Newell, Mark A; Jannink, Jean-Luc
Genomic selection (GS) is a method to predict the genetic value of selection candidates based on the genomic estimated breeding value (GEBV) predicted from high-density markers positioned throughout the genome. Unlike marker-assisted selection, the GEBV is based on all markers including both minor and major marker effects. Thus, the GEBV may capture more of the genetic variation for the particular trait under selection.
Roos, de A.P.W.
The objectives of this Ph.D. thesis were (1) to optimise genomic selection in dairy cattle with respect to the accuracy of predicting total genetic merit and (2) to optimise a dairy cattle breeding program using genomic selection. The study was performed using a combination of real data sets and
Lam, Raymond K.; Ohara, Catherine M.; Green, Joseph J.; Bikkannavar, Siddarayappa A.; Basinger, Scott A.; Redding, David C.; Shi, Fang
The Modified Gerchberg-Saxton (MGS) algorithm is an image-based wavefront-sensing method that can turn any science instrument focal plane into a wavefront sensor. MGS characterizes optical systems by estimating the wavefront errors in the exit pupil using only intensity images of a star or other point source of light. This innovative implementation of MGS significantly accelerates the MGS phase retrieval algorithm by using stream-processing hardware on conventional graphics cards. Stream processing is a relatively new, yet powerful, paradigm to allow parallel processing of certain applications that apply single instructions to multiple data (SIMD). These stream processors are designed specifically to support large-scale parallel computing on a single graphics chip. Computationally intensive algorithms, such as the Fast Fourier Transform (FFT), are particularly well suited for this computing environment. This high-speed version of MGS exploits commercially available hardware to accomplish the same objective in a fraction of the original time. The exploit involves performing matrix calculations in nVidia graphic cards. The graphical processor unit (GPU) is hardware that is specialized for computationally intensive, highly parallel computation. From the software perspective, a parallel programming model is used, called CUDA, to transparently scale multicore parallelism in hardware. This technology gives computationally intensive applications access to the processing power of the nVidia GPUs through a C/C++ programming interface. The AAMGS (Accelerated Adaptive MGS) software takes advantage of these advanced technologies, to accelerate the optical phase error characterization. With a single PC that contains four nVidia GTX-280 graphic cards, the new implementation can process four images simultaneously to produce a JWST (James Webb Space Telescope) wavefront measurement 60 times faster than the previous code.
Isik, Fikret; Bartholomé, Jérôme; Farjat, Alfredo; Chancerel, Emilie; Raffin, Annie; Sanchez, Leopoldo; Plomion, Christophe; Bouffier, Laurent
A two-generation maritime pine (Pinus pinaster Ait.) breeding population (n=661) was genotyped using 2500 SNP markers. The extent of linkage disequilibrium and utility of genomic selection for growth and stem straightness improvement were investigated. The overall intra-chromosomal linkage disequilibrium was r(2)=0.01. Linkage disequilibrium corrected for genomic relationships derived from markers was smaller (rV(2)=0.006). Genomic BLUP, Bayesian ridge regression and Bayesian LASSO regression statistical models were used to obtain genomic estimated breeding values. Two validation methods (random sampling 50% of the population and 10% of the progeny generation as validation sets) were used with 100 replications. The average predictive ability across statistical models and validation methods was about 0.49 for stem sweep, and 0.47 and 0.43 for total height and tree diameter, respectively. The sensitivity analysis suggested that prior densities (variance explained by markers) had little or no discernible effect on posterior means (residual variance) in Bayesian prediction models. Sampling from the progeny generation for model validation increased the predictive ability of markers for tree diameter and stem sweep but not for total height. The results are promising despite low linkage disequilibrium and low marker coverage of the genome (∼1.39 markers/cM). Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Li, Heng-De; Bao, Zhen-Min; Sun, Xiao-Wen
Selective breeding is very important in agricultural production and breeding value estimation is the core of selective breeding. With the development of genetic markers, especially high throughput genotyping technology, it becomes available to estimate breeding value at genome level, i.e. genomic selection (GS). In this review, the methods of GS was categorized into two groups: one is to predict genomic estimated breeding value (GEBV) based on the allele effect, such as least squares, random regression - best linear unbiased prediction (RR-BLUP), Bayes and principle component analysis, etc; the other is to predict GEBV with genetic relationship matrix, which constructs genetic relationship matrix via high throughput genetic markers and then predicts GEBV through linear mixed model, i.e. GBLUP. The basic principles of these methods were also introduced according to the above two classifications. Factors affecting GS accuracy include markers of type and density, length of haplotype, the size of reference population, the extent between marker-QTL and so on. Among the methods of GS, Bayes and GBLUP are usually more accurate than the others and least squares is the worst. GBLUP is time-efficient and can combine pedigree with genotypic information, hence it is superior to other methods. Although progress was made in GS, there are still some challenges, for examples, united breeding, long-term genetic gain with GS, and disentangling markers with and without contribution to the traits. GS has been applied in animal and plant breeding practice and also has the potential to predict genetic predisposition in humans and study evolutionary dynamics. GS, which is more precise than the traditional method, is a breakthrough at measuring genetic relationship. Therefore, GS will be a revolutionary event in the history of animal and plant breeding.
Desta, Zeratsion Abera; Ortiz, Rodomiro
Association analysis is used to measure relations between markers and quantitative trait loci (QTL). Their estimation ignores genes with small effects that trigger underpinning quantitative traits. By contrast, genome-wide selection estimates marker effects across the whole genome on the target population based on a prediction model developed in the training population (TP). Whole-genome prediction models estimate all marker effects in all loci and capture small QTL effects. Here, we review several genomic selection (GS) models with respect to both the prediction accuracy and genetic gain from selection. Phenotypic selection or marker-assisted breeding protocols can be replaced by selection, based on whole-genome predictions in which phenotyping updates the model to build up the prediction accuracy. Copyright © 2014 Elsevier Ltd. All rights reserved.
Esfandyari, Hadi; Sørensen, Anders Christian; Bijma, Piter
Background In livestock production, many animals are crossbred, with two distinct advantages: heterosis and breed complementarity. Genomic selection (GS) can be used to select purebred parental lines for crossbred performance (CP). Dominance being the likely genetic basis of heterosis, explicitly...
Sverrisdóttir, Elsa; Janss, Luc; Byrne, Stephen
Breeding for more space and resource efficient crops is important to feed the world’s increasing population. Potatoes produce approximately twice the amount of calories per hectare compared to cereals. The traditional “mate and phenotype” breeding approach is costly and time-consuming; however......, the completion of the genome sequence of potato has enabled the application of genomics-assisted breeding technologies. Genomic selection using genome-wide molecular markers is becoming increasingly applicable to crops as the genotyping costs continue to reduce and it is thus an attractive breeding alternative...... selection, can be obtained with good prediction accuracies in tetraploid potato....
Esfandyari, Hadi; Sørensen, Anders Christian; Bijma, Pieter
Genomic selection (GS) can be used to select purebreds for crossbred performance (CP). As dominance is the likely genetic basis of heterosis, explicitly including dominance in the GS model may be beneficial for selection of purebreds for CP, when estimating allelic effects from pure line data. Th...
Full Text Available In the last decade, genomic selection has become a standard in the genetic evaluation of livestock populations. However, most procedures for the implementation of genomic selection only consider the additive effects associated with SNP (Single Nucleotide Polymorphism markers used to calculate the prediction of the breeding values of candidates for selection. Nevertheless, the availability of estimates of non-additive effects is of interest because: (i they contribute to an increase in the accuracy of the prediction of breeding values and the genetic response; (ii they allow the definition of mate allocation procedures between candidates for selection; and (iii they can be used to enhance non-additive genetic variation through the definition of appropriate crossbreeding or purebred breeding schemes. This study presents a review of methods for the incorporation of non-additive genetic effects into genomic selection procedures and their potential applications in the prediction of future performance, mate allocation, crossbreeding, and purebred selection. The work concludes with a brief outline of some ideas for future lines of that may help the standard inclusion of non-additive effects in genomic selection.
Legarra, Andrés; Robert-Granié, Christèle; Manfredi, Eduardo; Elsen, Jean-Michel
Selection plans in plant and animal breeding are driven by genetic evaluation. Recent developments suggest using massive genetic marker information, known as “genomic selection.” There is little evidence of its performance, though. We empirically compared three strategies for selection: (1) use of pedigree and phenotypic information, (2) use of genomewide markers and phenotypic information, and (3) the combination of both. We analyzed four traits from a heterogeneous mouse population (http://...
de Haas, Yvette; Pryce, Jennie E; Wall, Eileen
Climate change is a growing area of international concern, and it is well established that the release of greenhouse gases (GHG) is a contributing factor. Of the various GHG produced by ruminants, enteric methane (CH4 ) is the most important contributor. One mitigation strategy is to reduce methane...... emission through genetic selection. Our first attempt used beef cattle and a GWAS to identify genes associated with several CH4 traits in Angus beef cattle. The Angus population consisted of 1020 animals with phenotypes on methane production (MeP), dry matter intake (DMI), and weight (WT). Additionally......, two new methane traits: residual genetic methane (RGM) and residual phenotypic methane (RPM) were calculated by adjusting CH4 for DMI and WT. Animals were genotyped using the 800k Illumina Bovine HD Array. Estimated heritabilities were 0.30, 0.19 and 0.15 for MeP, RGM and RPM respectively...
McCauley, Stephen; de Groot, Saskia; Mailund, Thomas
Motivation: Viral genomes tend to code in overlapping reading frames to maximize information content. This may result in atypical codon bias and particular evolutionary constraints. Due to the fast mutation rate of viruses, there is additional strong evidence for varying selection between intra......- and intergenomic regions. The presence of multiple coding regions complicates the concept of Ka/Ks ratio, and thus begs for an alternative approach when investigating selection strengths. Building on the paper by McCauley & Hein (2006), we develop a method for annotating a viral genome coding in overlapping...... may thus achieve an annotation both of coding regions as well as selection strengths, allowing us to investigate different selection patterns and hypotheses. Results: We illustrate our method by applying it to a multiple alignment of four HIV2 sequences, as well as four Hepatitis B sequences. We...
Cassava (Manihot esculenta Crantz) is a clonally propagated staple food crop in the tropics. Genomic selection (GS) has been implemented at three breeding institutions in Africa in order to reduce cycle times. Initial studies provided promising estimates of predictive abilities. Here, we expand on p...
Li, Hong-wei; Wang, Rui-jun; Wang, Zhi-ying; Li, Xue-wu; Wang, Zhen-yu; Yanjun, Zhang; Rui, Su; Zhihong, Liu; Jinquan, Li
With the development of gene chip and breeding technology, genomic selection in plants and animals has become research hotspots in recent years. Genomic selection has been extensively applied to all kinds of economic livestock, due to its high accuracy, short generation intervals and low breeding costs. In this review, we summarize genotyping technology and the methods for genomic breeding value estimation, the latter including the least square method, RR-BLUP, GBLUP, ssGBLUP, BayesA and BayesB. We also cover basic principles of genomic selection and compare their genetic marker ranges, genomic selection accuracy and operational speed. In addition, we list common indicators, methods and influencing factors that are related to genomic selection accuracy. Lastly, we discuss latest applications and the current problems of genomic selection at home and abroad. Importantly, we envision future status of genomic selection research, including multi-trait and multi-population genomic selection, as well as impact of whole genome sequencing and dominant effects on genomic selection. This review will provide some venues for other breeders to further understand genome selection.
Gois, I B; Borém, A; Cristofani-Yaly, M; de Resende, M D V; Azevedo, C F; Bastianel, M; Novelli, V M; Machado, M A
Genome wide selection (GWS) is essential for the genetic improvement of perennial species such as Citrus because of its ability to increase gain per unit time and to enable the efficient selection of characteristics with low heritability. This study assessed GWS efficiency in a population of Citrus and compared it with selection based on phenotypic data. A total of 180 individual trees from a cross between Pera sweet orange (Citrus sinensis Osbeck) and Murcott tangor (Citrus sinensis Osbeck x Citrus reticulata Blanco) were evaluated for 10 characteristics related to fruit quality. The hybrids were genotyped using 5287 DArT_seq TM (diversity arrays technology) molecular markers and their effects on phenotypes were predicted using the random regression - best linear unbiased predictor (rr-BLUP) method. The predictive ability, prediction bias, and accuracy of GWS were estimated to verify its effectiveness for phenotype prediction. The proportion of genetic variance explained by the markers was also computed. The heritability of the traits, as determined by markers, was 16-28%. The predictive ability of these markers ranged from 0.53 to 0.64, and the regression coefficients between predicted and observed phenotypes were close to unity. Over 35% of the genetic variance was accounted for by the markers. Accuracy estimates with GWS were lower than those obtained by phenotypic analysis; however, GWS was superior in terms of genetic gain per unit time. Thus, GWS may be useful for Citrus breeding as it can predict phenotypes early and accurately, and reduce the length of the selection cycle. This study demonstrates the feasibility of genomic selection in Citrus.
Blondel, Mathieu; Onogi, Akio; Iwata, Hiroyoshi; Ueda, Naonori
Genomic selection (GS) is a recent selective breeding method which uses predictive models based on whole-genome molecular markers. Until now, existing studies formulated GS as the problem of modeling an individual's breeding value for a particular trait of interest, i.e., as a regression problem. To assess predictive accuracy of the model, the Pearson correlation between observed and predicted trait values was used. In this paper, we propose to formulate GS as the problem of ranking individuals according to their breeding value. Our proposed framework allows us to employ machine learning methods for ranking which had previously not been considered in the GS literature. To assess ranking accuracy of a model, we introduce a new measure originating from the information retrieval literature called normalized discounted cumulative gain (NDCG). NDCG rewards more strongly models which assign a high rank to individuals with high breeding value. Therefore, NDCG reflects a prerequisite objective in selective breeding: accurate selection of individuals with high breeding value. We conducted a comparison of 10 existing regression methods and 3 new ranking methods on 6 datasets, consisting of 4 plant species and 25 traits. Our experimental results suggest that tree-based ensemble methods including McRank, Random Forests and Gradient Boosting Regression Trees achieve excellent ranking accuracy. RKHS regression and RankSVM also achieve good accuracy when used with an RBF kernel. Traditional regression methods such as Bayesian lasso, wBSR and BayesC were found less suitable for ranking. Pearson correlation was found to correlate poorly with NDCG. Our study suggests two important messages. First, ranking methods are a promising research direction in GS. Second, NDCG can be a useful evaluation measure for GS.
Nakaya, Akihiro; Isobe, Sachiko N.
Background Genomic selection or genome-wide selection (GS) has been highlighted as a new approach for marker-assisted selection (MAS) in recent years. GS is a form of MAS that selects favourable individuals based on genomic estimated breeding values. Previous studies have suggested the utility of GS, especially for capturing small-effect quantitative trait loci, but GS has not become a popular methodology in the field of plant breeding, possibly because there is insufficient information avail...
Kadarmideen, Haja; Do, Duy Ngoc; Janss, Luc
Genomic selection is widely used in both animal and plant species, however, it is performed with no input from known genomic or biological role of genetic variants and therefore is a black box approach in a genomic era. This study investigated the role of different genomic regions and detected QTLs...... in their contribution to estimated genomic variances and in prediction of genomic breeding values by applying SNP annotation approaches to feed efficiency. Ensembl Variant Predictor (EVP) and Pig QTL database were used as the source of genomic annotation for 60K chip. Genomic prediction was performed using the Bayes...... classes. Predictive accuracy was 0.531, 0.532, 0.302, and 0.344 for DFI, RFI, ADG and BF, respectively. The contribution per SNP to total genomic variance was similar among annotated classes across different traits. Predictive performance of SNP classes did not significantly differ from randomized SNP...
Poland, Jesse; Rutkoski, Jessica
Breeding for disease resistance is a central focus of plant breeding programs, as any successful variety must have the complete package of high yield, disease resistance, agronomic performance, and end-use quality. With the need to accelerate the development of improved varieties, genomics-assisted breeding is becoming an important tool in breeding programs. With marker-assisted selection, there has been success in breeding for disease resistance; however, much of this work and research has focused on identifying, mapping, and selecting for major resistance genes that tend to be highly effective but vulnerable to breakdown with rapid changes in pathogen races. In contrast, breeding for minor-gene quantitative resistance tends to produce more durable varieties but is a more challenging breeding objective. As the genetic architecture of resistance shifts from single major R genes to a diffused architecture of many minor genes, the best approach for molecular breeding will shift from marker-assisted selection to genomic selection. Genomics-assisted breeding for quantitative resistance will therefore necessitate whole-genome prediction models and selection methodology as implemented for classical complex traits such as yield. Here, we examine multiple case studies testing whole-genome prediction models and genomic selection for disease resistance. In general, whole-genome models for disease resistance can produce prediction accuracy suitable for application in breeding. These models also largely outperform multiple linear regression as would be applied in marker-assisted selection. With the implementation of genomic selection for yield and other agronomic traits, whole-genome marker profiles will be available for the entire set of breeding lines, enabling genomic selection for disease at no additional direct cost. In this context, the scope of implementing genomics selection for disease resistance, and specifically for quantitative resistance and quarantined pathogens
Anthony T. Slater
Full Text Available Potato ( L. breeders consider a large number of traits during cultivar development and progress in conventional breeding can be slow. There is accumulating evidence that some of these traits, such as yield, are affected by a large number of genes with small individual effects. Recently, significant efforts have been applied to the development of genomic resources to improve potato breeding, culminating in a draft genome sequence and the identification of a large number of single nucleotide polymorphisms (SNPs. The availability of these genome-wide SNPs is a prerequisite for implementing genomic selection for improvement of polygenic traits such as yield. In this review, we investigate opportunities for the application of genomic selection to potato, including novel breeding program designs. We have considered a number of factors that will influence this process, including the autotetraploid and heterozygous genetic nature of potato, the rate of decay of linkage disequilibrium, the number of required markers, the design of a reference population, and trait heritability. Based on estimates of the effective population size derived from a potato breeding program, we have calculated the expected accuracy of genomic selection for four key traits of varying heritability and propose that it will be reasonably accurate. We compared the expected genetic gain from genomic selection with the expected gain from phenotypic and pedigree selection, and found that genetic gain can be substantially improved by using genomic selection.
Xia, Jun Hong; Bai, Zhiyi; Meng, Zining; Zhang, Yong; Wang, Le; Liu, Feng; Jing, Wu; Wan, Zi Yi; Li, Jiale; Lin, Haoran; Yue, Gen Hua
Natural selection and selective breeding for genetic improvement have left detectable signatures within the genome of a species. Identification of selection signatures is important in evolutionary biology and for detecting genes that facilitate to accelerate genetic improvement. However, selection signatures, including artificial selection and natural selection, have only been identified at the whole genome level in several genetically improved fish species. Tilapia is one of the most important genetically improved fish species in the world. Using next-generation sequencing, we sequenced the genomes of 47 tilapia individuals. We identified a total of 1.43 million high-quality SNPs and found that the LD block sizes ranged from 10-100 kb in tilapia. We detected over a hundred putative selective sweep regions in each line of tilapia. Most selection signatures were located in non-coding regions of the tilapia genome. The Wnt signaling, gonadotropin-releasing hormone receptor and integrin signaling pathways were under positive selection in all improved tilapia lines. Our study provides a genome-wide map of genetic variation and selection footprints in tilapia, which could be important for genetic studies and accelerating genetic improvement of tilapia.
Dimitrijevic, Aleksandra; Horn, Renate
In sunflower, molecular markers for simple traits as, e.g., fertility restoration, high oleic acid content, herbicide tolerance or resistances to Plasmopara halstedii, Puccinia helianthi , or Orobanche cumana have been successfully used in marker-assisted breeding programs for years. However, agronomically important complex quantitative traits like yield, heterosis, drought tolerance, oil content or selection for disease resistance, e.g., against Sclerotinia sclerotiorum have been challenging and will require genome-wide approaches. Plant genetic resources for sunflower are being collected and conserved worldwide that represent valuable resources to study complex traits. Sunflower association panels provide the basis for genome-wide association studies, overcoming disadvantages of biparental populations. Advances in technologies and the availability of the sunflower genome sequence made novel approaches on the whole genome level possible. Genotype-by-sequencing, and whole genome sequencing based on next generation sequencing technologies facilitated the production of large amounts of SNP markers for high density maps as well as SNP arrays and allowed genome-wide association studies and genomic selection in sunflower. Genome wide or candidate gene based association studies have been performed for traits like branching, flowering time, resistance to Sclerotinia head and stalk rot. First steps in genomic selection with regard to hybrid performance and hybrid oil content have shown that genomic selection can successfully address complex quantitative traits in sunflower and will help to speed up sunflower breeding programs in the future. To make sunflower more competitive toward other oil crops higher levels of resistance against pathogens and better yield performance are required. In addition, optimizing plant architecture toward a more complex growth type for higher plant densities has the potential to considerably increase yields per hectare. Integrative approaches
Dimitrijevic, Aleksandra; Horn, Renate
In sunflower, molecular markers for simple traits as, e.g., fertility restoration, high oleic acid content, herbicide tolerance or resistances to Plasmopara halstedii, Puccinia helianthi, or Orobanche cumana have been successfully used in marker-assisted breeding programs for years. However, agronomically important complex quantitative traits like yield, heterosis, drought tolerance, oil content or selection for disease resistance, e.g., against Sclerotinia sclerotiorum have been challenging and will require genome-wide approaches. Plant genetic resources for sunflower are being collected and conserved worldwide that represent valuable resources to study complex traits. Sunflower association panels provide the basis for genome-wide association studies, overcoming disadvantages of biparental populations. Advances in technologies and the availability of the sunflower genome sequence made novel approaches on the whole genome level possible. Genotype-by-sequencing, and whole genome sequencing based on next generation sequencing technologies facilitated the production of large amounts of SNP markers for high density maps as well as SNP arrays and allowed genome-wide association studies and genomic selection in sunflower. Genome wide or candidate gene based association studies have been performed for traits like branching, flowering time, resistance to Sclerotinia head and stalk rot. First steps in genomic selection with regard to hybrid performance and hybrid oil content have shown that genomic selection can successfully address complex quantitative traits in sunflower and will help to speed up sunflower breeding programs in the future. To make sunflower more competitive toward other oil crops higher levels of resistance against pathogens and better yield performance are required. In addition, optimizing plant architecture toward a more complex growth type for higher plant densities has the potential to considerably increase yields per hectare. Integrative approaches
Full Text Available In sunflower, molecular markers for simple traits as, e.g., fertility restoration, high oleic acid content, herbicide tolerance or resistances to Plasmopara halstedii, Puccinia helianthi, or Orobanche cumana have been successfully used in marker-assisted breeding programs for years. However, agronomically important complex quantitative traits like yield, heterosis, drought tolerance, oil content or selection for disease resistance, e.g., against Sclerotinia sclerotiorum have been challenging and will require genome-wide approaches. Plant genetic resources for sunflower are being collected and conserved worldwide that represent valuable resources to study complex traits. Sunflower association panels provide the basis for genome-wide association studies, overcoming disadvantages of biparental populations. Advances in technologies and the availability of the sunflower genome sequence made novel approaches on the whole genome level possible. Genotype-by-sequencing, and whole genome sequencing based on next generation sequencing technologies facilitated the production of large amounts of SNP markers for high density maps as well as SNP arrays and allowed genome-wide association studies and genomic selection in sunflower. Genome wide or candidate gene based association studies have been performed for traits like branching, flowering time, resistance to Sclerotinia head and stalk rot. First steps in genomic selection with regard to hybrid performance and hybrid oil content have shown that genomic selection can successfully address complex quantitative traits in sunflower and will help to speed up sunflower breeding programs in the future. To make sunflower more competitive toward other oil crops higher levels of resistance against pathogens and better yield performance are required. In addition, optimizing plant architecture toward a more complex growth type for higher plant densities has the potential to considerably increase yields per hectare
Zhao, Yusheng; Gowda, Manje; Liu, Wenxin; Würschum, Tobias; Maurer, Hans P; Longin, Friedrich H; Ranc, Nicolas; Reif, Jochen C
Genomic selection is a promising breeding strategy for rapid improvement of complex traits. The objective of our study was to investigate the prediction accuracy of genomic breeding values through cross validation. The study was based on experimental data of six segregating populations from a half-diallel mating design with 788 testcross progenies from an elite maize breeding program. The plants were intensively phenotyped in multi-location field trials and fingerprinted with 960 SNP markers. We used random regression best linear unbiased prediction in combination with fivefold cross validation. The prediction accuracy across populations was higher for grain moisture (0.90) than for grain yield (0.58). The accuracy of genomic selection realized for grain yield corresponds to the precision of phenotyping at unreplicated field trials in 3-4 locations. As for maize up to three generations are feasible per year, selection gain per unit time is high and, consequently, genomic selection holds great promise for maize breeding programs.
Hedge, Jessica; Wilson, Daniel J.
Microbial genome evolution is shaped by a variety of selective pressures. Understanding how these processes occur can help to address important problems in microbiology by explaining observed differences in phenotypes, including virulence and resistance to antibiotics. Greater access to whole-genome sequencing provides microbiologists with the opportunity to perform large-scale analyses of selection in novel settings, such as within individual hosts. This tutorial aims to guide researchers th...
Thomasen, Jørn Rind
on optimization of genomc selction for a small dairy cattle breed such as Danish Jersey. Implementing genetic superior breeding schemes thus requires more accurate genomc predictions. Besides international collaboration, genotyping of cows is an efficient way to obtain more accurate genomic predictions...
Bouquet, A; Juga, J
Extensive genetic progress has been achieved in dairy cattle populations on many traits of economic importance because of efficient breeding programmes. Success of these programmes has relied on progeny testing of the best young males to accurately assess their genetic merit and hence their potential for breeding. Over the last few years, the integration of dense genomic information into statistical tools used to make selection decisions, commonly referred to as genomic selection, has enabled gains in predicting accuracy of breeding values for young animals without own performance. The possibility to select animals at an early stage allows defining new breeding strategies aimed at boosting genetic progress while reducing costs. The first objective of this article was to review methods used to model and optimize breeding schemes integrating genomic selection and to discuss their relative advantages and limitations. The second objective was to summarize the main results and perspectives on the use of genomic selection in practical breeding schemes, on the basis of the example of dairy cattle populations. Two main designs of breeding programmes integrating genomic selection were studied in dairy cattle. Genomic selection can be used either for pre-selecting males to be progeny tested or for selecting males to be used as active sires in the population. The first option produces moderate genetic gains without changing the structure of breeding programmes. The second option leads to large genetic gains, up to double those of conventional schemes because of a major reduction in the mean generation interval, but it requires greater changes in breeding programme structure. The literature suggests that genomic selection becomes more attractive when it is coupled with embryo transfer technologies to further increase selection intensity on the dam-to-sire pathway. The use of genomic information also offers new opportunities to improve preservation of genetic variation. However
Nielsen, Rasmus; Hellmann, Ines; Hubisz, Melissa
The recent availability of genome-scale genotyping data has led to the identification of regions of the human genome that seem to have been targeted by selection. These findings have increased our understanding of the evolutionary forces that affect the human genome, have augmented our knowledge...... of gene function and promise to increase our understanding of the genetic basis of disease. However, inferences of selection are challenged by several confounding factors, especially the complex demographic history of human populations, and concordance between studies is variable. Although such studies...
Full Text Available Abstract Background Array comparative genomic hybridization is a fast and cost-effective method for detecting, genotyping, and comparing the genomic sequence of unknown bacterial isolates. This method, as with all microarray applications, requires adequate coverage of probes targeting the regions of interest. An unbiased tiling of probes across the entire length of the genome is the most flexible design approach. However, such a whole-genome tiling requires that the genome sequence is known in advance. For the accurate analysis of uncharacterized bacteria, an array must query a fully representative set of sequences from the species' pan-genome. Prior microarrays have included only a single strain per array or the conserved sequences of gene families. These arrays omit potentially important genes and sequence variants from the pan-genome. Results This paper presents a new probe selection algorithm (PanArray that can tile multiple whole genomes using a minimal number of probes. Unlike arrays built on clustered gene families, PanArray uses an unbiased, probe-centric approach that does not rely on annotations, gene clustering, or multi-alignments. Instead, probes are evenly tiled across all sequences of the pan-genome at a consistent level of coverage. To minimize the required number of probes, probes conserved across multiple strains in the pan-genome are selected first, and additional probes are used only where necessary to span polymorphic regions of the genome. The viability of the algorithm is demonstrated by array designs for seven different bacterial pan-genomes and, in particular, the design of a 385,000 probe array that fully tiles the genomes of 20 different Listeria monocytogenes strains with overlapping probes at greater than twofold coverage. Conclusion PanArray is an oligonucleotide probe selection algorithm for tiling multiple genome sequences using a minimal number of probes. It is capable of fully tiling all genomes of a species on
Schielzeth, Holger; Streitner, Corinna; Lampe, Ulrike; Franzke, Alexandra; Reinhold, Klaus
Genome size is largely uncorrelated to organismal complexity and adaptive scenarios. Genetic drift as well as intragenomic conflict have been put forward to explain this observation. We here study the impact of genome size on sexual attractiveness in the bow-winged grasshopper Chorthippus biguttulus. Grasshoppers show particularly large variation in genome size due to the high prevalence of supernumerary chromosomes that are considered (mildly) selfish, as evidenced by non-Mendelian inheritance and fitness costs if present in high numbers. We ranked male grasshoppers by song characteristics that are known to affect female preferences in this species and scored genome sizes of attractive and unattractive individuals from the extremes of this distribution. We find that attractive singers have significantly smaller genomes, demonstrating that genome size is reflected in male courtship songs and that females prefer songs of males with small genomes. Such a genome size dependent mate preference effectively selects against selfish genetic elements that tend to increase genome size. The data therefore provide a novel example of how sexual selection can reinforce natural selection and can act as an agent in an intragenomic arms race. Furthermore, our findings indicate an underappreciated route of how choosy females could gain indirect benefits. © 2014 The Author(s). Evolution © 2014 The Society for the Study of Evolution.
Rogério Faria Vieira
Full Text Available Mungbean cultivar MGS Esmeralda was developed by Asian Vegetable Research and Development Center (Shanhua, Taiwan, as a result of crossing between the lines VC 1973A and VC 2768A. In ten trials conducted in the State of Minas Gerais, Brazil, it produced 13.5% more grains than 'Ouro Verde MG-2' (control cultivar, and its highest yield was 2,550 kg ha-1. The cultivar MGS Esmeralda is more susceptible to lodging, and its pods mature more uniformly than Ouro Verde MG-2 pods. One hundred-seed mass of 'MGS Esmeralda' ranged between 5.5 and 6.8 g. Both cultivars are susceptible to powdery mildew and cercospora leaf spot.A cultivar de mungo-verde MGS Esmeralda foi criada pelo Asian Vegetable Research and Development Center, localizado em Shanhua, Formosa. Ela é resultado do cruzamento entre as linhagens VC 1973A e VC 2768A. Em dez ensaios conduzidos em Minas Gerais, ela produziu 13,5% mais grãos do que a cultivar Ouro Verde MG-2 (testemunha, e sua produtividade mais alta foi 2.550 kg ha-1. A cultivar MGS Esmeralda é mais suscetível ao acamamento do que a Ouro Verde MG-2, mas suas vagens amadurecem mais uniformemente. A massa de 100 grãos da 'MGS Esmeralda' varia de 5,5 a 6,8 g. Ambas as cultivares são suscetíveis ao oídio e à cercosporiose.
Full Text Available Microbial genome evolution is shaped by a variety of selective pressures. Understanding how these processes occur can help to address important problems in microbiology by explaining observed differences in phenotypes, including virulence and resistance to antibiotics. Greater access to whole-genome sequencing provides microbiologists with the opportunity to perform large-scale analyses of selection in novel settings, such as within individual hosts. This tutorial aims to guide researchers through the fundamentals underpinning popular methods for measuring selection in pathogens. These methods are transferable to a wide variety of organisms, and the exercises provided are designed for researchers with any level of programming experience.
Hedge, Jessica; Wilson, Daniel J
Microbial genome evolution is shaped by a variety of selective pressures. Understanding how these processes occur can help to address important problems in microbiology by explaining observed differences in phenotypes, including virulence and resistance to antibiotics. Greater access to whole-genome sequencing provides microbiologists with the opportunity to perform large-scale analyses of selection in novel settings, such as within individual hosts. This tutorial aims to guide researchers through the fundamentals underpinning popular methods for measuring selection in pathogens. These methods are transferable to a wide variety of organisms, and the exercises provided are designed for researchers with any level of programming experience.
Calus, M.P.L.; Veerkamp, R.F.
Background Genomic selection has become a very important tool in animal genetics and is rapidly emerging in plant genetics. It holds the promise to be particularly beneficial to select for traits that are difficult or expensive to measure, such as traits that are measured in one environment and
Jonas, Elisabeth; de Koning, Dirk-Jan
Plant breeding largely depends on phenotypic selection in plots and only for some, often disease-resistance-related traits, uses genetic markers. The more recently developed concept of genomic selection, using a black box approach with no need of prior knowledge about the effect or function of individual markers, has also been proposed as a great opportunity for plant breeding. Several empirical and theoretical studies have focused on the possibility to implement this as a novel molecular method across various species. Although we do not question the potential of genomic selection in general, in this Opinion, we emphasize that genomic selection approaches from dairy cattle breeding cannot be easily applied to complex plant breeding. Copyright © 2013 Elsevier Ltd. All rights reserved.
Mars Global Surveyor (MGS) avionics system architecture comprises much of the electronics on board the spacecraft: electrical power, attitude and articulation control, command and data handling, telecommunications, and flight software. Schedule and cost constraints dictated a mix of new and inherited designs, especially hardware upgrades based on findings of the Mars Observer failure review boards.
Full Text Available High-throughput computing (HTC uses computer clusters to solve advanced computational problems, with the goal of accomplishing high throughput over relatively long periods of time. In genomic selection, for example, a set of markers covering the entire genome is used to train a model based on known data, and the resulting model is used to predict the genetic merit of selection candidates. Sophisticated models are very computationally demanding and, with several traits to be evaluated sequentially, computing time is long and output is low. In this paper, we present scenarios and basic principles of how HTC can be used in genomic selection, implemented using various techniques from simple batch processing to pipelining in distributed computer clusters. Various scripting languages, such as shell scripting, Perl and R, are also very useful to devise pipelines. By pipelining, we can reduce total computing time and consequently increase throughput. In comparison to the traditional data processing pipeline residing on the central processors, performing general purpose computation on a graphics processing unit (GPU provide a new-generation approach to massive parallel computing in genomic selection. While the concept of HTC may still be new to many researchers in animal breeding, plant breeding, and genetics, HTC infrastructures have already been built in many institutions, such as the University of Wisconsin – Madison, which can be leveraged for genomic selection, in terms of central processing unit (CPU capacity, network connectivity, storage availability, and middleware connectivity. Exploring existing HTC infrastructures as well as general purpose computing environments will further expand our capability to meet increasing computing demands posed by unprecedented genomic data that we have today. We anticipate that HTC will impact genomic selection via better statistical models, faster solutions, and more competitive products (e.g., from design of
Rubin, Carl-Johan; Megens, Hendrik-Jan; Barrio, Alvaro Martinez
Domestication of wild boar (Sus scrofa) and subsequent selection have resulted in dramatic phenotypic changes in domestic pigs for a number of traits, including behavior, body composition, reproduction, and coat color. Here we have used whole-genome resequencing to reveal some of the loci that un...... to strong directional selection.......Domestication of wild boar (Sus scrofa) and subsequent selection have resulted in dramatic phenotypic changes in domestic pigs for a number of traits, including behavior, body composition, reproduction, and coat color. Here we have used whole-genome resequencing to reveal some of the loci...... that underlie phenotypic evolution in European domestic pigs. Selective sweep analyses revealed strong signatures of selection at three loci harboring quantitative trait loci that explain a considerable part of one of the most characteristic morphological changes in the domestic pig—the elongation of the back...
Zhao, Yusheng; Gowda, Manje; Longin, Friedrich H; Würschum, Tobias; Ranc, Nicolas; Reif, Jochen C
Estimating marker effects based on routinely generated phenotypic data of breeding programs is a cost-effective strategy to implement genomic selection. Truncation selection in breeding populations, however, could have a strong impact on the accuracy to predict genomic breeding values. The main objective of our study was to investigate the influence of phenotypic selection on the accuracy and bias of genomic selection. We used experimental data of 788 testcross progenies from an elite maize breeding program. The testcross progenies were evaluated in unreplicated field trials in ten environments and fingerprinted with 857 SNP markers. Random regression best linear unbiased prediction method was used in combination with fivefold cross-validation based on genotypic sampling. We observed a substantial loss in the accuracy to predict genomic breeding values in unidirectional selected populations. In contrast, estimating marker effects based on bidirectional selected populations led to only a marginal decrease in the prediction accuracy of genomic breeding values. We concluded that bidirectional selection is a valuable approach to efficiently implement genomic selection in applied plant breeding programs.
Wu, Xiao-Lin; Beissinger, Timothy M; Bauck, Stewart; Woodward, Brent; Rosa, Guilherme J M; Weigel, Kent A; Gatti, Natalia de Leon; Gianola, Daniel
High-throughput computing (HTC) uses computer clusters to solve advanced computational problems, with the goal of accomplishing high-throughput over relatively long periods of time. In genomic selection, for example, a set of markers covering the entire genome is used to train a model based on known data, and the resulting model is used to predict the genetic merit of selection candidates. Sophisticated models are very computationally demanding and, with several traits to be evaluated sequentially, computing time is long, and output is low. In this paper, we present scenarios and basic principles of how HTC can be used in genomic selection, implemented using various techniques from simple batch processing to pipelining in distributed computer clusters. Various scripting languages, such as shell scripting, Perl, and R, are also very useful to devise pipelines. By pipelining, we can reduce total computing time and consequently increase throughput. In comparison to the traditional data processing pipeline residing on the central processors, performing general-purpose computation on a graphics processing unit provide a new-generation approach to massive parallel computing in genomic selection. While the concept of HTC may still be new to many researchers in animal breeding, plant breeding, and genetics, HTC infrastructures have already been built in many institutions, such as the University of Wisconsin-Madison, which can be leveraged for genomic selection, in terms of central processing unit capacity, network connectivity, storage availability, and middleware connectivity. Exploring existing HTC infrastructures as well as general-purpose computing environments will further expand our capability to meet increasing computing demands posed by unprecedented genomic data that we have today. We anticipate that HTC will impact genomic selection via better statistical models, faster solutions, and more competitive products (e.g., from design of marker panels to realized
Mandel, Jennifer R.; Nambeesan, Savithri; Bowers, John E.; Marek, Laura F.; Ebert, Daniel; Rieseberg, Loren H.; Knapp, Steven J.; Burke, John M.
The combination of large-scale population genomic analyses and trait-based mapping approaches has the potential to provide novel insights into the evolutionary history and genome organization of crop plants. Here, we describe the detailed genotypic and phenotypic analysis of a sunflower (Helianthus annuus L.) association mapping population that captures nearly 90% of the allelic diversity present within the cultivated sunflower germplasm collection. We used these data to characterize overall patterns of genomic diversity and to perform association analyses on plant architecture (i.e., branching) and flowering time, successfully identifying numerous associations underlying these agronomically and evolutionarily important traits. Overall, we found variable levels of linkage disequilibrium (LD) across the genome. In general, islands of elevated LD correspond to genomic regions underlying traits that are known to have been targeted by selection during the evolution of cultivated sunflower. In many cases, these regions also showed significantly elevated levels of differentiation between the two major sunflower breeding groups, consistent with the occurrence of divergence due to strong selection. One of these regions, which harbors a major branching locus, spans a surprisingly long genetic interval (ca. 25 cM), indicating the occurrence of an extended selective sweep in an otherwise recombinogenic interval. PMID:23555290
Full Text Available Many economically important traits in plant breeding have low heritability or are difficult to measure. For these traits, genomic selection has attractive features and may boost genetic gains. Our goal was to evaluate alternative scenarios to implement genomic selection for yield components in soybean (Glycine max L. merr. We used a nested association panel with cross validation to evaluate the impacts of training population size, genotyping density, and prediction model on the accuracy of genomic prediction. Our results indicate that training population size was the factor most relevant to improvement in genome-wide prediction, with greatest improvement observed in training sets up to 2000 individuals. We discuss assumptions that influence the choice of the prediction model. Although alternative models had minor impacts on prediction accuracy, the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB. Higher genotyping density marginally improved accuracy. Our study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set.
Xavier, Alencar; Muir, William M; Rainey, Katy Martin
Many economically important traits in plant breeding have low heritability or are difficult to measure. For these traits, genomic selection has attractive features and may boost genetic gains. Our goal was to evaluate alternative scenarios to implement genomic selection for yield components in soybean (Glycine max L. merr). We used a nested association panel with cross validation to evaluate the impacts of training population size, genotyping density, and prediction model on the accuracy of genomic prediction. Our results indicate that training population size was the factor most relevant to improvement in genome-wide prediction, with greatest improvement observed in training sets up to 2000 individuals. We discuss assumptions that influence the choice of the prediction model. Although alternative models had minor impacts on prediction accuracy, the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB. Higher genotyping density marginally improved accuracy. Our study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set. Copyright © 2016 Xavie et al.
Full Text Available The hotspots of structural polymorphisms and structural mutability in the human genome remain to be explained mechanistically. We examine associations of structural mutability with germline DNA methylation and with non-allelic homologous recombination (NAHR mediated by low-copy repeats (LCRs. Combined evidence from four human sperm methylome maps, human genome evolution, structural polymorphisms in the human population, and previous genomic and disease studies consistently points to a strong association of germline hypomethylation and genomic instability. Specifically, methylation deserts, the ~1% fraction of the human genome with the lowest methylation in the germline, show a tenfold enrichment for structural rearrangements that occurred in the human genome since the branching of chimpanzee and are highly enriched for fast-evolving loci that regulate tissue-specific gene expression. Analysis of copy number variants (CNVs from 400 human samples identified using a custom-designed array comparative genomic hybridization (aCGH chip, combined with publicly available structural variation data, indicates that association of structural mutability with germline hypomethylation is comparable in magnitude to the association of structural mutability with LCR-mediated NAHR. Moreover, rare CNVs occurring in the genomes of individuals diagnosed with schizophrenia, bipolar disorder, and developmental delay and de novo CNVs occurring in those diagnosed with autism are significantly more concentrated within hypomethylated regions. These findings suggest a new connection between the epigenome, selective mutability, evolution, and human disease.
Crossa, José; Pérez-Rodríguez, Paulino; Cuevas, Jaime; Montesinos-López, Osval; Jarquín, Diego; de Los Campos, Gustavo; Burgueño, Juan; González-Camacho, Juan M; Pérez-Elizalde, Sergio; Beyene, Yoseph; Dreisigacker, Susanne; Singh, Ravi; Zhang, Xuecai; Gowda, Manje; Roorkiwal, Manish; Rutkoski, Jessica; Varshney, Rajeev K
Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype×environment (G×E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation. GS applied to maize breeding has shown tangible genetic gains. Based on GP results, we speculate how GS in germplasm enhancement (i.e., prebreeding) programs could accelerate the flow of genes from gene bank accessions to elite lines. Recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding. Copyright © 2017 Elsevier Ltd. All rights reserved.
Wen, Zixiang; Boyse, John F; Song, Qijian; Cregan, Perry B; Wang, Dechun
Crop improvement always involves selection of specific alleles at genes controlling traits of agronomic importance, likely resulting in detectable signatures of selection within the genome of modern soybean (Glycine max L. Merr.). The identification of these signatures of selection is meaningful from the perspective of evolutionary biology and for uncovering the genetic architecture of agronomic traits. To this end, two populations of soybean, consisting of 342 landraces and 1062 improved lines, were genotyped with the SoySNP50K Illumina BeadChip containing 52,041 single nucleotide polymorphisms (SNPs), and systematically phenotyped for 9 agronomic traits. A cross-population composite likelihood ratio (XP-CLR) method was used to screen the signals of selective sweeps. A total of 125 candidate selection regions were identified, many of which harbored genes potentially involved in crop improvement. To further investigate whether these candidate regions were in fact enriched for genes affected by selection, genome-wide association studies (GWAS) were conducted on 7 selection traits targeted in soybean breeding (grain yield, plant height, lodging, maturity date, seed coat color, seed protein and oil content) and 2 non-selection traits (pubescence and flower color). Major genomic regions associated with selection traits overlapped with candidate selection regions, whereas no overlap of this kind occurred for the non-selection traits, suggesting that the selection sweeps identified are associated with traits of agronomic importance. Multiple novel loci and refined map locations of known loci related to these traits were also identified. These findings illustrate that comparative genomic analyses, especially when combined with GWAS, are a promising approach to dissect the genetic architecture of complex traits.
Villumsen, Trine Michelle; Janss, Luc
Breeding values for animals with marker data are estimated using a genomic selection approach where data is analyzed using Bayesian multi-marker association models. Fourteen model scenarios with varying haplotype lengths, hyper parameter and prior distributions were compared to find the scenario ...
Full Text Available Abstract Background With the establishment of high-throughput sequencing technologies and new methods for rapid and extensive single nucleotide (SNP discovery, marker-based genome scans in search of signatures of divergent selection between populations occupying ecologically distinct environments are becoming increasingly popular. Methods and Results On the basis of genome-wide SNP marker data generated by RAD sequencing of lake and stream stickleback populations, we show that the outcome of such studies can be systematically biased if markers with a low minor allele frequency are included in the analysis. The reason is that these ‘uninformative’ polymorphisms lack the adequate potential to capture signatures of drift and hitchhiking, the focal processes in ecological genome scans. Bias associated with uninformative polymorphisms is not eliminated by just avoiding technical artifacts in the data (PCR and sequencing errors, as a high proportion of SNPs with a low minor allele frequency is a general biological feature of natural populations. Conclusions We suggest that uninformative markers should be excluded from genome scans based on empirical criteria derived from careful inspection of the data, and that these criteria should be reported explicitly. Together, this should increase the quality and comparability of genome scans, and hence promote our understanding of the processes driving genomic differentiation.
Garner, J. B.; Douglas, M. L.; Williams, S. R. O; Wales, W. J.; Marett, L. C.; Nguyen, T. T. T.; Reich, C. M.; Hayes, B. J.
Dairy products are a key source of valuable proteins and fats for many millions of people worldwide. Dairy cattle are highly susceptible to heat-stress induced decline in milk production, and as the frequency and duration of heat-stress events increases, the long term security of nutrition from dairy products is threatened. Identification of dairy cattle more tolerant of heat stress conditions would be an important progression towards breeding better adapted dairy herds to future climates. Breeding for heat tolerance could be accelerated with genomic selection, using genome wide DNA markers that predict tolerance to heat stress. Here we demonstrate the value of genomic predictions for heat tolerance in cohorts of Holstein cows predicted to be heat tolerant and heat susceptible using controlled-climate chambers simulating a moderate heatwave event. Not only was the heat challenge stimulated decline in milk production less in cows genomically predicted to be heat-tolerant, physiological indicators such as rectal and intra-vaginal temperatures had reduced increases over the 4 day heat challenge. This demonstrates that genomic selection for heat tolerance in dairy cattle is a step towards securing a valuable source of nutrition and improving animal welfare facing a future with predicted increases in heat stress events. PMID:27682591
Biazzi, Elisa; Nazzicari, Nelson; Pecetti, Luciano; Brummer, E Charles; Palmonari, Alberto; Tava, Aldo; Annicchiarico, Paolo
Genetic progress for forage quality has been poor in alfalfa (Medicago sativa L.), the most-grown forage legume worldwide. This study aimed at exploring opportunities for marker-assisted selection (MAS) and genomic selection of forage quality traits based on breeding values of parent plants. Some 154 genotypes from a broadly-based reference population were genotyped by genotyping-by-sequencing (GBS), and phenotyped for leaf-to-stem ratio, leaf and stem contents of protein, neutral detergent fiber (NDF) and acid detergent lignin (ADL), and leaf and stem NDF digestibility after 24 hours (NDFD), of their dense-planted half-sib progenies in three growing conditions (summer harvest, full irrigation; summer harvest, suspended irrigation; autumn harvest). Trait-marker analyses were performed on progeny values averaged over conditions, owing to modest germplasm × condition interaction. Genomic selection exploited 11,450 polymorphic SNP markers, whereas a subset of 8,494 M. truncatula-aligned markers were used for a genome-wide association study (GWAS). GWAS confirmed the polygenic control of quality traits and, in agreement with phenotypic correlations, indicated substantially different genetic control of a given trait in stems and leaves. It detected several SNPs in different annotated genes that were highly linked to stem protein content. Also, it identified a small genomic region on chromosome 8 with high concentration of annotated genes associated with leaf ADL, including one gene probably involved in the lignin pathway. Three genomic selection models, i.e., Ridge-regression BLUP, Bayes B and Bayesian Lasso, displayed similar prediction accuracy, whereas SVR-lin was less accurate. Accuracy values were moderate (0.3-0.4) for stem NDFD and leaf protein content, modest for leaf ADL and NDFD, and low to very low for the other traits. Along with previous results for the same germplasm set, this study indicates that GBS data can be exploited to improve both quality traits
Xu, Zhuofei; Zhou, Rui
As is well known, pathogenic microbes evolve rapidly to escape from the host immune system and antibiotics. Genetic variations among microbial populations occur frequently during the long-term pathogen–host evolutionary arms race, and individual mutation beneficial for the fitness can be fixed...... to scan genome-wide alignments for evidence of positive Darwinian selection, recombination, and other evolutionary forces operating on the coding regions. In this chapter, we describe an integrative analysis pipeline and its application to tracking featured evolutionary trajectories on the genome...
Liu, Stephen V; Lenkiewicz, Elizabeth; Evers, Lisa; Holley, Tara; Kiefer, Jeffrey; Demeure, Michael J; Ramanathan, Ramesh K; Von Hoff, Daniel D; Barrett, Michael T; Ruiz, Christian; Glatz, Katharina; Bubendorf, Lukas; Eng, Cathy
It is widely accepted that many cancers arise as a result of an acquired genomic instability and the subsequent evolution of tumor cells with variable patterns of selected and background aberrations. The presence and behaviors of distinct neoplastic cell populations within a patient's tumor may underlie multiple clinical phenotypes in cancers. A goal of many current cancer genome studies is the identification of recurring selected driver events that can be advanced for the development of personalized therapies. Unfortunately, in the majority of rare tumors, this type of analysis can be particularly challenging. Large series of specimens for analysis are simply not available, allowing recurring patterns to remain hidden. In this paper, we highlight the use of DNA content-based flow sorting to identify and isolate DNA-diploid and DNA-aneuploid populations from tumor biopsies as a strategy to comprehensively study the genomic composition and behaviors of individual cancers in a series of rare solid tumors: intrahepatic cholangiocarcinoma, anal carcinoma, adrenal leiomyosarcoma, and pancreatic neuroendocrine tumors. We propose that the identification of highly selected genomic events in distinct tumor populations within each tumor can identify candidate driver events that can facilitate the development of novel, personalized treatment strategies for patients with cancer. (paper)
Nygaard, Sanne; Braunstein, Alexander; Malsen, Gareth; Van Dongen, Stijn; Gardner, Paul P.; Krogh, Anders; Otto, Thomas D.; Pain, Arnab; Berriman, Matthew; McAuliffe, Jon; Dermitzakis, Emmanouil T.; Jeffares, Daniel C.
of these genomes. Although evolutionary processes have a significant impact on malaria control, the selective pressures within Plasmodium genomes are poorly understood, particularly in the non-protein-coding portion of the genome. We use evolutionary methods
Rank, Christian; Larsen, Thomas Ostenfeld; Frisvad, Jens Christian
The advances in gene sequencing will in the near future enable researchers to affordably acquire the full genomes of handpicked isolates. We here present a method to evaluate the chemical potential of an entire species and select representatives for genome sequencing. The selection criteria for new...... strains to be sequenced can be manifold, but for studying the functional phenotype, using a metabolome based approach offers a cheap and rapid assessment of critical strains to cover the chemical diversity. We have applied this methodology on the complex A. flavus/A. oryzae group. Though these two species...... are in principal identical, they represent two different phenotypes. This is clearly presented through a correspondence analysis of selected extrolites, in which the subtle chemical differences are visually dispersed. The results points to a handful of strains, which, if sequenced, will likely enhance our...
Kosiol, Carolin; Vinar, Tomás; da Fonseca, Rute R
Genome-wide scans for positively selected genes (PSGs) in mammals have provided insight into the dynamics of genome evolution, the genetic basis of differences between species, and the functions of individual genes. However, previous scans have been limited in power and accuracy owing to small...... several new lineage- and clade-specific tests to be applied. Of approximately 16,500 human genes with high-confidence orthologs in at least two other species, 400 genes showed significant evidence of positive selection (FDR... showed evidence of positive selection on particular lineages or clades. As in previous studies, the identified PSGs were enriched for roles in defense/immunity, chemosensory perception, and reproduction, but enrichments were also evident for more specific functions, such as complement-mediated immunity...
Gouy, M; Rousselle, Y; Bastianelli, D; Lecomte, P; Bonnal, L; Roques, D; Efile, J-C; Rocher, S; Daugrois, J; Toubi, L; Nabeneza, S; Hervouet, C; Telismart, H; Denis, M; Thong-Chane, A; Glaszmann, J C; Hoarau, J-Y; Nibouche, S; Costet, L
Sugarcane cultivars are interspecific hybrids with an aneuploid, highly heterozygous polyploid genome. The complexity of the sugarcane genome is the main obstacle to the use of marker-assisted selection in sugarcane breeding. Given the promising results of recent studies of plant genomic selection, we explored the feasibility of genomic selection in this complex polyploid crop. Genetic values were predicted in two independent panels, each composed of 167 accessions representing sugarcane genetic diversity worldwide. Accessions were genotyped with 1,499 DArT markers. One panel was phenotyped in Reunion Island and the other in Guadeloupe. Ten traits concerning sugar and bagasse contents, digestibility and composition of the bagasse, plant morphology, and disease resistance were used. We used four statistical predictive models: bayesian LASSO, ridge regression, reproducing kernel Hilbert space, and partial least square regression. The accuracy of the predictions was assessed through the correlation between observed and predicted genetic values by cross validation within each panel and between the two panels. We observed equivalent accuracy among the four predictive models for a given trait, and marked differences were observed among traits. Depending on the trait concerned, within-panel cross validation yielded median correlations ranging from 0.29 to 0.62 in the Reunion Island panel and from 0.11 to 0.5 in the Guadeloupe panel. Cross validation between panels yielded correlations ranging from 0.13 for smut resistance to 0.55 for brix. This level of correlations is promising for future implementations. Our results provide the first validation of genomic selection in sugarcane.
Full Text Available The aim of this study was to identify the evidence of recent selection based on estimation of the integrated Haplotype Score (iHS, population differentiation index (FST and characterize affected regions near QTL associated with traits under strong selection in Pinzgau cattle. In total 21 Austrian and 19 Slovak purebreed bulls genotyped with Illumina bovineHD and bovineSNP50 BeadChip were used to identify genomic regions under selection. Only autosomal loci with call rate higher than 90%, minor allele frequency higher than 0.01 and Hardy-Weinberg equlibrium limit of 0.001 were included in the subsequent analyses of selection sweeps presence. The final dataset was consisted from 30538 SNPs with 81.86 kb average adjacent SNPs spacing. The iHS score were averaged into non-overlapping 500 kb segments across the genome. The FST values were also plotted against genome position based on sliding windows approach and averaged over 8 consecutive SNPs. Based on integrated Haplotype Score evaluation only 7 regions with iHS score higher than 1.7 was found. The average iHS score observed for each adjacent syntenic regions indicated slight effect of recent selection in analysed group of Pinzgau bulls. The level of genetic differentiation between Austrian and Slovak bulls estimated based on FST index was low. Only 24% of FST values calculated for each SNP was greather than 0.01. By using sliding windows approach was found that 5% of analysed windows had higher value than 0.01. Our results indicated use of similar selection scheme in breeding programs of Slovak and Austrian Pinzgau bulls. The evidence for genome-wide association between signatures of selection and regions affecting complex traits such as milk production was insignificant, because the loci in segments identified as affected by selection were very distant from each other. Identification of genomic regions that may be under pressure of selection for phenotypic traits to better understanding of the
Full Text Available Identifying the signals of artificial selection can contribute to further shaping economically important traits. Here, a chicken 600k SNP-array was employed to detect the signals of artificial selection using 331 individuals from 9 breeds, including Jingfen (JF, Jinghong (JH, Araucanas (AR, White Leghorn (WL, Pekin-Bantam (PB, Shamo (SH, Gallus-Gallus-Spadiceus (GA, Rheinlander (RH and Vorwerkhuhn (VO. Per the population genetic structure, 9 breeds were combined into 5 breed-pools, and a 'two-step' strategy was used to reveal the signals of artificial selection. GA, which has little artificial selection, was defined as the reference population, and a total of 204, 155, 305 and 323 potential artificial selection signals were identified in AR_VO, PB, RH_WL and JH_JF, respectively. We also found signals derived from standing and de-novo genetic variations have contributed to adaptive evolution during artificial selection. Further enrichment analysis suggests that the genomic regions of artificial selection signals harbour genes, including THSR, PTHLH and PMCH, responsible for economic traits, such as fertility, growth and immunization. Overall, this study found a series of genes that contribute to the improvement of chicken breeds and revealed the genetic mechanisms of adaptive evolution, which can be used as fundamental information in future chicken functional genomics study.
Nakaya, Akihiro; Isobe, Sachiko N
Genomic selection or genome-wide selection (GS) has been highlighted as a new approach for marker-assisted selection (MAS) in recent years. GS is a form of MAS that selects favourable individuals based on genomic estimated breeding values. Previous studies have suggested the utility of GS, especially for capturing small-effect quantitative trait loci, but GS has not become a popular methodology in the field of plant breeding, possibly because there is insufficient information available on GS for practical use. In this review, GS is discussed from a practical breeding viewpoint. Statistical approaches employed in GS are briefly described, before the recent progress in GS studies is surveyed. GS practices in plant breeding are then reviewed before future prospects are discussed. Statistical concepts used in GS are discussed with genetic models and variance decomposition, heritability, breeding value and linear model. Recent progress in GS studies is reviewed with a focus on empirical studies. For the practice of GS in plant breeding, several specific points are discussed including linkage disequilibrium, feature of populations and genotyped markers and breeding scheme. Currently, GS is not perfect, but it is a potent, attractive and valuable approach for plant breeding. This method will be integrated into many practical breeding programmes in the near future with further advances and the maturing of its theory.
Oakey, Helena; Cullis, Brian; Thompson, Robin; Comadran, Jordi; Halpin, Claire; Waugh, Robbie
Genomic selection in crop breeding introduces modeling challenges not found in animal studies. These include the need to accommodate replicate plants for each line, consider spatial variation in field trials, address line by environment interactions, and capture nonadditive effects. Here, we propose a flexible single-stage genomic selection approach that resolves these issues. Our linear mixed model incorporates spatial variation through environment-specific terms, and also randomization-based design terms. It considers marker, and marker by environment interactions using ridge regression best linear unbiased prediction to extend genomic selection to multiple environments. Since the approach uses the raw data from line replicates, the line genetic variation is partitioned into marker and nonmarker residual genetic variation (i.e., additive and nonadditive effects). This results in a more precise estimate of marker genetic effects. Using barley height data from trials, in 2 different years, of up to 477 cultivars, we demonstrate that our new genomic selection model improves predictions compared to current models. Analyzing single trials revealed improvements in predictive ability of up to 5.7%. For the multiple environment trial (MET) model, combining both year trials improved predictive ability up to 11.4% compared to a single environment analysis. Benefits were significant even when fewer markers were used. Compared to a single-year standard model run with 3490 markers, our partitioned MET model achieved the same predictive ability using between 500 and 1000 markers depending on the trial. Our approach can be used to increase accuracy and confidence in the selection of the best lines for breeding and/or, to reduce costs by using fewer markers. Copyright © 2016 Oakey et al.
Galagan James E
Full Text Available Abstract Background Natural selection has traditionally been understood as a force responsible for pushing genes to states of higher translational efficiency, whereas lower translational efficiency has been explained by neutral mutation and genetic drift. We looked for evidence of directional selection resulting in increased unpreferred codon usage (and presumably reduced translational efficiency in three divergent clusters of eukaryotic genomes using a simple optimal-codon-based metric (Kp/Ku. Results Here we show that for some genes natural selection is indeed responsible for causing accelerated unpreferred codon substitution, and document the scope of this selection. In Cryptococcus and to a lesser extent Drosophila, we find many genes showing a statistically significant signal of selection for unpreferred codon usage in one or more lineages. We did not find evidence for this type of selection in Saccharomyces. The signal of positive selection observed from unpreferred synonymous codon substitutions is coincident in Cryptococcus and Drosophila with the distribution of upstream open reading frames (uORFs, another genic feature known to reduce translational efficiency. Functional enrichment analysis of genes exhibiting low Kp/Ku ratios reveals that genes in regulatory roles are particularly subject to this type of selection. Conclusion Through genome-wide scans, we find recent selection for unpreferred codon usage at approximately 1% of genetic loci in a Cryptococcus and several genes in Drosophila. Unpreferred codons can impede translation efficiency, and we find that genes with translation-impeding uORFs are enriched for this selection signal. We find that regulatory genes are particularly likely to be subject to selection for unpreferred codon usage. Given that expression noise can propagate through regulatory cascades, and that low translational efficiency can reduce expression noise, this finding supports the hypothesis that translational
Eliot C Bush
Full Text Available An important challenge for human evolutionary biology is to understand the genetic basis of human-chimpanzee differences. One influential idea holds that such differences depend, to a large extent, on adaptive changes in gene expression. An important step in assessing this hypothesis involves gaining a better understanding of selective constraint on noncoding regions of hominid genomes. In noncoding sequence, functional elements are frequently small and can be separated by large nonfunctional regions. For this reason, constraint in hominid genomes is likely to be patchy. Here we use conservation in more distantly related mammals and amniotes as a way of identifying small sequence windows that are likely to be functional. We find that putatively functional noncoding elements defined in this manner are subject to significant selective constraint in hominids.
Full Text Available An important challenge for human evolutionary biology is to understand the genetic basis of human-chimpanzee differences. One influential idea holds that such differences depend, to a large extent, on adaptive changes in gene expression. An important step in assessing this hypothesis involves gaining a better understanding of selective constraint on noncoding regions of hominid genomes. In noncoding sequence, functional elements are frequently small and can be separated by large nonfunctional regions. For this reason, constraint in hominid genomes is likely to be patchy. Here we use conservation in more distantly related mammals and amniotes as a way of identifying small sequence windows that are likely to be functional. We find that putatively functional noncoding elements defined in this manner are subject to significant selective constraint in hominids.
Andrés, Aida M; Hubisz, Melissa J; Indap, Amit
Balancing selection is potentially an important biological force for maintaining advantageous genetic diversity in populations, including variation that is responsible for long-term adaptation to the environment. By serving as a means to maintain genetic variation, it may be particularly relevant...... to maintaining phenotypic variation in natural populations. Nevertheless, its prevalence and specific targets in the human genome remain largely unknown. We have analyzed the patterns of diversity and divergence of 13,400 genes in two human populations using an unbiased single-nucleotide polymorphism data set......, a genome-wide approach, and a method that incorporates demography in neutrality tests. We identified an unbiased catalog of genes with signatures of long-term balancing selection, which includes immunity genes as well as genes encoding keratins and membrane channels; the catalog also shows enrichment...
Meuwissen, Theo H E; Indahl, Ulf G; Ødegård, Jørgen
Non-linear Bayesian genomic prediction models such as BayesA/B/C/R involve iteration and mostly Markov chain Monte Carlo (MCMC) algorithms, which are computationally expensive, especially when whole-genome sequence (WGS) data are analyzed. Singular value decomposition (SVD) of the genotype matrix can facilitate genomic prediction in large datasets, and can be used to estimate marker effects and their prediction error variances (PEV) in a computationally efficient manner. Here, we developed, implemented, and evaluated a direct, non-iterative method for the estimation of marker effects for the BayesC genomic prediction model. The BayesC model assumes a priori that markers have normally distributed effects with probability [Formula: see text] and no effect with probability (1 - [Formula: see text]). Marker effects and their PEV are estimated by using SVD and the posterior probability of the marker having a non-zero effect is calculated. These posterior probabilities are used to obtain marker-specific effect variances, which are subsequently used to approximate BayesC estimates of marker effects in a linear model. A computer simulation study was conducted to compare alternative genomic prediction methods, where a single reference generation was used to estimate marker effects, which were subsequently used for 10 generations of forward prediction, for which accuracies were evaluated. SVD-based posterior probabilities of markers having non-zero effects were generally lower than MCMC-based posterior probabilities, but for some regions the opposite occurred, resulting in clear signals for QTL-rich regions. The accuracies of breeding values estimated using SVD- and MCMC-based BayesC analyses were similar across the 10 generations of forward prediction. For an intermediate number of generations (2 to 5) of forward prediction, accuracies obtained with the BayesC model tended to be slightly higher than accuracies obtained using the best linear unbiased prediction of SNP
Sachdeva, Himani; Barton, Nicholas H
Adaptive introgression is common in nature and can be driven by selection acting on multiple, linked genes. We explore the effects of polygenic selection on introgression under the infinitesimal model with linkage. This model assumes that the introgressing block has an effectively infinite number of loci, each with an infinitesimal effect on the trait under selection. The block is assumed to introgress under directional selection within a native population that is genetically homogeneous. We use individual-based simulations and a branching process approximation to compute various statistics of the introgressing block, and explore how these depend on parameters such as the map length and initial trait value associated with the introgressing block, the genetic variability along the block, and the strength of selection. Our results show that the introgression dynamics of a block under infinitesimal selection are qualitatively different from the dynamics of neutral introgression. We also find that in the long run, surviving descendant blocks are likely to have intermediate lengths, and clarify how their length is shaped by the interplay between linkage and infinitesimal selection. Our results suggest that it may be difficult to distinguish the long-term introgression of a block of genome with a single strongly selected locus from the introgression of a block with multiple, tightly linked and weakly selected loci. Copyright © 2018, Genetics.
Gu, Jingjing; Orr, Nick; Park, Stephen D; Katz, Lisa M; Sulimova, Galina; MacHugh, David E; Hill, Emmeline W
Thoroughbred horses have been selected for exceptional racing performance resulting in system-wide structural and functional adaptations contributing to elite athletic phenotypes. Because selection has been recent and intense in a closed population that stems from a small number of founder animals Thoroughbreds represent a unique population within which to identify genomic contributions to exercise-related traits. Employing a population genetics-based hitchhiking mapping approach we performed a genome scan using 394 autosomal and X chromosome microsatellite loci and identified positively selected loci in the extreme tail-ends of the empirical distributions for (1) deviations from expected heterozygosity (Ewens-Watterson test) in Thoroughbred (n = 112) and (2) global differentiation among four geographically diverse horse populations (F(ST)). We found positively selected genomic regions in Thoroughbred enriched for phosphoinositide-mediated signalling (3.2-fold enrichment; PThoroughbred athletic phenotype. We report for the first time candidate athletic-performance genes within regions targeted by selection in Thoroughbred horses that are principally responsible for fatty acid oxidation, increased insulin sensitivity and muscle strength: ACSS1 (acyl-CoA synthetase short-chain family member 1), ACTA1 (actin, alpha 1, skeletal muscle), ACTN2 (actinin, alpha 2), ADHFE1 (alcohol dehydrogenase, iron containing, 1), MTFR1 (mitochondrial fission regulator 1), PDK4 (pyruvate dehydrogenase kinase, isozyme 4) and TNC (tenascin C). Understanding the genetic basis for exercise adaptation will be crucial for the identification of genes within the complex molecular networks underlying obesity and its consequential pathologies, such as type 2 diabetes. Therefore, we propose Thoroughbred as a novel in vivo large animal model for understanding molecular protection against metabolic disease.
Duforet-Frebourg, Nicolas; Luu, Keurcien; Laval, Guillaume; Bazin, Eric; Blum, Michael G B
To characterize natural selection, various analytical methods for detecting candidate genomic regions have been developed. We propose to perform genome-wide scans of natural selection using principal component analysis (PCA). We show that the common FST index of genetic differentiation between populations can be viewed as the proportion of variance explained by the principal components. Considering the correlations between genetic variants and each principal component provides a conceptual framework to detect genetic variants involved in local adaptation without any prior definition of populations. To validate the PCA-based approach, we consider the 1000 Genomes data (phase 1) considering 850 individuals coming from Africa, Asia, and Europe. The number of genetic variants is of the order of 36 millions obtained with a low-coverage sequencing depth (3×). The correlations between genetic variation and each principal component provide well-known targets for positive selection (EDAR, SLC24A5, SLC45A2, DARC), and also new candidate genes (APPBPP2, TP1A1, RTTN, KCNMA, MYO5C) and noncoding RNAs. In addition to identifying genes involved in biological adaptation, we identify two biological pathways involved in polygenic adaptation that are related to the innate immune system (beta defensins) and to lipid metabolism (fatty acid omega oxidation). An additional analysis of European data shows that a genome scan based on PCA retrieves classical examples of local adaptation even when there are no well-defined populations. PCA-based statistics, implemented in the PCAdapt R package and the PCAdapt fast open-source software, retrieve well-known signals of human adaptation, which is encouraging for future whole-genome sequencing project, especially when defining populations is difficult. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
Full Text Available Traditional breeding strategies for selecting superior genotypes depending on phenotypic traits have proven to be of limited success, as this direct selection is hindered by low heritability, genetic interactions such as epistasis, environmental-genotype interactions, and polygenic effects. With the advent of new genomic tools, breeders have paved a way for selecting superior breeds. Genomic selection (GS has emerged as one of the most important approaches for predicting genotype performance. Here, we tested the breeding values of 240 maize subtropical lines phenotyped for drought at different environments using 29,619 cured SNPs. Prediction accuracies of seven genomic selection models (ridge regression, LASSO, elastic net, random forest, reproducing kernel Hilbert space, Bayes A and Bayes B were tested for their agronomic traits. Though prediction accuracies of Bayes B, Bayes A and RKHS were comparable, Bayes B outperformed the other models by predicting highest Pearson correlation coefficient in all three environments. From Bayes B, a set of the top 1053 significant SNPs with higher marker effects was selected across all datasets to validate the genes and QTLs. Out of these 1053 SNPs, 77 SNPs associated with 10 drought-responsive transcription factors. These transcription factors were associated with different physiological and molecular functions (stomatal closure, root development, hormonal signaling and photosynthesis. Of several models, Bayes B has been shown to have the highest level of prediction accuracy for our data sets. Our experiments also highlighted several SNPs based on their performance and relative importance to drought tolerance. The result of our experiments is important for the selection of superior genotypes and candidate genes for breeding drought-tolerant maize hybrids.
Benjamin F Voight
Full Text Available The identification of signals of very recent positive selection provides information about the adaptation of modern humans to local conditions. We report here on a genome-wide scan for signals of very recent positive selection in favor of variants that have not yet reached fixation. We describe a new analytical method for scanning single nucleotide polymorphism (SNP data for signals of recent selection, and apply this to data from the International HapMap Project. In all three continental groups we find widespread signals of recent positive selection. Most signals are region-specific, though a significant excess are shared across groups. Contrary to some earlier low resolution studies that suggested a paucity of recent selection in sub-Saharan Africans, we find that by some measures our strongest signals of selection are from the Yoruba population. Finally, since these signals indicate the existence of genetic variants that have substantially different fitnesses, they must indicate loci that are the source of significant phenotypic variation. Though the relevant phenotypes are generally not known, such loci should be of particular interest in mapping studies of complex traits. For this purpose we have developed a set of SNPs that can be used to tag the strongest approximately 250 signals of recent selection in each population.
Chandonia, John-Marc; Brenner, Steven E.
The structural genomics project is an international effort to determine the three-dimensional shapes of all important biological macromolecules, with a primary focus on proteins. Target proteins should be selected according to a strategy which is medically and biologically relevant, of good value, and tractable. As an option to consider, we present the Pfam5000 strategy, which involves selecting the 5000 most important families from the Pfam database as sources for targets. We compare the Pfam5000 strategy to several other proposed strategies that would require similar numbers of targets. These include including complete solution of several small to moderately sized bacterial proteomes, partial coverage of the human proteome, and random selection of approximately 5000 targets from sequenced genomes. We measure the impact that successful implementation of these strategies would have upon structural interpretation of the proteins in Swiss-Prot, TrEMBL, and 131 complete proteomes (including 10 of eukaryotes) from the Proteome Analysis database at EBI. Solving the structures of proteins from the 5000 largest Pfam families would allow accurate fold assignment for approximately 68 percent of all prokaryotic proteins (covering 59 percent of residues) and 61 percent of eukaryotic proteins (40 percent of residues). More fine-grained coverage which would allow accurate modeling of these proteins would require an order of magnitude more targets. The Pfam5000 strategy may be modified in several ways, for example to focus on larger families, bacterial sequences, or eukaryotic sequences; as long as secondary consideration is given to large families within Pfam, coverage results vary only slightly. In contrast, focusing structural genomics on a single tractable genome would have only a limited impact in structural knowledge of other proteomes: a significant fraction (about 30-40 percent of the proteins, and 40-60 percent of the residues) of each proteome is classified in small
Full Text Available Thoroughbred horses have been selected for exceptional racing performance resulting in system-wide structural and functional adaptations contributing to elite athletic phenotypes. Because selection has been recent and intense in a closed population that stems from a small number of founder animals Thoroughbreds represent a unique population within which to identify genomic contributions to exercise-related traits. Employing a population genetics-based hitchhiking mapping approach we performed a genome scan using 394 autosomal and X chromosome microsatellite loci and identified positively selected loci in the extreme tail-ends of the empirical distributions for (1 deviations from expected heterozygosity (Ewens-Watterson test in Thoroughbred (n = 112 and (2 global differentiation among four geographically diverse horse populations (F(ST. We found positively selected genomic regions in Thoroughbred enriched for phosphoinositide-mediated signalling (3.2-fold enrichment; P<0.01, insulin receptor signalling (5.0-fold enrichment; P<0.01 and lipid transport (2.2-fold enrichment; P<0.05 genes. We found a significant overrepresentation of sarcoglycan complex (11.1-fold enrichment; P<0.05 and focal adhesion pathway (1.9-fold enrichment; P<0.01 genes highlighting the role for muscle strength and integrity in the Thoroughbred athletic phenotype. We report for the first time candidate athletic-performance genes within regions targeted by selection in Thoroughbred horses that are principally responsible for fatty acid oxidation, increased insulin sensitivity and muscle strength: ACSS1 (acyl-CoA synthetase short-chain family member 1, ACTA1 (actin, alpha 1, skeletal muscle, ACTN2 (actinin, alpha 2, ADHFE1 (alcohol dehydrogenase, iron containing, 1, MTFR1 (mitochondrial fission regulator 1, PDK4 (pyruvate dehydrogenase kinase, isozyme 4 and TNC (tenascin C. Understanding the genetic basis for exercise adaptation will be crucial for the identification of genes
Full Text Available Studies using in-situ Auger electron spectroscopy and reflection high energy electron diffraction, and ex-situ high resolution X-ray diffraction and electron backscatter diffraction reveal that a MgS thin film grown directly on a GaAs (100 substrate by molecular beam epitaxy adopts its most stable phase, the rocksalt structure, with a lattice constant of 5.20 Å. A Au/MgS/n+-GaAs (100 Schottky-barrier photodiode was fabricated and its room temperature photoresponse was measured to have a sharp fall-off edge at 235 nm with rejection of more than three orders at 400 nm and higher than five orders at 500 nm, promising for various solar-blind UV detection applications.
Determination of the maximum Midwest Guardrail System (MGS) mounting height was performed in two phases. : Phase I concentrated on crash testing: two full-scale crash tests were performed on the MGS with top-rail mounting heights : of 34 in. (864 mm)...
Full Text Available Commercial sheep raised for mutton grow faster than traditional Chinese sheep breeds. Here, we aimed to evaluate genetic selection among three different types of sheep breed: two well-known commercial mutton breeds and one indigenous Chinese breed.We first combined locus-specific branch lengths and di statistical methods to detect candidate regions targeted by selection in the three different populations. The results showed that the genetic distances reached at least medium divergence for each pairwise combination. We found these two methods were highly correlated, and identified many growth-related candidate genes undergoing artificial selection. For production traits, APOBR and FTO are associated with body mass index. For meat traits, ALDOA, STK32B and FAM190A are related to marbling. For reproduction traits, CCNB2 and SLC8A3 affect oocyte development. We also found two well-known genes, GHR (which affects meat production and quality and EDAR (associated with hair thickness were associated with German mutton merino sheep. Furthermore, four genes (POL, RPL7, MSL1 and SHISA9 were associated with pre-weaning gain in our previous genome-wide association study.Our results indicated that combine locus-specific branch lengths and di statistical approaches can reduce the searching ranges for specific selection. And we got many credible candidate genes which not only confirm the results of previous reports, but also provide a suite of novel candidate genes in defined breeds to guide hybridization breeding.
Wang, Huihua; Zhang, Li; Cao, Jiaxve; Wu, Mingming; Ma, Xiaomeng; Liu, Zhen; Liu, Ruizao; Zhao, Fuping; Wei, Caihong; Du, Lixin
Commercial sheep raised for mutton grow faster than traditional Chinese sheep breeds. Here, we aimed to evaluate genetic selection among three different types of sheep breed: two well-known commercial mutton breeds and one indigenous Chinese breed. We first combined locus-specific branch lengths and di statistical methods to detect candidate regions targeted by selection in the three different populations. The results showed that the genetic distances reached at least medium divergence for each pairwise combination. We found these two methods were highly correlated, and identified many growth-related candidate genes undergoing artificial selection. For production traits, APOBR and FTO are associated with body mass index. For meat traits, ALDOA, STK32B and FAM190A are related to marbling. For reproduction traits, CCNB2 and SLC8A3 affect oocyte development. We also found two well-known genes, GHR (which affects meat production and quality) and EDAR (associated with hair thickness) were associated with German mutton merino sheep. Furthermore, four genes (POL, RPL7, MSL1 and SHISA9) were associated with pre-weaning gain in our previous genome-wide association study. Our results indicated that combine locus-specific branch lengths and di statistical approaches can reduce the searching ranges for specific selection. And we got many credible candidate genes which not only confirm the results of previous reports, but also provide a suite of novel candidate genes in defined breeds to guide hybridization breeding.
Jannink, Jean-Luc; Lorenz, Aaron J; Iwata, Hiroyoshi
We intuitively believe that the dramatic drop in the cost of DNA marker information we have experienced should have immediate benefits in accelerating the delivery of crop varieties with improved yield, quality and biotic and abiotic stress tolerance. But these traits are complex and affected by many genes, each with small effect. Traditional marker-assisted selection has been ineffective for such traits. The introduction of genomic selection (GS), however, has shifted that paradigm. Rather than seeking to identify individual loci significantly associated with a trait, GS uses all marker data as predictors of performance and consequently delivers more accurate predictions. Selection can be based on GS predictions, potentially leading to more rapid and lower cost gains from breeding. The objectives of this article are to review essential aspects of GS and summarize the important take-home messages from recent theoretical, simulation and empirical studies. We then look forward and consider research needs surrounding methodological questions and the implications of GS for long-term selection.
Full Text Available Genotype by environment interactions (GxE are very common in livestock and hamper genetic improvement. On the other hand, GxE is a source of genetic variation: genetic variation in response to environment, e.g. environmental perturbations such as heat stress or disease. In livestock breeding, there is tendency to ignore GxE because of increased complexity of models for genetic evaluations and lack of accuracy in extreme environments. GxE, however, creates opportunities to increase resilience of animals towards environmental perturbations. The main aim of the paper is to investigate to which extent GxE can be exploited with traditional and genomic selection methods. Furthermore, we investigated the benefit of reaction norm models compared to conventional methods ignoring GxE. The questions were addressed with selection index theory. GxE was modelled according to a linear reaction norm model in which the environmental gradient is the contemporary group mean. Economic values were based on linear and non-linear profit equations.Accuracies of environment-specific (GEBV were highest in intermediate environments and lowest in extreme environments. Reaction norm models had higher accuracies of (GEBV in extreme environments than conventional models ignoring GxE. Genomic selection always resulted in higher response to selection in all environments than sib or progeny testing schemes. The increase in response was with genomic selection between 9% and 140% compared to sib testing and between 11% and 114% compared to progeny testing when the reference population consisted of 1 million animals across all environments. When the aim was to decrease environmental sensitivity, the response in slope of the reaction norm model with genomic selection was between 1.09 and 319 times larger than with sib or progeny testing and in the right direction in contrast to sib and progeny testing that still increased environmental sensitivity. This shows that genomic selection
Daetwyler, Hans D.; Hayden, Matthew J.; Spangenberg, German C.; Hayes, Ben J.
Doubled haploids are routinely created and phenotypically selected in plant breeding programs to accelerate the breeding cycle. Genomic selection, which makes use of both phenotypes and genotypes, has been shown to further improve genetic gain through prediction of performance before or without phenotypic characterization of novel germplasm. Additional opportunities exist to combine genomic prediction methods with the creation of doubled haploids. Here we propose an extension to genomic selec...
Full Text Available Genomic selection (GS is a promising strategy for enhancing genetic gain. We investigated the accuracy of genomic estimated breeding values (GEBV in four inter-related synthetic populations that underwent several cycles of recurrent selection in an upland rice-breeding program. A total of 343 S2:4 lines extracted from those populations were phenotyped for flowering time, plant height, grain yield and panicle weight, and genotyped with an average density of one marker per 44.8 kb. The relative effect of the linkage disequilibrium (LD and minor allele frequency (MAF thresholds for selecting markers, the relative size of the training population (TP and of the validation population (VP, the selected trait and the genomic prediction models (frequentist and Bayesian on the accuracy of GEBVs was investigated in 540 cross validation experiments with 100 replicates. The effect of kinship between the training and validation populations was tested in an additional set of 840 cross validation experiments with a single genomic prediction model. LD was high (average r2 = 0.59 at 25 kb and decreased slowly, distribution of allele frequencies at individual loci was markedly skewed toward unbalanced frequencies (MAF average value 15.2% and median 9.6%, and differentiation between the four synthetic populations was low (FST ≤0.06. The accuracy of GEBV across all cross validation experiments ranged from 0.12 to 0.54 with an average of 0.30. Significant differences in accuracy were observed among the different levels of each factor investigated. Phenotypic traits had the biggest effect, and the size of the incidence matrix had the smallest. Significant first degree interaction was observed for GEBV accuracy between traits and all the other factors studied, and between prediction models and LD, MAF and composition of the TP. The potential of GS to accelerate genetic gain and breeding options to increase the accuracy of predictions are discussed.
Grenier, Cécile; Cao, Tuong-Vi; Ospina, Yolima; Quintero, Constanza; Châtel, Marc Henri; Tohme, Joe; Courtois, Brigitte; Ahmadi, Nourollah
Genomic selection (GS) is a promising strategy for enhancing genetic gain. We investigated the accuracy of genomic estimated breeding values (GEBV) in four inter-related synthetic populations that underwent several cycles of recurrent selection in an upland rice-breeding program. A total of 343 S2:4 lines extracted from those populations were phenotyped for flowering time, plant height, grain yield and panicle weight, and genotyped with an average density of one marker per 44.8 kb. The relative effect of the linkage disequilibrium (LD) and minor allele frequency (MAF) thresholds for selecting markers, the relative size of the training population (TP) and of the validation population (VP), the selected trait and the genomic prediction models (frequentist and Bayesian) on the accuracy of GEBVs was investigated in 540 cross validation experiments with 100 replicates. The effect of kinship between the training and validation populations was tested in an additional set of 840 cross validation experiments with a single genomic prediction model. LD was high (average r2 = 0.59 at 25 kb) and decreased slowly, distribution of allele frequencies at individual loci was markedly skewed toward unbalanced frequencies (MAF average value 15.2% and median 9.6%), and differentiation between the four synthetic populations was low (FST ≤0.06). The accuracy of GEBV across all cross validation experiments ranged from 0.12 to 0.54 with an average of 0.30. Significant differences in accuracy were observed among the different levels of each factor investigated. Phenotypic traits had the biggest effect, and the size of the incidence matrix had the smallest. Significant first degree interaction was observed for GEBV accuracy between traits and all the other factors studied, and between prediction models and LD, MAF and composition of the TP. The potential of GS to accelerate genetic gain and breeding options to increase the accuracy of predictions are discussed.
Zhang, Xuecai; Pérez-Rodríguez, Paulino; Burgueño, Juan; Olsen, Michael; Buckler, Edward; Atlin, Gary; Prasanna, Boddupalli M; Vargas, Mateo; San Vicente, Félix; Crossa, José
Genomic selection (GS) increases genetic gain by reducing the length of the selection cycle, as has been exemplified in maize using rapid cycling recombination of biparental populations. However, no results of GS applied to maize multi-parental populations have been reported so far. This study is the first to show realized genetic gains of rapid cycling genomic selection (RCGS) for four recombination cycles in a multi-parental tropical maize population. Eighteen elite tropical maize lines were intercrossed twice, and self-pollinated once, to form the cycle 0 (C 0 ) training population. A total of 1000 ear-to-row C 0 families was genotyped with 955,690 genotyping-by-sequencing SNP markers; their testcrosses were phenotyped at four optimal locations in Mexico to form the training population. Individuals from families with the best plant types, maturity, and grain yield were selected and intermated to form RCGS cycle 1 (C 1 ). Predictions of the genotyped individuals forming cycle C 1 were made, and the best predicted grain yielders were selected as parents of C 2 ; this was repeated for more cycles (C 2 , C 3 , and C 4 ), thereby achieving two cycles per year. Multi-environment trials of individuals from populations C 0, C 1 , C 2 , C 3 , and C 4 , together with four benchmark checks were evaluated at two locations in Mexico. Results indicated that realized grain yield from C 1 to C 4 reached 0.225 ton ha -1 per cycle, which is equivalent to 0.100 ton ha -1 yr -1 over a 4.5-yr breeding period from the initial cross to the last cycle. Compared with the original 18 parents used to form cycle 0 (C 0 ), genetic diversity narrowed only slightly during the last GS cycles (C 3 and C 4 ). Results indicate that, in tropical maize multi-parental breeding populations, RCGS can be an effective breeding strategy for simultaneously conserving genetic diversity and achieving high genetic gains in a short period of time. Copyright © 2017 Zhang et al.
Isidro, Julio; Jannink, Jean-Luc; Akdemir, Deniz; Poland, Jesse; Heslot, Nicolas; Sorrells, Mark E
Population structure must be evaluated before optimization of the training set population. Maximizing the phenotypic variance captured by the training set is important for optimal performance. The optimization of the training set (TRS) in genomic selection has received much interest in both animal and plant breeding, because it is critical to the accuracy of the prediction models. In this study, five different TRS sampling algorithms, stratified sampling, mean of the coefficient of determination (CDmean), mean of predictor error variance (PEVmean), stratified CDmean (StratCDmean) and random sampling, were evaluated for prediction accuracy in the presence of different levels of population structure. In the presence of population structure, the most phenotypic variation captured by a sampling method in the TRS is desirable. The wheat dataset showed mild population structure, and CDmean and stratified CDmean methods showed the highest accuracies for all the traits except for test weight and heading date. The rice dataset had strong population structure and the approach based on stratified sampling showed the highest accuracies for all traits. In general, CDmean minimized the relationship between genotypes in the TRS, maximizing the relationship between TRS and the test set. This makes it suitable as an optimization criterion for long-term selection. Our results indicated that the best selection criterion used to optimize the TRS seems to depend on the interaction of trait architecture and population structure.
Calus, M P L; Bijma, P; Veerkamp, R F
Our objective was to investigate the economic effect of prioritizing heifers for replacement at the herd level based on genomic estimated breeding values, and to compute break-even genotyping costs across a wide range of scenarios. Specifically, we aimed to determine the optimal proportion of preselection based on parent average information for all scenarios considered. Considered replacement strategies include a range of different selection intensities by considering different numbers of heifers available for replacement (15-45 in a herd with 100 dairy cows) as well as different replacement rates (15-40%). Use of conventional versus sexed semen was considered, where the latter resulted in having twice as many heifers available for replacement. The baseline scenario relies on prioritization of replacement heifers based on parent average. The first alternative scenario involved genomic selection of heifers, considering that all heifers were genotyped. The benefits of genomic selection in this scenario were computed using a simple formula that only requires the number of lactating animals, the difference in accuracy between parent average and genomic selection (GS), and the selection intensity as input. When all heifers were genotyped, using GS for replacement of heifers was beneficial in most scenarios for current genotyping prices, provided some room exists for selection, in the sense that at least 2 more heifers are available than needed for replacement. In those scenarios, minimum break-even genotyping costs were equal to half the economic value of a standard deviation of the breeding goal. The second alternative scenario involved a preselection based on parent average, followed by GS among all the preselected heifers. It was in almost all cases beneficial to genotype all heifers when conventional semen was used (i.e., to do no preselection). The optimal proportion of preselection based on parent average was at least 0.63 when sexed semen was used. Use of sexed
Roos, de A.P.W.; Schrooten, C.; Veerkamp, R.F.; Arendonk, van J.A.M.
Genomic selection has the potential to revolutionize dairy cattle breeding because young animals can be accurately selected as parents, leading to a much shorter generation interval and higher rates of genetic gain. The aims of this study were to assess the effects of genomic selection and reduction
Lombaert, R.; de Vries, B.L.; de Koter, A.; Decin, L.; Min, M.; Smolders, K.; Mutschke, H.; Waters, L.B.F.M.
The broad 30 μm feature in carbon stars is commonly attributed to MgS dust particles. However, reproducing the 30 μm feature with homogeneous MgS grains would require much more sulfur relative to the solar abundance. Direct gas-phase condensation of MgS occurs at a low efficiency. Precipitation of
Brankovics, Balázs; Zhang, Hao; van Diepeningen, Anne D; van der Lee, Theo A J; Waalwijk, Cees; de Hoog, G Sybren
GRAbB (Genomic Region Assembly by Baiting) is a new program that is dedicated to assemble specific genomic regions from NGS data. This approach is especially useful when dealing with multi copy regions, such as mitochondrial genome and the rDNA repeat region, parts of the genome that are often
Taye, Mengistie; Lee, Wonseok; Caetano-Anolles, Kelsey; Dessie, Tadelle; Hanotte, Olivier; Mwai, Okeyo Ally; Kemp, Stephen; Cho, Seoae; Oh, Sung Jong; Lee, Hak-Kyo; Kim, Heebal
As African indigenous cattle evolved in a hot tropical climate, they have developed an inherent thermotolerance; survival mechanisms include a light-colored and shiny coat, increased sweating, and cellular and molecular mechanisms to cope with high environmental temperature. Here, we report the positive selection signature of genes in African cattle breeds which contribute for their heat tolerance mechanisms. We compared the genomes of five indigenous African cattle breeds with the genomes of four commercial cattle breeds using cross-population composite likelihood ratio (XP-CLR) and cross-population extended haplotype homozygosity (XP-EHH) statistical methods. We identified 296 (XP-EHH) and 327 (XP-CLR) positively selected genes. Gene ontology analysis resulted in 41 biological process terms and six Kyoto Encyclopedia of Genes and Genomes pathways. Several genes and pathways were found to be involved in oxidative stress response, osmotic stress response, heat shock response, hair and skin properties, sweat gland development and sweating, feed intake and metabolism, and reproduction functions. The genes and pathways identified directly or indirectly contribute to the superior heat tolerance mechanisms in African cattle populations. The result will improve our understanding of the biological mechanisms of heat tolerance in African cattle breeds and opens an avenue for further study. © 2017 Japanese Society of Animal Science.
Jonas, Elisabeth; de Koning, Dirk Jan
Genomic Selection is an important topic in quantitative genetics and breeding. Not only does it allow the full use of current molecular genetic technologies, it stimulates also the development of new methods and models. Genomic selection, if fully implemented in commercial farming, should have a major impact on the productivity of various agricultural systems. But suggested approaches need to be applicable in commercial breeding populations. Many of the published research studies focus on methodologies. We conclude from the reviewed publications, that a stronger focus on strategies for the implementation of genomic selection in advanced breeding lines, introduction of new varieties, hybrids or multi-line crosses is needed. Efforts to find solutions for a better prediction and integration of environmental influences need to continue within applied breeding schemes. Goals of the implementation of genomic selection into crop breeding should be carefully defined and crop breeders in the private sector will play a substantial part in the decision-making process. However, the lack of published results from studies within, or in collaboration with, private companies diminishes the knowledge on the status of genomic selection within applied breeding programmes. Studies on the implementation of genomic selection in plant breeding need to evaluate models and methods with an enhanced emphasis on population-specific requirements and production environments. Adaptation of methods to breeding schemes or changes to breeding programmes for a better integration of genomic selection strategies are needed across species. More openness with a continuous exchange will contribute to successes.
Genomic selection (GS) uses genome-wide molecular marker data to predict the genetic value of selection candidates in breeding programs. In plant breeding, the ability to produce large numbers of progeny per cross allows GS to be conducted within each family. However, this approach requires phenotyp...
Full Text Available Genomic selection is a promising development in agriculture, aiming improved production by exploiting molecular genetic markers to design novel breeding programs and to develop new markers-based models for genetic evaluation. It opens opportunities for research, as novel algorithms and lab methodologies are developed. Genomic selection can be applied in many breeds and species. Further research on the implementation of genomic selection in breeding programs is highly desirable not only for the common good, but also the private sector (breeding companies. It has been projected that this approach will improve selection routines, especially in species with long reproduction cycles, late or sex-limited or expensive trait recording and for complex traits. The task of integrating genomic selection into existing breeding programs is, however, not straightforward. Despite successful integration into breeding programs for dairy cattle, it has yet to be shown how much emphasis can be given to the genomic information and how much additional phenotypic information is needed from new selection candidates. Genomic selection is already part of future planning in many breeding companies of pigs and beef cattle among others, but further research is needed to fully estimate how effective the use of genomic information will be for the prediction of the performance of future breeding stock. Genomic prediction of production in crossbreeding and across-breed schemes, costs and choice of individuals for genotyping are reasons for a reluctance to fully rely on genomic information for selection decisions. Breeding objectives are highly dependent on the industry and the additional gain when using genomic information has to be considered carefully. This review synthesizes some of the suggested approaches in selected livestock species including cattle, pig, chicken and fish. It outlines tasks to help understanding possible consequences when applying genomic information in
Hinson, D. P.
The Sun-synchronous, polar orbit of Mars Global Surveyor (MGS) provides frequent opportunities for radio occultation sounding of the neutral atmosphere. The basic result of each experiment is a profile of pressure and temperature versus planetocentric radius and geopotential. More than 4000 profiles were obtained during the 687-day mapping phase of the mission, and additional observations are underway. These measurements allow detailed characterization of planetary-scale dynamics, including stationary planetary (or Rossby) waves and transient waves produced by instability. For example, both types of dynamics were observed near 67° S during midwinter of the southern hemisphere (Ls=134° --160° ). Planetary waves are the most prominent dynamical feature in this subset of data. At zonal wave number s=1, both the temperature and geopotential fields tilt westward with increasing height, as expected for vertically-propagating planetary waves forced at the surface. The wave-2 structure is more nearly barotropic. The amplitude in geopotential height at Ls=150° increases from ~200 m near the surface to ~700 m at 10 Pa. The corresponding meridional wind speed increases from ~5 m s-1 near the surface to ~20 m s-1 at 10 Pa. Traveling ``baroclinic'' waves also appear intermittently during this interval. The dominant mode has a period of ~2 sols, s=3, and a peak amplitude of ~7 K at 300 Pa. Stong zonal variations in eddy amplitude signal the presence of a possible ``storm zone'' at 150° --330° E longitude. This talk will include other examples of these phenomena as well as comparisons with computer simulations by a Martian general circulation model (MGCM).
Clarke, Thomas H; Brinkac, Lauren M; Sutton, Granger; Fouts, Derrick E
The vast number of available sequenced bacterial genomes occasionally exceeds the facilities of comparative genomic methods or is dominated by a single outbreak strain, and thus a diverse and representative subset is required. Generation of the reduced subset currently requires a priori supervised clustering and sequence-only selection of medoid genomic sequences, independent of any additional genome metrics or strain attributes. The GGRaSP R-package described below generates a reduced subset of genomes that prioritizes maintaining genomes of interest to the user as well as minimizing the loss of genetic variation. The package also allows for unsupervised clustering by modeling the genomic relationships using a Gaussian Mixture Model to select an appropriate cluster threshold. We demonstrate the capabilities of GGRaSP by generating a reduced list of 315 genomes from a genomic dataset of 4600 Escherichia coli genomes, prioritizing selection by type strain and by genome completeness. GGRaSP is available at https://github.com/JCVenterInstitute/ggrasp/. email@example.com. Supplementary data are available at the GitHub site.
Ibanez-Escriche, N.; Gonzalez-Recio, O.
The aim of this work was to review the main challenges and pitfalls of the implementation of genomic selection in the breeding programs of different livestock species. Genomic selection is now one of the main challenges in animal breeding and genetics. Its application could considerably increase the genetic gain in traits of interest. However, the success of its practical implementation depends on the selection scheme characteristics, and these must be studied for each particular case. In dairy cattle, especially in Holsteins, genomic selection is a reality. However, in other livestock species (beef cattle, small ruminants, monogastrics and fish) genomic selection has mainly been used experimentally. The main limitation for its implementation in the mentioned livestock species is the high geno typing costs compared to the low selection value of the candidate. Nevertheless, nowadays the possibility of using single-nucleotide polymorphism (SNP) chips of low density to make genomic selection applications economically feasible is under study. Economic studies may optimize the benefits of genomic selection (GS) to include new traits in the breeding goals. It is evident that genomic selection offers great potential; however, a suitable geno typing strategy and recording system for each case is needed in order to properly exploit it. (Author) 50 refs.
Daetwyler, Hans D; Hayden, Matthew J; Spangenberg, German C; Hayes, Ben J
Doubled haploids are routinely created and phenotypically selected in plant breeding programs to accelerate the breeding cycle. Genomic selection, which makes use of both phenotypes and genotypes, has been shown to further improve genetic gain through prediction of performance before or without phenotypic characterization of novel germplasm. Additional opportunities exist to combine genomic prediction methods with the creation of doubled haploids. Here we propose an extension to genomic selection, optimal haploid value (OHV) selection, which predicts the best doubled haploid that can be produced from a segregating plant. This method focuses selection on the haplotype and optimizes the breeding program toward its end goal of generating an elite fixed line. We rigorously tested OHV selection breeding programs, using computer simulation, and show that it results in up to 0.6 standard deviations more genetic gain than genomic selection. At the same time, OHV selection preserved a substantially greater amount of genetic diversity in the population than genomic selection, which is important to achieve long-term genetic gain in breeding populations. Copyright © 2015 by the Genetics Society of America.
Plasmodium parasites, the causal agents of malaria, result in more than 1 million deaths annually. Plasmodium are unicellular eukaryotes with small ~23 Mb genomes encoding ~5200 protein-coding genes. The protein-coding genes comprise about half of these genomes. Although evolutionary processes have a significant impact on malaria control, the selective pressures within Plasmodium genomes are poorly understood, particularly in the non-protein-coding portion of the genome. We use evolutionary methods to describe selective processes in both the coding and non-coding regions of these genomes. Based on genome alignments of seven Plasmodium species, we show that protein-coding, intergenic and intronic regions are all subject to purifying selection and we identify 670 conserved non-genic elements. We then use genome-wide polymorphism data from P. falciparum to describe short-term selective processes in this species and identify some candidate genes for balancing (diversifying) selection. Our analyses suggest that there are many functional elements in the non-genic regions of these genomes and that adaptive evolution has occurred more frequently in the protein-coding regions of the genome. © 2010 Nygaard et al.
Su, Fei; Ou, Hong-Yu; Tao, Fei; Tang, Hongzhi; Xu, Ping
With genomic sequences of many closely related bacterial strains made available by deep sequencing, it is now possible to investigate trends in prokaryotic microevolution. Positive selection is a sub-process of microevolution, in which a particular mutation is favored, causing the allele frequency to continuously shift in one direction. Wide scanning of prokaryotic genomes has shown that positive selection at the molecular level is much more frequent than expected. Genes with significant positive selection may play key roles in bacterial adaption to different environmental pressures. However, selection pressure analyses are computationally intensive and awkward to configure. Here we describe an open access web server, which is designated as PSP (Positive Selection analysis for Prokaryotic genomes) for performing evolutionary analysis on orthologous coding genes, specially designed for rapid comparison of dozens of closely related prokaryotic genomes. Remarkably, PSP facilitates functional exploration at the multiple levels by assignments and enrichments of KO, GO or COG terms. To illustrate this user-friendly tool, we analyzed Escherichia coli and Bacillus cereus genomes and found that several genes, which play key roles in human infection and antibiotic resistance, show significant evidence of positive selection. PSP is freely available to all users without any login requirement at: http://db-mml.sjtu.edu.cn/PSP/. PSP ultimately allows researchers to do genome-scale analysis for evolutionary selection across multiple prokaryotic genomes rapidly and easily, and identify the genes undergoing positive selection, which may play key roles in the interactions of host-pathogen and/or environmental adaptation.
Jonas, Elisabeth; de Koning, Dirk-Jan
Genomic selection is a promising development in agriculture, aiming improved production by exploiting molecular genetic markers to design novel breeding programs and to develop new markers-based models for genetic evaluation. It opens opportunities for research, as novel algorithms and lab methodologies are developed. Genomic selection can be applied in many breeds and species. Further research on the implementation of genomic selection (GS) in breeding programs is highly desirable not only for the common good, but also the private sector (breeding companies). It has been projected that this approach will improve selection routines, especially in species with long reproduction cycles, late or sex-limited or expensive trait recording and for complex traits. The task of integrating GS into existing breeding programs is, however, not straightforward. Despite successful integration into breeding programs for dairy cattle, it has yet to be shown how much emphasis can be given to the genomic information and how much additional phenotypic information is needed from new selection candidates. Genomic selection is already part of future planning in many breeding companies of pigs and beef cattle among others, but further research is needed to fully estimate how effective the use of genomic information will be for the prediction of the performance of future breeding stock. Genomic prediction of production in crossbreeding and across-breed schemes, costs and choice of individuals for genotyping are reasons for a reluctance to fully rely on genomic information for selection decisions. Breeding objectives are highly dependent on the industry and the additional gain when using genomic information has to be considered carefully. This review synthesizes some of the suggested approaches in selected livestock species including cattle, pig, chicken, and fish. It outlines tasks to help understanding possible consequences when applying genomic information in breeding scenarios.
Leichty, Aaron R; Brisson, Dustin
Population genomic analyses have demonstrated power to address major questions in evolutionary and molecular microbiology. Collecting populations of genomes is hindered in many microbial species by the absence of a cost effective and practical method to collect ample quantities of sufficiently pure genomic DNA for next-generation sequencing. Here we present a simple method to amplify genomes of a target microbial species present in a complex, natural sample. The selective whole genome amplification (SWGA) technique amplifies target genomes using nucleotide sequence motifs that are common in the target microbe genome, but rare in the background genomes, to prime the highly processive phi29 polymerase. SWGA thus selectively amplifies the target genome from samples in which it originally represented a minor fraction of the total DNA. The post-SWGA samples are enriched in target genomic DNA, which are ideal for population resequencing. We demonstrate the efficacy of SWGA using both laboratory-prepared mixtures of cultured microbes as well as a natural host-microbe association. Targeted amplification of Borrelia burgdorferi mixed with Escherichia coli at genome ratios of 1:2000 resulted in >10(5)-fold amplification of the target genomes with genomic extracts from Wolbachia pipientis-infected Drosophila melanogaster resulted in up to 70% of high-throughput resequencing reads mapping to the W. pipientis genome. By contrast, 2-9% of sequencing reads were derived from W. pipientis without prior amplification. The SWGA technique results in high sequencing coverage at a fraction of the sequencing effort, thus allowing population genomic studies at affordable costs. Copyright © 2014 by the Genetics Society of America.
Dong, Linsong; Xiao, Shijun; Chen, Junwei; Wan, Liang; Wang, Zhiyong
Genomic selection (GS) is an effective method to improve predictive accuracies of genetic values. However, high cost in genotyping will limit the application of this technology in some species. Therefore, it is necessary to find some methods to reduce the genotyping costs in genomic selection. Large yellow croaker is one of the most commercially important marine fish species in southeast China and Eastern Asia. In this study, genotyping-by-sequencing was used to construct the libraries for the NGS sequencing and find 29,748 SNPs in the genome. Two traits, eviscerated weight (EW) and the ratio between eviscerated weight and whole body weight (REW), were chosen to study. Two strategies to reduce the costs were proposed as follows: selecting extreme phenotypes (EP) for genotyping in reference population or pre-selecting SNPs to construct low-density marker panels in candidates. Three methods of pre-selection of SNPs, i.e., pre-selecting SNPs by absolute effects (SE), by single marker analysis (SMA), and by fixed intervals of sequence number (EL), were studied. The results showed that using EP was a feasible method to save the genotyping costs in reference population. Heritability did not seem to have obvious influences on the predictive abilities estimated by EP. Using SMA was the most feasible method to save the genotyping costs in candidates. In addition, the combination of EP and SMA in genomic selection also showed good results, especially for trait of REW. We also described how to apply the new methods in genomic selection and compared the genotyping costs before and after using the new methods. Our study may not only offer a reference for aquatic genomic breeding but also offer a reference for genomic prediction in other species including livestock and plants, etc.
Amaral, A.J.; Ferretti, L.; Megens, H.J.W.C.; Crooijmans, R.P.M.A.; Nie, H.; Ramos-Onsins, S.E.; Perez-Enciso, M.; Schook, L.B.; Groenen, M.A.M.
Background Artificial selection has caused rapid evolution in domesticated species. The identification of selection footprints across domesticated genomes can contribute to uncover the genetic basis of phenotypic diversity. Methodology/Main Findings Genome wide footprints of pig domestication and
Paul J Wichgers Schreur
Full Text Available The bunyavirus genome comprises a small (S, medium (M, and large (L RNA segment of negative polarity. Although genome segmentation confers evolutionary advantages by enabling genome reassortment events with related viruses, genome segmentation also complicates genome replication and packaging. Accumulating evidence suggests that genomes of viruses with eight or more genome segments are incorporated into virions by highly selective processes. Remarkably, little is known about the genome packaging process of the tri-segmented bunyaviruses. Here, we evaluated, by single-molecule RNA fluorescence in situ hybridization (FISH, the intracellular spatio-temporal distribution and replication kinetics of the Rift Valley fever virus (RVFV genome and determined the segment composition of mature virions. The results reveal that the RVFV genome segments start to replicate near the site of infection before spreading and replicating throughout the cytoplasm followed by translocation to the virion assembly site at the Golgi network. Despite the average intracellular S, M and L genome segments approached a 1:1:1 ratio, major differences in genome segment ratios were observed among cells. We also observed a significant amount of cells lacking evidence of M-segment replication. Analysis of two-segmented replicons and four-segmented viruses subsequently confirmed the previous notion that Golgi recruitment is mediated by the Gn glycoprotein. The absence of colocalization of the different segments in the cytoplasm and the successful rescue of a tri-segmented variant with a codon shuffled M-segment suggested that inter-segment interactions are unlikely to drive the copackaging of the different segments into a single virion. The latter was confirmed by direct visualization of RNPs inside mature virions which showed that the majority of virions lack one or more genome segments. Altogether, this study suggests that RVFV genome packaging is a non-selective process.
Li, Zhengcao; Chen, Jiucheng; Wang, Zhen; Pan, Yuchun; Wang, Qishan; Xu, Ningying; Wang, Zhengguang
Chinese pigs have been undergoing both natural and artificial selection for thousands of years. Jinhua pigs are of great importance, as they can be a valuable model for exploring the genetic mechanisms linked to meat quality and other traits such as disease resistance, reproduction and production. The purpose of this study was to identify distinctive footprints of selection between Jinhua pigs and other breeds utilizing genome-wide SNP data. Genotyping by genome reducing and sequencing was implemented in order to perform cross-population extended haplotype homozygosity to reveal strong signatures of selection for those economically important traits. This work was performed at a 2% genome level, which comprised 152 006 SNPs genotyped in a total of 517 individuals. Population-specific footprints of selective sweeps were searched for in the genome of Jinhua pigs using six native breeds and three European breeds as reference groups. Several candidate genes associated with meat quality, health and reproduction, such as GH1, CRHR2, TRAF4 and CCK, were found to be overlapping with the significantly positive outliers. Additionally, the results revealed that some genomic regions associated with meat quality, immune response and reproduction in Jinhua pigs have evolved directionally under domestication and subsequent selections. The identified genes and biological pathways in Jinhua pigs showed different selection patterns in comparison with the Chinese and European breeds. © 2016 Stichting International Foundation for Animal Genetics.
Bastiaansen, J.W.M.; Bink, M.C.A.M.; Coster, A.; Maliepaard, C.A.; Calus, M.P.L.
Background - Genomic selection, the use of markers across the whole genome, receives increasing amounts of attention and is having more and more impact on breeding programs. Development of statistical and computational methods to estimate breeding values based on markers is a very active area of
Albrechtsen, Anders; Moltke, Ida; Nielsen, Rasmus
There has recently been considerable interest in detecting natural selection in the human genome. Selection will usually tend to increase identity-by-descent (IBD) among individuals in a population, and many methods for detecting recent and ongoing positive selection indirectly take advantage...... of this. In this article we show that excess IBD sharing is a general property of natural selection and we show that this fact makes it possible to detect several types of selection including a type that is otherwise difficult to detect: selection acting on standing genetic variation. Motivated by this......, we use a recently developed method for identifying IBD sharing among individuals from genome-wide data to scan populations from the new HapMap phase 3 project for regions with excess IBD sharing in order to identify regions in the human genome that have been under strong, very recent selection...
Henryon, Mark; Berg, Peer; Sørensen, Anders Christian
allocated to male and female candidates at ratios of 100:0, 75:25, 50:50, 25:75, and 0:100. For genotyped candidates, a direct-genomic value (DGV) was sampled with reliabilities 0.10, 0.50, and 0.90. Ten sires and 300 dams with the highest breeding values after genotyping were selected at each generation......We reasoned that there are diminishing marginal returns from genomic selection as the proportion of genotyped selection candidates is increased and breeding values based on a priori information are used to choose the candidates that are genotyped. We tested this premise by stochastic simulation...... of breeding schemes that resembled those used for pigs. We estimated rates of genetic gain and inbreeding realized by genomic selection in breeding schemes where candidates were phenotyped before genotyping and 0-100% of the candidates were genotyped based on predicted breeding values. Genotypings were...
Valen, Eivind; Sandelin, Albin Gustav
A central question in cellular biology is how the cell regulates transcription and discerns when and where to initiate it. Locating transcription start sites (TSSs), the signals that specify them, and ultimately elucidating the mechanisms of regulated initiation has therefore been a recurrent theme....... In recent years substantial progress has been made towards this goal, spurred by the possibility of applying genome-wide, sequencing-based analysis. We now have a large collection of high-resolution datasets identifying locations of TSSs, protein-DNA interactions, and chromatin features over whole genomes...
Smaragdov, M G
Genomic selection is a method based on the use of single nucleotide polymorphisms (SNPs) as markers for detecting animal or plant genotype values. The review describes the genomic selection of milk cattle 5 years after the design of dense SNP chips. References to the application of genomic selection to other animal and plant species are given. The main principles of constructing linear and nonlinear mathematical models that allow one to determine genomic estimates in animals are briefly described. Particular attention is focused on the accuracy and the phenomenon of the additivity ofgenomic estimates, as well as to the prospective use of various genomic selection schemes that consider it over dozens of generations. Information including international organizations that provide the consolidation of genomic information from different countries aimed at designing global reference populations of milk cattle is reported. The results of the practical application of genomic selection to detecting of the breeding value of milk cattle over 5 years are demonstrated in the table, which makes it possible to visually assess the achievements of this highly technological field of cattle breeding.
Fariello, María Inés; Boitard, Simon; Mercier, Sabine; Robelin, David; Faraut, Thomas; Arnould, Cécile; Recoquillay, Julien; Bouchez, Olivier; Salin, Gérald; Dehais, Patrice; Gourichon, David; Leroux, Sophie; Pitel, Frédérique; Leterrier, Christine; SanCristobal, Magali
Detecting genomic footprints of selection is an important step in the understanding of evolution. Accounting for linkage disequilibrium in genome scans increases detection power, but haplotype-based methods require individual genotypes and are not applicable on pool-sequenced samples. We propose to take advantage of the local score approach to account for linkage disequilibrium in genome scans for selection, cumulating (possibly small) signals from single markers over a genomic segment, to clearly pinpoint a selection signal. Using computer simulations, we demonstrate that this approach detects selection with higher power than several state-of-the-art single-marker, windowing or haplotype-based approaches. We illustrate this on two benchmark data sets including individual genotypes, for which we obtain similar results with the local score and one haplotype-based approach. Finally, we apply the local score approach to Pool-Seq data obtained from a divergent selection experiment on behaviour in quail and obtain precise and biologically coherent selection signals: while competing methods fail to highlight any clear selection signature, our method detects several regions involving genes known to act on social responsiveness or autistic traits. Although we focus here on the detection of positive selection from multiple population data, the local score approach is general and can be applied to other genome scans for selection or other genomewide analyses such as GWAS. © 2017 John Wiley & Sons Ltd.
Muranty, Hélène; Troggio, Michela; Sadok, Ben Inès; Rifaï, Al Mehdi; Auwerkerken, Annemarie; Banchi, E.; Velasco, Riccardo; Stevanato, P.; Weg, van de W.E.; Guardo, Di M.; Kumar, S.; Laurens, François; Bink, M.C.A.M.
The application of genomic selection in fruit tree crops is expected to enhance breeding efficiency by increasing prediction accuracy, increasing selection intensity and decreasing generation interval. The objectives of this study were to assess the accuracy of prediction and selection response in
Ramos, Barbara; González-Acuña, Daniel; Loyola, David E.; Johnson, Warren E.; Parker, Patricia G.; Massaro, Melanie; Dantas, Gisele P. M.; Miranda, Marcelo D.; Vianna, Juliana A.
Background Mitochondria play a key role in the balance of energy and heat production, and therefore the mitochondrial genome is under natural selection by environmental temperature and food availability, since starvation can generate more efficient coupling of energy production. However, selection over mitochondrial DNA (mtDNA) genes has usually been evaluated at the population level. We sequenced by NGS 12 mitogenomes and with four published genomes, assessed genetic variation in ten penguin...
Jia, Yi; Jannink, Jean-Luc
Genetic correlations between quantitative traits measured in many breeding programs are pervasive. These correlations indicate that measurements of one trait carry information on other traits. Current single-trait (univariate) genomic selection does not take advantage of this information. Multivariate genomic selection on multiple traits could accomplish this but has been little explored and tested in practical breeding programs. In this study, three multivariate linear models (i.e., GBLUP, BayesA, and BayesCπ) were presented and compared to univariate models using simulated and real quantitative traits controlled by different genetic architectures. We also extended BayesA with fixed hyperparameters to a full hierarchical model that estimated hyperparameters and BayesCπ to impute missing phenotypes. We found that optimal marker-effect variance priors depended on the genetic architecture of the trait so that estimating them was beneficial. We showed that the prediction accuracy for a low-heritability trait could be significantly increased by multivariate genomic selection when a correlated high-heritability trait was available. Further, multiple-trait genomic selection had higher prediction accuracy than single-trait genomic selection when phenotypes are not available on all individuals and traits. Additional factors affecting the performance of multiple-trait genomic selection were explored. PMID:23086217
Jiang, Peng; Shi, Feng-Xue; Li, Ming-Rui; Liu, Bao; Wen, Jun; Xiao, Hong-Xing; Li, Lin-Feng
Panax L. (the ginseng genus) is a shade-demanding group within the family Araliaceae and all of its species are of crucial significance in traditional Chinese medicine. Phylogenetic and biogeographic analyses demonstrated that two rounds of whole genome duplications accompanying with geographic and ecological isolations promoted the diversification of Panax species. However, contributions of the cytoplasmic genomes to the adaptive evolution of Panax species remained largely uninvestigated. In this study, we sequenced the chloroplast and mitochondrial genomes of 11 accessions belonging to seven Panax species. Our results show that heterogeneity in nucleotide substitution rate is abundant in both of the two cytoplasmic genomes, with the mitochondrial genome possessing more variants at the total level but the chloroplast showing higher sequence polymorphisms at the genic regions. Genome-wide scanning of positive selection identified five and 12 genes from the chloroplast and mitochondrial genomes, respectively. Functional analyses further revealed that these selected genes play important roles in plant development, cellular metabolism and adaptation. We therefore conclude that positive selection might be one of the potential evolutionary forces that shaped nucleotide variation pattern of these Panax species. In particular, the mitochondrial genes evolved under stronger selective pressure compared to the chloroplast genes.
Lohmueller, Kirk E; Albrechtsen, Anders; Li, Yingrui; Kim, Su Yeon; Korneliussen, Thorfinn; Vinckenbosch, Nicolas; Tian, Geng; Huerta-Sanchez, Emilia; Feder, Alison F; Grarup, Niels; Jørgensen, Torben; Jiang, Tao; Witte, Daniel R; Sandbæk, Annelli; Hellmann, Ines; Lauritzen, Torsten; Hansen, Torben; Pedersen, Oluf; Wang, Jun; Nielsen, Rasmus
A major question in evolutionary biology is how natural selection has shaped patterns of genetic variation across the human genome. Previous work has documented a reduction in genetic diversity in regions of the genome with low recombination rates. However, it is unclear whether other summaries of genetic variation, like allele frequencies, are also correlated with recombination rate and whether these correlations can be explained solely by negative selection against deleterious mutations or whether positive selection acting on favorable alleles is also required. Here we attempt to address these questions by analyzing three different genome-wide resequencing datasets from European individuals. We document several significant correlations between different genomic features. In particular, we find that average minor allele frequency and diversity are reduced in regions of low recombination and that human diversity, human-chimp divergence, and average minor allele frequency are reduced near genes. Population genetic simulations show that either positive natural selection acting on favorable mutations or negative natural selection acting against deleterious mutations can explain these correlations. However, models with strong positive selection on nonsynonymous mutations and little negative selection predict a stronger negative correlation between neutral diversity and nonsynonymous divergence than observed in the actual data, supporting the importance of negative, rather than positive, selection throughout the genome. Further, we show that the widespread presence of weakly deleterious alleles, rather than a small number of strongly positively selected mutations, is responsible for the correlation between neutral genetic diversity and recombination rate. This work suggests that natural selection has affected multiple aspects of linked neutral variation throughout the human genome and that positive selection is not required to explain these observations.
Lohmueller, Kirk E; Albrechtsen, Anders; Li, Yingrui
A major question in evolutionary biology is how natural selection has shaped patterns of genetic variation across the human genome. Previous work has documented a reduction in genetic diversity in regions of the genome with low recombination rates. However, it is unclear whether other summaries...... these questions by analyzing three different genome-wide resequencing datasets from European individuals. We document several significant correlations between different genomic features. In particular, we find that average minor allele frequency and diversity are reduced in regions of low recombination...... and that human diversity, human-chimp divergence, and average minor allele frequency are reduced near genes. Population genetic simulations show that either positive natural selection acting on favorable mutations or negative natural selection acting against deleterious mutations can explain these correlations...
Northcutt Sally L
Full Text Available Abstract Background Molecular estimates of breeding value are expected to increase selection response due to improvements in the accuracy of selection and a reduction in generation interval, particularly for traits that are difficult or expensive to record or are measured late in life. Several statistical methods for incorporating molecular data into breeding value estimation have been proposed, however, most studies have utilized simulated data in which the generated linkage disequilibrium may not represent the targeted livestock population. A genomic relationship matrix was developed for 698 Angus steers and 1,707 Angus sires using 41,028 single nucleotide polymorphisms and breeding values were estimated using feed efficiency phenotypes (average daily feed intake, residual feed intake, and average daily gain recorded on the steers. The number of SNPs needed to accurately estimate a genomic relationship matrix was evaluated in this population. Results Results were compared to estimates produced from pedigree-based mixed model analysis of 862 Angus steers with 34,864 identified paternal relatives but no female ancestors. Estimates of additive genetic variance and breeding value accuracies were similar for AFI and RFI using the numerator and genomic relationship matrices despite fewer animals in the genomic analysis. Bootstrap analyses indicated that 2,500-10,000 markers are required for robust estimation of genomic relationship matrices in cattle. Conclusions This research shows that breeding values and their accuracies may be estimated for commercially important sires for traits recorded in experimental populations without the need for pedigree data to establish identity by descent between members of the commercial and experimental populations when at least 2,500 SNPs are available for the generation of a genomic relationship matrix.
Full Text Available DNA methylation at CpG islands (CGIs is one of the most intensively studied epigenetic mechanisms. It is fundamental for cellular differentiation and control of transcriptional potential. DNA methylation is involved also in several processes that are central to evolutionary biology, including phenotypic plasticity and evolvability. In this study, we explored the relationship between CpG islands methylation and signatures of selective pressure in Homo Sapiens, using a computational biology approach. By analyzing methylation data of 25 cell lines from the Encyclopedia of DNA Elements (ENCODE Consortium, we compared the DNA methylation of CpG islands in genomic regions under selective pressure with the methylation of CpG islands in the remaining part of the genome. To define genomic regions under selective pressure, we used three different methods, each oriented to provide distinct information about selective events. Independently of the method and of the cell type used, we found evidences of undermethylation of CGIs in human genomic regions under selective pressure. Additionally, by analyzing SNP frequency in CpG islands, we demonstrated that CpG islands in regions under selective pressure show lower genetic variation. Our findings suggest that the CpG islands in regions under selective pressure seem to be somehow more "protected" from methylation when compared with other regions of the genome.
Michel, Sebastian; Kummer, Christian; Gallee, Martin; Hellinger, Jakob; Ametz, Christian; Akgöl, Batuhan; Epure, Doru; Löschenberger, Franziska; Buerstmayr, Hermann
Genomic selection shows great promise for pre-selecting lines with superior bread baking quality in early generations, 3 years ahead of labour-intensive, time-consuming, and costly quality analysis. The genetic improvement of baking quality is one of the grand challenges in wheat breeding as the assessment of the associated traits often involves time-consuming, labour-intensive, and costly testing forcing breeders to postpone sophisticated quality tests to the very last phases of variety development. The prospect of genomic selection for complex traits like grain yield has been shown in numerous studies, and might thus be also an interesting method to select for baking quality traits. Hence, we focused in this study on the accuracy of genomic selection for laborious and expensive to phenotype quality traits as well as its selection response in comparison with phenotypic selection. More than 400 genotyped wheat lines were, therefore, phenotyped for protein content, dough viscoelastic and mixing properties related to baking quality in multi-environment trials 2009-2016. The average prediction accuracy across three independent validation populations was r = 0.39 and could be increased to r = 0.47 by modelling major QTL as fixed effects as well as employing multi-trait prediction models, which resulted in an acceptable prediction accuracy for all dough rheological traits (r = 0.38-0.63). Genomic selection can furthermore be applied 2-3 years earlier than direct phenotypic selection, and the estimated selection response was nearly twice as high in comparison with indirect selection by protein content for baking quality related traits. This considerable advantage of genomic selection could accordingly support breeders in their selection decisions and aid in efficiently combining superior baking quality with grain yield in newly developed wheat varieties.
McElroy, Michel S; Navarro, Alberto J R; Mustiga, Guiliana; Stack, Conrad; Gezan, Salvador; Peña, Geover; Sarabia, Widem; Saquicela, Diego; Sotomayor, Ignacio; Douglas, Gavin M; Migicovsky, Zoë; Amores, Freddy; Tarqui, Omar; Myles, Sean; Motamayor, Juan C
Cacao ( Theobroma cacao ) is a globally important crop, and its yield is severely restricted by disease. Two of the most damaging diseases, witches' broom disease (WBD) and frosty pod rot disease (FPRD), are caused by a pair of related fungi: Moniliophthora perniciosa and Moniliophthora roreri , respectively. Resistant cultivars are the most effective long-term strategy to address Moniliophthora diseases, but efficiently generating resistant and productive new cultivars will require robust methods for screening germplasm before field testing. Marker-assisted selection (MAS) and genomic selection (GS) provide two potential avenues for predicting the performance of new genotypes, potentially increasing the selection gain per unit time. To test the effectiveness of these two approaches, we performed a genome-wide association study (GWAS) and GS on three related populations of cacao in Ecuador genotyped with a 15K single nucleotide polymorphism (SNP) microarray for three measures of WBD infection (vegetative broom, cushion broom, and chirimoya pod), one of FPRD (monilia pod) and two productivity traits (total fresh weight of pods and % healthy pods produced). GWAS yielded several SNPs associated with disease resistance in each population, but none were significantly correlated with the same trait in other populations. Genomic selection, using one population as a training set to estimate the phenotypes of the remaining two (composed of different families), varied among traits, from a mean prediction accuracy of 0.46 (vegetative broom) to 0.15 (monilia pod), and varied between training populations. Simulations demonstrated that selecting seedlings using GWAS markers alone generates no improvement over selecting at random, but that GS improves the selection process significantly. Our results suggest that the GWAS markers discovered here are not sufficiently predictive across diverse germplasm to be useful for MAS, but that using all markers in a GS framework holds
Michel S. McElroy
Full Text Available Cacao (Theobroma cacao is a globally important crop, and its yield is severely restricted by disease. Two of the most damaging diseases, witches’ broom disease (WBD and frosty pod rot disease (FPRD, are caused by a pair of related fungi: Moniliophthora perniciosa and Moniliophthora roreri, respectively. Resistant cultivars are the most effective long-term strategy to address Moniliophthora diseases, but efficiently generating resistant and productive new cultivars will require robust methods for screening germplasm before field testing. Marker-assisted selection (MAS and genomic selection (GS provide two potential avenues for predicting the performance of new genotypes, potentially increasing the selection gain per unit time. To test the effectiveness of these two approaches, we performed a genome-wide association study (GWAS and GS on three related populations of cacao in Ecuador genotyped with a 15K single nucleotide polymorphism (SNP microarray for three measures of WBD infection (vegetative broom, cushion broom, and chirimoya pod, one of FPRD (monilia pod and two productivity traits (total fresh weight of pods and % healthy pods produced. GWAS yielded several SNPs associated with disease resistance in each population, but none were significantly correlated with the same trait in other populations. Genomic selection, using one population as a training set to estimate the phenotypes of the remaining two (composed of different families, varied among traits, from a mean prediction accuracy of 0.46 (vegetative broom to 0.15 (monilia pod, and varied between training populations. Simulations demonstrated that selecting seedlings using GWAS markers alone generates no improvement over selecting at random, but that GS improves the selection process significantly. Our results suggest that the GWAS markers discovered here are not sufficiently predictive across diverse germplasm to be useful for MAS, but that using all markers in a GS framework holds
Dembowski, Jill A; DeLuca, Neal A
Much of the HSV-1 life cycle is carried out in the cell nucleus, including the expression, replication, repair, and packaging of viral genomes. Viral proteins, as well as cellular factors, play essential roles in these processes. Isolation of proteins on nascent DNA (iPOND) was developed to label and purify cellular replication forks. We adapted aspects of this method to label viral genomes to both image, and purify replicating HSV-1 genomes for the identification of associated proteins. Many viral and cellular factors were enriched on viral genomes, including factors that mediate DNA replication, repair, chromatin remodeling, transcription, and RNA processing. As infection proceeded, packaging and structural components were enriched to a greater extent. Among the more abundant proteins that copurified with genomes were the viral transcription factor ICP4 and the replication protein ICP8. Furthermore, all seven viral replication proteins were enriched on viral genomes, along with cellular PCNA and topoisomerases, while other cellular replication proteins were not detected. The chromatin-remodeling complexes present on viral genomes included the INO80, SWI/SNF, NURD, and FACT complexes, which may prevent chromatinization of the genome. Consistent with this conclusion, histones were not readily recovered with purified viral genomes, and imaging studies revealed an underrepresentation of histones on viral genomes. RNA polymerase II, the mediator complex, TFIID, TFIIH, and several other transcriptional activators and repressors were also affinity purified with viral DNA. The presence of INO80, NURD, SWI/SNF, mediator, TFIID, and TFIIH components is consistent with previous studies in which these complexes copurified with ICP4. Therefore, ICP4 is likely involved in the recruitment of these key cellular chromatin remodeling and transcription factors to viral genomes. Taken together, iPOND is a valuable method for the study of viral genome dynamics during infection and
Jill A Dembowski
Full Text Available Much of the HSV-1 life cycle is carried out in the cell nucleus, including the expression, replication, repair, and packaging of viral genomes. Viral proteins, as well as cellular factors, play essential roles in these processes. Isolation of proteins on nascent DNA (iPOND was developed to label and purify cellular replication forks. We adapted aspects of this method to label viral genomes to both image, and purify replicating HSV-1 genomes for the identification of associated proteins. Many viral and cellular factors were enriched on viral genomes, including factors that mediate DNA replication, repair, chromatin remodeling, transcription, and RNA processing. As infection proceeded, packaging and structural components were enriched to a greater extent. Among the more abundant proteins that copurified with genomes were the viral transcription factor ICP4 and the replication protein ICP8. Furthermore, all seven viral replication proteins were enriched on viral genomes, along with cellular PCNA and topoisomerases, while other cellular replication proteins were not detected. The chromatin-remodeling complexes present on viral genomes included the INO80, SWI/SNF, NURD, and FACT complexes, which may prevent chromatinization of the genome. Consistent with this conclusion, histones were not readily recovered with purified viral genomes, and imaging studies revealed an underrepresentation of histones on viral genomes. RNA polymerase II, the mediator complex, TFIID, TFIIH, and several other transcriptional activators and repressors were also affinity purified with viral DNA. The presence of INO80, NURD, SWI/SNF, mediator, TFIID, and TFIIH components is consistent with previous studies in which these complexes copurified with ICP4. Therefore, ICP4 is likely involved in the recruitment of these key cellular chromatin remodeling and transcription factors to viral genomes. Taken together, iPOND is a valuable method for the study of viral genome dynamics
Su, Hailin; Li, Hengde; Wang, Shi; Wang, Yangfan; Bao, Zhenmin
Genomic selection is more and more popular in animal and plant breeding industries all around the world, as it can be applied early in life without impacting selection candidates. The objective of this study was to bring the advantages of genomic selection to scallop breeding. Two different genomic selection tools MixP and gsbay were applied on genomic evaluation of simulated data and Zhikong scallop ( Chlamys farreri) field data. The data were compared with genomic best linear unbiased prediction (GBLUP) method which has been applied widely. Our results showed that both MixP and gsbay could accurately estimate single-nucleotide polymorphism (SNP) marker effects, and thereby could be applied for the analysis of genomic estimated breeding values (GEBV). In simulated data from different scenarios, the accuracy of GEBV acquired was ranged from 0.20 to 0.78 by MixP; it was ranged from 0.21 to 0.67 by gsbay; and it was ranged from 0.21 to 0.61 by GBLUP. Estimations made by MixP and gsbay were expected to be more reliable than those estimated by GBLUP. Predictions made by gsbay were more robust, while with MixP the computation is much faster, especially in dealing with large-scale data. These results suggested that both algorithms implemented by MixP and gsbay are feasible to carry out genomic selection in scallop breeding, and more genotype data will be necessary to produce genomic estimated breeding values with a higher accuracy for the industry.
Lipka, Alexander E.; Lu, Fei; Cherney, Jerome H.; Buckler, Edward S.; Casler, Michael D.; Costich, Denise E.
Switchgrass (Panicum virgatum L.) is a perennial grass undergoing development as a biofuel feedstock. One of the most important factors hindering breeding efforts in this species is the need for accurate measurement of biomass yield on a per-hectare basis. Genomic selection on simple-to-measure traits that approximate biomass yield has the potential to significantly speed up the breeding cycle. Recent advances in switchgrass genomic and phenotypic resources are now making it possible to evaluate the potential of genomic selection of such traits. We leveraged these resources to study the ability of three widely-used genomic selection models to predict phenotypic values of morphological and biomass quality traits in an association panel consisting of predominantly northern adapted upland germplasm. High prediction accuracies were obtained for most of the traits, with standability having the highest ten-fold cross validation prediction accuracy (0.52). Moreover, the morphological traits generally had higher prediction accuracies than the biomass quality traits. Nevertheless, our results suggest that the quality of current genomic and phenotypic resources available for switchgrass is sufficiently high for genomic selection to significantly impact breeding efforts for biomass yield. PMID:25390940
Nielsen, Rasmus; Williamson, Scott; Kim, Yuseob
of the selection coefficient. To illustrate the method, we apply our approach to data from the Seattle SNP project and to Chromosome 2 data from the HapMap project. In Chromosome 2, the most extreme signal is found in the lactase gene, which previously has been shown to be undergoing positive selection. Evidence...
Liu, Huiming; Sørensen, Anders Christian; Meuwissen, Theo H E
of inbreeding due to changes in allele frequencies and hitch-hiking. This study aimed at understanding the impact of using long-term genomic selection on changes in allele frequencies, genetic variation and the level of inbreeding. Methods Selection was performed in simulated scenarios with a population of 400......-BLUP, Genomic BLUP and Bayesian Lasso. Changes in allele frequencies at QTL, markers and linked neutral loci were investigated for the different selection criteria and different scenarios, along with the loss of favourable alleles and the rate of inbreeding measured by pedigree and runs of homozygosity. Results...
Toro Miguel A
Full Text Available Abstract Estimation of non-additive genetic effects in animal breeding is important because it increases the accuracy of breeding value prediction and the value of mate allocation procedures. With the advent of genomic selection these ideas should be revisited. The objective of this study was to quantify the efficiency of including dominance effects and practising mating allocation under a whole-genome evaluation scenario. Four strategies of selection, carried out during five generations, were compared by simulation techniques. In the first scenario (MS, individuals were selected based on their own phenotypic information. In the second (GSA, they were selected based on the prediction generated by the Bayes A method of whole-genome evaluation under an additive model. In the third (GSD, the model was expanded to include dominance effects. These three scenarios used random mating to construct future generations, whereas in the fourth one (GSD + MA, matings were optimized by simulated annealing. The advantage of GSD over GSA ranges from 9 to 14% of the expected response and, in addition, using mate allocation (GSD + MA provides an additional response ranging from 6% to 22%. However, mate selection can improve the expected genetic response over random mating only in the first generation of selection. Furthermore, the efficiency of genomic selection is eroded after a few generations of selection, thus, a continued collection of phenotypic data and re-evaluation will be required.
Heidaritabar, M.; Vereijken, A.; Muir, W.M.; Meuwissen, T.H.E.; Cheng, H.; Megens, H.J.W.C.; Groenen, M.; Bastiaansen, J.W.M.
Genomic selection (GS) is a DNA-based method of selecting for quantitative traits in animal and plant breeding, and offers a potentially superior alternative to traditional breeding methods that rely on pedigree and phenotype information. Using a 60¿K SNP chip with markers spaced throughout the
Mourier, Tobias; Willerslev, Eske
in generating and maintaining retroelement-free regions in the human genome. METHODOLOGY/PRINCIPAL FINDINGS: Based on the known transcriptional properties of retroelements, we expect long interspersed elements (LINEs) to be able to display a high degree of transcriptional interference. In contrast, we expect......BACKGROUND: Eukaryotic genomes are scattered with retroelements that proliferate through retrotransposition. Although retroelements make up around 40 percent of the human genome, large regions are found to be completely devoid of retroelements. This has been hypothesised to be a result of genomic...... activity of LINEs has been identified previously. CONCLUSIONS/SIGNIFICANCE: Our observations are consistent with the notion that selection against transcriptional interference has contributed to the maintenance and/or generation of retroelement-free regions in the human genome....
Huang, Zhicong; Ayday, Erman; Lin, Huang; Aiyar, Raeka S; Molyneaux, Adam; Xu, Zhenyu; Fellay, Jacques; Steinmetz, Lars M; Hubaux, Jean-Pierre
In clinical genomics, the continuous evolution of bioinformatic algorithms and sequencing platforms makes it beneficial to store patients' complete aligned genomic data in addition to variant calls relative to a reference sequence. Due to the large size of human genome sequence data files (varying from 30 GB to 200 GB depending on coverage), two major challenges facing genomics laboratories are the costs of storage and the efficiency of the initial data processing. In addition, privacy of genomic data is becoming an increasingly serious concern, yet no standard data storage solutions exist that enable compression, encryption, and selective retrieval. Here we present a privacy-preserving solution named SECRAM (Selective retrieval on Encrypted and Compressed Reference-oriented Alignment Map) for the secure storage of compressed aligned genomic data. Our solution enables selective retrieval of encrypted data and improves the efficiency of downstream analysis (e.g., variant calling). Compared with BAM, the de facto standard for storing aligned genomic data, SECRAM uses 18% less storage. Compared with CRAM, one of the most compressed nonencrypted formats (using 34% less storage than BAM), SECRAM maintains efficient compression and downstream data processing, while allowing for unprecedented levels of security in genomic data storage. Compared with previous work, the distinguishing features of SECRAM are that (1) it is position-based instead of read-based, and (2) it allows random querying of a subregion from a BAM-like file in an encrypted form. Our method thus offers a space-saving, privacy-preserving, and effective solution for the storage of clinical genomic data. © 2016 Huang et al.; Published by Cold Spring Harbor Laboratory Press.
Chavez-Galarza, Julio; Johnston, J. Spencer; Azevedo, João; Muñoz, Irene; De la Rúa, Pilar; Patton, John C.; Pinto, M. Alice
Dissecting genome-wide (expansions, contractions, admixture) from genome-specific effects (selection) is a goal of central importance in evolutionary biology because it leads to more robust inferences of demographic history and to identification of adaptive divergence. The publication of the honey bee genome and the development of high-density SNPs genotyping, provide us with powerful tools, allowing us to identify signatures of selection in the honey bee genome. These signatur...
Hansen Axelsson, H; Fikse, W F; Kargo, Morten
The aim of this simulation study was to test the hypothesis that phenotype information of specific indicator traits of environmental importance recorded on a small-scale can be implemented in breeding schemes with genomic selection to reduce the environmental impact of milk production. A stochastic...... was, however, best in the scenarios where the genetic correlation between IT and EI was ≥0.30 and the accuracy of direct genomic value was ≥0.40. The genetic gain in EI was 26 to 34% higher when indicator traits such as greenhouse gases in the breath of the cow and methane recorded in respiration...... of direct genomic values will be reasonably high...
Full Text Available Abstract Background It is commonly assumed that prediction of genome-wide breeding values in genomic selection is achieved by capitalizing on linkage disequilibrium between markers and QTL but also on genetic relationships. Here, we investigated the reliability of predicting genome-wide breeding values based on population-wide linkage disequilibrium information, based on identity-by-descent relationships within the known pedigree, and to what extent linkage disequilibrium information improves predictions based on identity-by-descent genomic relationship information. Methods The study was performed on milk, fat, and protein yield, using genotype data on 35 706 SNP and deregressed proofs of 1086 Italian Brown Swiss bulls. Genome-wide breeding values were predicted using a genomic identity-by-state relationship matrix and a genomic identity-by-descent relationship matrix (averaged over all marker loci. The identity-by-descent matrix was calculated by linkage analysis using one to five generations of pedigree data. Results We showed that genome-wide breeding values prediction based only on identity-by-descent genomic relationships within the known pedigree was as or more reliable than that based on identity-by-state, which implicitly also accounts for genomic relationships that occurred before the known pedigree. Furthermore, combining the two matrices did not improve the prediction compared to using identity-by-descent alone. Including different numbers of generations in the pedigree showed that most of the information in genome-wide breeding values prediction comes from animals with known common ancestors less than four generations back in the pedigree. Conclusions Our results show that, in pedigreed breeding populations, the accuracy of genome-wide breeding values obtained by identity-by-descent relationships was not improved by identity-by-state information. Although, in principle, genomic selection based on identity-by-state does not require
Full Text Available GRAbB (Genomic Region Assembly by Baiting is a new program that is dedicated to assemble specific genomic regions from NGS data. This approach is especially useful when dealing with multi copy regions, such as mitochondrial genome and the rDNA repeat region, parts of the genome that are often neglected or poorly assembled, although they contain interesting information from phylogenetic or epidemiologic perspectives, but also single copy regions can be assembled. The program is capable of targeting multiple regions within a single run. Furthermore, GRAbB can be used to extract specific loci from NGS data, based on homology, like sequences that are used for barcoding. To make the assembly specific, a known part of the region, such as the sequence of a PCR amplicon or a homologous sequence from a related species must be specified. By assembling only the region of interest, the assembly process is computationally much less demanding and may lead to assemblies of better quality. In this study the different applications and functionalities of the program are demonstrated such as: exhaustive assembly (rDNA region and mitochondrial genome, extracting homologous regions or genes (IGS, RPB1, RPB2 and TEF1a, as well as extracting multiple regions within a single run. The program is also compared with MITObim, which is meant for the exhaustive assembly of a single target based on a similar query sequence. GRAbB is shown to be more efficient than MITObim in terms of speed, memory and disk usage. The other functionalities (handling multiple targets simultaneously and extracting homologous regions of the new program are not matched by other programs. The program is available with explanatory documentation at https://github.com/b-brankovics/grabb. GRAbB has been tested on Ubuntu (12.04 and 14.04, Fedora (23, CentOS (7.1.1503 and Mac OS X (10.7. Furthermore, GRAbB is available as a docker repository: brankovics/grabb (https://hub.docker.com/r/brankovics/grabb/.
Piotrowski Jeff S
Full Text Available Abstract Background Interspecific hybridization occurs in every eukaryotic kingdom. While hybrid progeny are frequently at a selective disadvantage, in some instances their increased genome size and complexity may result in greater stress resistance than their ancestors, which can be adaptively advantageous at the edges of their ancestors' ranges. While this phenomenon has been repeatedly documented in the field, the response of hybrid populations to long-term selection has not often been explored in the lab. To fill this knowledge gap we crossed the two most distantly related members of the Saccharomyces sensu stricto group, S. cerevisiae and S. uvarum, and established a mixed population of homoploid and aneuploid hybrids to study how different types of selection impact hybrid genome structure. Results As temperature was raised incrementally from 31°C to 46.5°C over 500 generations of continuous culture, selection favored loss of the S. uvarum genome, although the kinetics of genome loss differed among independent replicates. Temperature-selected isolates exhibited greater inherent and induced thermal tolerance than parental species and founding hybrids, and also exhibited ethanol resistance. In contrast, as exogenous ethanol was increased from 0% to 14% over 500 generations of continuous culture, selection favored euploid S. cerevisiae x S. uvarum hybrids. Ethanol-selected isolates were more ethanol tolerant than S. uvarum and one of the founding hybrids, but did not exhibit resistance to temperature stress. Relative to parental and founding hybrids, temperature-selected strains showed heritable differences in cell wall structure in the forms of increased resistance to zymolyase digestion and Micafungin, which targets cell wall biosynthesis. Conclusions This is the first study to show experimentally that the genomic fate of newly-formed interspecific hybrids depends on the type of selection they encounter during the course of evolution
Josep M Comeron
Full Text Available The constant removal of deleterious mutations by natural selection causes a reduction in neutral diversity and efficacy of selection at genetically linked sites (a process called Background Selection, BGS. Population genetic studies, however, often ignore BGS effects when investigating demographic events or the presence of other types of selection. To obtain a more realistic evolutionary expectation that incorporates the unavoidable consequences of deleterious mutations, we generated high-resolution landscapes of variation across the Drosophila melanogaster genome under a BGS scenario independent of polymorphism data. We find that BGS plays a significant role in shaping levels of variation across the entire genome, including long introns and intergenic regions distant from annotated genes. We also find that a very large percentage of the observed variation in diversity across autosomes can be explained by BGS alone, up to 70% across individual chromosome arms at 100-kb scale, thus indicating that BGS predictions can be used as baseline to infer additional types of selection and demographic events. This approach allows detecting several outlier regions with signal of recent adaptive events and selective sweeps. The use of a BGS baseline, however, is particularly appropriate to investigate the presence of balancing selection and our study exposes numerous genomic regions with the predicted signature of higher polymorphism than expected when a BGS context is taken into account. Importantly, we show that these conclusions are robust to the mutation and selection parameters of the BGS model. Finally, analyses of protein evolution together with previous comparisons of genetic maps between Drosophila species, suggest temporally variable recombination landscapes and, thus, local BGS effects that may differ between extant and past phases. Because genome-wide BGS and temporal changes in linkage effects can skew approaches to estimate demographic and
Guosheng, Su; Madsen, Per; Nielsen, Ulrik Sander
This study investigated the accuracy of direct genomic breeding values (DGV) using a genomic BLUP model, genomic enhanced breeding values (GEBV) using a one-step blending approach, and GEBV using a selection index blending approach for 15 traits of Nordic Red Cattle. The data comprised 6,631 bulls...... genotyped and nongenotyped bulls for one-step blending, and to scale DGV and its expected reliability in the selection index blending. Weighting (scaling) factors had a small influence on reliabilities of GEBV, but a large influence on the variation of GEBV. Based on the validation analyses, averaged over...... the 15 traits, the reliability of DGV for bulls without daughter records was 11.0 percentage points higher than the reliability of conventional pedigree index. Further gain of 0.9 percentage points was achieved by combining information from conventional pedigree index using the selection index blending...
Wijchers, P.J.; de Laat, W.
Chromosomal rearrangements occur as a consequence of the erroneous repair of DNA double-stranded breaks, and often underlie disease. The recurrent detection of specific tumorigenic rearrangements suggests that there is a mechanism behind chromosomal partner selection involving the shape of the
Schierup, Mikkel Heide; Vekemans, Xavier
closely related species. We review recent empirical studies demonstrating these features and relate the empirical findings to theoretical predictions. We show how these features are being exploited in searches for other genes under multi-allelic balancing selection and for inference on recent breakdown...
Wang, Minjuan; Li, Xiang; Gao, Mingxia; Pan, Hongge; Liu, Yongfeng
Highlights: • Nanocrystallite MgS was synthesized by means of a reaction of MgH 2 of S via ball milling. • MgS was firstly investigated as anode material for lithium-ion batteries (LIBs). • MgS with acetylene black introduced by ball milling shows superior electrochemical property. • The mechanisms of the lithium insertion and extraction processes of MgS are discussed. • The work is considered helpful in developing new electrode material for LIBs. - Abstract: MgS was firstly investigated as an anode material for lithium-ion batteries (LIBs). A novel method for the synthesis of nano-sized MgS was conducted, i.e., by means of a reaction of MgH 2 of S via ball milling. Acetylene black (AB) was used as electron conductive agent and introduced by two approaches to the MgS anode material: the one is ball milling AB with the as-prepared MgS derived from MgH 2 and S; the other is pre-milling AB with S and then further milling the mixture with MgH 2 . X-ray diffraction, scanning electron microscopy, transmission electron microscopy (TEM) and high resolution TEM analyses show that MgS/AB composites with MgS nanocrystallites embedded in the AB matrix are formed via either of the approaches. The MgS anode derived from MgH 2 and the pre-milled S/AB mixture shows high capacity. Capacity fading occurs mainly in the initial several cycles. A capacity of 630 mA h/g is retained after 80 cycles. The electrochemical property is much better than that of the MgS/AB derived from MgS and AB, due to the much homogenous microstructure of the former. The mechanism of the lithium insertion and extraction process of MgS is primarily discussed. The work is considered helpful in developing new synthesis method for MgS and new electrode material for LIBs
Mina, Marco; Raynaud, Franck; Tavernari, Daniele; Battistello, Elena; Sungalee, Stephanie; Saghafinia, Sadegh; Laessle, Titouan; Sanchez-Vega, Francisco; Schultz, Nikolaus; Oricchio, Elisa; Ciriello, Giovanni
Cancer evolves through the emergence and selection of molecular alterations. Cancer genome profiling has revealed that specific events are more or less likely to be co-selected, suggesting that the selection of one event depends on the others. However, the nature of these evolutionary dependencies and their impact remain unclear. Here, we designed SELECT, an algorithmic approach to systematically identify evolutionary dependencies from alteration patterns. By analyzing 6,456 genomes from multiple tumor types, we constructed a map of oncogenic dependencies associated with cellular pathways, transcriptional readouts, and therapeutic response. Finally, modeling of cancer evolution shows that alteration dependencies emerge only under conditional selection. These results provide a framework for the design of strategies to predict cancer progression and therapeutic response. Copyright © 2017 Elsevier Inc. All rights reserved.
Pujolar, J. M.; Jacobsen, M. W.; Als, Thomas Damm
Next-generation sequencing and the collection of genome-wide data allow identifying adaptive variation and footprints of directional selection. Using a large SNP data set from 259 RAD-sequenced European eel individuals (glass eels) from eight locations between 34 and 64oN, we examined the patterns...... of genome-wide genetic diversity across locations. We tested for local selection by searching for increased population differentiation using FST-based outlier tests and by testing for significant associations between allele frequencies and environmental variables. The overall low genetic differentiation...... with single-generation signatures of spatially varying selection acting on glass eels. After screening 50 354 SNPs, a total of 754 potentially locally selected SNPs were identified. Candidate genes for local selection constituted a wide array of functions, including calcium signalling, neuroactive ligand...
Ryu, J; Lee, C
Selection signals of Korean cattle might be attributed largely to artificial selection for meat quality. Rapidly increased intragenic markers of newly annotated genes in the bovine genome would help overcome limited findings of genetic markers associated with meat quality at the selection signals in a previous study. The present study examined genetic associations of marbling score (MS) with intragenic nucleotide variants at selection signals of Korean cattle. A total of 39 092 nucleotide variants of 407 Korean cattle were utilized in the association analysis. A total of 129 variants were selected within newly annotated genes in the bovine genome. Their genetic associations were analyzed using the mixed model with random polygenic effects based on identical-by-state genetic relationships among animals in order to control for spurious associations produced by population structure. Genetic associations of MS were found (Pdirectional selection for greater MS and remain selection signals in the bovine genome. Further studies of fine mapping would be useful to incorporate favorable alleles in marker-assisted selection for MS of Korean cattle.
Cagliani, Rachele; Sironi, Manuela
Infectious diseases and epidemics have always accompanied and characterized human history, representing one of the main causes of death. Even today, despite progress in sanitation and medical research, infections are estimated to account for about 15% of deaths. The hypothesis whereby infectious diseases have been acting as a powerful selective pressure was formulated long ago, but it was not until the availability of large-scale genetic data and the development of novel methods to study molecular evolution that we could assess how pervasively infectious agents have shaped human genetic diversity. Indeed, recent evidences indicated that among the diverse environmental factors that acted as selective pressures during the evolution of our species, pathogen load had the strongest influence. Beside the textbook example of the major histocompatibility complex, selection signatures left by pathogen-exerted pressure can be identified at several human loci, including genes not directly involved in immune response. In the future, high-throughput technologies and the availability of genetic data from different populations are likely to provide novel insights into the evolutionary relationships between the human host and its pathogens. Hopefully, this will help identify the genetic determinants modulating the susceptibility to infectious diseases and will translate into new treatment strategies.
McClosky, Benjamin; LaCombe, Jason; Tanksley, Steven D
Self-fertilization (selfing) is commonly used for population development in plant breeding, and it is well established that selfing increases genetic variance between lines, thus increasing response to phenotypic selection. Furthermore, numerous studies have explored how selfing can be deployed to maximal benefit in the context of traditional plant breeding programs (Cornish in Heredity 65:201-211,1990a, Heredity 65:213-220,1990b; Liu et al. in Theor Appl Genet 109:370-376, 2004; Pooni and Jinks in Heredity 54:255-260, 1985). However, the impact of selfing on response to genomic selection has not been explored. In the current study we examined how selfing impacts the two key aspects of genomic selection-GEBV prediction (training) and selection response. We reach the following conclusions: (1) On average, selfing increases genomic selection gains by more than 70 %. (2) The gains in genomic selection response attributable to selfing hold over a wide range population sizes (100-500), heritabilities (0.2-0.8), and selection intensities (0.01-0.1). However, the benefits of selfing are dramatically reduced as the number of QTLs drops below 20. (3) The major cause of the improved response to genomic selection with selfing is through an increase in the occurrence of superior genotypes and not through improved GEBV predictions. While performance of the training population improves with selfing (especially with low heritability and small population sizes), the magnitude of these improvements is relatively small compared with improvements observed in the selection population. To illustrate the value of these insights, we propose a practical genomic selection scheme that substantially shortens the number of generations required to fully capture the benefits of selfing. Specifically, we provide simulation evidence that indicates the proposed scheme matches or exceeds the selection gains observed in advanced populations (i.e. F 8 and doubled haploid) across a broad range of
Full Text Available Natural selection at one site shapes patterns of genetic variation at linked sites. Quantifying the effects of "linked selection" on levels of genetic diversity is key to making reliable inference about demography, building a null model in scans for targets of adaptation, and learning about the dynamics of natural selection. Here, we introduce the first method that jointly infers parameters of distinct modes of linked selection, notably background selection and selective sweeps, from genome-wide diversity data, functional annotations and genetic maps. The central idea is to calculate the probability that a neutral site is polymorphic given local annotations, substitution patterns, and recombination rates. Information is then combined across sites and samples using composite likelihood in order to estimate genome-wide parameters of distinct modes of selection. In addition to parameter estimation, this approach yields a map of the expected neutral diversity levels along the genome. To illustrate the utility of our approach, we apply it to genome-wide resequencing data from 125 lines in Drosophila melanogaster and reliably predict diversity levels at the 1Mb scale. Our results corroborate estimates of a high fraction of beneficial substitutions in proteins and untranslated regions (UTR. They allow us to distinguish between the contribution of sweeps and other modes of selection around amino acid substitutions and to uncover evidence for pervasive sweeps in untranslated regions (UTRs. Our inference further suggests a substantial effect of other modes of linked selection and of adaptation in particular. More generally, we demonstrate that linked selection has had a larger effect in reducing diversity levels and increasing their variance in D. melanogaster than previously appreciated.
Elyashiv, Eyal; Sattath, Shmuel; Hu, Tina T; Strutsovsky, Alon; McVicker, Graham; Andolfatto, Peter; Coop, Graham; Sella, Guy
Natural selection at one site shapes patterns of genetic variation at linked sites. Quantifying the effects of "linked selection" on levels of genetic diversity is key to making reliable inference about demography, building a null model in scans for targets of adaptation, and learning about the dynamics of natural selection. Here, we introduce the first method that jointly infers parameters of distinct modes of linked selection, notably background selection and selective sweeps, from genome-wide diversity data, functional annotations and genetic maps. The central idea is to calculate the probability that a neutral site is polymorphic given local annotations, substitution patterns, and recombination rates. Information is then combined across sites and samples using composite likelihood in order to estimate genome-wide parameters of distinct modes of selection. In addition to parameter estimation, this approach yields a map of the expected neutral diversity levels along the genome. To illustrate the utility of our approach, we apply it to genome-wide resequencing data from 125 lines in Drosophila melanogaster and reliably predict diversity levels at the 1Mb scale. Our results corroborate estimates of a high fraction of beneficial substitutions in proteins and untranslated regions (UTR). They allow us to distinguish between the contribution of sweeps and other modes of selection around amino acid substitutions and to uncover evidence for pervasive sweeps in untranslated regions (UTRs). Our inference further suggests a substantial effect of other modes of linked selection and of adaptation in particular. More generally, we demonstrate that linked selection has had a larger effect in reducing diversity levels and increasing their variance in D. melanogaster than previously appreciated.
Cabrera-Bosquet, Llorenç; Crossa, José; von Zitzewitz, Jarislav; Serret, María Dolors; Araus, José Luis
Genomic selection (GS) and high-throughput phenotyping have recently been captivating the interest of the crop breeding community from both the public and private sectors world-wide. Both approaches promise to revolutionize the prediction of complex traits, including growth, yield and adaptation to stress. Whereas high-throughput phenotyping may help to improve understanding of crop physiology, most powerful techniques for high-throughput field phenotyping are empirical rather than analytical and comparable to genomic selection. Despite the fact that the two methodological approaches represent the extremes of what is understood as the breeding process (phenotype versus genome), they both consider the targeted traits (e.g. grain yield, growth, phenology, plant adaptation to stress) as a black box instead of dissecting them as a set of secondary traits (i.e. physiological) putatively related to the target trait. Both GS and high-throughput phenotyping have in common their empirical approach enabling breeders to use genome profile or phenotype without understanding the underlying biology. This short review discusses the main aspects of both approaches and focuses on the case of genomic selection of maize flowering traits and near-infrared spectroscopy (NIRS) and plant spectral reflectance as high-throughput field phenotyping methods for complex traits such as crop growth and yield. © 2012 Institute of Botany, Chinese Academy of Sciences.
Moon, Sunjin; Kim, Tae-Hun; Lee, Kyung-Tai; Kwak, Woori; Lee, Taeheon; Lee, Si-Woo; Kim, Myung-Jick; Cho, Kyuho; Kim, Namshin; Chung, Won-Hyong; Sung, Samsun; Park, Taesung; Cho, Seoae; Groenen, Martien Am; Nielsen, Rasmus; Kim, Yuseob; Kim, Heebal
Animal domestication involved drastic phenotypic changes driven by strong artificial selection and also resulted in new populations of breeds, established by humans. This study aims to identify genes that show evidence of recent artificial selection during pig domestication. Whole-genome resequencing of 30 individual pigs from domesticated breeds, Landrace and Yorkshire, and 10 Asian wild boars at ~16-fold coverage was performed resulting in over 4.3 million SNPs for 19,990 genes. We constructed a comprehensive genome map of directional selection by detecting selective sweeps using an F ST-based approach that detects directional selection in lineages leading to the domesticated breeds and using a haplotype-based test that detects ongoing selective sweeps within the breeds. We show that candidate genes under selection are significantly enriched for loci implicated in quantitative traits important to pig reproduction and production. The candidate gene with the strongest signals of directional selection belongs to group III of the metabolomics glutamate receptors, known to affect brain functions associated with eating behavior, suggesting that loci under strong selection include loci involved in behaviorial traits in domesticated pigs including tameness. We show that a significant proportion of selection signatures coincide with loci that were previously inferred to affect phenotypic variation in pigs. We further identify functional enrichment related to behavior, such as signal transduction and neuronal activities, for those targets of selection during domestication in pigs.
Heidaritabar, M; Vereijken, A; Muir, W M; Meuwissen, T; Cheng, H; Megens, H-J; Groenen, M A M; Bastiaansen, J W M
Genomic selection (GS) is a DNA-based method of selecting for quantitative traits in animal and plant breeding, and offers a potentially superior alternative to traditional breeding methods that rely on pedigree and phenotype information. Using a 60 K SNP chip with markers spaced throughout the entire chicken genome, we compared the impact of GS and traditional BLUP (best linear unbiased prediction) selection methods applied side-by-side in three different lines of egg-laying chickens. Differences were demonstrated between methods, both at the level and genomic distribution of allele frequency changes. In all three lines, the average allele frequency changes were larger with GS, 0.056 0.064 and 0.066, compared with BLUP, 0.044, 0.045 and 0.036 for lines B1, B2 and W1, respectively. With BLUP, 35 selected regions (empirical P selected regions were identified. Empirical thresholds for local allele frequency changes were determined from gene dropping, and differed considerably between GS (0.167-0.198) and BLUP (0.105-0.126). Between lines, the genomic regions with large changes in allele frequencies showed limited overlap. Our results show that GS applies selection pressure much more locally than BLUP, resulting in larger allele frequency changes. With these results, novel insights into the nature of selection on quantitative traits have been gained and important questions regarding the long-term impact of GS are raised. The rapid changes to a part of the genetic architecture, while another part may not be selected, at least in the short term, require careful consideration, especially when selection occurs before phenotypes are observed.
Somavilla, A L; Sonstegard, T S; Higa, R H; Rosa, A N; Siqueira, F; Silva, L O C; Torres Júnior, R A A; Coutinho, L L; Mudadu, M A; Alencar, M M; Regitano, L C A
Brazilian Nellore cattle (Bos indicus) have been selected for growth traits for over more than four decades. In recent years, reproductive and meat quality traits have become more important because of increasing consumption, exports and consumer demand. The identification of genome regions altered by artificial selection can potentially permit a better understanding of the biology of specific phenotypes that are useful for the development of tools designed to increase selection efficiency. Therefore, the aims of this study were to detect evidence of recent selection signatures in Nellore cattle using extended haplotype homozygosity methodology and BovineHD marker genotypes (>777,000 single nucleotide polymorphisms) as well as to identify corresponding genes underlying these signals. Thirty-one significant regions (P meat quality, fatty acid profiles and immunity. In addition, 545 genes were identified in regions harboring selection signatures. Within this group, 58 genes were associated with growth, muscle and adipose tissue metabolism, reproductive traits or the immune system. Using relative extended haplotype homozygosity to analyze high-density single nucleotide polymorphism marker data allowed for the identification of regions potentially under artificial selection pressure in the Nellore genome, which might be used to better understand autozygosity and the effects of selection on the Nellore genome. © 2014 Stichting International Foundation for Animal Genetics.
Kadarmideen, Haja; Do, Duy Ngoc
growth will increase the demand for food as well as animal products, particularly in emerging economic giants like India. Moreover, the urbanization has considerable impact on patterns of food consumption in general and on demand for livestock products, in particular and the increased income growth led......Global livestock production has increased substantially during the last decades, in both number of animals and productivity. Meanwhile, the human population is projected to reach 9.6 billions by 2050 and most of the increase in the projection takes place in developing countries. Rapid population...... production (OPU-IVP) of embryos will have a considerable impact in the future. This paper attempts to provide basic concepts of using genomic tools for livestock production with the focus on genomic prediction and selection methods and discuss about the potential application of genomic selection to increase...
K. Jun Tong
Full Text Available Genomes evolve through a combination of mutation, drift, and selection, all of which act heterogeneously across genes and lineages. This leads to differences in branch-length patterns among gene trees. Genes that yield trees with the same branch-length patterns can be grouped together into clusters. Here, we propose a novel phylogenetic approach to explain the factors that influence the number and distribution of these gene-tree clusters. We apply our method to a genomic dataset from insects, an ancient and diverse group of organisms. We find some evidence that when drift is the dominant evolutionary process, each cluster tends to contain a large number of fast-evolving genes. In contrast, strong negative selection leads to many distinct clusters, each of which contains only a few slow-evolving genes. Our work, although preliminary in nature, illustrates the use of phylogenetic methods to shed light on the factors driving rate variation in genomic evolution.
Schmidt, Malthe; Kollers, Sonja; Maasberg-Prelle, Anja; Großer, Jörg; Schinkel, Burkhard; Tomerius, Alexandra; Graner, Andreas; Korzun, Viktor
Genomic prediction of malting quality traits in barley shows the potential of applying genomic selection to improve selection for malting quality and speed up the breeding process. Genomic selection has been applied to various plant species, mostly for yield or yield-related traits such as grain dry matter yield or thousand kernel weight, and improvement of resistances against diseases. Quality traits have not been the main scope of analysis for genomic selection, but have rather been addressed by marker-assisted selection. In this study, the potential to apply genomic selection to twelve malting quality traits in two commercial breeding programs of spring and winter barley (Hordeum vulgare L.) was assessed. Phenotypic means were calculated combining multilocational field trial data from 3 or 4 years, depending on the trait investigated. Three to five locations were available in each of these years. Heritabilities for malting traits ranged between 0.50 and 0.98. Predictive abilities (PA), as derived from cross validation, ranged between 0.14 to 0.58 for spring barley and 0.40-0.80 for winter barley. Small training sets were shown to be sufficient to obtain useful PAs, possibly due to the narrow genetic base in this breeding material. Deployment of genomic selection in malting barley breeding clearly has the potential to reduce cost intensive phenotyping for quality traits, increase selection intensity and to shorten breeding cycles.
Atanur, Santosh S.; Diaz, Ana Garcia; Maratou, Klio; Sarkis, Allison; Rotival, Maxime; Game, Laurence; Tschannen, Michael R.; Kaisaki, Pamela J.; Otto, Georg W.; Ma, Man Chun John; Keane, Thomas M.; Hummel, Oliver; Saar, Kathrin; Chen, Wei; Guryev, Victor; Gopalakrishnan, Kathirvel; Garrett, Michael R.; Joe, Bina; Citterio, Lorena; Bianchi, Giuseppe; McBride, Martin; Dominiczak, Anna; Adams, David J.; Serikawa, Tadao; Flicek, Paul; Cuppen, Edwin; Hubner, Norbert; Petretto, Enrico; Gauguier, Dominique; Kwitek, Anne; Jacob, Howard; Aitman, Timothy J.
Large numbers of inbred laboratory rat strains have been developed for a range of complex disease phenotypes. To gain insights into the evolutionary pressures underlying selection for these phenotypes, we sequenced the genomes of 27 rat strains, including 11 models of hypertension, diabetes, and
Hartman, Y.; Uwimana, B; Hooftman, D.A.P.; Schranz, M.E.; van de Wiel, C.C.M.; Smulders, M.J.M.; Visser, R.G.F.; van Tienderen, P.H.
Genomic selection patterns and hybrid performance influence the chance that crop (trans)genes can spread to wild relatives. We measured fitness(-related) traits in two different field environments employing two different crop-wild crosses of lettuce. We performed quantitative trait loci (QTL)
Hartman, Y.; Uwimana, B.; Hooftman, D.A.P.; Schranz, M.E.; Wiel, van de C.C.M.; Smulders, M.J.M.; Visser, R.G.F.; Tienderen, van P.H.
Genomic selection patterns and hybrid performance influence the chance that crop (trans)genes can spread to wild relatives. We measured fitness(-related) traits in two different field environments employing two different crop–wild crosses of lettuce. We performed quantitative trait loci (QTL)
Jenko, J; Wiggans, G R; Cooper, T A; Eaglen, S A E; Luff, W G de L; Bichard, M; Pong-Wong, R; Woolliams, J A
This study compares how different cow genotyping strategies increase the accuracy of genomic estimated breeding values (EBV) in dairy cattle breeds with low numbers. In these breeds, few sires have progeny records, and genotyping cows can improve the accuracy of genomic EBV. The Guernsey breed is a small dairy cattle breed with approximately 14,000 recorded individuals worldwide. Predictions of phenotypes of milk yield, fat yield, protein yield, and calving interval were made for Guernsey cows from England and Guernsey Island using genomic EBV, with training sets including 197 de-regressed proofs of genotyped bulls, with cows selected from among 1,440 genotyped cows using different genotyping strategies. Accuracies of predictions were tested using 10-fold cross-validation among the cows. Genomic EBV were predicted using 4 different methods: (1) pedigree BLUP, (2) genomic BLUP using only bulls, (3) univariate genomic BLUP using bulls and cows, and (4) bivariate genomic BLUP. Genotyping cows with phenotypes and using their data for the prediction of single nucleotide polymorphism effects increased the correlation between genomic EBV and phenotypes compared with using only bulls by 0.163±0.022 for milk yield, 0.111±0.021 for fat yield, and 0.113±0.018 for protein yield; a decrease of 0.014±0.010 for calving interval from a low base was the only exception. Genetic correlation between phenotypes from bulls and cows were approximately 0.6 for all yield traits and significantly different from 1. Only a very small change occurred in correlation between genomic EBV and phenotypes when using the bivariate model. It was always better to genotype all the cows, but when only half of the cows were genotyped, a divergent selection strategy was better compared with the random or directional selection approach. Divergent selection of 30% of the cows remained superior for the yield traits in 8 of 10 folds. Copyright © 2017 American Dairy Science Association. Published by
He, Qiang; Kim, Kyu?Won; Park, Yong?Jin
Summary Weedy rice is the same biological species as cultivated rice (Oryza sativa); it is also a noxious weed infesting rice fields worldwide. Its formation and population?selective or ?adaptive signatures are poorly understood. In this study, we investigated the phylogenetics, population structure and signatures of selection of Korean weedy rice by determining the whole genomes of 30 weedy rice, 30 landrace rice and ten wild rice samples. The phylogenetic tree and results of ancestry infere...
Haberland, A M; König von Borstel, U; Simianer, H; König, S
Reliable selection criteria are required for young riding horses to increase genetic gain by increasing accuracy of selection and decreasing generation intervals. In this study, selection strategies incorporating genomic breeding values (GEBVs) were evaluated. Relevant stages of selection in sport horse breeding programs were analyzed by applying selection index theory. Results in terms of accuracies of indices (r(TI) ) and relative selection response indicated that information on single nucleotide polymorphism (SNP) genotypes considerably increases the accuracy of breeding values estimated for young horses without own or progeny performance. In a first scenario, the correlation between the breeding value estimated from the SNP genotype and the true breeding value (= accuracy of GEBV) was fixed to a relatively low value of r(mg) = 0.5. For a low heritability trait (h(2) = 0.15), and an index for a young horse based only on information from both parents, additional genomic information doubles r(TI) from 0.27 to 0.54. Including the conventional information source 'own performance' into the before mentioned index, additional SNP information increases r(TI) by 40%. Thus, particularly with regard to traits of low heritability, genomic information can provide a tool for well-founded selection decisions early in life. In a further approach, different sources of breeding values (e.g. GEBV and estimated breeding values (EBVs) from different countries) were combined into an overall index when altering accuracies of EBVs and correlations between traits. In summary, we showed that genomic selection strategies have the potential to contribute to a substantial reduction in generation intervals in horse breeding programs.
Bacterial cold water disease (BCWD) causes significant economic losses in salmonid aquaculture, and traditional family-based breeding programs aimed at improving BCWD resistance have been limited to exploiting only between-family variation. We used genomic selection (GS) models to predict genomic br...
Zhang, Jiaoping; Song, Qijian; Cregan, Perry B; Jiang, Guo-Liang
Twenty-two loci for soybean SW and candidate genes conditioning seed development were identified; and prediction accuracies of GS and MAS were estimated through cross-validation and validation with unrelated populations. Soybean (Glycine max) is a major crop for plant protein and oil production, and seed weight (SW) is important for yield and quality in food/vegetable uses of soybean. However, our knowledge of genes controlling SW remains limited. To better understand the molecular mechanism underlying the trait and explore marker-based breeding approaches, we conducted a genome-wide association study in a population of 309 soybean germplasm accessions using 31,045 single nucleotide polymorphisms (SNPs), and estimated the prediction accuracy of genomic selection (GS) and marker-assisted selection (MAS) for SW. Twenty-two loci of minor effect associated with SW were identified, including hotspots on Gm04 and Gm19. The mixed model containing these loci explained 83.4% of phenotypic variation. Candidate genes with Arabidopsis orthologs conditioning SW were also proposed. The prediction accuracies of GS and MAS by cross-validation were 0.75-0.87 and 0.62-0.75, respectively, depending on the number of SNPs used and the size of training population. GS also outperformed MAS when the validation was performed using unrelated panels across a wide range of maturities, with an average prediction accuracy of 0.74 versus 0.53. This study convincingly demonstrated that soybean SW is controlled by numerous minor-effect loci. It greatly enhances our understanding of the genetic basis of SW in soybean and facilitates the identification of genes controlling the trait. It also suggests that GS holds promise for accelerating soybean breeding progress. The results are helpful for genetic improvement and genomic prediction of yield in soybean.
Full Text Available Deedu (DU Mongolians, who migrated from the Mongolian steppes to the Qinghai-Tibetan Plateau approximately 500 years ago, are challenged by environmental conditions similar to native Tibetan highlanders. Identification of adaptive genetic factors in this population could provide insight into coordinated physiological responses to this environment. Here we examine genomic and phenotypic variation in this unique population and present the first complete analysis of a Mongolian whole-genome sequence. High-density SNP array data demonstrate that DU Mongolians share genetic ancestry with other Mongolian as well as Tibetan populations, specifically in genomic regions related with adaptation to high altitude. Several selection candidate genes identified in DU Mongolians are shared with other Asian groups (e.g., EDAR, neighboring Tibetan populations (including high-altitude candidates EPAS1, PKLR, and CYP2E1, as well as genes previously hypothesized to be associated with metabolic adaptation (e.g., PPARG. Hemoglobin concentration, a trait associated with high-altitude adaptation in Tibetans, is at an intermediate level in DU Mongolians compared to Tibetans and Han Chinese at comparable altitude. Whole-genome sequence from a DU Mongolian (Tianjiao1 shows that about 2% of the genomic variants, including more than 300 protein-coding changes, are specific to this individual. Our analyses of DU Mongolians and the first Mongolian genome provide valuable insight into genetic adaptation to extreme environments.
Wragg, David; Marti-Marimon, Maria; Basso, Benjamin; Bidanel, Jean-Pierre; Labarthe, Emmanuelle; Bouchez, Olivier; Le Conte, Yves; Vignal, Alain
Four main evolutionary lineages of A. mellifera have been described including eastern Europe (C) and western and northern Europe (M). Many apiculturists prefer bees from the C lineage due to their docility and high productivity. In France, the routine importation of bees from the C lineage has resulted in the widespread admixture of bees from the M lineage. The haplodiploid nature of the honeybee Apis mellifera, and its small genome size, permits affordable and extensive genomics studies. As a pilot study of a larger project to characterise French honeybee populations, we sequenced 60 drones sampled from two commercial populations managed for the production of honey and royal jelly. Results indicate a C lineage origin, whilst mitochondrial analysis suggests two drones originated from the O lineage. Analysis of heterozygous SNPs identified potential copy number variants near to genes encoding odorant binding proteins and several cytochrome P450 genes. Signatures of selection were detected using the hapFLK haplotype-based method, revealing several regions under putative selection for royal jelly production. The framework developed during this study will be applied to a broader sampling regime, allowing the genetic diversity of French honeybees to be characterised in detail.
Full Text Available BACKGROUND: Eukaryotic genomes are scattered with retroelements that proliferate through retrotransposition. Although retroelements make up around 40 percent of the human genome, large regions are found to be completely devoid of retroelements. This has been hypothesised to be a result of genomic regions being intolerant to insertions of retroelements. The inadvertent transcriptional activity of retroelements may affect neighbouring genes, which in turn could be detrimental to an organism. We speculate that such retroelement transcription, or transcriptional interference, is a contributing factor in generating and maintaining retroelement-free regions in the human genome. METHODOLOGY/PRINCIPAL FINDINGS: Based on the known transcriptional properties of retroelements, we expect long interspersed elements (LINEs to be able to display a high degree of transcriptional interference. In contrast, we expect short interspersed elements (SINEs to display very low levels of transcriptional interference. We find that genomic regions devoid of long interspersed elements (LINEs are enriched for protein-coding genes, but that this is not the case for regions devoid of short interspersed elements (SINEs. This is expected if genes are subject to selection against transcriptional interference. We do not find microRNAs to be associated with genomic regions devoid of either SINEs or LINEs. We further observe an increased relative activity of genes overlapping LINE-free regions during early embryogenesis, where activity of LINEs has been identified previously. CONCLUSIONS/SIGNIFICANCE: Our observations are consistent with the notion that selection against transcriptional interference has contributed to the maintenance and/or generation of retroelement-free regions in the human genome.
Nielsen, Henrik Bjørn; Almeida, Mathieu; Juncker, Agnieszka
of microbial genomes without the need for reference sequences. We demonstrate the method on data from 396 human gut microbiome samples and identify 7,381 co-abundance gene groups (CAGs), including 741 metagenomic species (MGS). We use these to assemble 238 high-quality microbial genomes and identify...
Full Text Available Since the divergence of humans and chimpanzees about 5 million years ago, these species have undergone a remarkable evolution with drastic divergence in anatomy and cognitive abilities. At the molecular level, despite the small overall magnitude of DNA sequence divergence, we might expect such evolutionary changes to leave a noticeable signature throughout the genome. We here compare 13,731 annotated genes from humans to their chimpanzee orthologs to identify genes that show evidence of positive selection. Many of the genes that present a signature of positive selection tend to be involved in sensory perception or immune defenses. However, the group of genes that show the strongest evidence for positive selection also includes a surprising number of genes involved in tumor suppression and apoptosis, and of genes involved in spermatogenesis. We hypothesize that positive selection in some of these genes may be driven by genomic conflict due to apoptosis during spermatogenesis. Genes with maximal expression in the brain show little or no evidence for positive selection, while genes with maximal expression in the testis tend to be enriched with positively selected genes. Genes on the X chromosome also tend to show an elevated tendency for positive selection. We also present polymorphism data from 20 Caucasian Americans and 19 African Americans for the 50 annotated genes showing the strongest evidence for positive selection. The polymorphism analysis further supports the presence of positive selection in these genes by showing an excess of high-frequency derived nonsynonymous mutations.
Elferink, Martin G.; Megens, Hendrik-Jan; Vereijken, Addie; Hu, Xiaoxiang; Crooijmans, Richard P. M. A.; Groenen, Martien A. M.
Identifying genomics regions that are affected by selection is important to understand the domestication and selection history of the domesticated chicken, as well as understanding molecular pathways underlying phenotypic traits and breeding goals. While whole-genome approaches, either high-density SNP chips or massively parallel sequencing, have been successfully applied to identify evidence for selective sweeps in chicken, it has been difficult to distinguish patterns of selection and stochastic and breed specific effects. Here we present a study to identify selective sweeps in a large number of chicken breeds (67 in total) using a high-density (58 K) SNP chip. We analyzed commercial chickens representing all major breeding goals. In addition, we analyzed non-commercial chicken diversity for almost all recognized traditional Dutch breeds and a selection of representative breeds from China. Based on their shared history or breeding goal we in silico grouped the breeds into 14 breed groups. We identified 396 chromosomal regions that show suggestive evidence of selection in at least one breed group with 26 of these regions showing strong evidence of selection. Of these 26 regions, 13 were previously described and 13 yield new candidate genes for performance traits in chicken. Our approach demonstrates the strength of including many different populations with similar, and breed groups with different selection histories to reduce stochastic effects based on single populations. PMID:22384281
Mariana F Nery
Full Text Available Cetaceans are unique in being the only mammals completely adapted to an aquatic environment. This adaptation has required complex changes and sometimes a complete restructuring of physiology, behavior and morphology. Identifying genes that have been subjected to selection pressure during cetacean evolution would greatly enhance our knowledge of the ways in which genetic variation in this mammalian order has been shaped by natural selection. Here, we performed a genome-wide scan for positive selection in the dolphin lineage. We employed models of codon substitution that account for variation of selective pressure over branches on the tree and across sites in a sequence. We analyzed 7,859 nuclear-coding ortholog genes and using a series of likelihood ratio tests (LRTs, we identified 376 genes (4.8% with molecular signatures of positive selection in the dolphin lineage. We used the cow as the sister group and compared estimates of selection in the cetacean genome to this using the same methods. This allowed us to define which genes have been exclusively under positive selection in the dolphin lineage. The enrichment analysis found that the identified positively selected genes are significantly over-represented for three exclusive functional categories only in the dolphin lineage: segment specification, mesoderm development and system development. Of particular interest for cetacean adaptation to an aquatic life are the following GeneOntology targets under positive selection: genes related to kidney, heart, lung, eye, ear and nervous system development.
Full Text Available The extraordinary phenotypic diversity of dog breeds has been sculpted by a unique population history accompanied by selection for novel and desirable traits. Here we perform a comprehensive analysis using multiple test statistics to identify regions under selection in 509 dogs from 46 diverse breeds using a newly developed high-density genotyping array consisting of >170,000 evenly spaced SNPs. We first identify 44 genomic regions exhibiting extreme differentiation across multiple breeds. Genetic variation in these regions correlates with variation in several phenotypic traits that vary between breeds, and we identify novel associations with both morphological and behavioral traits. We next scan the genome for signatures of selective sweeps in single breeds, characterized by long regions of reduced heterozygosity and fixation of extended haplotypes. These scans identify hundreds of regions, including 22 blocks of homozygosity longer than one megabase in certain breeds. Candidate selection loci are strongly enriched for developmental genes. We chose one highly differentiated region, associated with body size and ear morphology, and characterized it using high-throughput sequencing to provide a list of variants that may directly affect these traits. This study provides a catalogue of genomic regions showing extreme reduction in genetic variation or population differentiation in dogs, including many linked to phenotypic variation. The many blocks of reduced haplotype diversity observed across the genome in dog breeds are the result of both selection and genetic drift, but extended blocks of homozygosity on a megabase scale appear to be best explained by selection. Further elucidation of the variants under selection will help to uncover the genetic basis of complex traits and disease.
Charles E Chapple
Full Text Available BACKGROUND: Selenoproteins are a diverse family of proteins notable for the presence of the 21st amino acid, selenocysteine. Until very recently, all metazoan genomes investigated encoded selenoproteins, and these proteins had therefore been believed to be essential for animal life. Challenging this assumption, recent comparative analyses of insect genomes have revealed that some insect genomes appear to have lost selenoprotein genes. METHODOLOGY/PRINCIPAL FINDINGS: In this paper we investigate in detail the fate of selenoproteins, and that of selenoprotein factors, in all available arthropod genomes. We use a variety of in silico comparative genomics approaches to look for known selenoprotein genes and factors involved in selenoprotein biosynthesis. We have found that five insect species have completely lost the ability to encode selenoproteins and that selenoprotein loss in these species, although so far confined to the Endopterygota infraclass, cannot be attributed to a single evolutionary event, but rather to multiple, independent events. Loss of selenoproteins and selenoprotein factors is usually coupled to the deletion of the entire no-longer functional genomic region, rather than to sequence degradation and consequent pseudogenisation. Such dynamics of gene extinction are consistent with the high rate of genome rearrangements observed in Drosophila. We have also found that, while many selenoprotein factors are concomitantly lost with the selenoproteins, others are present and conserved in all investigated genomes, irrespective of whether they code for selenoproteins or not, suggesting that they are involved in additional, non-selenoprotein related functions. CONCLUSIONS/SIGNIFICANCE: Selenoproteins have been independently lost in several insect species, possibly as a consequence of the relaxation in insects of the selective constraints acting across metazoans to maintain selenoproteins. The dispensability of selenoproteins in insects may
Annicchiarico, Paolo; Nazzicari, Nelson; Li, Xuehui; Wei, Yanling; Pecetti, Luciano; Brummer, E Charles
Genomic selection based on genotyping-by-sequencing (GBS) data could accelerate alfalfa yield gains, if it displayed moderate ability to predict parent breeding values. Its interest would be enhanced by predicting ability also for germplasm/reference populations other than those for which it was defined. Predicting accuracy may be influenced by statistical models, SNP calling procedures and missing data imputation strategies. Landrace and variety material from two genetically-contrasting reference populations, i.e., 124 elite genotypes adapted to the Po Valley (sub-continental climate; PV population) and 154 genotypes adapted to Mediterranean-climate environments (Me population), were genotyped by GBS and phenotyped in separate environments for dry matter yield of their dense-planted half-sib progenies. Both populations showed no sub-population genetic structure. Predictive accuracy was higher by joint rather than separate SNP calling for the two data sets, and using random forest imputation of missing data. Highest accuracy was obtained using Support Vector Regression (SVR) for PV, and Ridge Regression BLUP and SVR for Me germplasm. Bayesian methods (Bayes A, Bayes B and Bayesian Lasso) tended to be less accurate. Random Forest Regression was the least accurate model. Accuracy attained about 0.35 for Me in the range of 0.30-0.50 missing data, and 0.32 for PV at 0.50 missing data, using at least 10,000 SNP markers. Cross-population predictions based on a smaller subset of common SNPs implied a relative loss of accuracy of about 25% for Me and 30% for PV. Genome-wide association analyses based on large subsets of M. truncatula-aligned markers revealed many SNPs with modest association with yield, and some genome areas hosting putative QTLs. A comparison of genomic vs. conventional selection for parent breeding value assuming 1-year vs. 5-year selection cycles, respectively, indicated over three-fold greater predicted yield gain per unit time for genomic selection
The results of this thesis show that the probability of introgression of a putative transgene to wild relatives indeed depends strongly on the insertion location of the transgene. The study of genomic selection patterns can identify crop genomic regions under negative selection in multiple
Doolittle, W Ford; Brunet, Tyler D P
The idea that much of our genome is irrelevant to fitness-is not the product of positive natural selection at the organismal level-remains viable. Claims to the contrary, and specifically that the notion of "junk DNA" should be abandoned, are based on conflating meanings of the word "function". Recent estimates suggest that perhaps 90% of our DNA, though biochemically active, does not contribute to fitness in any sequence-dependent way, and possibly in no way at all. Comparisons to vertebrates with much larger and smaller genomes (the lungfish and the pufferfish) strongly align with such a conclusion, as they have done for the last half-century.
Sergey I Nikolaev
Full Text Available Detection of the rare polymorphisms and causative mutations of genetic diseases in a targeted genomic area has become a major goal in order to understand genomic and phenotypic variability. We have interrogated repeat-masked regions of 8.9 Mb on human chromosomes 21 (7.8 Mb and 7 (1.1 Mb from an individual from the International HapMap Project (NA12872. We have optimized a method of genomic selection for high throughput sequencing. Microarray-based selection and sequencing resulted in 260-fold enrichment, with 41% of reads mapping to the target region. 83% of SNPs in the targeted region had at least 4-fold sequence coverage and 54% at least 15-fold. When assaying HapMap SNPs in NA12872, our sequence genotypes are 91.3% concordant in regions with coverage > or = 4-fold, and 97.9% concordant in regions with coverage > or = 15-fold. About 81% of the SNPs recovered with both thresholds are listed in dbSNP. We observed that regions with low sequence coverage occur in close proximity to low-complexity DNA. Validation experiments using Sanger sequencing were performed for 46 SNPs with 15-20 fold coverage, with a confirmation rate of 96%, suggesting that DNA selection provides an accurate and cost-effective method for identifying rare genomic variants.
Carolina M. Voloch
Full Text Available Lyssavirus is a diverse genus of viruses that infect a variety of mammalian hosts, typically causing encephalitis. The evolution of this lineage, particularly the rabies virus, has been a focus of research because of the extensive occurrence of cross-species transmission, and the distinctive geographical patterns present throughout the diversification of these viruses. Although numerous studies have examined pattern-related questions concerning Lyssavirus evolution, analyses of the evolutionary processes acting on Lyssavirus diversification are scarce. To clarify the relevance of positive natural selection in Lyssavirus diversification, we conducted a comprehensive scan for episodic diversifying selection across all lineages and codon sites of the five coding regions in lyssavirus genomes. Although the genomes of these viruses are generally conserved, the glycoprotein (G, RNA-dependent RNA polymerase (L and polymerase (P genes were frequently targets of adaptive evolution during the diversification of the genus. Adaptive evolution is particularly manifest in the glycoprotein gene, which was inferred to have experienced the highest density of positively selected codon sites along branches. Substitutions in the L gene were found to be associated with the early diversification of phylogroups. A comparison between the number of positively selected sites inferred along the branches of RABV population branches and Lyssavirus intespecies branches suggested that the occurrence of positive selection was similar on the five coding regions of the genome in both groups.
Voloch, Carolina M; Capellão, Renata T; Mello, Beatriz; Schrago, Carlos G
Lyssavirus is a diverse genus of viruses that infect a variety of mammalian hosts, typically causing encephalitis. The evolution of this lineage, particularly the rabies virus, has been a focus of research because of the extensive occurrence of cross-species transmission, and the distinctive geographical patterns present throughout the diversification of these viruses. Although numerous studies have examined pattern-related questions concerning Lyssavirus evolution, analyses of the evolutionary processes acting on Lyssavirus diversification are scarce. To clarify the relevance of positive natural selection in Lyssavirus diversification, we conducted a comprehensive scan for episodic diversifying selection across all lineages and codon sites of the five coding regions in lyssavirus genomes. Although the genomes of these viruses are generally conserved, the glycoprotein (G), RNA-dependent RNA polymerase (L) and polymerase (P) genes were frequently targets of adaptive evolution during the diversification of the genus. Adaptive evolution is particularly manifest in the glycoprotein gene, which was inferred to have experienced the highest density of positively selected codon sites along branches. Substitutions in the L gene were found to be associated with the early diversification of phylogroups. A comparison between the number of positively selected sites inferred along the branches of RABV population branches and Lyssavirus intespecies branches suggested that the occurrence of positive selection was similar on the five coding regions of the genome in both groups.
Full Text Available The use of breeding programs for the Pacific white shrimp (Penaeus (Litopenaeus vannamei based on mixed linear models with pedigreed data are described. The application of these classic breeding methods yielded continuous progress of great value to increase the profitability of the shrimp industry in several countries. Recent advances in such areas as genomics in shrimp will allow for the development of new breeding programs in the near future that will increase genetic progress. In particular, these novel techniques may help increase disease resistance to specific emerging diseases, which is today a very important component of shrimp breeding programs. Thanks to increased selection accuracy, simulated genetic advance using genomic selection for survival to a disease challenge was up to 2.6 times that of phenotypic sib selection.
Elliot L. Heffner
Full Text Available Genomic selection (GS uses genome-wide molecular marker data to predict the genetic value of selection candidates in breeding programs. In plant breeding, the ability to produce large numbers of progeny per cross allows GS to be conducted within each family. However, this approach requires phenotypes of lines from each cross before conducting GS. This will prolong the selection cycle and may result in lower gains per year than approaches that estimate marker-effects with multiple families from previous selection cycles. In this study, phenotypic selection (PS, conventional marker-assisted selection (MAS, and GS prediction accuracy were compared for 13 agronomic traits in a population of 374 winter wheat ( L. advanced-cycle breeding lines. A cross-validation approach that trained and validated prediction accuracy across years was used to evaluate effects of model selection, training population size, and marker density in the presence of genotype × environment interactions (G×E. The average prediction accuracies using GS were 28% greater than with MAS and were 95% as accurate as PS. For net merit, the average accuracy across six selection indices for GS was 14% greater than for PS. These results provide empirical evidence that multifamily GS could increase genetic gain per unit time and cost in plant breeding.
Emile R Chimusa
Full Text Available We report a study of genome-wide, dense SNP (∼ 900K and copy number polymorphism data of indigenous southern Africans. We demonstrate the genetic contribution to southern and eastern African populations, which involved admixture between indigenous San, Niger-Congo-speaking and populations of Eurasian ancestry. This finding illustrates the need to account for stratification in genome-wide association studies, and that admixture mapping would likely be a successful approach in these populations. We developed a strategy to detect the signature of selection prior to and following putative admixture events. Several genomic regions show an unusual excess of Niger-Kordofanian, and unusual deficiency of both San and Eurasian ancestry, which were considered the footprints of selection after population admixture. Several SNPs with strong allele frequency differences were observed predominantly between the admixed indigenous southern African populations, and their ancestral Eurasian populations. Interestingly, many candidate genes, which were identified within the genomic regions showing signals for selection, were associated with southern African-specific high-risk, mostly communicable diseases, such as malaria, influenza, tuberculosis, and human immunodeficiency virus/AIDs. This observation suggests a potentially important role that these genes might have played in adapting to the environment. Additionally, our analyses of haplotype structure, linkage disequilibrium, recombination, copy number variation and genome-wide admixture highlight, and support the unique position of San relative to both African and non-African populations. This study contributes to a better understanding of population ancestry and selection in south-eastern African populations; and the data and results obtained will support research into the genetic contributions to infectious as well as non-communicable diseases in the region.
Chimusa, Emile R; Meintjies, Ayton; Tchanga, Milaine; Mulder, Nicola; Seoighe, Cathal; Seioghe, Cathal; Soodyall, Himla; Ramesar, Rajkumar
We report a study of genome-wide, dense SNP (∼ 900K) and copy number polymorphism data of indigenous southern Africans. We demonstrate the genetic contribution to southern and eastern African populations, which involved admixture between indigenous San, Niger-Congo-speaking and populations of Eurasian ancestry. This finding illustrates the need to account for stratification in genome-wide association studies, and that admixture mapping would likely be a successful approach in these populations. We developed a strategy to detect the signature of selection prior to and following putative admixture events. Several genomic regions show an unusual excess of Niger-Kordofanian, and unusual deficiency of both San and Eurasian ancestry, which were considered the footprints of selection after population admixture. Several SNPs with strong allele frequency differences were observed predominantly between the admixed indigenous southern African populations, and their ancestral Eurasian populations. Interestingly, many candidate genes, which were identified within the genomic regions showing signals for selection, were associated with southern African-specific high-risk, mostly communicable diseases, such as malaria, influenza, tuberculosis, and human immunodeficiency virus/AIDs. This observation suggests a potentially important role that these genes might have played in adapting to the environment. Additionally, our analyses of haplotype structure, linkage disequilibrium, recombination, copy number variation and genome-wide admixture highlight, and support the unique position of San relative to both African and non-African populations. This study contributes to a better understanding of population ancestry and selection in south-eastern African populations; and the data and results obtained will support research into the genetic contributions to infectious as well as non-communicable diseases in the region.
Full Text Available Abstract Background Chlamydia trachomatis is an obligate intracellular bacterial parasite, which causes several severe and debilitating diseases in humans. This study uses comparative genomic analyses of 12 complete published C. trachomatis genomes to assess the contribution of recombination and selection in this pathogen and to understand the major evolutionary forces acting on the genome of this bacterium. Results The conserved core genes of C. trachomatis are a large proportion of the pan-genome: we identified 836 core genes in C. trachomatis out of a range of 874-927 total genes in each genome. The ratio of recombination events compared to mutation (ρ/θ was 0.07 based on ancestral reconstructions using the ClonalFrame tool, but recombination had a significant effect on genetic diversification (r/m = 0.71. The distance-dependent decay of linkage disequilibrium also indicated that C. trachomatis populations behaved intermediately between sexual and clonal extremes. Fifty-five genes were identified as having a history of recombination and 92 were under positive selection based on statistical tests. Twenty-three genes showed evidence of being under both positive selection and recombination, which included genes with a known role in virulence and pathogencity (e.g., ompA, pmps, tarp. Analysis of inter-clade recombination flux indicated non-uniform currents of recombination between clades, which suggests the possibility of spatial population structure in C. trachomatis infections. Conclusions C. trachomatis is the archetype of a bacterial species where recombination is relatively frequent yet gene gains by horizontal gene transfer (HGT and losses (by deletion are rare. Gene conversion occurs at sites across the whole C. trachomatis genome but may be more often fixed in genes that are under diversifying selection. Furthermore, genome sequencing will reveal patterns of serotype specific gene exchange and selection that will generate important
Murray, Gemma G R; Soares, André E R; Novak, Ben J; Schaefer, Nathan K; Cahill, James A; Baker, Allan J; Demboski, John R; Doll, Andrew; Da Fonseca, Rute R; Fulton, Tara L; Gilbert, M Thomas P; Heintzman, Peter D; Letts, Brandon; McIntosh, George; O'Connell, Brendan L; Peck, Mark; Pipes, Marie-Lorraine; Rice, Edward S; Santos, Kathryn M; Sohrweide, A Gregory; Vohr, Samuel H; Corbett-Detig, Russell B; Green, Richard E; Shapiro, Beth
The extinct passenger pigeon was once the most abundant bird in North America, and possibly the world. Although theory predicts that large populations will be more genetically diverse, passenger pigeon genetic diversity was surprisingly low. To investigate this disconnect, we analyzed 41 mitochondrial and 4 nuclear genomes from passenger pigeons and 2 genomes from band-tailed pigeons, which are passenger pigeons' closest living relatives. Passenger pigeons' large population size appears to have allowed for faster adaptive evolution and removal of harmful mutations, driving a huge loss in their neutral genetic diversity. These results demonstrate the effect that selection can have on a vertebrate genome and contradict results that suggested that population instability contributed to this species's surprisingly rapid extinction. Copyright © 2017, American Association for the Advancement of Science.
Yoshizumi, Takeshi; Oikawa, Kazusato; Chuah, Jo-Ann; Kodama, Yutaka; Numata, Keiji
Selective gene delivery into organellar genomes (mitochondrial and plastid genomes) has been limited because of a lack of appropriate platform technology, even though these organelles are essential for metabolite and energy production. Techniques for selective organellar modification are needed to functionally improve organelles and produce transplastomic/transmitochondrial plants. However, no method for mitochondrial genome modification has yet been established for multicellular organisms including plants. Likewise, modification of plastid genomes has been limited to a few plant species and algae. In the present study, we developed ionic complexes of fusion peptides containing organellar targeting signal and plasmid DNA for selective delivery of exogenous DNA into the plastid and mitochondrial genomes of intact plants. This is the first report of exogenous DNA being integrated into the mitochondrial genomes of not only plants, but also multicellular organisms in general. This fusion peptide-mediated gene delivery system is a breakthrough platform for both plant organellar biotechnology and gene therapy for mitochondrial diseases in animals.
Full Text Available Abstract Background Mammalian genomes consist of regions differing in GC content, referred to as isochores or GC-content domains. The scientific debate is still open as to whether such compositional heterogeneity is a selected or neutral trait. Results Here we analyze SNP allele frequencies, retrotransposon insertion polymorphisms (RIPs, as well as fixed substitutions accumulated in the human lineage since its divergence from chimpanzee to indicate that biased gene conversion (BGC has been playing a role in within-genome GC content variation. Yet, a distinct contribution to GC content evolution is accounted for by a selective process. Accordingly, we searched for independent evidences that GC content distribution does not conform to neutral expectations. Indeed, after correcting for possible biases, we show that intron GC content and size display isochore-specific correlations. Conclusion We consider that the more parsimonious explanation for our results is that GC content is subjected to the action of both weak selection and BGC in the human genome with features such as nucleosome positioning or chromatin conformation possibly representing the final target of selective processes. This view might reconcile previous contrasting findings and add some theoretical background to recent evidences suggesting that GC content domains display different behaviors with respect to highly regulated biological processes such as developmentally-stage related gene expression and programmed replication timing during neural stem cell differentiation.
Hartman, Yorike; Uwimana, Brigitte; Hooftman, Danny A P; Schranz, Michael E; van de Wiel, Clemens C M; Smulders, Marinus J M; Visser, Richard G F; van Tienderen, Peter H
Genomic selection patterns and hybrid performance influence the chance that crop (trans)genes can spread to wild relatives. We measured fitness(-related) traits in two different field environments employing two different crop–wild crosses of lettuce. We performed quantitative trait loci (QTL) analyses and estimated the fitness distribution of early- and late-generation hybrids. We detected consistent results across field sites and crosses for a fitness QTL at linkage group 7, where a selective advantage was conferred by the wild allele. Two fitness QTL were detected on linkage group 5 and 6, which were unique to one of the crop–wild crosses. Average hybrid fitness was lower than the fitness of the wild parent, but several hybrid lineages outperformed the wild parent, especially in a novel habitat for the wild type. In early-generation hybrids, this may partly be due to heterosis effects, whereas in late-generation hybrids transgressive segregation played a major role. The study of genomic selection patterns can identify crop genomic regions under negative selection across multiple environments and cultivar–wild crosses that might be applicable in transgene mitigation strategies. At the same time, results were cultivar-specific, so that a case-by-case environmental risk assessment is still necessary, decreasing its general applicability. PMID:23789025
Hartman, Yorike; Uwimana, Brigitte; Hooftman, Danny A P; Schranz, Michael E; van de Wiel, Clemens C M; Smulders, Marinus J M; Visser, Richard G F; van Tienderen, Peter H
Genomic selection patterns and hybrid performance influence the chance that crop (trans)genes can spread to wild relatives. We measured fitness(-related) traits in two different field environments employing two different crop-wild crosses of lettuce. We performed quantitative trait loci (QTL) analyses and estimated the fitness distribution of early- and late-generation hybrids. We detected consistent results across field sites and crosses for a fitness QTL at linkage group 7, where a selective advantage was conferred by the wild allele. Two fitness QTL were detected on linkage group 5 and 6, which were unique to one of the crop-wild crosses. Average hybrid fitness was lower than the fitness of the wild parent, but several hybrid lineages outperformed the wild parent, especially in a novel habitat for the wild type. In early-generation hybrids, this may partly be due to heterosis effects, whereas in late-generation hybrids transgressive segregation played a major role. The study of genomic selection patterns can identify crop genomic regions under negative selection across multiple environments and cultivar-wild crosses that might be applicable in transgene mitigation strategies. At the same time, results were cultivar-specific, so that a case-by-case environmental risk assessment is still necessary, decreasing its general applicability.
Repar, Jelena; Warnecke, Tobias
Inversions are a major contributor to structural genome evolution in prokaryotes. Here, using a novel alignment-based method, we systematically compare 1,651 bacterial and 98 archaeal genomes to show that inversion landscapes are frequently biased toward (symmetric) inversions around the origin-terminus axis. However, symmetric inversion bias is not a universal feature of prokaryotic genome evolution but varies considerably across clades. At the extremes, inversion landscapes in Bacillus-Clostridium and Actinobacteria are dominated by symmetric inversions, while there is little or no systematic bias favoring symmetric rearrangements in archaea with a single origin of replication. Within clades, we find strong but clade-specific relationships between symmetric inversion bias and different features of adaptive genome architecture, including the distance of essential genes to the origin of replication and the preferential localization of genes on the leading strand. We suggest that heterogeneous selection pressures have converged to produce similar patterns of structural genome evolution across prokaryotes. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
Frank M. You
Full Text Available Flax is an important economic crop for seed oil and stem fiber. Phenotyping of traits such as seed yield, seed quality, stem fiber yield, and quality characteristics is expensive and time consuming. Genomic selection (GS refers to a breeding approach aimed at selecting preferred individuals based on genomic estimated breeding values predicted by a statistical model based on the relationship between phenotypes and genome-wide genetic markers. We evaluated the prediction accuracy of GS (rMP and the efficiency of GS relative to phenotypic selection (RE for three GS models: ridge regression best linear unbiased prediction (RR-BLUP, Bayesian LASSO (BL, and Bayesian ridge regression (BRR, for seed yield, oil content, iodine value, linoleic, and linolenic acid content with a full and a common set of genome-wide simple sequence repeat markers in each of three biparental populations. The three GS models generated similar rMP and RE, while BRR displayed a higher coefficient of determination (R2 of the fitted models than did RR-BLUP or BL. The mean rMP and RE varied for traits with different heritabilities and was affected by the genetic variation of the traits in the populations. GS for seed yield generated a mean RE of 1.52 across populations and marker sets, a value significantly superior to that for direct phenotypic selection. Our empirical results provide the first validation of GS in flax and demonstrate that GS could increase genetic gain per unit time for linseed breeding. Further studies for selection of training populations and markers are warranted.
Nellåker, Christoffer; Keane, Thomas M; Yalcin, Binnaz; Wong, Kim; Agam, Avigail; Belgard, T Grant; Flint, Jonathan; Adams, David J; Frankel, Wayne N; Ponting, Chris P
Transposable element (TE)-derived sequence dominates the landscape of mammalian genomes and can modulate gene function by dysregulating transcription and translation. Our current knowledge of TEs in laboratory mouse strains is limited primarily to those present in the C57BL/6J reference genome, with most mouse TEs being drawn from three distinct classes, namely short interspersed nuclear elements (SINEs), long interspersed nuclear elements (LINEs) and the endogenous retrovirus (ERV) superfamily. Despite their high prevalence, the different genomic and gene properties controlling whether TEs are preferentially purged from, or are retained by, genetic drift or positive selection in mammalian genomes remain poorly defined. Using whole genome sequencing data from 13 classical laboratory and 4 wild-derived mouse inbred strains, we developed a comprehensive catalogue of 103,798 polymorphic TE variants. We employ this extensive data set to characterize TE variants across the Mus lineage, and to infer neutral and selective processes that have acted over 2 million years. Our results indicate that the majority of TE variants are introduced though the male germline and that only a minority of TE variants exert detectable changes in gene expression. However, among genes with differential expression across the strains there are twice as many TE variants identified as being putative causal variants as expected. Most TE variants that cause gene expression changes appear to be purged rapidly by purifying selection. Our findings demonstrate that past TE insertions have often been highly deleterious, and help to prioritize TE variants according to their likely contribution to gene expression or phenotype variation.
Deng, Lian; Ruiz-Linares, Andrés; Xu, Shuhua; Wang, Sijia
Latin American populations stem from the admixture of Europeans, Africans and Native Americans, which started over 400 years ago and had lasted for several centuries. Extreme deviation over the genome-wide average in ancestry estimations at certain genomic locations could reflect recent natural selection. We evaluated the distribution of ancestry estimations using 678 genome-wide microsatellite markers in 249 individuals from 13 admixed populations across Latin America. We found significant deviations in ancestry estimations including three locations with more than 3.5 times standard deviations from the genome-wide average: an excess of European ancestry at 1p36 and 14q32, and an excess of African ancestry at 6p22. Using simulations, we could show that at least the deviation at 6p22 was unlikely to result from genetic drift alone. By applying different linguistic groups as well as the most likely ancestral Native American populations as the ancestry, we showed that the choice of Native American ancestry could affect the local ancestry estimation. However, the signal at 6p22 consistently appeared in most of the analyses using various ancestral groups. This study provided important insights for recent natural selection in the context of the unique history of the New World and implications for disease mapping.
Dowell, Robin; Odell, Aaron; Richmond, Phillip; Malmer, Daniel; Halper-Stromberg, Eitan; Bennett, Beth; Larson, Colin; Leach, Sonia; Radcliffe, Richard A
The Inbred Long- and Short-Sleep (ILS, ISS) mouse lines were selected for differences in acute ethanol sensitivity using the loss of righting response (LORR) as the selection trait. The lines show an over tenfold difference in LORR and, along with a recombinant inbred panel derived from them (the LXS), have been widely used to dissect the genetic underpinnings of acute ethanol sensitivity. Here we have sequenced the genomes of the ILS and ISS to investigate the DNA variants that contribute to their sensitivity difference. We identified ~2.7 million high-confidence SNPs and small indels and ~7000 structural variants between the lines; variants were found to occur in 6382 annotated genes. Using a hidden Markov model, we were able to reconstruct the genome-wide ancestry patterns of the eight inbred progenitor strains from which the ILS and ISS were derived, and found that quantitative trait loci that have been mapped for LORR were slightly enriched for DNA variants. Finally, by mapping and quantifying RNA-seq reads from the ILS and ISS to their strain-specific genomes rather than to the reference genome, we found a substantial improvement in a differential expression analysis between the lines. This work will help in identifying and characterizing the DNA sequence variants that contribute to the difference in ethanol sensitivity between the ILS and ISS and will also aid in accurate quantification of RNA-seq data generated from the LXS RIs.
Riedelsheimer, Christian; Melchinger, Albrecht E
We developed a universally applicable planning tool for optimizing the allocation of resources for one cycle of genomic selection in a biparental population. The framework combines selection theory with constraint numerical optimization and considers genotype × environment interactions. Genomic selection (GS) is increasingly implemented in plant breeding programs to increase selection gain but little is known how to optimally allocate the resources under a given budget. We investigated this problem with model calculations by combining quantitative genetic selection theory with constraint numerical optimization. We assumed one selection cycle where both the training and prediction sets comprised double haploid (DH) lines from the same biparental population. Grain yield for testcrosses of maize DH lines was used as a model trait but all parameters can be adjusted in a freely available software implementation. An extension of the expected selection accuracy given by Daetwyler et al. (2008) was developed to correctly balance between the number of environments for phenotyping the training set and its population size in the presence of genotype × environment interactions. Under small budget, genotyping costs mainly determine whether GS is superior over phenotypic selection. With increasing budget, flexibility in resource allocation increases greatly but selection gain leveled off quickly requiring balancing the number of populations with the budget spent for each population. The use of an index combining phenotypic and GS predicted values in the training set was especially beneficial under limited resources and large genotype × environment interactions. Once a sufficiently high selection accuracy is achieved in the prediction set, further selection gain can be achieved most efficiently by massively expanding its size. Thus, with increasing budget, reducing the costs for producing a DH line becomes increasingly crucial for successfully exploiting the
Cros, David; Denis, Marie; Sánchez, Leopoldo; Cochard, Benoit; Flori, Albert; Durand-Gasselin, Tristan; Nouy, Bruno; Omoré, Alphonse; Pomiès, Virginie; Riou, Virginie; Suryana, Edyana; Bouvet, Jean-Marc
Genomic selection empirically appeared valuable for reciprocal recurrent selection in oil palm as it could account for family effects and Mendelian sampling terms, despite small populations and low marker density. Genomic selection (GS) can increase the genetic gain in plants. In perennial crops, this is expected mainly through shortened breeding cycles and increased selection intensity, which requires sufficient GS accuracy in selection candidates, despite often small training populations. Our objective was to obtain the first empirical estimate of GS accuracy in oil palm (Elaeis guineensis), the major world oil crop. We used two parental populations involved in conventional reciprocal recurrent selection (Deli and Group B) with 131 individuals each, genotyped with 265 SSR. We estimated within-population GS accuracies when predicting breeding values of non-progeny-tested individuals for eight yield traits. We used three methods to sample training sets and five statistical methods to estimate genomic breeding values. The results showed that GS could account for family effects and Mendelian sampling terms in Group B but only for family effects in Deli. Presumably, this difference between populations originated from their contrasting breeding history. The GS accuracy ranged from -0.41 to 0.94 and was positively correlated with the relationship between training and test sets. Training sets optimized with the so-called CDmean criterion gave the highest accuracies, ranging from 0.49 (pulp to fruit ratio in Group B) to 0.94 (fruit weight in Group B). The statistical methods did not affect the accuracy. Finally, Group B could be preselected for progeny tests by applying GS to key yield traits, therefore increasing the selection intensity. Our results should be valuable for breeding programs with small populations, long breeding cycles, or reduced effective size.
Wimmer, Valentin; Lehermeier, Christina; Albrecht, Theresa; Auinger, Hans-Jürgen; Wang, Yu; Schön, Chris-Carolin
In genome-based prediction there is considerable uncertainty about the statistical model and method required to maximize prediction accuracy. For traits influenced by a small number of quantitative trait loci (QTL), predictions are expected to benefit from methods performing variable selection [e.g., BayesB or the least absolute shrinkage and selection operator (LASSO)] compared to methods distributing effects across the genome [ridge regression best linear unbiased prediction (RR-BLUP)]. We investigate the assumptions underlying successful variable selection by combining computer simulations with large-scale experimental data sets from rice (Oryza sativa L.), wheat (Triticum aestivum L.), and Arabidopsis thaliana (L.). We demonstrate that variable selection can be successful when the number of phenotyped individuals is much larger than the number of causal mutations contributing to the trait. We show that the sample size required for efficient variable selection increases dramatically with decreasing trait heritabilities and increasing extent of linkage disequilibrium (LD). We contrast and discuss contradictory results from simulation and experimental studies with respect to superiority of variable selection methods over RR-BLUP. Our results demonstrate that due to long-range LD, medium heritabilities, and small sample sizes, superiority of variable selection methods cannot be expected in plant breeding populations even for traits like FRIGIDA gene expression in Arabidopsis and flowering time in rice, assumed to be influenced by a few major QTL. We extend our conclusions to the analysis of whole-genome sequence data and infer upper bounds for the number of causal mutations which can be identified by LASSO. Our results have major impact on the choice of statistical method needed to make credible inferences about genetic architecture and prediction accuracy of complex traits.
Full Text Available An increasing interest is being placed in the detection of genes, or genomic regions, that have been targeted by selection because identifying signatures of selection can lead to a better understanding of genotype-phenotype relationships. A common strategy for the detection of selection signatures is to compare samples from distinct populations and to search for genomic regions with outstanding genetic differentiation. The aim of this study was to detect selective signatures in layer chicken populations using a recently proposed approach, hapFLK, which exploits linkage disequilibrium information while accounting appropriately for the hierarchical structure of populations. We performed the analysis on 70 individuals from three commercial layer breeds (White Leghorn, White Rock and Rhode Island Red, genotyped for approximately 1 million SNPs. We found a total of 41 and 107 regions with outstanding differentiation or similarity using hapFLK and its single SNP counterpart FLK respectively. Annotation of selection signature regions revealed various genes and QTL corresponding to productions traits, for which layer breeds were selected. A number of the detected genes were associated with growth and carcass traits, including IGF-1R, AGRP and STAT5B. We also annotated an interesting gene associated with the dark brown feather color mutational phenotype in chickens (SOX10. We compared FST, FLK and hapFLK and demonstrated that exploiting linkage disequilibrium information and accounting for hierarchical population structure decreased the false detection rate.
Hartman, Y.; Uwimana, B.; Hooftman, D.A.P.; Schranz, M.E.; Wiel, van de, C.C.M.; Smulders, M.J.M.; Visser, R.G.F.; Tienderen, van, P.H.
Genomic selection patterns and hybrid performance influence the chance that crop (trans)genes can spread to wild relatives. We measured fitness(-related) traits in two different field environments employing two different crop?wild crosses of lettuce. We performed quantitative trait loci (QTL) analyses and estimated the fitness distribution of early- and late-generation hybrids. We detected consistent results across field sites and crosses for a fitness QTL at linkage group 7, where a selectiv...
Glebes, Tirzah Y; Sandoval, Nicholas R; Gillis, Jacob H; Gill, Ryan T
Engineering both feedstock and product tolerance is important for transitioning towards next-generation biofuels derived from renewable sources. Tolerance to chemical inhibitors typically results in complex phenotypes, for which multiple genetic changes must often be made to confer tolerance. Here, we performed a genome-wide search for furfural-tolerant alleles using the TRackable Multiplex Recombineering (TRMR) method (Warner et al. (2010), Nature Biotechnology), which uses chromosomally integrated mutations directed towards increased or decreased expression of virtually every gene in Escherichia coli. We employed various growth selection strategies to assess the role of selection design towards growth enrichments. We also compared genes with increased fitness from our TRMR selection to those from a previously reported genome-wide identification study of furfural tolerance genes using a plasmid-based genomic library approach (Glebes et al. (2014) PLOS ONE). In several cases, growth improvements were observed for the chromosomally integrated promoter/RBS mutations but not for the plasmid-based overexpression constructs. Through this assessment, four novel tolerance genes, ahpC, yhjH, rna, and dicA, were identified and confirmed for their effect on improving growth in the presence of furfural. © 2014 Wiley Periodicals, Inc.
Yang, Bin; Peng, Yu; Leung, Henry Chi-Ming; Yiu, Siu-Ming; Chen, Jing-Chi; Chin, Francis Yuk-Lun
With the rapid development of genome sequencing techniques, traditional research methods based on the isolation and cultivation of microorganisms are being gradually replaced by metagenomics, which is also known as environmental genomics. The first step, which is still a major bottleneck, of metagenomics is the taxonomic characterization of DNA fragments (reads) resulting from sequencing a sample of mixed species. This step is usually referred as "binning". Existing binning methods are based on supervised or semi-supervised approaches which rely heavily on reference genomes of known microorganisms and phylogenetic marker genes. Due to the limited availability of reference genomes and the bias and instability of marker genes, existing binning methods may not be applicable in many cases. In this paper, we present an unsupervised binning method based on the distribution of a carefully selected set of l-mers (substrings of length l in DNA fragments). From our experiments, we show that our method can accurately bin DNA fragments with various lengths and relative species abundance ratios without using any reference and training datasets. Another feature of our method is its error robustness. The binning accuracy decreases by less than 1% when the sequencing error rate increases from 0% to 5%. Note that the typical sequencing error rate of existing commercial sequencing platforms is less than 2%. We provide a new and effective tool to solve the metagenome binning problem without using any reference datasets or markers information of any known reference genomes (species). The source code of our software tool, the reference genomes of the species for generating the test datasets and the corresponding test datasets are available at http://i.cs.hku.hk/~alse/MetaCluster/.
Full Text Available Genome-wide molecular markers are often being used to evaluate genetic diversity in germplasm collections and for making genomic selections in breeding programs. To accurately predict phenotypes and assay genetic diversity, molecular markers should assay a representative sample of the polymorphisms in the population under study. Ascertainment bias arises when marker data is not obtained from a random sample of the polymorphisms in the population of interest. Genotyping-by-sequencing (GBS is rapidly emerging as a low-cost genotyping platform, even for the large, complex, and polyploid wheat (Triticum aestivum L. genome. With GBS, marker discovery and genotyping occur simultaneously, resulting in minimal ascertainment bias. The previous platform of choice for whole-genome genotyping in many species such as wheat was DArT (Diversity Array Technology and has formed the basis of most of our knowledge about cereals genetic diversity. This study compared GBS and DArT marker platforms for measuring genetic diversity and genomic selection (GS accuracy in elite U.S. soft winter wheat. From a set of 365 breeding lines, 38,412 single nucleotide polymorphism GBS markers were discovered and genotyped. The GBS SNPs gave a higher GS accuracy than 1,544 DArT markers on the same lines, despite 43.9% missing data. Using a bootstrap approach, we observed significantly more clustering of markers and ascertainment bias with DArT relative to GBS. The minor allele frequency distribution of GBS markers had a deficit of rare variants compared to DArT markers. Despite the ascertainment bias of the DArT markers, GS accuracy for three traits out of four was not significantly different when an equal number of markers were used for each platform. This suggests that the gain in accuracy observed using GBS compared to DArT markers was mainly due to a large increase in the number of markers available for the analysis.
Heslot, Nicolas; Rutkoski, Jessica; Poland, Jesse; Jannink, Jean-Luc; Sorrells, Mark E.
Genome-wide molecular markers are often being used to evaluate genetic diversity in germplasm collections and for making genomic selections in breeding programs. To accurately predict phenotypes and assay genetic diversity, molecular markers should assay a representative sample of the polymorphisms in the population under study. Ascertainment bias arises when marker data is not obtained from a random sample of the polymorphisms in the population of interest. Genotyping-by-sequencing (GBS) is rapidly emerging as a low-cost genotyping platform, even for the large, complex, and polyploid wheat (Triticum aestivum L.) genome. With GBS, marker discovery and genotyping occur simultaneously, resulting in minimal ascertainment bias. The previous platform of choice for whole-genome genotyping in many species such as wheat was DArT (Diversity Array Technology) and has formed the basis of most of our knowledge about cereals genetic diversity. This study compared GBS and DArT marker platforms for measuring genetic diversity and genomic selection (GS) accuracy in elite U.S. soft winter wheat. From a set of 365 breeding lines, 38,412 single nucleotide polymorphism GBS markers were discovered and genotyped. The GBS SNPs gave a higher GS accuracy than 1,544 DArT markers on the same lines, despite 43.9% missing data. Using a bootstrap approach, we observed significantly more clustering of markers and ascertainment bias with DArT relative to GBS. The minor allele frequency distribution of GBS markers had a deficit of rare variants compared to DArT markers. Despite the ascertainment bias of the DArT markers, GS accuracy for three traits out of four was not significantly different when an equal number of markers were used for each platform. This suggests that the gain in accuracy observed using GBS compared to DArT markers was mainly due to a large increase in the number of markers available for the analysis. PMID:24040295
Genova, Antonio; Goossens, Sander; Lemoine, Frank G.; Mazarico, Erwan; Smith, David E.; Zuber, Maria T.
The Mars Global Surveyor (MGS), Mars Odyssey (ODY), and Mars Reconnaissance Orbiter (MRO) missions have enabled NASA to conduct reconnaissance and exploration of Mars from orbit for sixteen consecutive years. These radio systems on these spacecraft enabled radio science in orbit around Mars to improve the knowledge of the static structure of the Martian gravitational field. The continuity of the radio tracking data, which cover more than a solar cycle, also provides useful information to characterize the temporal variability of the gravity field, relevant to the planet's internal dynamics and the structure and dynamics of the atmosphere . MGS operated for more than 7 years, between 1999 and 2006, in a frozen sun-synchronous, near-circular, polar orbit with the periapsis at approximately 370 km altitude. ODY and MRO have been orbiting Mars in two separate sun-synchronous orbits at different local times and altitudes. ODY began its mapping phase in 2002 with the periapis at approximately 390 km altitude and 4-5pm Local Solar Time (LST), whereas the MRO science mission started in November 2006 with the periapis at approximately 255 km altitude and 3pm LST. The 16 years of radio tracking data provide useful information on the atmospheric density in the Martian upper atmosphere. We used ODY and MRO radio data to recover the long-term periodicity of the major atmospheric constituents -- CO2, O, and He -- at the orbit altitudes of these two spacecraft . The improved atmospheric model provides a better prediction of the annual and semi-annual variability of the dominant species. Therefore, the inclusion of the recovered model leads to improved orbit determination and an improved gravity field model of Mars with MGS, ODY, and MRO radio tracking data.
Nielsen, Rasmus; Bustamente, Carlos; Clark, Andrew G.
Since the divergence of humans and chimpanzees about 5 million years ago, these species have undergone a remarkable evolution with drastic divergence in anatomy and cognitive abilities. At the molecular level, despite the small overall magnitude of DNA sequence divergence, we might expect...... such evolutionary changes to leave a noticeable signature throughout the genome. We here compare 13,731 annotated genes from humans to their chimpanzee orthologs to identify genes that show evidence of positive selection. Many of the genes that present a signature of positive selection tend to be involved...
Yabe, Shiori; Yamasaki, Masanori; Ebana, Kaworu; Hayashi, Takeshi; Iwata, Hiroyoshi
Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS), which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an "island model" inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the potential of genomic
Full Text Available Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS, which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an "island model" inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the
The stability of the zinc-blende structured MgS is studied using a constant pressure ab initio molecular dynamics technique. A phase transition into a rocksalt structure is observed through the simulation. The zinc-blende to rocksalt phase transformation proceeds via two rhombohedral intermediate phases within R3m (No:160) and R3-barm (No:166) symmetries and does not involve any bond breaking. This mechanism is different from the previously observed mechanism in molecular dynamics simulations. (fast track communication)
Bhering, L L; Junqueira, V S; Peixoto, L A; Cruz, C D; Laviola, B G
The aim of this study was to evaluate different methods used in genomic selection, and to verify those that select a higher proportion of individuals with superior genotypes. Thus, F2 populations of different sizes were simulated (100, 200, 500, and 1000 individuals) with 10 replications each. These consisted of 10 linkage groups (LG) of 100 cM each, containing 100 equally spaced markers per linkage group, of which 200 controlled the characteristics, defined as the 20 initials of each LG. Genetic and phenotypic values were simulated assuming binomial distribution of effects for each LG, and the absence of dominance. For phenotypic values, heritabilities of 20, 50, and 80% were considered. To compare methodologies, the analysis processing time, coefficient of coincidence (selection of 5, 10, and 20% of superior individuals), and Spearman correlation between true genetic values, and the genomic values predicted by each methodology were determined. Considering the processing time, the three methodologies were statistically different, rrBLUP was the fastest, and Bayesian LASSO was the slowest. Spearman correlation revealed that the rrBLUP and GBLUP methodologies were equivalent, and Bayesian LASSO provided the lowest correlation values. Similar results were obtained in coincidence variables among the individuals selected, in which Bayesian LASSO differed statistically and presented a lower value than the other methodologies. Therefore, for the scenarios evaluated, rrBLUP is the best methodology for the selection of genetically superior individuals.
Li, Yongle; Ruperao, Pradeep; Batley, Jacqueline; Edwards, David; Khan, Tanveer; Colmer, Timothy D; Pang, Jiayin; Siddique, Kadambot H M; Sutton, Tim
Drought tolerance is a complex trait that involves numerous genes. Identifying key causal genes or linked molecular markers can facilitate the fast development of drought tolerant varieties. Using a whole-genome resequencing approach, we sequenced 132 chickpea varieties and advanced breeding lines and found more than 144,000 single nucleotide polymorphisms (SNPs). We measured 13 yield and yield-related traits in three drought-prone environments of Western Australia. The genotypic effects were significant for all traits, and many traits showed highly significant correlations, ranging from 0.83 between grain yield and biomass to -0.67 between seed weight and seed emergence rate. To identify candidate genes, the SNP and trait data were incorporated into the SUPER genome-wide association study (GWAS) model, a modified version of the linear mixed model. We found that several SNPs from auxin-related genes, including auxin efflux carrier protein (PIN3), p-glycoprotein, and nodulin MtN21/EamA-like transporter, were significantly associated with yield and yield-related traits under drought-prone environments. We identified four genetic regions containing SNPs significantly associated with several different traits, which was an indication of pleiotropic effects. We also investigated the possibility of incorporating the GWAS results into a genomic selection (GS) model, which is another approach to deal with complex traits. Compared to using all SNPs, application of the GS model using subsets of SNPs significantly associated with the traits under investigation increased the prediction accuracies of three yield and yield-related traits by more than twofold. This has important implication for implementing GS in plant breeding programs.
Full Text Available Drought tolerance is a complex trait that involves numerous genes. Identifying key causal genes or linked molecular markers can facilitate the fast development of drought tolerant varieties. Using a whole-genome resequencing approach, we sequenced 132 chickpea varieties and advanced breeding lines and found more than 144,000 single nucleotide polymorphisms (SNPs. We measured 13 yield and yield-related traits in three drought-prone environments of Western Australia. The genotypic effects were significant for all traits, and many traits showed highly significant correlations, ranging from 0.83 between grain yield and biomass to -0.67 between seed weight and seed emergence rate. To identify candidate genes, the SNP and trait data were incorporated into the SUPER genome-wide association study (GWAS model, a modified version of the linear mixed model. We found that several SNPs from auxin-related genes, including auxin efflux carrier protein (PIN3, p-glycoprotein, and nodulin MtN21/EamA-like transporter, were significantly associated with yield and yield-related traits under drought-prone environments. We identified four genetic regions containing SNPs significantly associated with several different traits, which was an indication of pleiotropic effects. We also investigated the possibility of incorporating the GWAS results into a genomic selection (GS model, which is another approach to deal with complex traits. Compared to using all SNPs, application of the GS model using subsets of SNPs significantly associated with the traits under investigation increased the prediction accuracies of three yield and yield-related traits by more than twofold. This has important implication for implementing GS in plant breeding programs.
Cagan, Alex; Blass, Torsten
Dogs [Canis lupus familiaris] were the first animal species to be domesticated and continue to occupy an important place in human societies. Recent studies have begun to reveal when and where dog domestication occurred. While much progress has been made in identifying the genetic basis of phenotypic differences between dog breeds we still know relatively little about the genetic changes underlying the phenotypes that differentiate all dogs from their wild progenitors, wolves [Canis lupus]. In particular, dogs generally show reduced aggression and fear towards humans compared to wolves. Therefore, selection for tameness was likely a necessary prerequisite for dog domestication. With the increasing availability of whole-genome sequence data it is possible to try and directly identify the genetic variants contributing to the phenotypic differences between dogs and wolves. We analyse the largest available database of genome-wide polymorphism data in a global sample of dogs 69 and wolves 7. We perform a scan to identify regions of the genome that are highly differentiated between dogs and wolves. We identify putatively functional genomic variants that are segregating or at high frequency [> = 0.75 Fst] for alternative alleles between dogs and wolves. A biological pathways analysis of the genes containing these variants suggests that there has been selection on the 'adrenaline and noradrenaline biosynthesis pathway', well known for its involvement in the fight-or-flight response. We identify 11 genes with putatively functional variants fixed for alternative alleles between dogs and wolves. The segregating variants in these genes are strong candidates for having been targets of selection during early dog domestication. We present the first genome-wide analysis of the different categories of putatively functional variants that are fixed or segregating at high frequency between a global sampling of dogs and wolves. We find evidence that selection has been strongest
Allison David B
Full Text Available Abstract Background HIV susceptibility and pathogenicity exhibit both interindividual and intergroup variability. The etiology of intergroup variability is still poorly understood, and could be partly linked to genetic differences among racial/ethnic groups. These genetic differences may be traceable to different regimes of natural selection in the 60,000 years since the human radiation out of Africa. Here, we examine population differentiation and haplotype patterns at several loci identified through genome-wide association studies on HIV-1 control, as determined by viral-load setpoint, in European and African-American populations. We use genome-wide data from the Human Genome Diversity Project, consisting of 53 world-wide populations, to compare measures of FST and relative extended haplotype homozygosity (REHH at these candidate loci to the rest of the respective chromosome. Results We find that the Europe-Middle East and Europe-South Asia pairwise FST in the most strongly associated region are elevated compared to most pairwise comparisons with the sub-Saharan African group, which exhibit very low FST. We also find genetic signatures of recent positive selection (higher REHH at these associated regions among all groups except for sub-Saharan Africans and Native Americans. This pattern is consistent with one in which genetic differentiation, possibly due to diversifying/positive selection, occurred at these loci among Eurasians. Conclusions These findings are concordant with those from earlier studies suggesting recent evolutionary change at immunity-related genomic regions among Europeans, and shed light on the potential genetic and evolutionary origin of population differences in HIV-1 control.
Full Text Available Genomic selection (GS provides an attractive option for accelerating genetic gain in perennial ryegrass ( improvement given the long cycle times of most current breeding programs. The present study used simulation to investigate the level of genetic gain and inbreeding obtained from GS breeding strategies compared with traditional breeding strategies for key traits (persistency, yield, and flowering time. Base population genomes were simulated through random mating for 60,000 generations at an effective population size of 10,000. The degree of linkage disequilibrium (LD in the resulting population was compared with that obtained from empirical studies. Initial parental varieties were simulated to match diversity of current commercial cultivars. Genomic selection was designed to fit into a company breeding program at two selection points in the breeding cycle (spaced plants and miniplot. Genomic estimated breeding values (GEBVs for productivity traits were trained with phenotypes and genotypes from plots. Accuracy of GEBVs was 0.24 for persistency and 0.36 for yield for single plants, while for plots it was lower (0.17 and 0.19, respectively. Higher accuracy of GEBVs was obtained for flowering time (up to 0.7, partially as a result of the larger reference population size that was available from the clonal row stage. The availability of GEBVs permit a 4-yr reduction in cycle time, which led to at least a doubling and trebling genetic gain for persistency and yield, respectively, than the traditional program. However, a higher rate of inbreeding per cycle among varieties was also observed for the GS strategy.
Lin, Zibei; Cogan, Noel O I; Pembleton, Luke W; Spangenberg, German C; Forster, John W; Hayes, Ben J; Daetwyler, Hans D
Genomic selection (GS) provides an attractive option for accelerating genetic gain in perennial ryegrass () improvement given the long cycle times of most current breeding programs. The present study used simulation to investigate the level of genetic gain and inbreeding obtained from GS breeding strategies compared with traditional breeding strategies for key traits (persistency, yield, and flowering time). Base population genomes were simulated through random mating for 60,000 generations at an effective population size of 10,000. The degree of linkage disequilibrium (LD) in the resulting population was compared with that obtained from empirical studies. Initial parental varieties were simulated to match diversity of current commercial cultivars. Genomic selection was designed to fit into a company breeding program at two selection points in the breeding cycle (spaced plants and miniplot). Genomic estimated breeding values (GEBVs) for productivity traits were trained with phenotypes and genotypes from plots. Accuracy of GEBVs was 0.24 for persistency and 0.36 for yield for single plants, while for plots it was lower (0.17 and 0.19, respectively). Higher accuracy of GEBVs was obtained for flowering time (up to 0.7), partially as a result of the larger reference population size that was available from the clonal row stage. The availability of GEBVs permit a 4-yr reduction in cycle time, which led to at least a doubling and trebling genetic gain for persistency and yield, respectively, than the traditional program. However, a higher rate of inbreeding per cycle among varieties was also observed for the GS strategy. Copyright © 2016 Crop Science Society of America.
Parejo, M; Wragg, D; Henriques, D; Vignal, A; Neuditschko, M
Human-mediated selection has left signatures in the genomes of many domesticated animals, including the European dark honeybee, Apis mellifera mellifera, which has been selected by apiculturists for centuries. Using whole-genome sequence information, we investigated selection signatures in spatially separated honeybee subpopulations (Switzerland, n = 39 and France, n = 17). Three different test statistics were calculated in windows of 2 kb (fixation index, cross-population extended haplotype homozygosity and cross-population composite likelihood ratio) and combined into a recently developed composite selection score. Applying a stringent false discovery rate of 0.01, we identified six significant selective sweeps distributed across five chromosomes covering eight genes. These genes are associated with multiple molecular and biological functions, including regulation of transcription, receptor binding and signal transduction. Of particular interest is a selection signature on chromosome 1, which corresponds to the WNT4 gene, the family of which is conserved across the animal kingdom with a variety of functions. In Drosophila melanogaster, WNT4 alleles have been associated with differential wing, cross vein and abdominal phenotypes. Defining phenotypic characteristics of different Apis mellifera ssp., which are typically used as selection criteria, include colour and wing venation pattern. This signal is therefore likely to be a good candidate for human mediated-selection arising from different applied breeding practices in the two managed populations. © 2017 The Authors. Animal Genetics published by John Wiley & Sons Ltd on behalf of Stichting International Foundation for Animal Genetics.
Zhao, Meixia; Du, Jianchang; Lin, Feng; Tong, Chaobo; Yu, Jingyin; Huang, Shunmou; Wang, Xiaowu; Liu, Shengyi; Ma, Jianxin
Recent sequencing of the Brassica rapa and Brassica oleracea genomes revealed extremely contrasting genomic features such as the abundance and distribution of transposable elements between the two genomes. However, whether and how these structural differentiations may have influenced the evolutionary rates of the two genomes since their split from a common ancestor are unknown. Here, we investigated and compared the rates of nucleotide substitution between two long terminal repeats (LTRs) of individual orthologous LTR-retrotransposons, the rates of synonymous and non-synonymous substitution among triplicated genes retained in both genomes from a shared whole genome triplication event, and the rates of genetic recombination estimated/deduced by the comparison of physical and genetic distances along chromosomes and ratios of solo LTRs to intact elements. Overall, LTR sequences and genic sequences showed more rapid nucleotide substitution in B. rapa than in B. oleracea. Synonymous substitution of triplicated genes retained from a shared whole genome triplication was detected at higher rates in B. rapa than in B. oleracea. Interestingly, non-synonymous substitution was observed at lower rates in the former than in the latter, indicating shifted densities of purifying selection between the two genomes. In addition to evolutionary asymmetry, orthologous genes differentially regulated and/or disrupted by transposable elements between the two genomes were also characterized. Our analyses suggest that local genomic and epigenomic features, such as recombination rates and chromatin dynamics reshaped by independent proliferation of transposable elements and elimination between the two genomes, are perhaps partially the causes and partially the outcomes of the observed inter-specific asymmetric evolution. © 2013 Purdue University The Plant Journal © 2013 John Wiley & Sons Ltd.
Chan, Leong-Keat; Bendall, Matthew L.; Malfatti, Stephanie; Schwientek, Patrick; Tremblay, Julien; Schackwitz, Wendy; Martin, Joel; Pati, Amrita; Bushnell, Brian; Foster, Brian; Kang, Dongwan; Tringe, Susannah G.; Bertilsson, Stefan; Moran, Mary Ann; Shade, Ashley; Newton, Ryan J.; Stevens, Sarah; McMcahon, Katherine D.; Mamlstrom, Rex R.
Multiple evolutionary models have been proposed to explain the formation of genetically and ecologically distinct bacterial groups. Time-series metagenomics enables direct observation of evolutionary processes in natural populations, and if applied over a sufficiently long time frame, this approach could capture events such as gene-specific or genome-wide selective sweeps. Direct observations of either process could help resolve how distinct groups form in natural microbial assemblages. Here, from a three-year metagenomic study of a freshwater lake, we explore changes in single nucleotide polymorphism (SNP) frequencies and patterns of gene gain and loss in populations of Chlorobiaceae and Methylophilaceae. SNP analyses revealed substantial genetic heterogeneity within these populations, although the degree of heterogeneity varied considerably among closely related, co-occurring Methylophilaceae populations. SNP allele frequencies, as well as the relative abundance of certain genes, changed dramatically over time in each population. Interestingly, SNP diversity was purged at nearly every genome position in one of the Chlorobiaceae populations over the course of three years, while at the same time multiple genes either swept through or were swept from this population. These patterns were consistent with a genome-wide selective sweep, a process predicted by the ecotype model? of diversification, but not previously observed in natural populations.
Chan, Leong-Keat; Bendall, Matthew L.; Malfatti, Stephanie; Schwientek, Patrick; Tremblay, Julien; Schackwitz, Wendy; Martin, Joel; Pati, Amrita; Bushnell, Brian; Foster, Brian; Kang, Dongwan; Tringe, Susannah G.; Bertilsson, Stefan; Moran, Mary Ann; Shade, Ashley; Newton, Ryan J.; Stevens, Sarah; McMahon, Katherine D.; Malmstrom, Rex R.
Multiple evolutionary models have been proposed to explain the formation of genetically and ecologically distinct bacterial groups. Time-series metagenomics enables direct observation of evolutionary processes in natural populations, and if applied over a sufficiently long time frame, this approach could capture events such as gene-specific or genome-wide selective sweeps. Direct observations of either process could help resolve how distinct groups form in natural microbial assemblages. Here, from a three-year metagenomic study of a freshwater lake, we explore changes in single nucleotide polymorphism (SNP) frequencies and patterns of gene gain and loss in populations of Chlorobiaceae and Methylophilaceae. SNP analyses revealed substantial genetic heterogeneity within these populations, although the degree of heterogeneity varied considerably among closely related, co-occurring Methylophilaceae populations. SNP allele frequencies, as well as the relative abundance of certain genes, changed dramatically over time in each population. Interestingly, SNP diversity was purged at nearly every genome position in one of the Chlorobiaceae populations over the course of three years, while at the same time multiple genes either swept through or were swept from this population. These patterns were consistent with a genome-wide selective sweep, a process predicted by the ‘ecotype model’ of diversification, but not previously observed in natural populations.
Haasl, Ryan J.; Payseur, Bret A.
Genomewide scans for natural selection (GWSS) have become increasingly common over the last 15 years due to increased availability of genome-scale genetic data. Here, we report a representative survey of GWSS from 1999 to present and find that (i) between 1999 and 2009, 35 of 49 (71%) GWSS focused on human, while from 2010 to present, only 38 of 83 (46%) of GWSS focused on human, indicating increased focus on nonmodel organisms; (ii) the large majority of GWSS incorporate interpopulation or interspecific comparisons using, for example FST, cross-population extended haplotype homozygosity or the ratio of nonsynonymous to synonymous substitutions; (iii) most GWSS focus on detection of directional selection rather than other modes such as balancing selection; and (iv) in human GWSS, there is a clear shift after 2004 from microsatellite markers to dense SNP data. A survey of GWSS meant to identify loci positively selected in response to severe hypoxic conditions support an approach to GWSS in which a list of a priori candidate genes based on potential selective pressures are used to filter the list of significant hits a posteriori. We also discuss four frequently ignored determinants of genomic heterogeneity that complicate GWSS: mutation, recombination, selection and the genetic architecture of adaptive traits. We recommend that GWSS methodology should better incorporate aspects of genomewide heterogeneity using empirical estimates of relevant parameters and/or realistic, whole-chromosome simulations to improve interpretation of GWSS results. Finally, we argue that knowledge of potential selective agents improves interpretation of GWSS results and that new methods focused on correlations between environmental variables and genetic variation can help automate this approach. PMID:26224644
van der Lee, Robin; Wiel, Laurens; van Dam, Teunis J P; Huynen, Martijn A
Hotspots of rapid genome evolution hold clues about human adaptation. We present a comparative analysis of nine whole-genome sequenced primates to identify high-confidence targets of positive selection. We find strong statistical evidence for positive selection in 331 protein-coding genes (3%), pinpointing 934 adaptively evolving codons (0.014%). Our new procedure is stringent and reveals substantial artefacts (20% of initial predictions) that have inflated previous estimates. The final 331 positively selected genes (PSG) are strongly enriched for innate and adaptive immunity, secreted and cell membrane proteins (e.g. pattern recognition, complement, cytokines, immune receptors, MHC, Siglecs). We also find evidence for positive selection in reproduction and chromosome segregation (e.g. centromere-associated CENPO, CENPT), apolipoproteins, smell/taste receptors and mitochondrial proteins. Focusing on the virus-host interaction, we retrieve most evolutionary conflicts known to influence antiviral activity (e.g. TRIM5, MAVS, SAMHD1, tetherin) and predict 70 novel cases through integration with virus-human interaction data. Protein structure analysis further identifies positive selection in the interaction interfaces between viruses and their cellular receptors (CD4-HIV; CD46-measles, adenoviruses; CD55-picornaviruses). Finally, primate PSG consistently show high sequence variation in human exomes, suggesting ongoing evolution. Our curated dataset of positive selection is a rich source for studying the genetics underlying human (antiviral) phenotypes. Procedures and data are available at https://github.com/robinvanderlee/positive-selection. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
Amambua-Ngwa, Alfred; Tetteh, Kevin K A; Manske, Magnus; Gomez-Escobar, Natalia; Stewart, Lindsay B; Deerhake, M Elizabeth; Cheeseman, Ian H; Newbold, Christopher I; Holder, Anthony A; Knuepfer, Ellen; Janha, Omar; Jallow, Muminatou; Campino, Susana; Macinnis, Bronwyn; Kwiatkowski, Dominic P; Conway, David J
Acquired immunity in vertebrates maintains polymorphisms in endemic pathogens, leading to identifiable signatures of balancing selection. To comprehensively survey for genes under such selection in the human malaria parasite Plasmodium falciparum, we generated paired-end short-read sequences of parasites in clinical isolates from an endemic Gambian population, which were mapped to the 3D7 strain reference genome to yield high-quality genome-wide coding sequence data for 65 isolates. A minority of genes did not map reliably, including the hypervariable var, rifin, and stevor families, but 5,056 genes (90.9% of all in the genome) had >70% sequence coverage with minimum read depth of 5 for at least 50 isolates, of which 2,853 genes contained 3 or more single nucleotide polymorphisms (SNPs) for analysis of polymorphic site frequency spectra. Against an overall background of negatively skewed frequencies, as expected from historical population expansion combined with purifying selection, the outlying minority of genes with signatures indicating exceptionally intermediate frequencies were identified. Comparing genes with different stage-specificity, such signatures were most common in those with peak expression at the merozoite stage that invades erythrocytes. Members of clag, PfMC-2TM, surfin, and msp3-like gene families were highly represented, the strongest signature being in the msp3-like gene PF10_0355. Analysis of msp3-like transcripts in 45 clinical and 11 laboratory adapted isolates grown to merozoite-containing schizont stages revealed surprisingly low expression of PF10_0355. In diverse clonal parasite lines the protein product was expressed in a minority of mature schizonts (<1% in most lines and ∼10% in clone HB3), and eight sub-clones of HB3 cultured separately had an intermediate spectrum of positive frequencies (0.9 to 7.5%), indicating phase variable expression of this polymorphic antigen. This and other identified targets of balancing selection are now
Full Text Available Acquired immunity in vertebrates maintains polymorphisms in endemic pathogens, leading to identifiable signatures of balancing selection. To comprehensively survey for genes under such selection in the human malaria parasite Plasmodium falciparum, we generated paired-end short-read sequences of parasites in clinical isolates from an endemic Gambian population, which were mapped to the 3D7 strain reference genome to yield high-quality genome-wide coding sequence data for 65 isolates. A minority of genes did not map reliably, including the hypervariable var, rifin, and stevor families, but 5,056 genes (90.9% of all in the genome had >70% sequence coverage with minimum read depth of 5 for at least 50 isolates, of which 2,853 genes contained 3 or more single nucleotide polymorphisms (SNPs for analysis of polymorphic site frequency spectra. Against an overall background of negatively skewed frequencies, as expected from historical population expansion combined with purifying selection, the outlying minority of genes with signatures indicating exceptionally intermediate frequencies were identified. Comparing genes with different stage-specificity, such signatures were most common in those with peak expression at the merozoite stage that invades erythrocytes. Members of clag, PfMC-2TM, surfin, and msp3-like gene families were highly represented, the strongest signature being in the msp3-like gene PF10_0355. Analysis of msp3-like transcripts in 45 clinical and 11 laboratory adapted isolates grown to merozoite-containing schizont stages revealed surprisingly low expression of PF10_0355. In diverse clonal parasite lines the protein product was expressed in a minority of mature schizonts (<1% in most lines and ∼10% in clone HB3, and eight sub-clones of HB3 cultured separately had an intermediate spectrum of positive frequencies (0.9 to 7.5%, indicating phase variable expression of this polymorphic antigen. This and other identified targets of balancing
Qian, Wei; Wang, Yong; Li, Rui-Fu; Zhou, Xin; Liu, Jing; Peng, Dai-Zhi
BACKGROUND Lentiviral vectors have been successfully used for human skin cell gene transfer studies. Defining the selection of integration sites for retroviral vectors in the host genome is crucial in risk assessment analysis of gene therapy. However, genome-wide analyses of lentiviral integration sites in human keratinocytes, especially after prolonged growth, are poorly understood. MATERIAL AND METHODS In this study, 874 unique lentiviral vector integration sites in human HaCaT keratinocytes after long-term culture were identified and analyzed with the online tool GTSG-QuickMap and SPSS software. RESULTS The data indicated that lentiviral vectors showed integration site preferences for genes and gene-rich regions. CONCLUSIONS This study will likely assist in determining the relative risks of the lentiviral vector system and in the design of a safe lentiviral vector system in the gene therapy of skin diseases.
Bhat, Javaid A; Ali, Sajad; Salgotra, Romesh K; Mir, Zahoor A; Dutta, Sutapa; Jadon, Vasudha; Tyagi, Anshika; Mushtaq, Muntazir; Jain, Neelu; Singh, Pradeep K; Singh, Gyanendra P; Prabhu, K V
Genomic selection (GS) is a promising approach exploiting molecular genetic markers to design novel breeding programs and to develop new markers-based models for genetic evaluation. In plant breeding, it provides opportunities to increase genetic gain of complex traits per unit time and cost. The cost-benefit balance was an important consideration for GS to work in crop plants. Availability of genome-wide high-throughput, cost-effective and flexible markers, having low ascertainment bias, suitable for large population size as well for both model and non-model crop species with or without the reference genome sequence was the most important factor for its successful and effective implementation in crop species. These factors were the major limitations to earlier marker systems viz., SSR and array-based, and was unimaginable before the availability of next-generation sequencing (NGS) technologies which have provided novel SNP genotyping platforms especially the genotyping by sequencing. These marker technologies have changed the entire scenario of marker applications and made the use of GS a routine work for crop improvement in both model and non-model crop species. The NGS-based genotyping have increased genomic-estimated breeding value prediction accuracies over other established marker platform in cereals and other crop species, and made the dream of GS true in crop breeding. But to harness the true benefits from GS, these marker technologies will be combined with high-throughput phenotyping for achieving the valuable genetic gain from complex traits. Moreover, the continuous decline in sequencing cost will make the WGS feasible and cost effective for GS in near future. Till that time matures the targeted sequencing seems to be more cost-effective option for large scale marker discovery and GS, particularly in case of large and un-decoded genomes.
Brown, T. A. (Terence A.)
... of genome expression and replication processes, and transcriptomics and proteomics. This text is richly illustrated with clear, easy-to-follow, full color diagrams, which are downloadable from the book's website...
Full Text Available Host specialization is a key evolutionary process for the diversification and emergence of new pathogens. However, the molecular determinants of host range are poorly understood. Smut fungi are biotrophic pathogens that have distinct and narrow host ranges based on largely unknown genetic determinants. Hence, we aimed to expand comparative genomics analyses of smut fungi by including more species infecting different hosts and to define orphans and positively selected genes to gain further insights into the genetics basis of host specialization. We analyzed nine lineages of smut fungi isolated from eight crop and non-crop hosts: maize, barley, sugarcane, wheat, oats, Zizania latifolia (Manchurian rice, Echinochloa colona (a wild grass, and Persicaria sp. (a wild dicot plant. We assembled two new genomes: Ustilago hordei (strain Uhor01 isolated from oats and U. tritici (strain CBS 119.19 isolated from wheat. The smut genomes were of small sizes, ranging from 18.38 to 24.63 Mb. U. hordei species experienced genome expansions due to the proliferation of transposable elements and the amount of these elements varied among the two strains. Phylogenetic analysis confirmed that Ustilago is not a monophyletic genus and, furthermore, detected misclassification of the U. tritici specimen. The comparison between smut pathogens of crop and non-crop hosts did not reveal distinct signatures, suggesting that host domestication did not play a dominant role in shaping the evolution of smuts. We found that host specialization in smut fungi likely has a complex genetic basis: different functional categories were enriched in orphans and lineage-specific selected genes. The diversification and gain/loss of effector genes are probably the most important determinants of host specificity.
Gayk, Zach G; Le Duc, Diana; Horn, Jeffrey; Lindsay, Alec R
The common loon (Gavia immer) is one of five species that comprise the avian order Gaviiformes. Loons are specialized divers, reaching depths up to 60 m while staying submerged for intervals up to three minutes. In this study we used comparative genomics to investigate the genetic basis of the common loon adaptations to its ecological niche. We used Illumina short read DNA sequence data from a female bird to produce a draft assembly of the common loon (Gavia immer) genome. We identified 14,169 common loon genes, which based on well-resolved avian genomes, represent approximately 80.7% of common loon genes. Evolutionary analyses between common loon and Adelie penguin (Pygoscelis adeliae), red-throated loon (Gavia stellata), chicken (Gallus gallus), northern fulmar (Fulmarus glacialis), and rock pigeon (Columba livia) show 164 positively selected genes in common and red-throated loons. These genes were enriched for a number of protein classes, including those involved in muscle tissue development, immunoglobulin function, hemoglobin iron binding, G-protein coupled receptors, and ATP metabolism. Signatures of positive selection in these areas suggest the genus Gavia may have adapted for underwater diving by modulating their oxidative and metabolic pathways. While more research is required, these adaptations likely result in (1) compensations in oxygen respiration and energetic metabolism, (2) low-light visual acuity, and (3) elevated solute exchange. This work represents the first effort to understand the genomic adaptations of the common loon as well as other Gavia and may have implications for subsequent studies that target particular genes for loon population genetic, ecological or conservation studies.
Vamathevan, Jessica J., E-mail: firstname.lastname@example.org [Computational Biology, Quantitative Sciences, GlaxoSmithKline, Stevenage (United Kingdom); Hall, Matthew D.; Hasan, Samiul; Woollard, Peter M. [Computational Biology, Quantitative Sciences, GlaxoSmithKline, Stevenage (United Kingdom); Xu, Meng; Yang, Yulan; Li, Xin; Wang, Xiaoli [BGI-Shenzen, Shenzhen (China); Kenny, Steve [Safety Assessment, PTS, GlaxoSmithKline, Ware (United Kingdom); Brown, James R. [Computational Biology, Quantitative Sciences, GlaxoSmithKline, Collegeville, PA (United States); Huxley-Jones, Julie [UK Platform Technology Sciences (PTS) Operations and Planning, PTS, GlaxoSmithKline, Stevenage (United Kingdom); Lyon, Jon; Haselden, John [Safety Assessment, PTS, GlaxoSmithKline, Ware (United Kingdom); Min, Jiumeng [BGI-Shenzen, Shenzhen (China); Sanseau, Philippe [Computational Biology, Quantitative Sciences, GlaxoSmithKline, Stevenage (United Kingdom)
Improving drug attrition remains a challenge in pharmaceutical discovery and development. A major cause of early attrition is the demonstration of safety signals which can negate any therapeutic index previously established. Safety attrition needs to be put in context of clinical translation (i.e. human relevance) and is negatively impacted by differences between animal models and human. In order to minimize such an impact, an earlier assessment of pharmacological target homology across animal model species will enhance understanding of the context of animal safety signals and aid species selection during later regulatory toxicology studies. Here we sequenced the genomes of the Sus scrofa Göttingen minipig and the Canis familiaris beagle, two widely used animal species in regulatory safety studies. Comparative analyses of these new genomes with other key model organisms, namely mouse, rat, cynomolgus macaque, rhesus macaque, two related breeds (S. scrofa Duroc and C. familiaris boxer) and human reveal considerable variation in gene content. Key genes in toxicology and metabolism studies, such as the UGT2 family, CYP2D6, and SLCO1A2, displayed unique duplication patterns. Comparisons of 317 known human drug targets revealed surprising variation such as species-specific positive selection, duplication and higher occurrences of pseudogenized targets in beagle (41 genes) relative to minipig (19 genes). These data will facilitate the more effective use of animals in biomedical research. - Highlights: • Genomes of the minipig and beagle dog, two species used in pharmaceutical studies. • First systematic comparative genome analysis of human and six experimental animals. • Key drug toxicology genes display unique duplication patterns across species. • Comparison of 317 drug targets show species-specific evolutionary patterns.
Vamathevan, Jessica J.; Hall, Matthew D.; Hasan, Samiul; Woollard, Peter M.; Xu, Meng; Yang, Yulan; Li, Xin; Wang, Xiaoli; Kenny, Steve; Brown, James R.; Huxley-Jones, Julie; Lyon, Jon; Haselden, John; Min, Jiumeng; Sanseau, Philippe
Improving drug attrition remains a challenge in pharmaceutical discovery and development. A major cause of early attrition is the demonstration of safety signals which can negate any therapeutic index previously established. Safety attrition needs to be put in context of clinical translation (i.e. human relevance) and is negatively impacted by differences between animal models and human. In order to minimize such an impact, an earlier assessment of pharmacological target homology across animal model species will enhance understanding of the context of animal safety signals and aid species selection during later regulatory toxicology studies. Here we sequenced the genomes of the Sus scrofa Göttingen minipig and the Canis familiaris beagle, two widely used animal species in regulatory safety studies. Comparative analyses of these new genomes with other key model organisms, namely mouse, rat, cynomolgus macaque, rhesus macaque, two related breeds (S. scrofa Duroc and C. familiaris boxer) and human reveal considerable variation in gene content. Key genes in toxicology and metabolism studies, such as the UGT2 family, CYP2D6, and SLCO1A2, displayed unique duplication patterns. Comparisons of 317 known human drug targets revealed surprising variation such as species-specific positive selection, duplication and higher occurrences of pseudogenized targets in beagle (41 genes) relative to minipig (19 genes). These data will facilitate the more effective use of animals in biomedical research. - Highlights: • Genomes of the minipig and beagle dog, two species used in pharmaceutical studies. • First systematic comparative genome analysis of human and six experimental animals. • Key drug toxicology genes display unique duplication patterns across species. • Comparison of 317 drug targets show species-specific evolutionary patterns
Pérez-Enciso, Miguel; Rincón, Juan C; Legarra, Andrés
The development of next-generation sequencing technologies (NGS) has made the use of whole-genome sequence data for routine genetic evaluations possible, which has triggered a considerable interest in animal and plant breeding fields. Here, we investigated whether complete or partial sequence data can improve upon existing SNP (single nucleotide polymorphism) array-based selection strategies by simulation using a mixed coalescence - gene-dropping approach. We simulated 20 or 100 causal mutations (quantitative trait nucleotides, QTN) within 65 predefined 'gene' regions, each 10 kb long, within a genome composed of ten 3-Mb chromosomes. We compared prediction accuracy by cross-validation using a medium-density chip (7.5 k SNPs), a high-density (HD, 17 k) and sequence data (335 k). Genetic evaluation was based on a GBLUP method. The simulations showed: (1) a law of diminishing returns with increasing number of SNPs; (2) a modest effect of SNP ascertainment bias in arrays; (3) a small advantage of using whole-genome sequence data vs. HD arrays i.e. ~4%; (4) a minor effect of NGS errors except when imputation error rates are high (≥20%); and (5) if QTN were known, prediction accuracy approached 1. Since this is obviously unrealistic, we explored milder assumptions. We showed that, if all SNPs within causal genes were included in the prediction model, accuracy could also dramatically increase by ~40%. However, this criterion was highly sensitive to either misspecification (including wrong genes) or to the use of an incomplete gene list; in these cases, accuracy fell rapidly towards that reached when all SNPs from sequence data were blindly included in the model. Our study shows that, unless an accurate prior estimate on the functionality of SNPs can be included in the predictor, there is a law of diminishing returns with increasing SNP density. As a result, use of whole-genome sequence data may not result in a highly increased selection response over high
Kwong, Qi Bin; Teh, Chee Keng; Ong, Ai Ling; Chew, Fook Tim; Mayes, Sean; Kulaveerasingam, Harikrishna; Tammi, Martti; Yeoh, Suat Hui; Appleton, David Ross; Harikrishna, Jennifer Ann
Genomic selection (GS) uses genome-wide markers as an attempt to accelerate genetic gain in breeding programs of both animals and plants. This approach is particularly useful for perennial crops such as oil palm, which have long breeding cycles, and for which the optimal method for GS is still under debate. In this study, we evaluated the effect of different marker systems and modeling methods for implementing GS in an introgressed dura family derived from a Deli dura x Nigerian dura (Deli x Nigerian) with 112 individuals. This family is an important breeding source for developing new mother palms for superior oil yield and bunch characters. The traits of interest selected for this study were fruit-to-bunch (F/B), shell-to-fruit (S/F), kernel-to-fruit (K/F), mesocarp-to-fruit (M/F), oil per palm (O/P) and oil-to-dry mesocarp (O/DM). The marker systems evaluated were simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs). RR-BLUP, Bayesian A, B, Cπ, LASSO, Ridge Regression and two machine learning methods (SVM and Random Forest) were used to evaluate GS accuracy of the traits. The kinship coefficient between individuals in this family ranged from 0.35 to 0.62. S/F and O/DM had the highest genomic heritability, whereas F/B and O/P had the lowest. The accuracies using 135 SSRs were low, with accuracies of the traits around 0.20. The average accuracy of machine learning methods was 0.24, as compared to 0.20 achieved by other methods. The trait with the highest mean accuracy was F/B (0.28), while the lowest were both M/F and O/P (0.18). By using whole genomic SNPs, the accuracies for all traits, especially for O/DM (0.43), S/F (0.39) and M/F (0.30) were improved. The average accuracy of machine learning methods was 0.32, compared to 0.31 achieved by other methods. Due to high genomic resolution, the use of whole-genome SNPs improved the efficiency of GS dramatically for oil palm and is recommended for dura breeding programs. Machine learning slightly
Oyebola, Kolapo M; Idowu, Emmanuel T; Olukosi, Yetunde A; Awolola, Taiwo S; Amambua-Ngwa, Alfred
The burden of falciparum malaria is especially high in sub-Saharan Africa. Differences in pressure from host immunity and antimalarial drugs lead to adaptive changes responsible for high level of genetic variations within and between the parasite populations. Population-specific genetic studies to survey for genes under positive or balancing selection resulting from drug pressure or host immunity will allow for refinement of interventions. We performed a pooled sequencing (pool-seq) of the genomes of 100 Plasmodium falciparum isolates from Nigeria. We explored allele-frequency based neutrality test (Tajima's D) and integrated haplotype score (iHS) to identify genes under selection. Fourteen shared iHS regions that had at least 2 SNPs with a score > 2.5 were identified. These regions code for genes that were likely to have been under strong directional selection. Two of these genes were the chloroquine resistance transporter (CRT) on chromosome 7 and the multidrug resistance 1 (MDR1) on chromosome 5. There was a weak signature of selection in the dihydrofolate reductase (DHFR) gene on chromosome 4 and MDR5 genes on chromosome 13, with only 2 and 3 SNPs respectively identified within the iHS window. We observed strong selection pressure attributable to continued chloroquine and sulfadoxine-pyrimethamine use despite their official proscription for the treatment of uncomplicated malaria. There was also a major selective sweep on chromosome 6 which had 32 SNPs within the shared iHS region. Tajima's D of circumsporozoite protein (CSP), erythrocyte-binding antigen (EBA-175), merozoite surface proteins - MSP3 and MSP7, merozoite surface protein duffy binding-like (MSPDBL2) and serine repeat antigen (SERA-5) were 1.38, 1.29, 0.73, 0.84 and 0.21, respectively. We have demonstrated the use of pool-seq to understand genomic patterns of selection and variability in P. falciparum from Nigeria, which bears the highest burden of infections. This investigation identified known
Full Text Available High-altitude hypoxia (reduced inspired oxygen tension due to decreased barometric pressure exerts severe physiological stress on the human body. Two high-altitude regions where humans have lived for millennia are the Andean Altiplano and the Tibetan Plateau. Populations living in these regions exhibit unique circulatory, respiratory, and hematological adaptations to life at high altitude. Although these responses have been well characterized physiologically, their underlying genetic basis remains unknown. We performed a genome scan to identify genes showing evidence of adaptation to hypoxia. We looked across each chromosome to identify genomic regions with previously unknown function with respect to altitude phenotypes. In addition, groups of genes functioning in oxygen metabolism and sensing were examined to test the hypothesis that particular pathways have been involved in genetic adaptation to altitude. Applying four population genetic statistics commonly used for detecting signatures of natural selection, we identified selection-nominated candidate genes and gene regions in these two populations (Andeans and Tibetans separately. The Tibetan and Andean patterns of genetic adaptation are largely distinct from one another, with both populations showing evidence of positive natural selection in different genes or gene regions. Interestingly, one gene previously known to be important in cellular oxygen sensing, EGLN1 (also known as PHD2, shows evidence of positive selection in both Tibetans and Andeans. However, the pattern of variation for this gene differs between the two populations. Our results indicate that several key HIF-regulatory and targeted genes are responsible for adaptation to high altitude in Andeans and Tibetans, and several different chromosomal regions are implicated in the putative response to selection. These data suggest a genetic role in high-altitude adaption and provide a basis for future genotype/phenotype association
Azevedo Peixoto, Leonardo de; Laviola, Bruno Galvêas; Alves, Alexandre Alonso; Rosado, Tatiana Barbosa; Bhering, Leonardo Lopes
Genomic wide selection is a promising approach for improving the selection accuracy in plant breeding, particularly in species with long life cycles, such as Jatropha. Therefore, the objectives of this study were to estimate the genetic parameters for grain yield (GY) and the weight of 100 seeds (W100S) using restricted maximum likelihood (REML); to compare the performance of GWS methods to predict GY and W100S; and to estimate how many markers are needed to train the GWS model to obtain the maximum accuracy. Eight GWS models were compared in terms of predictive ability. The impact that the marker density had on the predictive ability was investigated using a varying number of markers, from 2 to 1,248. Because the genetic variance between evaluated genotypes was significant, it was possible to obtain selection gain. All of the GWS methods tested in this study can be used to predict GY and W100S in Jatropha. A training model fitted using 1,000 and 800 markers is sufficient to capture the maximum genetic variance and, consequently, maximum prediction ability of GY and W100S, respectively. This study demonstrated the applicability of genome-wide prediction to identify useful genetic sources of GY and W100S for Jatropha breeding. Further research is needed to confirm the applicability of the proposed approach to other complex traits.
Leonardo de Azevedo Peixoto
Full Text Available Genomic wide selection is a promising approach for improving the selection accuracy in plant breeding, particularly in species with long life cycles, such as Jatropha. Therefore, the objectives of this study were to estimate the genetic parameters for grain yield (GY and the weight of 100 seeds (W100S using restricted maximum likelihood (REML; to compare the performance of GWS methods to predict GY and W100S; and to estimate how many markers are needed to train the GWS model to obtain the maximum accuracy. Eight GWS models were compared in terms of predictive ability. The impact that the marker density had on the predictive ability was investigated using a varying number of markers, from 2 to 1,248. Because the genetic variance between evaluated genotypes was significant, it was possible to obtain selection gain. All of the GWS methods tested in this study can be used to predict GY and W100S in Jatropha. A training model fitted using 1,000 and 800 markers is sufficient to capture the maximum genetic variance and, consequently, maximum prediction ability of GY and W100S, respectively. This study demonstrated the applicability of genome-wide prediction to identify useful genetic sources of GY and W100S for Jatropha breeding. Further research is needed to confirm the applicability of the proposed approach to other complex traits.
Cornelia Di Gaetano
Full Text Available The peculiar position of Sardinia in the Mediterranean sea has rendered its population an interesting biogeographical isolate. The aim of this study was to investigate the genetic population structure, as well as to estimate Runs of Homozygosity and regions under positive selection, using about 1.2 million single nucleotide polymorphisms genotyped in 1077 Sardinian individuals. Using four different methods--fixation index, inflation factor, principal component analysis and ancestry estimation--we were able to highlight, as expected for a genetic isolate, the high internal homogeneity of the island. Sardinians showed a higher percentage of genome covered by RoHs>0.5 Mb (F(RoH%0.5 when compared to peninsular Italians, with the only exception of the area surrounding Alghero. We furthermore identified 9 genomic regions showing signs of positive selection and, we re-captured many previously inferred signals. Other regions harbor novel candidate genes for positive selection, like TMEM252, or regions containing long non coding RNA. With the present study we confirmed the high genetic homogeneity of Sardinia that may be explained by the shared ancestry combined with the action of evolutionary forces.
Full Text Available Korean Hanwoo cattle have been subjected to intensive artificial selection over the past four decades to improve meat production traits. Another three cattle varieties very closely related to Hanwoo reside in Korea (Jeju Black and Brindle and in China (Yanbian. These breeds have not been part of a breeding scheme to improve production traits. Here, we compare the selected Hanwoo against these similar but presumed to be unselected populations to identify genomic regions that have been under recent selection pressure due to the breeding program. Rsb statistics were used to contrast the genomes of Hanwoo versus a pooled sample of the three unselected population (UN. We identified 37 significant SNPs (FDR corrected in the HW/UN comparison and 21 known protein coding genes were within 1 MB to the identified SNPs. These genes were previously reported to affect traits important for meat production (14 genes, reproduction including mammary gland development (3 genes, coat color (2 genes, and genes affecting behavioral traits in a broader sense (2 genes. We subsequently sequenced (Illumina HiSeq 2000 platform 10 individuals of the brown Hanwoo and the Chinese Yanbian to identify SNPs within the candidate genomic regions. Based on allele frequency differences, haplotype structures, and literature research, we singled out one non-synonymous SNP in the APP gene (APP: c.569C>T, Ala199Val and predicted the mutational effect on the protein structure. We found that protein-protein interactions might be impaired due to increased exposed hydrophobic surfaces of the mutated protein. The APP gene has also been reported to affect meat tenderness in pigs and obesity in humans. Meat tenderness has been linked to intramuscular fat content, which is one of the main breeding goals for brown Hanwoo, potentially supporting a causal influence of the herein described nsSNP in the APP gene.
Lim, Dajeong; Strucken, Eva M; Choi, Bong Hwan; Chai, Han Ha; Cho, Yong Min; Jang, Gul Won; Kim, Tae-Hun; Gondro, Cedric; Lee, Seung Hwan
Korean Hanwoo cattle have been subjected to intensive artificial selection over the past four decades to improve meat production traits. Another three cattle varieties very closely related to Hanwoo reside in Korea (Jeju Black and Brindle) and in China (Yanbian). These breeds have not been part of a breeding scheme to improve production traits. Here, we compare the selected Hanwoo against these similar but presumed to be unselected populations to identify genomic regions that have been under recent selection pressure due to the breeding program. Rsb statistics were used to contrast the genomes of Hanwoo versus a pooled sample of the three unselected population (UN). We identified 37 significant SNPs (FDR corrected) in the HW/UN comparison and 21 known protein coding genes were within 1 MB to the identified SNPs. These genes were previously reported to affect traits important for meat production (14 genes), reproduction including mammary gland development (3 genes), coat color (2 genes), and genes affecting behavioral traits in a broader sense (2 genes). We subsequently sequenced (Illumina HiSeq 2000 platform) 10 individuals of the brown Hanwoo and the Chinese Yanbian to identify SNPs within the candidate genomic regions. Based on allele frequency differences, haplotype structures, and literature research, we singled out one non-synonymous SNP in the APP gene (APP: c.569C>T, Ala199Val) and predicted the mutational effect on the protein structure. We found that protein-protein interactions might be impaired due to increased exposed hydrophobic surfaces of the mutated protein. The APP gene has also been reported to affect meat tenderness in pigs and obesity in humans. Meat tenderness has been linked to intramuscular fat content, which is one of the main breeding goals for brown Hanwoo, potentially supporting a causal influence of the herein described nsSNP in the APP gene.
Rincent, R; Kuhn, E; Monod, H; Oury, F-X; Rousset, M; Allard, V; Le Gouis, J
We propose a statistical criterion to optimize multi-environment trials to predict genotype × environment interactions more efficiently, by combining crop growth models and genomic selection models. Genotype × environment interactions (GEI) are common in plant multi-environment trials (METs). In this context, models developed for genomic selection (GS) that refers to the use of genome-wide information for predicting breeding values of selection candidates need to be adapted. One promising way to increase prediction accuracy in various environments is to combine ecophysiological and genetic modelling thanks to crop growth models (CGM) incorporating genetic parameters. The efficiency of this approach relies on the quality of the parameter estimates, which depends on the environments composing this MET used for calibration. The objective of this study was to determine a method to optimize the set of environments composing the MET for estimating genetic parameters in this context. A criterion called OptiMET was defined to this aim, and was evaluated on simulated and real data, with the example of wheat phenology. The MET defined with OptiMET allowed estimating the genetic parameters with lower error, leading to higher QTL detection power and higher prediction accuracies. MET defined with OptiMET was on average more efficient than random MET composed of twice as many environments, in terms of quality of the parameter estimates. OptiMET is thus a valuable tool to determine optimal experimental conditions to best exploit MET and the phenotyping tools that are currently developed.
de Rezende Neves, Haroldo Henrique; Carvalheiro, Roberto; de Queiroz, Sandra Aidar
Simulation studies allow addressing consequences of selection schemes, helping to identify effective strategies to enable genetic gain and maintain genetic diversity. The aim of this study was to evaluate the long-term impact of genomic selection (GS) in genetic progress and genetic diversity of beef cattle. Forward-in-time simulation generated a population with pattern of linkage disequilibrium close to that previously reported for real beef cattle populations. Different scenarios of GS and traditional pedigree-based BLUP (PBLUP) selection were simulated for 15 generations, mimicking selection for female reproduction and meat quality. For GS scenarios, an alternative selection criterion was simulated (wGBLUP), intended to enhance long-term gains by attributing more weight to favorable alleles with low frequency. GS allowed genetic progress up to 40% greater than PBLUP, for female reproduction and meat quality. The alternative criterion wGBLUP did not increase long-term response, although allowed reducing inbreeding rates and loss of favorable alleles. The results suggest that GS outperforms PBLUP when the selected trait is under less polygenic background and that attributing more weight to low-frequency favorable alleles can reduce inbreeding rates and loss of favorable alleles in GS.
Jessica E. Rutkoski
Full Text Available Quantitative adult plant resistance (APR to stem rust ( f. sp. is an important breeding target in wheat ( L. and a potential target for genomic selection (GS. To evaluate the relative importance of known APR loci in applying GS, we characterized a set of CIMMYT germplasm at important APR loci and on a genome-wide profile using genotyping-by-sequencing (GBS. Using this germplasm, we describe the genetic architecture and evaluate prediction models for APR using data from the international Ug99 stem rust screening nurseries. Prediction models incorporating markers linked to important APR loci and seedling phenotype scores as fixed effects were evaluated along with the classic prediction models: Multiple linear regression (MLR, Genomic best linear unbiased prediction (G-BLUP, Bayesian Lasso (BL, and Bayes Cπ (BCπ. We found the region to play an important role in APR in this germplasm. A model using linked markers as fixed effects in G-BLUP was more accurate than MLR with linked markers (-value = 0.12, and ordinary G-BLUP (-value = 0.15. Incorporating seedling phenotype information as fixed effects in G-BLUP did not consistently increase accuracy. Overall, levels of prediction accuracy found in this study indicate that GS can be effectively applied to improve stem rust APR in this germplasm, and if genotypes at linked markers are available, modeling these genotypes as fixed effects could lead to better predictions.
Wichgers Schreur, Paul J.; Kortekaas, Jeroen
The bunyavirus genome comprises a small (S), medium (M), and large (L) RNA segment of negative polarity. Although genome segmentation confers evolutionary advantages by enabling genome reassortment events with related viruses, genome segmentation also complicates genome replication and packaging.
Ramos, Barbara; González-Acuña, Daniel; Loyola, David E; Johnson, Warren E; Parker, Patricia G; Massaro, Melanie; Dantas, Gisele P M; Miranda, Marcelo D; Vianna, Juliana A
Mitochondria play a key role in the balance of energy and heat production, and therefore the mitochondrial genome is under natural selection by environmental temperature and food availability, since starvation can generate more efficient coupling of energy production. However, selection over mitochondrial DNA (mtDNA) genes has usually been evaluated at the population level. We sequenced by NGS 12 mitogenomes and with four published genomes, assessed genetic variation in ten penguin species distributed from the equator to Antarctica. Signatures of selection of 13 mitochondrial protein-coding genes were evaluated by comparing among species within and among genera (Spheniscus, Pygoscelis, Eudyptula, Eudyptes and Aptenodytes). The genetic data were correlated with environmental data obtained through remote sensing (sea surface temperature [SST], chlorophyll levels [Chl] and a combination of SST and Chl [COM]) through the distribution of these species. We identified the complete mtDNA genomes of several penguin species, including ND6 and 8 tRNAs on the light strand and 12 protein coding genes, 14 tRNAs and two rRNAs positioned on the heavy strand. The highest diversity was found in NADH dehydrogenase genes and the lowest in COX genes. The lowest evolutionary divergence among species was between Humboldt (Spheniscus humboldti) and Galapagos (S. mendiculus) penguins (0.004), while the highest was observed between little penguin (Eudyptula minor) and Adélie penguin (Pygoscelis adeliae) (0.097). We identified a signature of purifying selection (Ka/Ks penguins. In contrast, COX1 had a signature of strong negative selection. ND4 Ka/Ks ratios were highly correlated with SST (Mantel, p-value: 0.0001; GLM, p-value: 0.00001) and thus may be related to climate adaptation throughout penguin speciation. These results identify mtDNA candidate genes under selection which could be involved in broad-scale adaptations of penguins to their environment. Such knowledge may be
Babbitt, Gregory A; Cotter, C R
One prominent pattern of mutational frequency, long appreciated in comparative genomics, is the bias of purine/pyrimidine conserving substitutions (transitions) over purine/pyrimidine altering substitutions (transversions). Traditionally, this transitional bias has been thought to be driven by the underlying rates of DNA mutation and/or repair. However, recent sequencing studies of mutation accumulation lines in model organisms demonstrate that substitutions generally do not accumulate at rates that would indicate a transitional bias. These observations have called into question a very basic assumption of molecular evolution; that naturally occurring patterns of molecular variation in noncoding regions accurately reflect the underlying processes of randomly accumulating neutral mutation in nuclear genomes. Here, in Saccharomyces yeasts, we report a very strong inverse association (r = -0.951, P < 0.004) between the genome-wide frequency of substitutions and their average energetic effect on nucleosome formation, as predicted by a structurally based energy model of DNA deformation around the nucleosome core. We find that transitions occurring at sites positioned nearest the nucleosome surface, which are believed to function most importantly in nucleosome formation, alter the deformation energy of DNA to the nucleosome core by only a fraction of the energy changes typical of most transversions. When we examined the same substitutions set against random background sequences as well as an existing study reporting substitutions arising in mutation accumulation lines of Saccharomyces cerevisiae, we failed to find a similar relationship. These results support the idea that natural selection acting to functionally conserve chromatin organization may contribute significantly to genome-wide transitional bias, even in noncoding regions. Because nucleosome core structure is highly conserved across eukaryotes, our observations may also help to further explain locally elevated
Buch, Line Hjortø; Kargo, Morten; Berg, Peer
Today, almost all reference populations consist of progeny tested bulls. However, older progeny tested bulls do not have reliable estimated breeding values (EBV) for new traits. Thus, to be able to select for these new traits, it is necessary to build a reference population. We used a deterministic...... of the direct genomic values (DGV) for a new functional trait, regardless of its heritability. For small-scale recording, we compared two scenarios where the reference population consisted of the 2000 cows with phenotypic records or the 30 sires of these cows in the first year with measurements of the new...... to achieve accuracies of the DGV that enable selection for new functional traits recorded on a large scale within 3 years from commencement of recording; and (iv) a higher heritability benefits a reference population of cows more than a reference population of bulls....
Schumer, Molly; Xu, Chenling; Powell, Daniel L; Durvasula, Arun; Skov, Laurits; Holland, Chris; Blazier, John C; Sankararaman, Sriram; Andolfatto, Peter; Rosenthal, Gil G; Przeworski, Molly
To investigate the consequences of hybridization between species, we studied three replicate hybrid populations that formed naturally between two swordtail fish species, estimating their fine-scale genetic map and inferring ancestry along the genomes of 690 individuals. In all three populations, ancestry from the "minor" parental species is more common in regions of high recombination and where there is linkage to fewer putative targets of selection. The same patterns are apparent in a reanalysis of human and archaic admixture. These results support models in which ancestry from the minor parental species is more likely to persist when rapidly uncoupled from alleles that are deleterious in hybrids. Our analyses further indicate that selection on swordtail hybrids stems predominantly from deleterious combinations of epistatically interacting alleles. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
Mustafa, Mohammad Razif Bin; Dhahi, Th S.; Ehfaed, Nuri. A. K. H.; Adam, Tijjani; Hashim, U.; Azizah, N.; Mohammed, Mohammed; Noriman, N. Z.
The nano structure based on silicon can be surface modified to be used as label-free biosensors that allow real-time measurements. The silicon nanowire surface was functionalized using 3-aminopropyltrimethoxysilane (APTES), which functions as a facilitator to immobilize biomolecules on the silicon nanowire surface. The process is simple, economical; this will pave the way for point-of-care applications. However, the surface modification and subsequent detection mechanism still not clear. Thus, study proposed step by step process of silicon nano surface modification and its possible in specific and selective target detection of Supra-genome 21 Mers Salmonella. The device captured the molecule with precisely; the approach took the advantages of strong binding chemistry created between APTES and biomolecule. The results indicated how modifications of the nanowires provide sensing capability with strong surface chemistries that can lead to specific and selective target detection.
Sun, Yu; Tamarit, Daniel
Abstract The major codon preference model suggests that codons read by tRNAs in high concentrations are preferentially utilized in highly expressed genes. However, the identity of the optimal codons differs between species although the forces driving such changes are poorly understood. We suggest that these questions can be tackled by placing codon usage studies in a phylogenetic framework and that bacterial genomes with extreme nucleotide composition biases provide informative model systems. Switches in the background substitution biases from GC to AT have occurred in Gardnerella vaginalis (GC = 32%), and from AT to GC in Lactobacillus delbrueckii (GC = 62%) and Lactobacillus fermentum (GC = 63%). We show that despite the large effects on codon usage patterns by these switches, all three species evolve under selection on synonymous sites. In G. vaginalis, the dramatic codon frequency changes coincide with shifts of optimal codons. In contrast, the optimal codons have not shifted in the two Lactobacillus genomes despite an increased fraction of GC-ending codons. We suggest that all three species are in different phases of an on-going shift of optimal codons, and attribute the difference to a stronger background substitution bias and/or longer time since the switch in G. vaginalis. We show that comparative and correlative methods for optimal codon identification yield conflicting results for genomes in flux and discuss possible reasons for the mispredictions. We conclude that switches in the direction of the background substitution biases can drive major shifts in codon preference patterns even under sustained selection on synonymous codon sites. PMID:27540085
Full Text Available Abstract Background Genome-wide gene-gene interaction analysis using single nucleotide polymorphisms (SNPs is an attractive way for identification of genetic components that confers susceptibility of human complex diseases. Individual hypothesis testing for SNP-SNP pairs as in common genome-wide association study (GWAS however involves difficulty in setting overall p-value due to complicated correlation structure, namely, the multiple testing problem that causes unacceptable false negative results. A large number of SNP-SNP pairs than sample size, so-called the large p small n problem, precludes simultaneous analysis using multiple regression. The method that overcomes above issues is thus needed. Results We adopt an up-to-date method for ultrahigh-dimensional variable selection termed the sure independence screening (SIS for appropriate handling of numerous number of SNP-SNP interactions by including them as predictor variables in logistic regression. We propose ranking strategy using promising dummy coding methods and following variable selection procedure in the SIS method suitably modified for gene-gene interaction analysis. We also implemented the procedures in a software program, EPISIS, using the cost-effective GPGPU (General-purpose computing on graphics processing units technology. EPISIS can complete exhaustive search for SNP-SNP interactions in standard GWAS dataset within several hours. The proposed method works successfully in simulation experiments and in application to real WTCCC (Wellcome Trust Case–control Consortium data. Conclusions Based on the machine-learning principle, the proposed method gives powerful and flexible genome-wide search for various patterns of gene-gene interaction.
Schulz-Streeck, Torben; Ogutu, Joseph O; Piepho, Hans-Peter
Genomic selection (GS) is a method for predicting breeding values of plants or animals using many molecular markers that is commonly implemented in two stages. In plant breeding the first stage usually involves computation of adjusted means for genotypes which are then used to predict genomic breeding values in the second stage. We compared two classical stage-wise approaches, which either ignore or approximate correlations among the means by a diagonal matrix, and a new method, to a single-stage analysis for GS using ridge regression best linear unbiased prediction (RR-BLUP). The new stage-wise method rotates (orthogonalizes) the adjusted means from the first stage before submitting them to the second stage. This makes the errors approximately independently and identically normally distributed, which is a prerequisite for many procedures that are potentially useful for GS such as machine learning methods (e.g. boosting) and regularized regression methods (e.g. lasso). This is illustrated in this paper using componentwise boosting. The componentwise boosting method minimizes squared error loss using least squares and iteratively and automatically selects markers that are most predictive of genomic breeding values. Results are compared with those of RR-BLUP using fivefold cross-validation. The new stage-wise approach with rotated means was slightly more similar to the single-stage analysis than the classical two-stage approaches based on non-rotated means for two unbalanced datasets. This suggests that rotation is a worthwhile pre-processing step in GS for the two-stage approaches for unbalanced datasets. Moreover, the predictive accuracy of stage-wise RR-BLUP was higher (5.0-6.1%) than that of componentwise boosting.
Sabeti, Pardis C; Varilly, Patrick; Fry, Ben; Lohmueller, Jason; Hostetter, Elizabeth; Cotsapas, Chris; Xie, Xiaohui; Byrne, Elizabeth H; McCarroll, Steven A; Gaudet, Rachelle; Schaffner, Stephen F; Lander, Eric S; Frazer, Kelly A; Ballinger, Dennis G; Cox, David R; Hinds, David A; Stuve, Laura L; Gibbs, Richard A; Belmont, John W; Boudreau, Andrew; Hardenbol, Paul; Leal, Suzanne M; Pasternak, Shiran; Wheeler, David A; Willis, Thomas D; Yu, Fuli; Yang, Huanming; Zeng, Changqing; Gao, Yang; Hu, Haoran; Hu, Weitao; Li, Chaohua; Lin, Wei; Liu, Siqi; Pan, Hao; Tang, Xiaoli; Wang, Jian; Wang, Wei; Yu, Jun; Zhang, Bo; Zhang, Qingrun; Zhao, Hongbin; Zhao, Hui; Zhou, Jun; Gabriel, Stacey B; Barry, Rachel; Blumenstiel, Brendan; Camargo, Amy; Defelice, Matthew; Faggart, Maura; Goyette, Mary; Gupta, Supriya; Moore, Jamie; Nguyen, Huy; Onofrio, Robert C; Parkin, Melissa; Roy, Jessica; Stahl, Erich; Winchester, Ellen; Ziaugra, Liuda; Altshuler, David; Shen, Yan; Yao, Zhijian; Huang, Wei; Chu, Xun; He, Yungang; Jin, Li; Liu, Yangfan; Shen, Yayun; Sun, Weiwei; Wang, Haifeng; Wang, Yi; Wang, Ying; Xiong, Xiaoyan; Xu, Liang; Waye, Mary M Y; Tsui, Stephen K W; Xue, Hong; Wong, J Tze-Fei; Galver, Luana M; Fan, Jian-Bing; Gunderson, Kevin; Murray, Sarah S; Oliphant, Arnold R; Chee, Mark S; Montpetit, Alexandre; Chagnon, Fanny; Ferretti, Vincent; Leboeuf, Martin; Olivier, Jean-François; Phillips, Michael S; Roumy, Stéphanie; Sallée, Clémentine; Verner, Andrei; Hudson, Thomas J; Kwok, Pui-Yan; Cai, Dongmei; Koboldt, Daniel C; Miller, Raymond D; Pawlikowska, Ludmila; Taillon-Miller, Patricia; Xiao, Ming; Tsui, Lap-Chee; Mak, William; Song, You Qiang; Tam, Paul K H; Nakamura, Yusuke; Kawaguchi, Takahisa; Kitamoto, Takuya; Morizono, Takashi; Nagashima, Atsushi; Ohnishi, Yozo; Sekine, Akihiro; Tanaka, Toshihiro; Tsunoda, Tatsuhiko; Deloukas, Panos; Bird, Christine P; Delgado, Marcos; Dermitzakis, Emmanouil T; Gwilliam, Rhian; Hunt, Sarah; Morrison, Jonathan; Powell, Don; Stranger, Barbara E; Whittaker, Pamela; Bentley, David R; Daly, Mark J; de Bakker, Paul I W; Barrett, Jeff; Chretien, Yves R; Maller, Julian; McCarroll, Steve; Patterson, Nick; Pe'er, Itsik; Price, Alkes; Purcell, Shaun; Richter, Daniel J; Sabeti, Pardis; Saxena, Richa; Schaffner, Stephen F; Sham, Pak C; Varilly, Patrick; Altshuler, David; Stein, Lincoln D; Krishnan, Lalitha; Smith, Albert Vernon; Tello-Ruiz, Marcela K; Thorisson, Gudmundur A; Chakravarti, Aravinda; Chen, Peter E; Cutler, David J; Kashuk, Carl S; Lin, Shin; Abecasis, Gonçalo R; Guan, Weihua; Li, Yun; Munro, Heather M; Qin, Zhaohui Steve; Thomas, Daryl J; McVean, Gilean; Auton, Adam; Bottolo, Leonardo; Cardin, Niall; Eyheramendy, Susana; Freeman, Colin; Marchini, Jonathan; Myers, Simon; Spencer, Chris; Stephens, Matthew; Donnelly, Peter; Cardon, Lon R; Clarke, Geraldine; Evans, David M; Morris, Andrew P; Weir, Bruce S; Tsunoda, Tatsuhiko; Johnson, Todd A; Mullikin, James C; Sherry, Stephen T; Feolo, Michael; Skol, Andrew; Zhang, Houcan; Zeng, Changqing; Zhao, Hui; Matsuda, Ichiro; Fukushima, Yoshimitsu; Macer, Darryl R; Suda, Eiko; Rotimi, Charles N; Adebamowo, Clement A; Ajayi, Ike; Aniagwu, Toyin; Marshall, Patricia A; Nkwodimmah, Chibuzor; Royal, Charmaine D M; Leppert, Mark F; Dixon, Missy; Peiffer, Andy; Qiu, Renzong; Kent, Alastair; Kato, Kazuto; Niikawa, Norio; Adewole, Isaac F; Knoppers, Bartha M; Foster, Morris W; Clayton, Ellen Wright; Watkin, Jessica; Gibbs, Richard A; Belmont, John W; Muzny, Donna; Nazareth, Lynne; Sodergren, Erica; Weinstock, George M; Wheeler, David A; Yakub, Imtaz; Gabriel, Stacey B; Onofrio, Robert C; Richter, Daniel J; Ziaugra, Liuda; Birren, Bruce W; Daly, Mark J; Altshuler, David; Wilson, Richard K; Fulton, Lucinda L; Rogers, Jane; Burton, John; Carter, Nigel P; Clee, Christopher M; Griffiths, Mark; Jones, Matthew C; McLay, Kirsten; Plumb, Robert W; Ross, Mark T; Sims, Sarah K; Willey, David L; Chen, Zhu; Han, Hua; Kang, Le; Godbout, Martin; Wallenburg, John C; L'Archevêque, Paul; Bellemare, Guy; Saeki, Koji; Wang, Hongguang; An, Daochang; Fu, Hongbo; Li, Qing; Wang, Zhen; Wang, Renwu; Holden, Arthur L; Brooks, Lisa D; McEwen, Jean E; Guyer, Mark S; Wang, Vivian Ota; Peterson, Jane L; Shi, Michael; Spiegel, Jack; Sung, Lawrence M; Zacharia, Lynn F; Collins, Francis S; Kennedy, Karen; Jamieson, Ruth; Stewart, John
With the advent of dense maps of human genetic variation, it is now possible to detect positive natural selection across the human genome. Here we report an analysis of over 3 million polymorphisms from the International HapMap Project Phase 2 (HapMap2). We used 'long-range haplotype' methods, which were developed to identify alleles segregating in a population that have undergone recent selection, and we also developed new methods that are based on cross-population comparisons to discover alleles that have swept to near-fixation within a population. The analysis reveals more than 300 strong candidate regions. Focusing on the strongest 22 regions, we develop a heuristic for scrutinizing these regions to identify candidate targets of selection. In a complementary analysis, we identify 26 non-synonymous, coding, single nucleotide polymorphisms showing regional evidence of positive selection. Examination of these candidates highlights three cases in which two genes in a common biological process have apparently undergone positive selection in the same population:LARGE and DMD, both related to infection by the Lassa virus, in West Africa;SLC24A5 and SLC45A2, both involved in skin pigmentation, in Europe; and EDAR and EDA2R, both involved in development of hair follicles, in Asia.
Madu, C.A.; Onwuagba, B.N.
The electronic and structural properties of MgS and CaS rocksalt structure are studied with the first principle full Potential Linearized Augmented Plane Wave (FP-LAPW) method. The exchange-correlation potential was calculated within the Generalized Gradient Approximation (GGA) using the Perdew-Burke-Ernzerhof (PBE-GGA) scheme. The scalar relativistic approach was adopted for the valence states, whereas the core states are treated fully relativistically. Energy band structures, density of states and structural parameters of both compounds are presented and discussed in context with the available theoretical and experimental studies. Our results are good and show reasonable agreement with previous results even though sufficient experimental values are not available for more realistic comparison. (author)
Full Text Available We describe here our project based in a search for sub-stellar companions (brown dwarfs and exo-planets around young ultra-cool dwarfs (UCDs and characterise their properties. We will use current and future technology (high contrast imaging, high-precision Doppler determinations from the ground and space (VLT, ELT and JWST, to find companions to young objects. Members of young moving groups (MGs have clear advantages in this field. We compiled a catalogue of young UCD objects and studied their membership to five known young moving groups: Local Association (Pleiades moving group, 20–150 Myr, Ursa Mayor group (Sirius supercluster, 300 Myr, Hyades supercluster (600 Myr, IC 2391 supercluster (35 Myr and Castor moving group (200 Myr. To assess them as members we used different kinematic and spectroscopic criteria.
Jeffrey B. Endelman
Full Text Available Many important traits in plant breeding are polygenic and therefore recalcitrant to traditional marker-assisted selection. Genomic selection addresses this complexity by including all markers in the prediction model. A key method for the genomic prediction of breeding values is ridge regression (RR, which is equivalent to best linear unbiased prediction (BLUP when the genetic covariance between lines is proportional to their similarity in genotype space. This additive model can be broadened to include epistatic effects by using other kernels, such as the Gaussian, which represent inner products in a complex feature space. To facilitate the use of RR and nonadditive kernels in plant breeding, a new software package for R called rrBLUP has been developed. At its core is a fast maximum-likelihood algorithm for mixed models with a single variance component besides the residual error, which allows for efficient prediction with unreplicated training data. Use of the rrBLUP software is demonstrated through several examples, including the identification of optimal crosses based on superior progeny value. In cross-validation tests, the prediction accuracy with nonadditive kernels was significantly higher than RR for wheat ( L. grain yield but equivalent for several maize ( L. traits.
Full Text Available Banana (Musa spp. is an important crop in the African Great Lakes region in terms of income and food security, with the highest per capita consumption worldwide. Pests, diseases and climate change hamper sustainable production of bananas. New breeding tools with increased crossbreeding efficiency are being investigated to breed for resistant, high yielding hybrids of East African Highland banana (EAHB. These include genomic selection (GS, which will benefit breeding through increased genetic gain per unit time. Understanding trait variation and the correlation among economically important traits is an essential first step in the development and selection of suitable GS models for banana. In this study, we tested the hypothesis that trait variations in bananas are not affected by cross combination, cycle, field management and their interaction with genotype. A training population created using EAHB breeding material and its progeny was phenotyped in two contrasting conditions. A high level of correlation among vegetative and yield related traits was observed. Therefore, genomic selection models could be developed for traits that are easily measured. It is likely that the predictive ability of traits that are difficult to phenotype will be similar to less difficult traits they are highly correlated with. Genotype response to cycle and field management practices varied greatly with respect to traits. Yield related traits accounted for 31-35% of principal component variation under low and high input field management conditions. Resistance to Black Sigatoka was stable across cycles but varied under different field management depending on the genotype. The best cross combination was 1201K-1xSH3217 based on selection response (R of hybrids. Genotyping using simple sequence repeat (SSR markers revealed that the training population was genetically diverse, reflecting a complex pedigree background, which was mostly influenced by the male parents.
Nyine, Moses; Uwimana, Brigitte; Swennen, Rony; Batte, Michael; Brown, Allan; Christelová, Pavla; Hřibová, Eva; Lorenzen, Jim; Doležel, Jaroslav
Banana (Musa spp.) is an important crop in the African Great Lakes region in terms of income and food security, with the highest per capita consumption worldwide. Pests, diseases and climate change hamper sustainable production of bananas. New breeding tools with increased crossbreeding efficiency are being investigated to breed for resistant, high yielding hybrids of East African Highland banana (EAHB). These include genomic selection (GS), which will benefit breeding through increased genetic gain per unit time. Understanding trait variation and the correlation among economically important traits is an essential first step in the development and selection of suitable GS models for banana. In this study, we tested the hypothesis that trait variations in bananas are not affected by cross combination, cycle, field management and their interaction with genotype. A training population created using EAHB breeding material and its progeny was phenotyped in two contrasting conditions. A high level of correlation among vegetative and yield related traits was observed. Therefore, genomic selection models could be developed for traits that are easily measured. It is likely that the predictive ability of traits that are difficult to phenotype will be similar to less difficult traits they are highly correlated with. Genotype response to cycle and field management practices varied greatly with respect to traits. Yield related traits accounted for 31-35% of principal component variation under low and high input field management conditions. Resistance to Black Sigatoka was stable across cycles but varied under different field management depending on the genotype. The best cross combination was 1201K-1xSH3217 based on selection response (R) of hybrids. Genotyping using simple sequence repeat (SSR) markers revealed that the training population was genetically diverse, reflecting a complex pedigree background, which was mostly influenced by the male parents.
Nyine, Moses; Uwimana, Brigitte; Swennen, Rony; Batte, Michael; Brown, Allan; Christelová, Pavla; Hřibová, Eva; Lorenzen, Jim
Banana (Musa spp.) is an important crop in the African Great Lakes region in terms of income and food security, with the highest per capita consumption worldwide. Pests, diseases and climate change hamper sustainable production of bananas. New breeding tools with increased crossbreeding efficiency are being investigated to breed for resistant, high yielding hybrids of East African Highland banana (EAHB). These include genomic selection (GS), which will benefit breeding through increased genetic gain per unit time. Understanding trait variation and the correlation among economically important traits is an essential first step in the development and selection of suitable GS models for banana. In this study, we tested the hypothesis that trait variations in bananas are not affected by cross combination, cycle, field management and their interaction with genotype. A training population created using EAHB breeding material and its progeny was phenotyped in two contrasting conditions. A high level of correlation among vegetative and yield related traits was observed. Therefore, genomic selection models could be developed for traits that are easily measured. It is likely that the predictive ability of traits that are difficult to phenotype will be similar to less difficult traits they are highly correlated with. Genotype response to cycle and field management practices varied greatly with respect to traits. Yield related traits accounted for 31–35% of principal component variation under low and high input field management conditions. Resistance to Black Sigatoka was stable across cycles but varied under different field management depending on the genotype. The best cross combination was 1201K-1xSH3217 based on selection response (R) of hybrids. Genotyping using simple sequence repeat (SSR) markers revealed that the training population was genetically diverse, reflecting a complex pedigree background, which was mostly influenced by the male parents. PMID:28586365
Lopez-Cruz, Marco; Crossa, Jose; Bonnett, David; Dreisigacker, Susanne; Poland, Jesse; Jannink, Jean-Luc; Singh, Ravi P; Autrique, Enrique; de los Campos, Gustavo
Genomic selection (GS) models use genome-wide genetic information to predict genetic values of candidates of selection. Originally, these models were developed without considering genotype × environment interaction(G×E). Several authors have proposed extensions of the single-environment GS model that accommodate G×E using either covariance functions or environmental covariates. In this study, we model G×E using a marker × environment interaction (M×E) GS model; the approach is conceptually simple and can be implemented with existing GS software. We discuss how the model can be implemented by using an explicit regression of phenotypes on markers or using co-variance structures (a genomic best linear unbiased prediction-type model). We used the M×E model to analyze three CIMMYT wheat data sets (W1, W2, and W3), where more than 1000 lines were genotyped using genotyping-by-sequencing and evaluated at CIMMYT's research station in Ciudad Obregon, Mexico, under simulated environmental conditions that covered different irrigation levels, sowing dates and planting systems. We compared the M×E model with a stratified (i.e., within-environment) analysis and with a standard (across-environment) GS model that assumes that effects are constant across environments (i.e., ignoring G×E). The prediction accuracy of the M×E model was substantially greater of that of an across-environment analysis that ignores G×E. Depending on the prediction problem, the M×E model had either similar or greater levels of prediction accuracy than the stratified analyses. The M×E model decomposes marker effects and genomic values into components that are stable across environments (main effects) and others that are environment-specific (interactions). Therefore, in principle, the interaction model could shed light over which variants have effects that are stable across environments and which ones are responsible for G×E. The data set and the scripts required to reproduce the analysis are
Nguyen, Thuy T T; Bowman, Phil J; Haile-Mariam, Mekonnen; Pryce, Jennie E; Hayes, Benjamin J
Temperature and humidity levels above a certain threshold decrease milk production in dairy cattle, and genetic variation is associated with the amount of lost production. To enable selection for improved heat tolerance, the aim of this study was to develop genomic estimated breeding values (GEBV) for heat tolerance in dairy cattle. Heat tolerance was defined as the rate of decline in production under heat stress. We combined herd test-day recording data from 366,835 Holstein and 76,852 Jersey cows with daily temperature and humidity measurements from weather stations closest to the tested herds for test days between 2003 and 2013. We used daily mean values of temperature-humidity index averaged for the day of test and the 4 previous days as the measure of heat stress. Tolerance to heat stress was estimated for each cow using a random regression model with a common threshold of temperature-humidity index=60 for all cows. The slope solutions for cows from this model were used to define the daughter trait deviations of their sires. Genomic best linear unbiased prediction was used to calculate GEBV for heat tolerance for milk, fat, and protein yield. Two reference populations were used, the first consisted of genotyped sires only (2,300 Holstein and 575 Jersey sires), and the other included genotyped sires and cows (2,189 Holstein and 1,188 Jersey cows). The remainder of the genotyped sires were used as a validation set. All animals had genotypes for 632,003 single nucleotide polymorphisms. When using only genotyped sires in the reference set and only the first parity data, the accuracy of GEBV for heat tolerance in relation to changes in milk, fat, and protein yield were 0.48, 0.50, and 0.49 in the Holstein validation sires and 0.44, 0.61, and 0.53 in the Jersey validation sires, respectively. Some slight improvement in the accuracy of prediction was achieved when cows were included in the reference population for Holsteins. No clear improvements in the accuracy of
Neves Haroldo HR
Full Text Available Abstract Background The availability of high-density panels of SNP markers has opened new perspectives for marker-assisted selection strategies, such that genotypes for these markers are used to predict the genetic merit of selection candidates. Because the number of markers is often much larger than the number of phenotypes, marker effect estimation is not a trivial task. The objective of this research was to compare the predictive performance of ten different statistical methods employed in genomic selection, by analyzing data from a heterogeneous stock mice population. Results For the five traits analyzed (W6W: weight at six weeks, WGS: growth slope, BL: body length, %CD8+: percentage of CD8+ cells, CD4+/ CD8+: ratio between CD4+ and CD8+ cells, within-family predictions were more accurate than across-family predictions, although this superiority in accuracy varied markedly across traits. For within-family prediction, two kernel methods, Reproducing Kernel Hilbert Spaces Regression (RKHS and Support Vector Regression (SVR, were the most accurate for W6W, while a polygenic model also had comparable performance. A form of ridge regression assuming that all markers contribute to the additive variance (RR_GBLUP figured among the most accurate for WGS and BL, while two variable selection methods ( LASSO and Random Forest, RF had the greatest predictive abilities for %CD8+ and CD4+/ CD8+. RF, RKHS, SVR and RR_GBLUP outperformed the remainder methods in terms of bias and inflation of predictions. Conclusions Methods with large conceptual differences reached very similar predictive abilities and a clear re-ranking of methods was observed in function of the trait analyzed. Variable selection methods were more accurate than the remainder in the case of %CD8+ and CD4+/CD8+ and these traits are likely to be influenced by a smaller number of QTL than the remainder. Judged by their overall performance across traits and computational requirements, RR
Wang, Jing; Street, Nathaniel R; Scofield, Douglas G; Ingvarsson, Pär K
A central aim of evolutionary genomics is to identify the relative roles that various evolutionary forces have played in generating and shaping genetic variation within and among species. Here we use whole-genome resequencing data to characterize and compare genome-wide patterns of nucleotide polymorphism, site frequency spectrum, and population-scaled recombination rates in three species of Populus: Populus tremula, P. tremuloides, and P. trichocarpa. We find that P. tremuloides has the highest level of genome-wide variation, skewed allele frequencies, and population-scaled recombination rates, whereas P. trichocarpa harbors the lowest. Our findings highlight multiple lines of evidence suggesting that natural selection, due to both purifying and positive selection, has widely shaped patterns of nucleotide polymorphism at linked neutral sites in all three species. Differences in effective population sizes and rates of recombination largely explain the disparate magnitudes and signatures of linked selection that we observe among species. The present work provides the first phylogenetic comparative study on a genome-wide scale in forest trees. This information will also improve our ability to understand how various evolutionary forces have interacted to influence genome evolution among related species. Copyright © 2016 by the Genetics Society of America.
Gaither, Michelle R; Bernal, Moisés A; Coleman, Richard R; Bowen, Brian W; Jones, Shelley A; Simison, W Brian; Rocha, Luiz A
The drivers of speciation remain among the most controversial topics in evolutionary biology. Initially, Darwin emphasized natural selection as a primary mechanism of speciation, but the architects of the modern synthesis largely abandoned that view in favour of divergence by geographic isolation. The balance between selection and isolation is still at the forefront of the evolutionary debate, especially for the world's tropical oceans where biodiversity is high, but isolating barriers are few. Here, we identify the drivers of speciation in Pacific reef fishes of the genus Acanthurus by comparative genome scans of two peripheral populations that split from a large Central-West Pacific lineage at roughly the same time. Mitochondrial sequences indicate that populations in the Hawaiian Archipelago and the Marquesas Islands became isolated approximately 0.5 Ma. The Hawaiian lineage is morphologically indistinguishable from the widespread Pacific form, but the Marquesan form is recognized as a distinct species that occupies an unusual tropical ecosystem characterized by upwelling, turbidity, temperature fluctuations, algal blooms and little coral cover. An analysis of 3737 SNPs reveals a strong signal of selection at the Marquesas, with 59 loci under disruptive selection including an opsin Rh2 locus. While both the Hawaiian and Marquesan populations indicate signals of drift, the former shows a weak signal of selection that is comparable with populations in the Central-West Pacific. This contrast between closely related lineages reveals one population diverging due primarily to geographic isolation and genetic drift, and the other achieving taxonomic species status under the influence of selection. © 2015 The Authors. Molecular Ecology Published by John Wiley & Sons Ltd.
Adam H Freedman
Full Text Available Controlling for background demographic effects is important for accurately identifying loci that have recently undergone positive selection. To date, the effects of demography have not yet been explicitly considered when identifying loci under selection during dog domestication. To investigate positive selection on the dog lineage early in the domestication, we examined patterns of polymorphism in six canid genomes that were previously used to infer a demographic model of dog domestication. Using an inferred demographic model, we computed false discovery rates (FDR and identified 349 outlier regions consistent with positive selection at a low FDR. The signals in the top 100 regions were frequently centered on candidate genes related to brain function and behavior, including LHFPL3, CADM2, GRIK3, SH3GL2, MBP, PDE7B, NTAN1, and GLRA1. These regions contained significant enrichments in behavioral ontology categories. The 3rd top hit, CCRN4L, plays a major role in lipid metabolism, that is supported by additional metabolism related candidates revealed in our scan, including SCP2D1 and PDXC1. Comparing our method to an empirical outlier approach that does not directly account for demography, we found only modest overlaps between the two methods, with 60% of empirical outliers having no overlap with our demography-based outlier detection approach. Demography-aware approaches have lower-rates of false discovery. Our top candidates for selection, in addition to expanding the set of neurobehavioral candidate genes, include genes related to lipid metabolism, suggesting a dietary target of selection that was important during the period when proto-dogs hunted and fed alongside hunter-gatherers.
Leplat, Florian Jean Victor
Manganese (Mn) deficiency remains an unsolved nutritional problem affecting crop production worldwide. The tolerance to Mn limiting conditions, known as Mn efficiency, is a quantitative abiotic stress trait, generally controlled by several genes. However the underlying genetic background of Mn...... functionality in Mn dependent pathways and processes. In a the second step, a genuine statistical method to assist breeding programs in selecting new varieties, named Genomic Selection (GS), was applied. It was demonstrated that GS is an effective tool to be used in breeding programs for selecting more...
Fernandez-Fueyo, Elena; Ruiz-Duenas, Francisco J.; Ferreira, Patrica; Floudas, Dimitrios; HIbbett, David S.; Canessa, Paulo; Larrondo, Luis F.; James, Tim Y.; Seelenfreund, Daniela; Lobos, Sergio; Polanco, Ruben; Tello, Mario; Honda, Yoichi; Watanabe, Takahito; Watanabe, Takashi; Ryu, Jae San; Kubicek, Christian P.; Schmoll, Monika; Gaskell, Jill; Hammel, Kenneth E.; John, Franz J.; Vanden Wymelenberg, Amber; Sabat, Grzegorz; Splinter BonDurant, Sandra; Syed, Khajamohiddin; Yadav, Jagjit S.; Doddapaneni, Harshavardhan; Subramanian, Venkataramanan; Lavin, Jose L.; Oguiza, Jose A.; Perez, Gumer; Pisabarro, Antonio G.; Ramirez, Lucia; Santoyo, Francisco; Master, Emma; Coutinho, Pedro M.; Henrissat, Bernard; Lombard, Vincent; Magnuson, Jon Karl; Kues, Ursula; Hori, Chiaki; Igarashi, Kiyohiko; Samejima, Masahiro; Held, Benjamin W.; Barry, Kerrie W.; LaButti, Kurt M.; Lapidus, Alla; Lindquist, Erika A.; Lucas, Susan M.; Riley, Robert; Salamov, Asaf A.; Hoffmeister, Dirk; Schwenk, Daniel; Hadar, Yitzhak; Yarden, Oded; de Vries, Ronald P.; Wiebenga, Ad; Stenlid, Jan; Eastwood, Daniel; Grigoriev, Igor V.; Berka, Randy M.; Blanchette, Robert A.; Kersten, Phil; Martinez, Angel T.; Vicuna, Rafael; Cullen, Dan
Efficient lignin depolymerization is unique to the wood decay basidiomycetes, collectively referred to as white rot fungi. Phanerochaete chrysosporium simultaneously degrades lignin and cellulose, whereas the closely related species, Ceriporiopsis subvermispora, also depolymerizes lignin but may do so with relatively little cellulose degradation. To investigate the basis for selective ligninolysis, we conducted comparative genome analysis of C. subvermispora and P. chrysosporium. Genes encoding manganese peroxidase numbered 13 and five in C. subvermispora and P. chrysosporium, respectively. In addition, the C. subvermispora genome contains at least seven genes predicted to encode laccases, whereas the P. chrysosporium genome contains none. We also observed expansion of the number of C. subvermispora desaturase-encoding genes putatively involved in lipid metabolism. Microarray-based transcriptome analysis showed substantial up-regulation of several desaturase and MnP genes in wood-containing medium. MS identified MnP proteins in C. subvermispora culture filtrates, but none in P. chrysosporium cultures. These results support the importance of MnP and a lignin degradation mechanism whereby cleavage of the dominant nonphenolic structures is mediated by lipid peroxidation products. Two C. subvermispora genes were predicted to encode peroxidases structurally similar to P. chrysosporium lignin peroxidase and, following heterologous expression in Escherichia coli, the enzymes were shown to oxidize high redox potential substrates, but not Mn2. Apart from oxidative lignin degradation, we also examined cellulolytic and hemicellulolytic systems in both fungi. In summary, the C. subvermispora genetic inventory and expression patterns exhibit increased oxidoreductase potential and diminished cellulolytic capability relative to P. chrysosporium.
Franco G. Asoro
Full Text Available Genomic selection (GS is a method to estimate the breeding values of individuals by using markers throughout the genome. We evaluated the accuracies of GS using data from five traits on 446 oat ( L. lines genotyped with 1005 Diversity Array Technology (DArT markers and two GS methods (ridge regression–best linear unbiased prediction [RR-BLUP] and BayesCπ under various training designs. Our objectives were to (i determine accuracy under increasing marker density and training population size, (ii assess accuracies when data is divided over time, and (iii examine accuracy in the presence of population structure. Accuracy increased as the number of markers and training size become larger. Including older lines in the training population increased or maintained accuracy, indicating that older generations retained information useful for predicting validation populations. The presence of population structure affected accuracy: when training and validation subpopulations were closely related accuracy was greater than when they were distantly related, implying that linkage disequilibrium (LD relationships changed across subpopulations. Across many scenarios involving large training populations, the accuracy of BayesCπ and RR-BLUP did not differ. This empirical study provided evidence regarding the application of GS to hasten the delivery of cultivars through the use of inexpensive and abundant molecular markers available to the public sector.
Leempoel, Kevin; Parisod, Christian; Geiser, Céline; Joost, Stéphane
Plant species are known to adapt locally to their environment, particularly in mountainous areas where conditions can vary drastically over short distances. The climate of such landscapes being largely influenced by topography, using fine-scale models to evaluate environmental heterogeneity may help detecting adaptation to micro-habitats. Here, we applied a multiscale landscape genomic approach to detect evidence of local adaptation in the alpine plant Biscutella laevigata . The two gene pools identified, experiencing limited gene flow along a 1-km ridge, were different in regard to several habitat features derived from a very high resolution (VHR) digital elevation model (DEM). A correlative approach detected signatures of selection along environmental gradients such as altitude, wind exposure, and solar radiation, indicating adaptive pressures likely driven by fine-scale topography. Using a large panel of DEM-derived variables as ecologically relevant proxies, our results highlighted the critical role of spatial resolution. These high-resolution multiscale variables indeed indicate that the robustness of associations between genetic loci and environmental features depends on spatial parameters that are poorly documented. We argue that the scale issue is critical in landscape genomics and that multiscale ecological variables are key to improve our understanding of local adaptation in highly heterogeneous landscapes.
Lee, Ciaran M; Cradick, Thomas J; Fine, Eli J; Bao, Gang
The rapid advancement in targeted genome editing using engineered nucleases such as ZFNs, TALENs, and CRISPR/Cas9 systems has resulted in a suite of powerful methods that allows researchers to target any genomic locus of interest. A complementary set of design tools has been developed to aid researchers with nuclease design, target site selection, and experimental validation. Here, we review the various tools available for target selection in designing engineered nucleases, and for quantifying nuclease activity and specificity, including web-based search tools and experimental methods. We also elucidate challenges in target selection, especially in predicting off-target effects, and discuss future directions in precision genome editing and its applications. PMID:26750397
Jason W Sahl
Full Text Available Burkholderia pseudomallei is the causative agent of melioidosis and a potential bioterrorism agent. In the development of medical countermeasures against B. pseudomallei infection, the US Food and Drug Administration (FDA animal Rule recommends using well-characterized strains in animal challenge studies. In this study, whole genome sequence data were generated for 6 B. pseudomallei isolates previously identified as candidates for animal challenge studies; an additional 5 isolates were sequenced that were associated with human inhalational melioidosis. A core genome single nucleotide polymorphism (SNP phylogeny inferred from a concatenated SNP alignment from the 11 isolates sequenced in this study and a diverse global collection of isolates demonstrated the diversity of the proposed Animal Rule isolates. To understand the genomic composition of each isolate, a large-scale blast score ratio (LS-BSR analysis was performed on the entire pan-genome; this demonstrated the variable composition of genes across the panel and also helped to identify genes unique to individual isolates. In addition, a set of ~550 genes associated with pathogenesis in B. pseudomallei were screened against the 11 sequenced genomes with LS-BSR. Differential gene distribution for 54 virulence-associated genes was observed between genomes and three of these genes were correlated with differential virulence observed in animal challenge studies using BALB/c mice. Differentially conserved genes and SNPs associated with disease severity were identified and could be the basis for future studies investigating the pathogenesis of B. pseudomallei. Overall, the genetic characterization of the 11 proposed Animal Rule isolates provides context for future studies involving B. pseudomallei pathogenesis, differential virulence, and efficacy to therapeutics.
Lin, Michael F; Kheradpour, Pouya; Washietl, Stefan
conservation compared to typical protein-coding genes—especially at synonymous sites. In this study, we use genome alignments of 29 placental mammals to systematically locate short regions within human ORFs that show conspicuously low estimated rates of synonymous substitution across these species. The 29......-species alignment provides statistical power to locate more than 10,000 such regions with resolution down to nine-codon windows, which are found within more than a quarter of all human protein-coding genes and contain ~2% of their synonymous sites. We collect numerous lines of evidence that the observed...... synonymous constraint in these regions reflects selection on overlapping functional elements including splicing regulatory elements, dual-coding genes, RNA secondary structures, microRNA target sites, and developmental enhancers. Our results show that overlapping functional elements are common in mammalian...
Bassi, Filippo M; Bentley, Alison R; Charmet, Gilles; Ortiz, Rodomiro; Crossa, Jose
In the last decade the breeding technology referred to as 'genomic selection' (GS) has been implemented in a variety of species, with particular success in animal breeding. Recent research shows the potential of GS to reshape wheat breeding. Many authors have concluded that the estimated genetic gain per year applying GS is several times that of conventional breeding. GS is, however, a new technology for wheat breeding and many programs worldwide are still struggling to identify the best strategy for its implementation. This article provides practical guidelines on the key considerations when implementing GS. A review of the existing GS literature for a range of species is provided and used to prime breeder-oriented considerations on the practical applications of GS. Furthermore, this article discusses potential breeding schemes for GS, genotyping considerations, and methods for effective training population design. The components of selection intensity, progress toward inbreeding in half- or full-sibs recurrent schemes, and the generation of selection are also presented. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.
Müller, Dominik; Schopp, Pascal; Melchinger, Albrecht E
Recurrent selection (RS) has been used in plant breeding to successively improve synthetic and other multiparental populations. Synthetics are generated from a limited number of parents [Formula: see text] but little is known about how [Formula: see text] affects genomic selection (GS) in RS, especially the persistency of prediction accuracy ([Formula: see text]) and genetic gain. Synthetics were simulated by intermating [Formula: see text]= 2-32 parent lines from an ancestral population with short- or long-range linkage disequilibrium ([Formula: see text]) and subjected to multiple cycles of GS. We determined [Formula: see text] and genetic gain across 30 cycles for different training set ( TS ) sizes, marker densities, and generations of recombination before model training. Contributions to [Formula: see text] and genetic gain from pedigree relationships, as well as from cosegregation and [Formula: see text] between QTL and markers, were analyzed via four scenarios differing in (i) the relatedness between TS and selection candidates and (ii) whether selection was based on markers or pedigree records. Persistency of [Formula: see text] was high for small [Formula: see text] where predominantly cosegregation contributed to [Formula: see text], but also for large [Formula: see text] where [Formula: see text] replaced cosegregation as the dominant information source. Together with increasing genetic variance, this compensation resulted in relatively constant long- and short-term genetic gain for increasing [Formula: see text] > 4, given long-range LD A in the ancestral population. Although our scenarios suggest that information from pedigree relationships contributed to [Formula: see text] for only very few generations in GS, we expect a longer contribution than in pedigree BLUP, because capturing Mendelian sampling by markers reduces selective pressure on pedigree relationships. Larger TS size ([Formula: see text]) and higher marker density improved persistency of
Full Text Available Recurrent selection (RS has been used in plant breeding to successively improve synthetic and other multiparental populations. Synthetics are generated from a limited number of parents ( Np , but little is known about how Np affects genomic selection (GS in RS, especially the persistency of prediction accuracy (rg , g ^ and genetic gain. Synthetics were simulated by intermating Np= 2–32 parent lines from an ancestral population with short- or long-range linkage disequilibrium (LDA and subjected to multiple cycles of GS. We determined rg , g ^ and genetic gain across 30 cycles for different training set (TS sizes, marker densities, and generations of recombination before model training. Contributions to rg , g ^ and genetic gain from pedigree relationships, as well as from cosegregation and LDA between QTL and markers, were analyzed via four scenarios differing in (i the relatedness between TS and selection candidates and (ii whether selection was based on markers or pedigree records. Persistency of rg , g ^ was high for small Np , where predominantly cosegregation contributed to rg , g ^ , but also for large Np , where LDA replaced cosegregation as the dominant information source. Together with increasing genetic variance, this compensation resulted in relatively constant long- and short-term genetic gain for increasing Np > 4, given long-range LDA in the ancestral population. Although our scenarios suggest that information from pedigree relationships contributed to rg , g ^ for only very few generations in GS, we expect a longer contribution than in pedigree BLUP, because capturing Mendelian sampling by markers reduces selective pressure on pedigree relationships. Larger TS size (NTS and higher marker density improved persistency of rg , g ^ and hence genetic gain, but additional recombinations could not increase genetic gain.
Full Text Available Investigations on the influence of nature vs. nurture on Alcoholism (Alcohol Use Disorder in human have yet to provide a clear view on potential genomic etiologies. To address this issue, we sequenced a replicated animal model system bidirectionally-selected for alcohol preference (AP. This model is uniquely suited to map genetic effects with high reproducibility, and resolution. The origin of the rat lines (an 8-way cross resulted in small haplotype blocks (HB with a corresponding high level of resolution. We sequenced DNAs from 40 samples (10 per line of each replicate to determine allele frequencies and HB. We achieved ~46X coverage per line and replicate. Excessive differentiation in the genomic architecture between lines, across replicates, termed signatures of selection (SS, were classified according to gene and region. We identified SS in 930 genes associated with AP. The majority (50% of the SS were confined to single gene regions, the greatest numbers of which were in promoters (284 and intronic regions (169 with the least in exon's (4, suggesting that differences in AP were primarily due to alterations in regulatory regions. We confirmed previously identified genes and found many new genes associated with AP. Of those newly identified genes, several demonstrated neuronal function involved in synaptic memory and reward behavior, e.g. ion channels (Kcnf1, Kcnn3, Scn5a, excitatory receptors (Grin2a, Gria3, Grip1, neurotransmitters (Pomc, and synapses (Snap29. This study not only reveals the polygenic architecture of AP, but also emphasizes the importance of regulatory elements, consistent with other complex traits.
Lo, Chiao-Ling; Lossie, Amy C; Liang, Tiebing; Liu, Yunlong; Xuei, Xiaoling; Lumeng, Lawrence; Zhou, Feng C; Muir, William M
Investigations on the influence of nature vs. nurture on Alcoholism (Alcohol Use Disorder) in human have yet to provide a clear view on potential genomic etiologies. To address this issue, we sequenced a replicated animal model system bidirectionally-selected for alcohol preference (AP). This model is uniquely suited to map genetic effects with high reproducibility, and resolution. The origin of the rat lines (an 8-way cross) resulted in small haplotype blocks (HB) with a corresponding high level of resolution. We sequenced DNAs from 40 samples (10 per line of each replicate) to determine allele frequencies and HB. We achieved ~46X coverage per line and replicate. Excessive differentiation in the genomic architecture between lines, across replicates, termed signatures of selection (SS), were classified according to gene and region. We identified SS in 930 genes associated with AP. The majority (50%) of the SS were confined to single gene regions, the greatest numbers of which were in promoters (284) and intronic regions (169) with the least in exon's (4), suggesting that differences in AP were primarily due to alterations in regulatory regions. We confirmed previously identified genes and found many new genes associated with AP. Of those newly identified genes, several demonstrated neuronal function involved in synaptic memory and reward behavior, e.g. ion channels (Kcnf1, Kcnn3, Scn5a), excitatory receptors (Grin2a, Gria3, Grip1), neurotransmitters (Pomc), and synapses (Snap29). This study not only reveals the polygenic architecture of AP, but also emphasizes the importance of regulatory elements, consistent with other complex traits.
Lorenz, Aaron J; Beissinger, Timothy M; Silva, Renato Rodrigues; de Leon, Natalia
Maize silage is forage of high quality and yield, and represents the second most important use of maize in the United States. The Wisconsin Quality Synthetic (WQS) maize population has undergone five cycles of recurrent selection for silage yield and composition, resulting in a genetically improved population. The application of high-density molecular markers allows breeders and geneticists to identify important loci through association analysis and selection mapping, as well as to monitor changes in the distribution of genetic diversity across the genome. The objectives of this study were to identify loci controlling variation for maize silage traits through association analysis and the assessment of selection signatures and to describe changes in the genomic distribution of gene diversity through selection and genetic drift in the WQS recurrent selection program. We failed to find any significant marker-trait associations using the historical phenotypic data from WQS breeding trials combined with 17,719 high-quality, informative single nucleotide polymorphisms. Likewise, no strong genomic signatures were left by selection on silage yield and quality in the WQS despite genetic gain for these traits. These results could be due to the genetic complexity underlying these traits, or the role of selection on standing genetic variation. Variation in loss of diversity through drift was observed across the genome. Some large regions experienced much greater loss in diversity than what is expected, suggesting limited recombination combined with small populations in recurrent selection programs could easily lead to fixation of large swaths of the genome. Copyright © 2015 Lorenz et al.
Wenzel, Marius A; Douglas, Alex; James, Marianne C; Redpath, Steve M; Piertney, Stuart B
Landscape genomics promises to provide novel insights into how neutral and adaptive processes shape genome-wide variation within and among populations. However, there has been little emphasis on examining whether individual-based phenotype-genotype relationships derived from approaches such as genome-wide association (GWAS) manifest themselves as a population-level signature of selection in a landscape context. The two may prove irreconcilable as individual-level patterns become diluted by high levels of gene flow and complex phenotypic or environmental heterogeneity. We illustrate this issue with a case study that examines the role of the highly prevalent gastrointestinal nematode Trichostrongylus tenuis in shaping genomic signatures of selection in red grouse (Lagopus lagopus scotica). Individual-level GWAS involving 384 SNPs has previously identified five SNPs that explain variation in T. tenuis burden. Here, we examine whether these same SNPs display population-level relationships between T. tenuis burden and genetic structure across a small-scale landscape of 21 sites with heterogeneous parasite pressure. Moreover, we identify adaptive SNPs showing signatures of directional selection using F(ST) outlier analysis and relate population- and individual-level patterns of multilocus neutral and adaptive genetic structure to T. tenuis burden. The five candidate SNPs for parasite-driven selection were neither associated with T. tenuis burden on a population level, nor under directional selection. Similarly, there was no evidence of parasite-driven selection in SNPs identified as candidates for directional selection. We discuss these results in the context of red grouse ecology and highlight the broader consequences for the utility of landscape genomics approaches for identifying signatures of selection. © 2015 John Wiley & Sons Ltd.
Onogi, Akio; Watanabe, Maya; Mochizuki, Toshihiro; Hayashi, Takeshi; Nakagawa, Hiroshi; Hasegawa, Toshihiro; Iwata, Hiroyoshi
It is suggested that accuracy in predicting plant phenotypes can be improved by integrating genomic prediction with crop modelling in a single hierarchical model. Accurate prediction of phenotypes is important for plant breeding and management. Although genomic prediction/selection aims to predict phenotypes on the basis of whole-genome marker information, it is often difficult to predict phenotypes of complex traits in diverse environments, because plant phenotypes are often influenced by genotype-environment interaction. A possible remedy is to integrate genomic prediction with crop/ecophysiological modelling, which enables us to predict plant phenotypes using environmental and management information. To this end, in the present study, we developed a novel method for integrating genomic prediction with phenological modelling of Asian rice (Oryza sativa, L.), allowing the heading date of untested genotypes in untested environments to be predicted. The method simultaneously infers the phenological model parameters and whole-genome marker effects on the parameters in a Bayesian framework. By cultivating backcross inbred lines of Koshihikari × Kasalath in nine environments, we evaluated the potential of the proposed method in comparison with conventional genomic prediction, phenological modelling, and two-step methods that applied genomic prediction to phenological model parameters inferred from Nelder-Mead or Markov chain Monte Carlo algorithms. In predicting heading dates of untested lines in untested environments, the proposed and two-step methods tended to provide more accurate predictions than the conventional genomic prediction methods, particularly in environments where phenotypes from environments similar to the target environment were unavailable for training genomic prediction. The proposed method showed greater accuracy in prediction than the two-step methods in all cross-validation schemes tested, suggesting the potential of the integrated approach in
He, Jun; Xu, Jiaqi; Wu, Xiao-Lin; Bauck, Stewart; Lee, Jungjae; Morota, Gota; Kachman, Stephen D; Spangler, Matthew L
SNP chips are commonly used for genotyping animals in genomic selection but strategies for selecting low-density (LD) SNPs for imputation-mediated genomic selection have not been addressed adequately. The main purpose of the present study was to compare the performance of eight LD (6K) SNP panels, each selected by a different strategy exploiting a combination of three major factors: evenly-spaced SNPs, increased minor allele frequencies, and SNP-trait associations either for single traits independently or for all the three traits jointly. The imputation accuracies from 6K to 80K SNP genotypes were between 96.2 and 98.2%. Genomic prediction accuracies obtained using imputed 80K genotypes were between 0.817 and 0.821 for daughter pregnancy rate, between 0.838 and 0.844 for fat yield, and between 0.850 and 0.863 for milk yield. The two SNP panels optimized on the three major factors had the highest genomic prediction accuracy (0.821-0.863), and these accuracies were very close to those obtained using observed 80K genotypes (0.825-0.868). Further exploration of the underlying relationships showed that genomic prediction accuracies did not respond linearly to imputation accuracies, but were significantly affected by genotype (imputation) errors of SNPs in association with the traits to be predicted. SNPs optimal for map coverage and MAF were favorable for obtaining accurate imputation of genotypes whereas trait-associated SNPs improved genomic prediction accuracies. Thus, optimal LD SNP panels were the ones that combined both strengths. The present results have practical implications on the design of LD SNP chips for imputation-enabled genomic prediction.
Toghiani, S; Aggrey, S E; Rekaya, R
Availability of high-density single nucleotide polymorphism (SNP) genotyping platforms provided unprecedented opportunities to enhance breeding programmes in livestock, poultry and plant species, and to better understand the genetic basis of complex traits. Using this genomic information, genomic breeding values (GEBVs), which are more accurate than conventional breeding values. The superiority of genomic selection is possible only when high-density SNP panels are used to track genes and QTLs affecting the trait. Unfortunately, even with the continuous decrease in genotyping costs, only a small fraction of the population has been genotyped with these high-density panels. It is often the case that a larger portion of the population is genotyped with low-density and low-cost SNP panels and then imputed to a higher density. Accuracy of SNP genotype imputation tends to be high when minimum requirements are met. Nevertheless, a certain rate of genotype imputation errors is unavoidable. Thus, it is reasonable to assume that the accuracy of GEBVs will be affected by imputation errors; especially, their cumulative effects over time. To evaluate the impact of multi-generational selection on the accuracy of SNP genotypes imputation and the reliability of resulting GEBVs, a simulation was carried out under varying updating of the reference population, distance between the reference and testing sets, and the approach used for the estimation of GEBVs. Using fixed reference populations, imputation accuracy decayed by about 0.5% per generation. In fact, after 25 generations, the accuracy was only 7% lower than the first generation. When the reference population was updated by either 1% or 5% of the top animals in the previous generations, decay of imputation accuracy was substantially reduced. These results indicate that low-density panels are useful, especially when the generational interval between reference and testing population is small. As the generational interval
Ferchaud, Anne-Laure; Hansen, Michael M
Heterogeneous genomic divergence between populations may reflect selection, but should also be seen in conjunction with gene flow and drift, particularly population bottlenecks. Marine and freshwater three-spine stickleback (Gasterosteus aculeatus) populations often exhibit different lateral armour plate morphs. Moreover, strikingly parallel genomic footprints across different marine-freshwater population pairs are interpreted as parallel evolution and gene reuse. Nevertheless, in some geographic regions like the North Sea and Baltic Sea, different patterns are observed. Freshwater populations in coastal regions are often dominated by marine morphs, suggesting that gene flow overwhelms selection, and genomic parallelism may also be less pronounced. We used RAD sequencing for analysing 28 888 SNPs in two marine and seven freshwater populations in Denmark, Europe. Freshwater populations represented a variety of environments: river populations accessible to gene flow from marine sticklebacks and large and small isolated lakes with and without fish predators. Sticklebacks in an accessible river environment showed minimal morphological and genomewide divergence from marine populations, supporting the hypothesis of gene flow overriding selection. Allele frequency spectra suggested bottlenecks in all freshwater populations, and particularly two small lake populations. However, genomic footprints ascribed to selection could nevertheless be identified. No genomic regions were consistent freshwater-marine outliers, and parallelism was much lower than in other comparable studies. Two genomic regions previously described to be under divergent selection in freshwater and marine populations were outliers between different freshwater populations. We ascribe these patterns to stronger environmental heterogeneity among freshwater populations in our study as compared to most other studies, although the demographic history involving bottlenecks should also be considered in the
Full Text Available In single-step analyses, missing genotypes are explicitly or implicitly imputed, and this requires centering the observed genotypes using the means of the unselected founders. If genotypes are only available for selected individuals, centering on the unselected founder mean is not straightforward. Here, computer simulation is used to study an alternative analysis that does not require centering genotypes but fits the mean μg of unselected individuals as a fixed effect. Starting with observed diplotypes from 721 cattle, a five-generation population was simulated with sire selection to produce 40,000 individuals with phenotypes, of which the 1000 sires had genotypes. The next generation of 8000 genotyped individuals was used for validation. Evaluations were undertaken with (J or without (N μg when marker covariates were not centered; and with (JC or without (C μg when all observed and imputed marker covariates were centered. Centering did not influence accuracy of genomic prediction, but fitting μg did. Accuracies were improved when the panel comprised only quantitative trait loci (QTL; models JC and J had accuracies of 99.4%, whereas models C and N had accuracies of 90.2%. When only markers were in the panel, the 4 models had accuracies of 80.4%. In panels that included QTL, fitting μg in the model improved accuracy, but had little impact when the panel contained only markers. In populations undergoing selection, fitting μg in the model is recommended to avoid bias and reduction in prediction accuracy due to selection.
A reassociation kinetics-based approach was used to reduce the complexity of genomic DNA from the Deutsch laboratory strain of the cattle tick, Rhipicephalus microplus, to facilitate genome sequencing. Selected genomic DNA (Cot value = 660) was sequenced using 454 GS FLX technology, resulting in 356...
Liu, Zhaohua; Ji, Zhibin; Wang, Guizhi; Chao, Tianle; Hou, Lei; Wang, Jianmin
Throughout a long period of adaptation and selection, sheep have thrived in a diverse range of ecological environments. Mongolian sheep is the common ancestor of the Chinese short fat-tailed sheep. Migration to different ecoregions leads to changes in selection pressures and results in microevolution. Mongolian sheep and its subspecies differ in a number of important traits, especially reproductive traits. Genome-wide intraspecific variation is required to dissect the genetic basis of these traits. This research resequenced 3 short fat-tailed sheep breeds with a 43.2-fold coverage of the sheep genome. We report more than 17 million single nucleotide polymorphisms and 2.9 million indels and identify 143 genomic regions with reduced pooled heterozygosity or increased genetic distance to each other breed that represent likely targets for selection during the migration. These regions harbor genes related to developmental processes, cellular processes, multicellular organismal processes, biological regulation, metabolic processes, reproduction, localization, growth and various components of the stress responses. Furthermore, we examined the haplotype diversity of 3 genomic regions involved in reproduction and found significant differences in TSHR and PRL gene regions among 8 sheep breeds. Our results provide useful genomic information for identifying genes or causal mutations associated with important economic traits in sheep and for understanding the genetic basis of adaptation to different ecological environments.
Brown, A; Ojango, J; Gibson, J; Coffey, M; Okeyo, M; Mrode, R
Due to the absence of accurate pedigree information, it has not been possible to implement genetic evaluations for crossbred cattle in African small-holder systems. Genomic selection techniques that do not rely on pedigree information could, therefore, be a useful alternative. The objective of this study was to examine the feasibility of using genomic selection techniques in a crossbred cattle population using data from Kenya provided by the Dairy Genetics East Africa Project. Genomic estimated breeding values for milk yield were estimated using 2 prediction methods, GBLUP and BayesC, and accuracies were calculated as the correlation between yield deviations and genomic breeding values included in the estimation process, mimicking the situation for young bulls. The accuracy of evaluation ranged from 0.28 to 0.41, depending on the validation population and prediction method used. No significant differences were found in accuracy between the 2 prediction methods. The results suggest that there is potential for implementing genomic selection for young bulls in crossbred small-holder cattle populations, and targeted genotyping and phenotyping should be pursued to facilitate this. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
Translational genomics is a critical phase in harnessing the rich genomic data available for sorghum. There is a need to transform nucleotide variation data between sorghum germplasm such as that derived from RNA seq, genotype by sequencing (gbs) or whole genome resequencing thru translation and...
Lado, Bettina; Battenfield, Sarah; Guzmán, Carlos; Quincke, Martín; Singh, Ravi P; Dreisigacker, Susanne; Peña, R Javier; Fritz, Allan; Silva, Paula; Poland, Jesse; Gutiérrez, Lucía
The single most important decision in plant breeding programs is the selection of appropriate crosses. The ideal cross would provide superior predicted progeny performance and enough diversity to maintain genetic gain. The aim of this study was to compare the best crosses predicted using combinations of mid-parent value and variance prediction accounting for linkage disequilibrium (V) or assuming linkage equilibrium (V). After predicting the mean and the variance of each cross, we selected crosses based on mid-parent value, the top 10% of the progeny, and weighted mean and variance within progenies for grain yield, grain protein content, mixing time, and loaf volume in two applied wheat ( L.) breeding programs: Instituto Nacional de Investigación Agropecuaria (INIA) Uruguay and CIMMYT Mexico. Although the variance of the progeny is important to increase the chances of finding superior individuals from transgressive segregation, we observed that the mid-parent values of the crosses drove the genetic gain but the variance of the progeny had a small impact on genetic gain for grain yield. However, the relative importance of the variance of the progeny was larger for quality traits. Overall, the genomic resources and the statistical models are now available to plant breeders to predict both the performance of breeding lines per se as well as the value of progeny from any potential crosses. Copyright © 2017 Crop Science Society of America.
Genova, Antonio; Goossens, Sander; Lemoine, Frank G.; Mazarico, Erwan; Neumann, Gregory A.; Smith, David E.; Zuber, Maria T.
We present a spherical harmonic solution of the static gravity field of Mars to degree and order 120, GMM-3, that has been calculated using the Deep Space Network tracking data of the NASA Mars missions, Mars Global Surveyor (MGS), Mars Odyssey (ODY), and the Mars Reconnaissance Orbiter (MRO). We have also jointly determined spherical harmonic solutions for the static and time-variable gravity field of Mars, and the Mars k 2 Love numbers, exclusive of the gravity contribution of the atmosphere. Consequently, the retrieved time-varying gravity coefficients and the Love number k 2 solely yield seasonal variations in the mass of the polar caps and the solid tides of Mars, respectively. We obtain a Mars Love number k 2 of 0.1697 +/-0.0027 (3- sigma). The inclusion of MRO tracking data results in improved seasonal gravity field coefficients C 30 and, for the first time, C 50 . Refinements of the atmospheric model in our orbit determination program have allowed us to monitor the odd zonal harmonic C 30 for approx.1.5 solar cycles (16 years). This gravity model shows improved correlations with MOLA topography up to 15% larger at higher harmonics ( l = 60–80) than previous solutions.
Full Text Available Abstract- This article proposes a probabilistic frame built on Scenario fabrication to considerate the uncertainties in the finest action managing of Micro Grids MGs. The MG contains different recoverable energy resources such as Wind Turbine WT Micro Turbine MT Photovoltaic PV Fuel Cell FC and one battery as the storing device. The advised frame is based on scenario generation and Roulette wheel mechanism to produce different circumstances for handling the uncertainties of altered factors. It habits typical spreading role as a probability scattering function of random factors. The uncertainties which are measured in this paper are grid bid alterations cargo request calculating error and PV and WT yield power productions. It is well-intentioned to asset that solving the MG difficult for 24 hours of a day by considering diverse uncertainties and different constraints needs one powerful optimization method that can converge fast when it doesnt fall in local optimal topic. Simultaneously single Group Search Optimization GSO system is presented to vision the total search space globally. The GSO algorithm is instigated from group active of beasts. Also the GSO procedure one change is similarly planned for this algorithm. The planned context and way is applied o one test grid-connected MG as a typical grid.
Full Text Available General parameters of selection, such as the frequency and strength of positive selection in natural populations or the role of introgression, are still insufficiently understood. The house mouse (Mus musculus is a particularly well-suited model system to approach such questions, since it has a defined history of splits into subspecies and populations and since extensive genome information is available. We have used high-density single-nucleotide polymorphism (SNP typing arrays to assess genomic patterns of positive selection and introgression of alleles in two natural populations of each of the subspecies M. m. domesticus and M. m. musculus. Applying different statistical procedures, we find a large number of regions subject to apparent selective sweeps, indicating frequent positive selection on rare alleles or novel mutations. Genes in the regions include well-studied imprinted loci (e.g. Plagl1/Zac1, homologues of human genes involved in adaptations (e.g. alpha-amylase genes or in genetic diseases (e.g. Huntingtin and Parkin. Haplotype matching between the two subspecies reveals a large number of haplotypes that show patterns of introgression from specific populations of the respective other subspecies, with at least 10% of the genome being affected by partial or full introgression. Using neutral simulations for comparison, we find that the size and the fraction of introgressed haplotypes are not compatible with a pure migration or incomplete lineage sorting model. Hence, it appears that introgressed haplotypes can rise in frequency due to positive selection and thus can contribute to the adaptive genomic landscape of natural populations. Our data support the notion that natural genomes are subject to complex adaptive processes, including the introgression of haplotypes from other differentiated populations or species at a larger scale than previously assumed for animals. This implies that some of the admixture found in inbred strains of mice
Marcio P. Arruda
Full Text Available Genomic selection (GS is a breeding method that uses marker–trait models to predict unobserved phenotypes. This study developed GS models for predicting traits associated with resistance to head blight (FHB in wheat ( L.. We used genotyping-by-sequencing (GBS to identify 5054 single-nucleotide polymorphisms (SNPs, which were then treated as predictor variables in GS analysis. We compared how the prediction accuracy of the genomic-estimated breeding values (GEBVs was affected by (i five genotypic imputation methods (random forest imputation [RFI], expectation maximization imputation [EMI], -nearest neighbor imputation [kNNI], singular value decomposition imputation [SVDI], and the mean imputation [MNI]; (ii three statistical models (ridge-regression best linear unbiased predictor [RR-BLUP], least absolute shrinkage and operator selector [LASSO], and elastic net; (iii marker density ( = 500, 1500, 3000, and 4500 SNPs; (iv training population (TP size ( = 96, 144, 192, and 218; (v marker-based and pedigree-based relationship matrices; and (vi control for relatedness in TPs and validation populations (VPs. No discernable differences in prediction accuracy were observed among imputation methods. The RR-BLUP outperformed other models in nearly all scenarios. Accuracies decreased substantially when marker number decreased to 3000 or 1500 SNPs, depending on the trait; when sample size of the training set was less than 192; when using pedigree-based instead of marker-based matrix; or when no control for relatedness was implemented. Overall, moderate to high prediction accuracies were observed in this study, suggesting that GS is a very promising breeding strategy for FHB resistance in wheat.
Shimizu, Masanori; Goto, Maki; Hanai, Moeko; Shimizu, Tsutomu; Izawa, Norihiko; Kanamoto, Hirosuke; Tomizawa, Ken-Ichi; Yokota, Akiho; Kobayashi, Hirokazu
Strategies employed for the production of genetically modified (GM) crops are premised on (1) the avoidance of gene transfer in the field; (2) the use of genes derived from edible organisms such as plants; (3) preventing the appearance of herbicide-resistant weeds; and (4) maintaining transgenes without obstructing plant cell propagation. To this end, we developed a novel vector system for chloroplast transformation with acetolactate synthase (ALS). ALS catalyzes the first step in the biosynthesis of the branched amino acids, and its enzymatic activity is inhibited by certain classes of herbicides. We generated a series of Arabidopsis (Arabidopsis thaliana) mutated ALS (mALS) genes and introduced constructs with mALS and the aminoglycoside 3'-adenyltransferase gene (aadA) into the tobacco (Nicotiana tabacum) chloroplast genome by particle bombardment. Transplastomic plants were selected using their resistance to spectinomycin. The effects of herbicides on transplastomic mALS activity were examined by a colorimetric assay using the leaves of transplastomic plants. We found that transplastomic G121A, A122V, and P197S plants were specifically tolerant to pyrimidinylcarboxylate, imidazolinon, and sulfonylurea/pyrimidinylcarboxylate herbicides, respectively. Transplastomic plants possessing mALSs were able to grow in the presence of various herbicides, thus affirming the relationship between mALSs and the associated resistance to herbicides. Our results show that mALS genes integrated into the chloroplast genome are useful sustainable markers that function to exclude plants other than those that are GM while maintaining transplastomic crops. This investigation suggests that the resistance management of weeds in the field amid growing GM crops is possible using (1) a series of mALSs that confer specific resistance to herbicides and (2) a strategy that employs herbicide rotation.
Lopes, Marcos S; Bovenhuis, Henk; Hidalgo, André M; van Arendonk, Johan A M; Knol, Egbert F; Bastiaansen, John W M
Breed-specific effects are observed when the same allele of a given genetic marker has a different effect depending on its breed origin, which results in different allele substitution effects across breeds. In such a case, single-breed breeding values may not be the most accurate predictors of crossbred performance. Our aim was to estimate the contribution of alleles from each parental breed to the genetic variance of traits that are measured in crossbred offspring, and to compare the prediction accuracies of estimated direct genomic values (DGV) from a traditional genomic selection model (GS) that are trained on purebred or crossbred data, with accuracies of DGV from a model that accounts for breed-specific effects (BS), trained on purebred or crossbred data. The final dataset was composed of 924 Large White, 924 Landrace and 924 two-way cross (F1) genotyped and phenotyped animals. The traits evaluated were litter size (LS) and gestation length (GL) in pigs. The genetic correlation between purebred and crossbred performance was higher than 0.88 for both LS and GL. For both traits, the additive genetic variance was larger for alleles inherited from the Large White breed compared to alleles inherited from the Landrace breed (0.74 and 0.56 for LS, and 0.42 and 0.40 for GL, respectively). The highest prediction accuracies of crossbred performance were obtained when training was done on crossbred data. For LS, prediction accuracies were the same for GS and BS DGV (0.23), while for GL, prediction accuracy for BS DGV was similar to the accuracy of GS DGV (0.53 and 0.52, respectively). In this study, training on crossbred data resulted in higher prediction accuracy than training on purebred data and evidence of breed-specific effects for LS and GL was demonstrated. However, when training was done on crossbred data, both GS and BS models resulted in similar prediction accuracies. In future studies, traits with a lower genetic correlation between purebred and crossbred
Huerta, Araceli M.; Francino, M. Pilar; Morett, Enrique; Collado-Vides, Julio
The evolutionary processes operating in the DNA regions that participate in the regulation of gene expression are poorly understood. In Escherichia coli, we have established a sequence pattern that distinguishes regulatory from nonregulatory regions. The density of promoter-like sequences, that are recognizable by RNA polymerase and may function as potential promoters, is high within regulatory regions, in contrast to coding regions and regions located between convergently-transcribed genes. Moreover, functional promoter sites identified experimentally are often found in the subregions of highest density of promoter-like signals, even when individual sites with higher binding affinity for RNA polymerase exist elsewhere within the regulatory region. In order to investigate the generality of this pattern, we have used position weight matrices describing the -35 and -10 promoter boxes of E. coli to search for these motifs in 43 additional genomes belonging to most established bacterial phyla, after specific calibration of the matrices according to the base composition of the noncoding regions of each genome. We have found that all bacterial species analyzed contain similar promoter-like motifs, and that, in most cases, these motifs follow the same genomic distribution observed in E. coli. Differential densities between regulatory and nonregulatory regions are detectable in most bacterial genomes, with the exception of those that have experienced evolutionary extreme genome reduction. Thus, the phylogenetic distribution of this pattern mirrors that of genes and other genomic features that require weak selection to be effective in order to persist. On this basis, we suggest that the loss of differential densities in the reduced genomes of host-restricted pathogens and symbionts is the outcome of a process of genome degradation resulting from the decreased efficiency of purifying selection in highly structured small populations. This implies that the differential
Eg Nielsen, Einar; Hansen, Jakob Hemmer; Poulsen, Nina Aagaard
-associated single nucleotide polymorphisms (SNPs) for evidence of selection in local populations of Atlantic cod (Gadus morhua L.) across the species distribution. Results: Our global genome scan analysis identified eight outlier gene loci with very high statistical support, likely to be subject to directional...... selection in local demes, or closely linked to loci under selection. Likewise, on a regional south/north transect of central and eastern Atlantic populations, seven loci displayed strongly elevated levels of genetic differentiation. Selection patterns among populations appeared to be relatively widespread...
Kim, Eui-Soo; Sonstegard, Tad S; da Silva, Marcos V G B; Gasbarre, Louis C; Van Tassell, Curtis P
Genetic markers associated with parasite indicator traits are ideal targets for study of marker assisted selection aimed at controlling infections that reduce herd use of anthelminthics. For this study, we collected gastrointestinal (GI) nematode fecal egg count (FEC) data from post-weaning animals of an Angus resource population challenged to a 26 week natural exposure on pasture. In all, data from 487 animals was collected over a 16 year period between 1992 and 2007, most of which were selected for a specific DRB1 allele to reduce the influence of potential allelic variant effects of the MHC locus. A genome-wide association study (GWAS) based on BovineSNP50 genotypes revealed six genomic regions located on bovine Chromosomes 3, 5, 8, 15 and 27; which were significantly associated (-log10 p=4.3) with Box-Cox transformed mean FEC (BC-MFEC). DAVID analysis of the genes within the significant genomic regions suggested a correlation between our results and annotation for genes involved in inflammatory response to infection. Furthermore, ROH and selection signature analyses provided strong evidence that the genomic regions associated BC-MFEC have not been affected by local autozygosity or recent experimental selection. These findings provide useful information for parasite resistance prediction for young grazing cattle and suggest new candidate gene targets for development of disease-modifying therapies or future studies of host response to GI parasite infection.
Eynard, Sonia E.; Croiseau, Pascal; Laloë, Denis; Fritz, Sebastien; Calus, Mario P.L.; Restoux, Gwendal
Genomic selection (GS) is commonly used in livestock and increasingly in plant breeding. Relying on phenotypes and genotypes of a reference population, GS allows performance prediction for young individuals having only genotypes. This is expected to achieve fast high genetic gain but with a
Full Text Available Genetic markers associated with parasite indicator traits are ideal targets for study of marker assisted selection aimed at controlling infections that reduce herd use of anthelminthics. For this study, we collected gastrointestinal (GI nematode fecal egg count (FEC data from post-weaning animals of an Angus resource population challenged to a 26 week natural exposure on pasture. In all, data from 487 animals was collected over a 16 year period between 1992 and 2007, most of which were selected for a specific DRB1 allele to reduce the influence of potential allelic variant effects of the MHC locus. A genome-wide association study (GWAS based on BovineSNP50 genotypes revealed six genomic regions located on bovine Chromosomes 3, 5, 8, 15 and 27; which were significantly associated (-log10 p=4.3 with Box-Cox transformed mean FEC (BC-MFEC. DAVID analysis of the genes within the significant genomic regions suggested a correlation between our results and annotation for genes involved in inflammatory response to infection. Furthermore, ROH and selection signature analyses provided strong evidence that the genomic regions associated BC-MFEC have not been affected by local autozygosity or recent experimental selection. These findings provide useful information for parasite resistance prediction for young grazing cattle and suggest new candidate gene targets for development of disease-modifying therapies or future studies of host response to GI parasite infection.
Full Text Available Genome-wide association studies (GWAS aim to identify genetic variants related to diseases by examining the associations between phenotypes and hundreds of thousands of genotyped markers. Because many genes are potentially involved in common diseases and a large number of markers are analyzed, it is crucial to devise an effective strategy to identify truly associated variants that have individual and/or interactive effects, while controlling false positives at the desired level. Although a number of model selection methods have been proposed in the literature, including marginal search, exhaustive search, and forward search, their relative performance has only been evaluated through limited simulations due to the lack of an analytical approach to calculating the power of these methods. This article develops a novel statistical approach for power calculation, derives accurate formulas for the power of different model selection strategies, and then uses the formulas to evaluate and compare these strategies in genetic model spaces. In contrast to previous studies, our theoretical framework allows for random genotypes, correlations among test statistics, and a false-positive control based on GWAS practice. After the accuracy of our analytical results is validated through simulations, they are utilized to systematically evaluate and compare the performance of these strategies in a wide class of genetic models. For a specific genetic model, our results clearly reveal how different factors, such as effect size, allele frequency, and interaction, jointly affect the statistical power of each strategy. An example is provided for the application of our approach to empirical research. The statistical approach used in our derivations is general and can be employed to address the model selection problems in other random predictor settings. We have developed an R package markerSearchPower to implement our formulas, which can be downloaded from the
Full Text Available The geographic mosaic of coevolution predicts parasite virulence should be locally adapted to the host community. Cotesia parasitoid wasps adapt to local lepidopteran species possibly through their symbiotic bracovirus. The virus, essential for the parasitism success, is at the heart of the complex coevolutionary relationship linking the wasps and their hosts. The large segmented genome contained in the virus particles encodes virulence genes involved in host immune and developmental suppression. Coevolutionary arms race should result in the positive selection of particular beneficial alleles. To understand the global role of bracoviruses in the local adaptation or specialization of parasitoid wasps to their hosts, we studied the molecular evolution of four bracoviruses associated with wasps of the genus Cotesia, including C congregata, C vestalis and new data and annotation on two ecologically differentiated populations of C sesamie, Kitale and Mombasa. Paired orthologs analyses revealed more genes under positive selection when comparing the two C sesamiae bracoviruses belonging to the same species, and more genes under strong evolutionary constraint between species. Furthermore branch-site evolutionary models showed that 17 genes, out of the 54 currently available shared by the four bracoviruses, harboured sites under positive selection including: the histone H4-like, a C-type lectin, two ep1-like, ep2, a viral ankyrin, CrV1, a ben-domain, a Serine-rich, and eight unknown genes. Lastly the phylogenetic analyses of the histone, ep2 and CrV1 genes in different African C sesamiae populations showed that each gene described differently the individual relationships. In particular we found recombination had happened between the ep2 and CrV1 genes, which are localized 37.5 kb apart on the wasp chromosomes. Involved in multidirectional coevolutionary interactions, C sesamiae wasps rely on different bracovirus mediated molecular pathways to overcome
Chandonia, John-Marc; Kim, Sung-Hou; Brenner, Steven E.
At the Berkeley Structural Genomics Center (BSGC), our goalis to obtain a near-complete structural complement of proteins in theminimal organisms Mycoplasma genitalium and M. pneumoniae, two closelyrelated pathogens. Current targets for structure determination have beenselected in six major stages, starting with those predicted to be mosttractable to high throughput study and likely to yield new structuralinformation. We report on the process used to select these proteins, aswell as our target deselection procedure. Target deselection reducesexperimental effort by eliminating targets similar to those recentlysolved by the structural biology community or other centers. We measurethe impact of the 69 structures solved at the BSGC as of July 2004 onstructure prediction coverage of the M. pneumoniae and M. genitaliumproteomes. The number of Mycoplasma proteins for which thefold couldfirst be reliably assigned based on structures solved at the BSGC (24 M.pneumoniae and 21 M. genitalium) is approximately 25 percent of the totalresulting from work at all structural genomics centers and the worldwidestructural biology community (94 M. pneumoniae and 86M. genitalium)during the same period. As the number of structures contributed by theBSGC during that period is less than 1 percent of the total worldwideoutput, the benefits of a focused target selection strategy are apparent.If the structures of all current targets were solved, the percentage ofM. pneumoniae proteins for which folds could be reliably assigned wouldincrease from approximately 57 percent (391 of 687) at present to around80 percent (550 of 687), and the percentage of the proteome that could beaccurately modeled would increase from around 37 percent (254 of 687) toabout 64 percent (438 of 687). In M. genitalium, the percentage of theproteome that could be structurally annotated based on structures of ourremaining targets would rise from 72 percent (348 of 486) to around 76percent (371 of 486), with the
Full Text Available Since the time of their domestication, goats (Capra hircus have evolved in a large variety of locally adapted populations in response to different human and environmental pressures. In the present era, many indigenous populations are threatened with extinction due to their substitution by cosmopolitan breeds, while they might represent highly valuable genomic resources. It is thus crucial to characterize the neutral and adaptive genetic diversity of indigenous populations. A fine characterization of whole genome variation in farm animals is now possible by using new sequencing technologies. We sequenced the complete genome at 12X coverage of 44 goats geographically representative of the three phenotypically distinct indigenous populations in Morocco. The study of mitochondrial genomes showed a high diversity exclusively restricted to the haplogroup A. The 44 nuclear genomes showed a very high diversity (24 million variants associated with low linkage disequilibrium. The overall genetic diversity was weakly structured according to geography and phenotypes. When looking for signals of positive selection in each population we identified many candidate genes, several of which gave insights into the metabolic pathways or biological processes involved in the adaptation to local conditions (e.g. panting in warm/desert conditions. This study highlights the interest of WGS data to characterize livestock genomic diversity. It illustrates the valuable genetic richness present in indigenous populations that have to be sustainably managed and may represent valuable genetic resources for the long-term preservation of the species.
Bastiaansen, John W M; Coster, Albart; Calus, Mario P L; van Arendonk, Johan A M; Bovenhuis, Henk
Genomic selection has become an important tool in the genetic improvement of animals and plants. The objective of this study was to investigate the impacts of breeding value estimation method, reference population structure, and trait genetic architecture, on long-term response to genomic selection without updating marker effects. Three methods were used to estimate genomic breeding values: a BLUP method with relationships estimated from genome-wide markers (GBLUP), a Bayesian method, and a partial least squares regression method (PLSR). A shallow (individuals from one generation) or deep reference population (individuals from five generations) was used with each method. The effects of the different selection approaches were compared under four different genetic architectures for the trait under selection. Selection was based on one of the three genomic breeding values, on pedigree BLUP breeding values, or performed at random. Selection continued for ten generations. Differences in long-term selection response were small. For a genetic architecture with a very small number of three to four quantitative trait loci (QTL), the Bayesian method achieved a response that was 0.05 to 0.1 genetic standard deviation higher than other methods in generation 10. For genetic architectures with approximately 30 to 300 QTL, PLSR (shallow reference) or GBLUP (deep reference) had an average advantage of 0.2 genetic standard deviation over the Bayesian method in generation 10. GBLUP resulted in 0.6% and 0.9% less inbreeding than PLSR and BM and on average a one third smaller reduction of genetic variance. Responses in early generations were greater with the shallow reference population while long-term response was not affected by reference population structure. The ranking of estimation methods was different with than without selection. Under selection, applying GBLUP led to lower inbreeding and a smaller reduction of genetic variance while a similar response to selection was
Lorenz, Aaron J
Allocating resources between population size and replication affects both genetic gain through phenotypic selection and quantitative trait loci detection power and effect estimation accuracy for marker-assisted selection (MAS). It is well known that because alleles are replicated across individuals in quantitative trait loci mapping and MAS, more resources should be allocated to increasing population size compared with phenotypic selection. Genomic selection is a form of MAS using all marker information simultaneously to predict individual genetic values for complex traits and has widely been found superior to MAS. No studies have explicitly investigated how resource allocation decisions affect success of genomic selection. My objective was to study the effect of resource allocation on response to MAS and genomic selection in a single biparental population of doubled haploid lines by using computer simulation. Simulation results were compared with previously derived formulas for the calculation of prediction accuracy under different levels of heritability and population size. Response of prediction accuracy to resource allocation strategies differed between genomic selection models (ridge regression best linear unbiased prediction [RR-BLUP], BayesCπ) and multiple linear regression using ordinary least-squares estimation (OLS), leading to different optimal resource allocation choices between OLS and RR-BLUP. For OLS, it was always advantageous to maximize population size at the expense of replication, but a high degree of flexibility was observed for RR-BLUP. Prediction accuracy of doubled haploid lines included in the training set was much greater than of those excluded from the training set, so there was little benefit to phenotyping only a subset of the lines genotyped. Finally, observed prediction accuracies in the simulation compared well to calculated prediction accuracies, indicating these theoretical formulas are useful for making resource allocation
Jeong, Hyeonsoo; Song, Ki-Duk; Seo, Minseok; Caetano-Anollés, Kelsey; Kim, Jaemin; Kwak, Woori; Oh, Jae-Don; Kim, EuiSoo; Jeong, Dong Kee; Cho, Seoae; Kim, Heebal; Lee, Hak-Kyo
Natural and artificial selection following domestication has led to the existence of more than a hundred pig breeds, as well as incredible variation in phenotypic traits. Berkshire pigs are regarded as having superior meat quality compared to other breeds. As the meat production industry seeks selective breeding approaches to improve profitable traits such as meat quality, information about genetic determinants of these traits is in high demand. However, most of the studies have been performed using trained sensory panel analysis without investigating the underlying genetic factors. Here we investigate the relationship between genomic composition and this phenotypic trait by scanning for signatures of positive selection in whole-genome sequencing data. We generated genomes of 10 Berkshire pigs at a total of 100.6 coverage depth, using the Illumina Hiseq2000 platform. Along with the genomes of 11 Landrace and 13 Yorkshire pigs, we identified genomic variants of 18.9 million SNVs and 3.4 million Indels in the mapped regions. We identified several associated genes related to lipid metabolism, intramuscular fatty acid deposition, and muscle fiber type which attribute to pork quality (TG, FABP1, AKIRIN2, GLP2R, TGFBR3, JPH3, ICAM2, and ERN1) by applying between population statistical tests (XP-EHH and XP-CLR). A statistical enrichment test was also conducted to detect breed specific genetic variation. In addition, de novo short sequence read assembly strategy identified several candidate genes (SLC25A14, IGF1, PI4KA, CACNA1A) as also contributing to lipid metabolism. Results revealed several candidate genes involved in Berkshire meat quality; most of these genes are involved in lipid metabolism and intramuscular fat deposition. These results can provide a basis for future research on the genomic characteristics of Berkshire pigs.
Campo, D; Lehmann, K; Fjeldsted, C; Souaiaia, T; Kao, J; Nuzhdin, S V
The prevailing demographic model for Drosophila melanogaster suggests that the colonization of North America occurred very recently from a subset of European flies that rapidly expanded across the continent. This model implies a sudden population growth and range expansion consistent with very low or no population subdivision. As flies adapt to new environments, local adaptation events may be expected. To describe demographic and selective events during North American colonization, we have generated a data set of 35 individual whole-genome sequences from inbred lines of D. melanogaster from a west coast US population (Winters, California, USA) and compared them with a public genome data set from Raleigh (Raleigh, North Carolina, USA). We analysed nuclear and mitochondrial genomes and described levels of variation and divergence within and between these two North American D. melanogaster populations. Both populations exhibit negative values of Tajima's D across the genome, a common signature of demographic expansion. We also detected a low but significant level of genome-wide differentiation between the two populations, as well as multiple allele surfing events, which can be the result of gene drift in local subpopulations on the edge of an expansion wave. In contrast to this genome-wide pattern, we uncovered a 50-kilobase segment in chromosome arm 3L that showed all the hallmarks of a soft selective sweep in both populations. A comparison of allele frequencies within this divergent region among six populations from three continents allowed us to cluster these populations in two differentiated groups, providing evidence for the action of natural selection on a global scale. © 2013 John Wiley & Sons Ltd.
Wang, Jing; Street, Nathaniel R; Scofield, Douglas G; Ingvarsson, Pär K
Despite the global economic and ecological importance of forest trees, the genomic basis of differential adaptation and speciation in tree species is still poorly understood. Populus tremula and Populus tremuloides are two of the most widespread tree species in the Northern Hemisphere. Using whole-genome re-sequencing data of 24 P. tremula and 22 P. tremuloides individuals, we find that the two species diverged ∼2.2-3.1 million years ago, coinciding with the severing of the Bering land bridge and the onset of dramatic climatic oscillations during the Pleistocene. Both species have experienced substantial population expansions following long-term declines after species divergence. We detect widespread and heterogeneous genomic differentiation between species, and in accordance with the expectation of allopatric speciation, coalescent simulations suggest that neutral evolutionary processes can account for most of the observed patterns of genetic differentiation. However, there is an excess of regions exhibiting extreme differentiation relative to those expected under demographic simulations, which is indicative of the action of natural selection. Overall genetic differentiation is negatively associated with recombination rate in both species, providing strong support for a role of linked selection in generating the heterogeneous genomic landscape of differentiation between species. Finally, we identify a number of candidate regions and genes that may have been subject to positive and/or balancing selection during the speciation process. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
Chapple, Charles E.; Guigó, Roderic
BACKGROUND: Selenoproteins are a diverse family of proteins notable for the presence of the 21st amino acid, selenocysteine. Until very recently, all metazoan genomes investigated encoded selenoproteins, and these proteins had therefore been believed to be essential for animal life. Challenging this assumption, recent comparative analyses of insect genomes have revealed that some insect genomes appear to have lost selenoprotein genes. METHODOLOGY/PRINCIPAL FINDINGS: In this paper we investiga...
Eduardo da Cruz Gouveia Pimentel
Full Text Available The aim of this study was to compare iterative and direct solvers for estimation of marker effects in genomic selection. One iterative and two direct methods were used: Gauss-Seidel with Residual Update, Cholesky Decomposition and Gentleman-Givens rotations. For resembling different scenarios with respect to number of markers and of genotyped animals, a simulated data set divided into 25 subsets was used. Number of markers ranged from 1,200 to 5,925 and number of animals ranged from 1,200 to 5,865. Methods were also applied to real data comprising 3081 individuals genotyped for 45181 SNPs. Results from simulated data showed that the iterative solver was substantially faster than direct methods for larger numbers of markers. Use of a direct solver may allow for computing (covariances of SNP effects. When applied to real data, performance of the iterative method varied substantially, depending on the level of ill-conditioning of the coefficient matrix. From results with real data, Gentleman-Givens rotations would be the method of choice in this particular application as it provided an exact solution within a fairly reasonable time frame (less than two hours. It would indeed be the preferred method whenever computer resources allow its use.
Pearson, Hillary; Granados, Diana Paola; Durette, Chantal; Bonneil, Eric; Courcelles, Mathieu; Rodenbrock, Anja; Laverdure, Jean-Philippe; Côté, Caroline; Thibault, Pierre
MHC class I–associated peptides (MAPs) define the immune self for CD8+ T lymphocytes and are key targets of cancer immunosurveillance. Here, the goals of our work were to determine whether the entire set of protein-coding genes could generate MAPs and whether specific features influence the ability of discrete genes to generate MAPs. Using proteogenomics, we have identified 25,270 MAPs isolated from the B lymphocytes of 18 individuals who collectively expressed 27 high-frequency HLA-A,B allotypes. The entire MAP repertoire presented by these 27 allotypes covered only 10% of the exomic sequences expressed in B lymphocytes. Indeed, 41% of expressed protein-coding genes generated no MAPs, while 59% of genes generated up to 64 MAPs, often derived from adjacent regions and presented by different allotypes. We next identified several features of transcripts and proteins associated with efficient MAP production. From these data, we built a logistic regression model that predicts with good accuracy whether a gene generates MAPs. Our results show preferential selection of MAPs from a limited repertoire of proteins with distinctive features. The notion that the MHC class I immunopeptidome presents only a small fraction of the protein-coding genome for monitoring by the immune system has profound implications in autoimmunity and cancer immunology. PMID:27841757
The development of a complex technology such as in vitro fertilization (IVF) requires years of experimentation, sometimes comparing several species to learn how to create the right in vitro environment for oocytes, spermatozoa, and early embryos. At the same time, individual species characteristics such as gamete physiology and gamete interaction are recently evolved traits and must be analysed within the context of each species. In the last 40 years since the birth of Louise Brown, IVF techniques progressed and are now used in multiple domestic and non-domestic animal species around the world. This does not mean that the technology is completely matured or satisfactory; a number of problems remain to be solved and several procedures still need to be optimized. The development of IVF in cattle is particularly interesting since agriculture practices permitted the commercial development of the procedure and it is now used at a scale comparable to human IVF (millions of newborns). The genomic selection of young animals or even embryos combined with sexing and freezing technologies is driving a new era of IVF in the Dairy sector. The time has come for a retrospective analysis of the success and pitfalls of the last 40 years of bovine IVF and for the description of the challenges to overcome in the years to come.
Full Text Available Genomic selection (GS is a breeding tool that estimates breeding values (GEBVs of individuals based solely on marker data by using a model built using phenotypic and marker data from a training population (TP. The effectiveness of GS increases as the correlation of GEBVs and phenotypes (accuracy increases. Using phenotypic and genotypic data from a TP of 470 soft winter wheat lines, we assessed the accuracy of GS for grain yield, Fusarium Head Blight (FHB resistance, softness equivalence (SE, and flour yield (FY. Four TP data sampling schemes were tested: (1 use all TP data, (2 use subsets of TP lines with low genotype-by-environment interaction, (3 use subsets of markers significantly associated with quantitative trait loci (QTL, and (4 a combination of 2 and 3. We also correlated the phenotypes of relatives of the TP to their GEBVs calculated from TP data. The GS accuracy within the TP using all TP data ranged from 0.35 (FHB to 0.62 (FY. On average, the accuracy of GS from using subsets of data increased by 54% relative to using all TP data. Using subsets of markers selected for significant association with the target trait had the greatest impact on GS accuracy. Between-environment prediction accuracy was also increased by using data subsets. The accuracy of GS when predicting the phenotypes of TP relatives ranged from 0.00 to 0.85. These results suggest that GS could be useful for these traits and GS accuracy can be greatly improved by using subsets of TP data.
Ma, Yan-Ping; Ke, Hao; Liang, Zhi-Ling; Liu, Zhen-Xing; Hao, Le; Ma, Jiang-Yao; Li, Yu-Gu
Streptococcus agalactiae is an important human and animal pathogen. To better understand the genetic features and evolution of S. agalactiae, multiple factors influencing synonymous codon usage patterns in S. agalactiae were analyzed in this study. A- and U-ending rich codons were used in S. agalactiae function genes through the overall codon usage analysis, indicating that Adenine (A)/Thymine (T) compositional constraints might contribute an important role to the synonymous codon usage pattern. The GC3% against the effective number of codon (ENC) value suggested that translational selection was the important factor for codon bias in the microorganism. Principal component analysis (PCA) showed that (i) mutational pressure was the most important factor in shaping codon usage of all open reading frames (ORFs) in the S. agalactiae genome; (ii) strand specific mutational bias was not capable of influencing the codon usage bias in the leading and lagging strands; and (iii) gene length was not the important factor in synonymous codon usage pattern in this organism. Additionally, the high correlation between tRNA adaptation index (tAI) value and codon adaptation index (CAI), frequency of optimal codons (Fop) value, reinforced the role of natural selection for efficient translation in S. agalactiae. Comparison of synonymous codon usage pattern between S. agalactiae and susceptible hosts (human and tilapia) showed that synonymous codon usage of S. agalactiae was independent of the synonymous codon usage of susceptible hosts. The study of codon usage in S. agalactiae may provide evidence about the molecular evolution of the bacterium and a greater understanding of evolutionary relationships between S. agalactiae and its hosts.
Full Text Available To evaluate the potential of genomic selection (GS, a selection experiment with GS and phenotypic selection (PS was performed in an allogamous crop, common buckwheat (Fagopyrum esculentum Moench. To indirectly select for seed yield per unit area, which cannot be measured on a single-plant basis, a selection index was constructed from seven agro-morphological traits measurable on a single plant basis. Over 3 years, we performed two GS and one PS cycles per year for improvement in the selection index. In GS, a prediction model was updated every year on the basis of genotypes of 14,598–50,000 markers and phenotypes. Plants grown from seeds derived from a series of generations of GS and PS populations were evaluated for the traits in the selection index and other yield-related traits. GS resulted in a 20.9% increase and PS in a 15.0% increase in the selection index in comparison with the initial population. Although the level of linkage disequilibrium in the breeding population was low, the target trait was improved with GS. Traits with higher weights in the selection index were improved more than those with lower weights, especially when prediction accuracy was high. No trait changed in an unintended direction in either GS or PS. The accuracy of genomic prediction models built in the first cycle decreased in the later cycles because the genetic bottleneck through the selection cycles changed linkage disequilibrium patterns in the breeding population. The present study emphasizes the importance of updating models in GS and demonstrates the potential of GS in mass selection of allogamous crop species, and provided a pilot example of successful application of GS to plant breeding.
Yabe, Shiori; Hara, Takashi; Ueno, Mariko; Enoki, Hiroyuki; Kimura, Tatsuro; Nishimura, Satoru; Yasui, Yasuo; Ohsawa, Ryo; Iwata, Hiroyoshi
To evaluate the potential of genomic selection (GS), a selection experiment with GS and phenotypic selection (PS) was performed in an allogamous crop, common buckwheat ( Fagopyrum esculentum Moench). To indirectly select for seed yield per unit area, which cannot be measured on a single-plant basis, a selection index was constructed from seven agro-morphological traits measurable on a single plant basis. Over 3 years, we performed two GS and one PS cycles per year for improvement in the selection index. In GS, a prediction model was updated every year on the basis of genotypes of 14,598-50,000 markers and phenotypes. Plants grown from seeds derived from a series of generations of GS and PS populations were evaluated for the traits in the selection index and other yield-related traits. GS resulted in a 20.9% increase and PS in a 15.0% increase in the selection index in comparison with the initial population. Although the level of linkage disequilibrium in the breeding population was low, the target trait was improved with GS. Traits with higher weights in the selection index were improved more than those with lower weights, especially when prediction accuracy was high. No trait changed in an unintended direction in either GS or PS. The accuracy of genomic prediction models built in the first cycle decreased in the later cycles because the genetic bottleneck through the selection cycles changed linkage disequilibrium patterns in the breeding population. The present study emphasizes the importance of updating models in GS and demonstrates the potential of GS in mass selection of allogamous crop species, and provided a pilot example of successful application of GS to plant breeding.
Bougher, S. W.; Engel, S.; Hinson, D. P.; Murphy, J. R.
Martian electron density profiles provided by the Mars Global Surveyor (MGS) Radio Science (RS) experiment over the 95-200 km altitude range indicate what the height of the electron peak and the longitudinal structure of the peak height are sensitive indicators of the physical state of the Mars lower and upper atmospheres. The present analysis is carried out on five sets of occultation profiles, all at high solar zenith angles (SZA). Variations spanning 2 Martian years are investigated near aphelion conditions at high northern latitudes (64.7 - 77.6 N) making use of four of these data sets. A mean ionospheric peak height of 133.5 - 135 km is obtained near SZA = 78 - 82 deg.; a corresponding mean peak density of 7.3 - 8.5 x l0(exp 4)/ qu cm is also measured during solar moderate conditions at Mars. Strong wave number 2 - 3 oscillations in peak heights are consistently observed as a function of longitude over the 2 Martian years. These observed ionospheric features are remarkably similar during aphelion conditions 1 Martian year apart. This year-to-year repeatability in the thermosphere-ionosphere structure is consistent with that observed in multiyear aphelion temperature data of the Mars lower atmosphere. Coupled Mars general circulation model (MGCM) and Mars thermospheric general circulation model (MTGCM) codes are run for Mars aphelion conditions, yielding mean and longitude variable ionospheric peak heights that reasonably match RS observations. A tidal decomposition of MTGCM thermospheric densities shows that observed ionospheric wave number 3 features are linked to a non-migrating tidal mode with semidiurnal period (sigma = 2) and zonal wave number 1 (s = -1) characteristics. The height of this photochemically determined ionospheric peak should be monitored regularly.
Nielsen, H Bjørn; Almeida, Mathieu; Juncker, Agnieszka Sierakowska; Rasmussen, Simon; Li, Junhua; Sunagawa, Shinichi; Plichta, Damian R; Gautier, Laurent; Pedersen, Anders G; Le Chatelier, Emmanuelle; Pelletier, Eric; Bonde, Ida; Nielsen, Trine; Manichanh, Chaysavanh; Arumugam, Manimozhiyan; Batto, Jean-Michel; Quintanilha Dos Santos, Marcelo B; Blom, Nikolaj; Borruel, Natalia; Burgdorf, Kristoffer S; Boumezbeur, Fouad; Casellas, Francesc; Doré, Joël; Dworzynski, Piotr; Guarner, Francisco; Hansen, Torben; Hildebrand, Falk; Kaas, Rolf S; Kennedy, Sean; Kristiansen, Karsten; Kultima, Jens Roat; Léonard, Pierre; Levenez, Florence; Lund, Ole; Moumen, Bouziane; Le Paslier, Denis; Pons, Nicolas; Pedersen, Oluf; Prifti, Edi; Qin, Junjie; Raes, Jeroen; Sørensen, Søren; Tap, Julien; Tims, Sebastian; Ussery, David W; Yamada, Takuji; Renault, Pierre; Sicheritz-Ponten, Thomas; Bork, Peer; Wang, Jun; Brunak, Søren; Ehrlich, S Dusko
Most current approaches for analyzing metagenomic data rely on comparisons to reference genomes, but the microbial diversity of many environments extends far beyond what is covered by reference databases. De novo segregation of complex metagenomic data into specific biological entities, such as particular bacterial strains or viruses, remains a largely unsolved problem. Here we present a method, based on binning co-abundant genes across a series of metagenomic samples, that enables comprehensive discovery of new microbial organisms, viruses and co-inherited genetic entities and aids assembly of microbial genomes without the need for reference sequences. We demonstrate the method on data from 396 human gut microbiome samples and identify 7,381 co-abundance gene groups (CAGs), including 741 metagenomic species (MGS). We use these to assemble 238 high-quality microbial genomes and identify affiliations between MGS and hundreds of viruses or genetic entities. Our method provides the means for comprehensive profiling of the diversity within complex metagenomic samples.
Varona, Luis; Legarra, Andrés; Herring, William; Vitezica, Zulma G
The quantitative genetics theory argues that inbreeding depression and heterosis are founded on the existence of directional dominance. However, most procedures for genomic selection that have included dominance effects assumed prior symmetrical distributions. To address this, two alternatives can be considered: (1) assume the mean of dominance effects different from zero, and (2) use skewed distributions for the regularization of dominance effects. The aim of this study was to compare these approaches using two pig datasets and to confirm the presence of directional dominance. Four alternative models were implemented in two datasets of pig litter size that consisted of 13,449 and 11,581 records from 3631 and 2612 sows genotyped with the Illumina PorcineSNP60 BeadChip. The models evaluated included (1) a model that does not consider directional dominance (Model SN), (2) a model with a covariate b for the average individual homozygosity (Model SC), (3) a model with a parameter λ that reflects asymmetry in the context of skewed Gaussian distributions (Model AN), and (4) a model that includes both b and λ (Model Full). The results of the analysis showed that posterior probabilities of a negative b or a positive λ under Models SC and AN were higher than 0.99, which indicate positive directional dominance. This was confirmed with the predictions of inbreeding depression under Models Full, SC and AN, that were higher than in the SN Model. In spite of differences in posterior estimates of variance components between models, comparison of models based on LogCPO and DIC indicated that Model SC provided the best fit for the two datasets analyzed. Our results confirmed the presence of positive directional dominance for pig litter size and suggested that it should be taken into account when dominance effects are included in genomic evaluation procedures. The consequences of ignoring directional dominance may affect predictions of breeding values and can lead to biased
Nygaard, Sanne; Braunstein, Alexander; Malsen, Gareth
Plasmodium parasites, the causal agents of malaria, result in more than 1 million deaths annually. Plasmodium are unicellular eukaryotes with small ~23 Mb genomes encoding ~5200 protein-coding genes. The protein-coding genes comprise about half of these genomes. Although evolutionary processes ha...
Peanut (Arachis hypogaea; 2n=4x=40) is a nutritious food and a good source of vitamins, minerals, and healthy fats. Expansion of genetic and genomic resources for genetic enhancement of cultivated peanut has gained momentum from the sequenced genomes of the diploid ancestors of cultivated peanut. ...
McNeal, Joel R; Kuehl, Jennifer V; Boore, Jeffrey L; de Pamphilis, Claude W
Plastid genome content and protein sequence are highly conserved across land plants and their closest algal relatives. Parasitic plants, which obtain some or all of their nutrition through an attachment to a host plant, are often a striking exception. Heterotrophy can lead to relaxed constraint on some plastid genes or even total gene loss. We sequenced plastid genomes of two species in the parasitic genus Cuscuta along with a non-parasitic relative, Ipomoea purpurea, to investigate changes in the plastid genome that may result from transition to the parasitic lifestyle. Aside from loss of all ndh genes, Cuscuta exaltata retains photosynthetic and photorespiratory genes that evolve under strong selective constraint. Cuscuta obtusiflora has incurred substantially more change to its plastid genome, including loss of all genes for the plastid-encoded RNA polymerase. Despite extensive change in gene content and greatly increased rate of overall nucleotide substitution, C. obtusiflora also retains all photosynthetic and photorespiratory genes with only one minor exception. Although Epifagus virginiana, the only other parasitic plant with its plastid genome sequenced to date, has lost a largely overlapping set of transfer-RNA and ribosomal genes as Cuscuta, it has lost all genes related to photosynthesis and maintains a set of genes which are among the most divergent in Cuscuta. Analyses demonstrate photosynthetic genes are under the highest constraint of any genes within the plastid genomes of Cuscuta, indicating a function involving RuBisCo and electron transport through photosystems is still the primary reason for retention of the plastid genome in these species.
Kuehl Jennifer V
Full Text Available Abstract Background Plastid genome content and protein sequence are highly conserved across land plants and their closest algal relatives. Parasitic plants, which obtain some or all of their nutrition through an attachment to a host plant, are often a striking exception. Heterotrophy can lead to relaxed constraint on some plastid genes or even total gene loss. We sequenced plastid genomes of two species in the parasitic genus Cuscuta along with a non-parasitic relative, Ipomoea purpurea, to investigate changes in the plastid genome that may result from transition to the parasitic lifestyle. Results Aside from loss of all ndh genes, Cuscuta exaltata retains photosynthetic and photorespiratory genes that evolve under strong selective constraint. Cuscuta obtusiflora has incurred substantially more change to its plastid genome, including loss of all genes for the plastid-encoded RNA polymerase. Despite extensive change in gene content and greatly increased rate of overall nucleotide substitution, C. obtusiflora also retains all photosynthetic and photorespiratory genes with only one minor exception. Conclusion Although Epifagus virginiana, the only other parasitic plant with its plastid genome sequenced to date, has lost a largely overlapping set of transfer-RNA and ribosomal genes as Cuscuta, it has lost all genes related to photosynthesis and maintains a set of genes which are among the most divergent in Cuscuta. Analyses demonstrate photosynthetic genes are under the highest constraint of any genes within the plastid genomes of Cuscuta, indicating a function involving RuBisCo and electron transport through photosystems is still the primary reason for retention of the plastid genome in these species.
Rasmusen, L. H.; Dargis, R.; Iversen, Katrine Højholt
observed in single gene analyses. Species identification based on single gene analysis showed their limitations when more strains were included. In contrast, analyses incorporating more sequence data, like MLSA, SNPs and core-genome analyses, provided more distinct clustering. The core-genome tree showed......Identification of Mitis group streptococci (MGS) to the species level is challenging for routine microbiology laboratories. Correct identification is crucial for the diagnosis of infective endocarditis, identification of treatment failure, and/or infection relapse. Eighty MGS from Danish patients...
Bhatia, Gaurav; Tandon, Arti; Patterson, Nick; Aldrich, Melinda C; Ambrosone, Christine B; Amos, Christopher; Bandera, Elisa V; Berndt, Sonja I; Bernstein, Leslie; Blot, William J; Bock, Cathryn H; Caporaso, Neil; Casey, Graham; Deming, Sandra L; Diver, W Ryan; Gapstur, Susan M; Gillanders, Elizabeth M; Harris, Curtis C; Henderson, Brian E; Ingles, Sue A; Isaacs, William; De Jager, Phillip L; John, Esther M; Kittles, Rick A; Larkin, Emma; McNeill, Lorna H; Millikan, Robert C; Murphy, Adam; Neslund-Dudas, Christine; Nyante, Sarah; Press, Michael F; Rodriguez-Gil, Jorge L; Rybicki, Benjamin A; Schwartz, Ann G; Signorello, Lisa B; Spitz, Margaret; Strom, Sara S; Tucker, Margaret A; Wiencke, John K; Witte, John S; Wu, Xifeng; Yamamura, Yuko; Zanetti, Krista A; Zheng, Wei; Ziegler, Regina G; Chanock, Stephen J; Haiman, Christopher A; Reich, David; Price, Alkes L
The extent of recent selection in admixed populations is currently an unresolved question. We scanned the genomes of 29,141 African Americans and failed to find any genome-wide-significant deviations in local ancestry, indicating no evidence of selection influencing ancestry after admixture. A recent analysis of data from 1,890 African Americans reported that there was evidence of selection in African Americans after their ancestors left Africa, both before and after admixture. Selection after admixture was reported on the basis of deviations in local ancestry, and selection before admixture was reported on the basis of allele-frequency differences between African Americans and African populations. The local-ancestry deviations reported by the previous study did not replicate in our very large sample, and we show that such deviations were expected purely by chance, given the number of hypotheses tested. We further show that the previous study's conclusion of selection in African Americans before admixture is also subject to doubt. This is because the FST statistics they used were inflated and because true signals of unusual allele-frequency differences between African Americans and African populations would be best explained by selection that occurred in Africa prior to migration to the Americas. Copyright © 2014 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
Schiavo, G; Galimberti, G; Calò, D G; Samorè, A B; Bertolini, F; Russo, V; Gallo, M; Buttazzoni, L; Fontanesi, L
In this study, we investigated at the genome-wide level if 20 years of artificial directional selection based on boar genetic evaluation obtained with a classical BLUP animal model shaped the genome of the Italian Large White pig breed. The most influential boars of this breed (n = 192), born from 1992 (the beginning of the selection program of this breed) to 2012, with an estimated breeding value reliability of >0.85, were genotyped with the Illumina Porcine SNP60 BeadChip. After grouping the boars in eight classes according to their year of birth, filtered single nucleotide polymorphisms (SNPs) were used to evaluate the effects of time on genotype frequency changes using multinomial logistic regression models. Of these markers, 493 had a PBonferroni selection program. The obtained results indicated that the genome of the Italian Large White pigs was shaped by a directional selection program derived by the application of methodologies assuming the infinitesimal model that captured a continuous trend of allele frequency changes in the boar population. © 2015 Stichting International Foundation for Animal Genetics.
Tellier Laurent C
Full Text Available Abstract Background Mitochondria are a valuable resource for studying the evolutionary process and deducing phylogeny. A few mitochondria genomes have been sequenced, but a comprehensive picture of the domestication event for silkworm mitochondria remains to be established. In this study, we integrate the extant data, and perform a whole genome resequencing of Japanese wild silkworm to obtain breakthrough results in silkworm mitochondrial (mt population, and finally use these to deduce a more comprehensive phylogeny of the Bombycidae. Results We identified 347 single nucleotide polymorphisms (SNPs in the mt genome, but found no past recombination event to have occurred in the silkworm progenitor. A phylogeny inferred from these whole genome SNPs resulted in a well-classified tree, confirming that the domesticated silkworm, Bombyx mori, most recently diverged from the Chinese wild silkworm, rather than from the Japanese wild silkworm. We showed that the population sizes of the domesticated and Chinese wild silkworms both experience neither expansion nor contraction. We also discovered that one mt gene, named cytochrome b, shows a strong signal of positive selection in the domesticated clade. This gene is related to energy metabolism, and may have played an important role during silkworm domestication. Conclusions We present a comparative analysis on 41 mt genomes of B. mori and B. mandarina from China and Japan. With these, we obtain a much clearer picture of the evolution history of the silkworm. The data and analyses presented here aid our understanding of the silkworm in general, and provide a crucial insight into silkworm phylogeny.
Kaddis Maldonado, Rebecca J.; Parent, Leslie J.
Infectious retrovirus particles contain two copies of unspliced viral RNA that serve as the viral genome. Unspliced retroviral RNA is transcribed in the nucleus by the host RNA polymerase II and has three potential fates: (1) it can be spliced into subgenomic messenger RNAs (mRNAs) for the translation of viral proteins; or it can remain unspliced to serve as either (2) the mRNA for the translation of Gag and Gag–Pol; or (3) the genomic RNA (gRNA) that is packaged into virions. The Gag structural protein recognizes and binds the unspliced viral RNA to select it as a genome, which is selected in preference to spliced viral RNAs and cellular RNAs. In this review, we summarize the current state of understanding about how retroviral packaging is orchestrated within the cell and explore potential new mechanisms based on recent discoveries in the field. We discuss the cis-acting elements in the unspliced viral RNA and the properties of the Gag protein that are required for their interaction. In addition, we discuss the role of host factors in influencing the fate of the newly transcribed viral RNA, current models for how retroviruses distinguish unspliced viral mRNA from viral genomic RNA, and the possible subcellular sites of genomic RNA dimerization and selection by Gag. Although this review centers primarily on the wealth of data available for the alpharetrovirus Rous sarcoma virus, in which a discrete RNA packaging sequence has been identified, we have also summarized the cis- and trans-acting factors as well as the mechanisms governing gRNA packaging of other retroviruses for comparison. PMID:27657110
Mattersdorfer, Karin; Koblmüller, Stephan; Sefc, Kristina M
Genome scan-based tests for selection are directly applicable to natural populations to study the genetic and evolutionary mechanisms behind phenotypic differentiation. We conducted AFLP genome scans in three distinct geographic colour morphs of the cichlid fish Tropheus moorii to assess whether the extant, allopatric colour pattern differentiation can be explained by drift and to identify markers mapping to genomic regions possibly involved in colour patterning. The tested morphs occupy adjacent shore sections in southern Lake Tanganyika and are separated from each other by major habitat barriers. The genome scans revealed significant genetic structure between morphs, but a very low proportion of loci fixed for alternative AFLP alleles in different morphs. This high level of polymorphism within morphs suggested that colour pattern differentiation did not result exclusively from neutral processes. Outlier detection methods identified six loci with excess differentiation in the comparison between a bluish and a yellow-blotch morph and five different outlier loci in comparisons of each of these morphs with a red morph. As population expansions and the genetic structure of Tropheus make the outlier approach prone to false-positive signals of selection, we examined the correlation between outlier locus alleles and colour phenotypes in a genetic and phenotypic cline between two morphs. Distributions of allele frequencies at one outlier locus were indeed consistent with linkage to a colour locus. Despite the challenges posed by population structure and demography, our results encourage the cautious application of genome scans to studies of divergent selection in subdivided and recently expanded populations. © 2012 Blackwell Publishing Ltd.
Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines.
Spindel, Jennifer; Begum, Hasina; Akdemir, Deniz; Virk, Parminder; Collard, Bertrand; Redoña, Edilberto; Atlin, Gary; Jannink, Jean-Luc; McCouch, Susan R
Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.
Genomic selection and association mapping in rice (Oryza sativa: effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines.
Full Text Available Genomic Selection (GS is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.
Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite, Tropical Rice Breeding Lines
Spindel, Jennifer; Begum, Hasina; Akdemir, Deniz; Virk, Parminder; Collard, Bertrand; Redoña, Edilberto; Atlin, Gary; Jannink, Jean-Luc; McCouch, Susan R.
Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline. PMID:25689273
Full Text Available The combination therapy of the Artemisinin-derivative Artemether (ART with Lumefantrine (LM (Coartem® is an important malaria treatment regimen in many endemic countries. Resistance to Artemisinin has already been reported, and it is feared that LM resistance (LMR could also evolve quickly. Therefore molecular markers which can be used to track Coartem® efficacy are urgently needed. Often, stable resistance arises from initial, unstable phenotypes that can be identified in vitro. Here we have used the Plasmodium falciparum multidrug resistant reference strain V1S to induce LMR in vitro by culturing the parasite under continuous drug pressure for 16 months. The initial IC(50 (inhibitory concentration that kills 50% of the parasite population was 24 nM. The resulting resistant strain V1S(LM, obtained after culture for an estimated 166 cycles under LM pressure, grew steadily in 378 nM of LM, corresponding to 15 times the IC(50 of the parental strain. However, after two weeks of culturing V1S(LM in drug-free medium, the IC(50 returned to that of the initial, parental strain V1S. This transient drug tolerance was associated with major changes in gene expression profiles: using the PFSANGER Affymetrix custom array, we identified 184 differentially expressed genes in V1S(LM. Among those are 18 known and putative transporters including the multidrug resistance gene 1 (pfmdr1, the multidrug resistance associated protein and the V-type H+ pumping pyrophosphatase 2 (pfvp2 as well as genes associated with fatty acid metabolism. In addition we detected a clear selective advantage provided by two genomic loci in parasites grown under LM drug pressure, suggesting that all, or some of those genes contribute to development of LM tolerance--they may prove useful as molecular markers to monitor P. falciparum LM susceptibility.
A. H. Sallam
Full Text Available Prediction accuracy of genomic selection (GS has been previously evaluated through simulation and cross-validation; however, validation based on progeny performance in a plant breeding program has not been investigated thoroughly. We evaluated several prediction models in a dynamic barley breeding population comprised of 647 six-row lines using four traits differing in genetic architecture and 1536 single nucleotide polymorphism (SNP markers. The breeding lines were divided into six sets designated as one parent set and five consecutive progeny sets comprised of representative samples of breeding lines over a 5-yr period. We used these data sets to investigate the effect of model and training population composition on prediction accuracy over time. We found little difference in prediction accuracy among the models confirming prior studies that found the simplest model, random regression best linear unbiased prediction (RR-BLUP, to be accurate across a range of situations. In general, we found that using the parent set was sufficient to predict progeny sets with little to no gain in accuracy from generating larger training populations by combining the parent set with subsequent progeny sets. The prediction accuracy ranged from 0.03 to 0.99 across the four traits and five progeny sets. We explored characteristics of the training and validation populations (marker allele frequency, population structure, and linkage disequilibrium, LD as well as characteristics of the trait (genetic architecture and heritability, . Fixation of markers associated with a trait over time was most clearly associated with reduced prediction accuracy for the mycotoxin trait DON. Higher trait in the training population and simpler trait architecture were associated with greater prediction accuracy.
Szymczak, Silke; Holzinger, Emily; Dasgupta, Abhijit; Malley, James D; Molloy, Anne M; Mills, James L; Brody, Lawrence C; Stambolian, Dwight; Bailey-Wilson, Joan E
Machine learning methods and in particular random forests (RFs) are a promising alternative to standard single SNP analyses in genome-wide association studies (GWAS). RFs provide variable importance measures (VIMs) to rank SNPs according to their predictive power. However, in contrast to the established genome-wide significance threshold, no clear criteria exist to determine how many SNPs should be selected for downstream analyses. We propose a new variable selection approach, recurrent relative variable importance measure (r2VIM). Importance values are calculated relative to an observed minimal importance score for several runs of RF and only SNPs with large relative VIMs in all of the runs are selected as important. Evaluations on simulated GWAS data show that the new method controls the number of false-positives under the null hypothesis. Under a simple alternative hypothesis with several independent main effects it is only slightly less powerful than logistic regression. In an experimental GWAS data set, the same strong signal is identified while the approach selects none of the SNPs in an underpowered GWAS. The novel variable selection method r2VIM is a promising extension to standard RF for objectively selecting relevant SNPs in GWAS while controlling the number of false-positive results.
Boschiero, Clarissa; Moreira, Gabriel Costa Monteiro; Gheyas, Almas Ara; Godoy, Thaís Fernanda; Gasparin, Gustavo; Mariani, Pilar Drummond Sampaio Corrêa; Paduan, Marcela; Cesar, Aline Silva Mello; Ledur, Mônica Corrêa; Coutinho, Luiz Lehmann
Meat and egg-type chickens have been selected for several generations for different traits. Artificial and natural selection for different phenotypes can change frequency of genetic variants, leaving particular genomic footprints throghtout the genome. Thus, the aims of this study were to sequence 28 chickens from two Brazilian lines (meat and white egg-type) and use this information to characterize genome-wide genetic variations, identify putative regions under selection using Fst method, and find putative pathways under selection. A total of 13.93 million SNPs and 1.36 million INDELs were identified, with more variants detected from the broiler (meat-type) line. Although most were located in non-coding regions, we identified 7255 intolerant non-synonymous SNPs, 512 stopgain/loss SNPs, 1381 frameshift and 1094 non-frameshift INDELs that may alter protein functions. Genes harboring intolerant non-synonymous SNPs affected metabolic pathways related mainly to reproduction and endocrine systems in the white-egg layer line, and lipid metabolism and metabolic diseases in the broiler line. Fst analysis in sliding windows, using SNPs and INDELs separately, identified over 300 putative regions of selection overlapping with more than 250 genes. For the first time in chicken, INDEL variants were considered for selection signature analysis, showing high level of correlation in results between SNP and INDEL data. The putative regions of selection signatures revealed interesting candidate genes and pathways related to important phenotypic traits in chicken, such as lipid metabolism, growth, reproduction, and cardiac development. In this study, Fst method was applied to identify high confidence putative regions under selection, providing novel insights into selection footprints that can help elucidate the functional mechanisms underlying different phenotypic traits relevant to meat and egg-type chicken lines. In addition, we generated a large catalog of line-specific and common
Nirea Kahsay G
Full Text Available Abstract Background Simulation studies have shown that accuracy and genetic gain are increased in genomic selection schemes compared to traditional aquaculture sib-based schemes. In genomic selection, accuracy of selection can be maximized by increasing the precision of the estimation of SNP effects and by maximizing the relationships between test sibs and candidate sibs. Another means of increasing the accuracy of the estimation of SNP effects is to create individuals in the test population with extreme genotypes. The latter approach was studied here with creation of double haploids and use of non-random mating designs. Methods Six alternative breeding schemes were simulated in which the design of the test population was varied: test sibs inherited maternal (Mat, paternal (Pat or a mixture of maternal and paternal (MatPat double haploid genomes or test sibs were obtained by maximum coancestry mating (MaxC, minimum coancestry mating (MinC, or random (RAND mating. Three thousand test sibs and 3000 candidate sibs were genotyped. The test sibs were recorded for a trait that could not be measured on the candidates and were used to estimate SNP effects. Selection was done by truncation on genome-wide estimated breeding values and 100 individuals were selected as parents each generation, equally divided between both sexes. Results Results showed a 7 to 19% increase in selection accuracy and a 6 to 22% increase in genetic gain in the MatPat scheme compared to the RAND scheme. These increases were greater with lower heritabilities. Among all other scenarios, i.e. Mat, Pat, MaxC, and MinC, no substantial differences in selection accuracy and genetic gain were observed. Conclusions In conclusion, a test population designed with a mixture of paternal and maternal double haploids, i.e. the MatPat scheme, increases substantially the accuracy of selection and genetic gain. This will be particularly interesting for traits that cannot be recorded on the
Full Text Available Understanding adaptive genetic variation and its relation to environmental factors are important for understanding how plants adapt to climate change and for managing genetic resources. Genome scans for the loci exhibiting either notably high or low levels of population differentiation (outlier loci provide one means of identifying genomic regions possibly associated with convergent or divergent selection. In this study, we combined AFLP genome scan and environmental association analysis to test for signals of natural selection in natural populations of Liriodendron chinense (Chinese Tulip Tree; Magnoliaceae along a latitudinal transect. We genotyped 276 individuals from 11 populations of L. chinense using 987 AFLP markers. Two complementary methods (Dfdist and BayeScan and association analysis between AFLP loci and climate factors were applied to detect outlier loci. Our analyses recovered both neutral and potentially adaptive genetic differentiation among populations of L. chinense. We found moderate genetic diversity within populations and high genetic differentiation among populations with reduced genetic diversity towards the periphery of the species ranges. Nine AFLP marker loci showed evidence of being outliers for population differentiation for both detection methods. Of these, six were strongly associated with at least one climate factor. Temperature, precipitation and radiation were found to be three important factors influencing local adaptation of L. chinense. The outlier AFLP loci are likely not the target of natural selection, but the neighboring genes of these loci might be involved in local adaptation. Hence, these candidates should be validated by further studies.
Rapp, M; Lein, V; Lacoudre, F; Lafferty, J; Müller, E; Vida, G; Bozhanova, V; Ibraliu, A; Thorwarth, P; Piepho, H P; Leiser, W L; Würschum, T; Longin, C F H
Simultaneous improvement of protein content and grain yield by index selection is possible but its efficiency largely depends on the weighting of the single traits. The genetic architecture of these indices is similar to that of the primary traits. Grain yield and protein content are of major importance in durum wheat breeding, but their negative correlation has hampered their simultaneous improvement. To account for this in wheat breeding, the grain protein deviation (GPD) and the protein yield were proposed as targets for selection. The aim of this work was to investigate the potential of different indices to simultaneously improve grain yield and protein content in durum wheat and to evaluate their genetic architecture towards genomics-assisted breeding. To this end, we investigated two different durum wheat panels comprising 159 and 189 genotypes, which were tested in multiple field locations across Europe and genotyped by a genotyping-by-sequencing approach. The phenotypic analyses revealed significant genetic variances for all traits and heritabilities of the phenotypic indices that were in a similar range as those of grain yield and protein content. The GPD showed a high and positive correlation with protein content, whereas protein yield was highly and positively correlated with grain yield. Thus, selecting for a high GPD would mainly increase the protein content whereas a selection based on protein yield would mainly improve grain yield, but a combination of both indices allows to balance this selection. The genome-wide association mapping revealed a complex genetic architecture for all traits with most QTL having small effects and being detected only in one germplasm set, thus limiting the potential of marker-assisted selection for trait improvement. By contrast, genome-wide prediction appeared promising but its performance strongly depends on the relatedness between training and prediction sets.
Taras K Oleksyk
Full Text Available When a selective sweep occurs in the chromosomal region around a target gene in two populations that have recently separated, it produces three dramatic genomic consequences: 1 decreased multi-locus heterozygosity in the region; 2 elevated or diminished genetic divergence (F(ST of multiple polymorphic variants adjacent to the selected locus between the divergent populations, due to the alternative fixation of alleles; and 3 a consequent regional increase in the variance of F(ST (S(2F(ST for the same clustered variants, due to the increased alternative fixation of alleles in the loci surrounding the selection target. In the first part of our study, to search for potential targets of directional selection, we developed and validated a resampling-based computational approach; we then scanned an array of 31 different-sized moving windows of SNP variants (5-65 SNPs across the human genome in a set of European and African American population samples with 183,997 SNP loci after correcting for the recombination rate variation. The analysis revealed 180 regions of recent selection with very strong evidence in either population or both. In the second part of our study, we compared the newly discovered putative regions to those sites previously postulated in the literature, using methods based on inspecting patterns of linkage disequilibrium, population divergence and other methodologies. The newly found regions were cross-validated with those found in nine other studies that have searched for selection signals. Our study was replicated especially well in those regions confirmed by three or more studies. These validated regions were independently verified, using a combination of different methods and different databases in other studies, and should include fewer false positives. The main strength of our analysis method compared to others is that it does not require dense genotyping and therefore can be used with data from population-based genome SNP scans
Goodman, Jessie L; Amendola, Laura M; Horike-Pyne, Martha; Trinidad, Susan B; Fullerton, Stephanie M; Burke, Wylie; Jarvik, Gail P
Legal and ethical questions arise regarding disseminating genetic research results to family members in the event of a research participant's death; failure to return or return to legal next of kin or estate executor may not reflect participant desires. We sought to determine participant preferences for whether and to whom they would like their data released in the case of their death prior to receiving genomic results, focusing on whether the person selected was also their estate executor. The University of Washington NEXT Medicine Study of the Clinical Sequencing Exploratory Research program previously reported participant preferences regarding designating an individual to receive genomic results in the event of death, including whether they want results shared, and if so, with what person. Participants were also asked whether this designee is executor of their will or estate. To date, 61 individuals were asked about the concordance of their study designee and legal representative: 42 (69%) reported having a will or estate plan and of these, 14 (33%) chose someone other than their executor to receive their results. For the 14 who chose someone other than their estate executor to receive genetic results, 12 (86%) chose a family member, typically a biological relative, as their designee. Those with a different genomic designee than their executor were less likely to be partnered ( P = 0.0024). For those partnered participants without an estate plan, spouses were not always chosen for return of genomic results. For one-third of our participants, the individual deemed most appropriate by the participant to receive their genomic results was not the executor. In the absence of an explicit designation, HIPAA may prohibit access to genomic results to persons other than the executor; hence asking for designation at the time of study enrollment (or initiation of clinical testing) is important.
Full Text Available Abstract Background At least three species of Borrelia burgdorferi sensu lato (Bbsl cause tick-borne Lyme disease. Previous work including the genome analysis of B. burgdorferi B31 and B. garinii PBi suggested a highly variable plasmid part. The frequent occurrence of duplicated sequence stretches, the observed plasmid redundancy, as well as the mainly unknown function and variability of plasmid encoded genes rendered the relationships between plasmids within and between species largely unresolvable. Results To gain further insight into Borreliae genome properties we completed the plasmid sequences of B. garinii PBi, added the genome of a further species, B. afzelii PKo, to our analysis, and compared for both species the genomes of pathogenic and apathogenic strains. The core of all Bbsl genomes consists of the chromosome and two plasmids collinear between all species. We also found additional groups of plasmids, which share large parts of their sequences. This makes it very likely that these plasmids are relatively stable and share common ancestors before the diversification of Borrelia species. The analysis of the differences between B. garinii PBi and B. afzelii PKo genomes of low and high passages revealed that the loss of infectivity is accompanied in both species by a loss of similar genetic material. Whereas B. garinii PBi suffered only from the break-off of a plasmid end, B. afzelii PKo lost more material, probably an entire plasmid. In both cases the vls gene locus encoding for variable surface proteins is affected. Conclusion The complete genome sequences of a B. garinii and a B. afzelii strain facilitate further comparative studies within the genus Borrellia. Our study shows that loss of infectivity can be traced back to only one single event in B. garinii PBi: the loss of the vls cassettes possibly due to error prone gene conversion. Similar albeit extended losses in B. afzelii PKo support the hypothesis that infectivity of Borrelia
Full Text Available Sheep are among the major economically important livestock species worldwide because the animals produce milk, wool, skin, and meat. In the present study, the Illumina OvineSNP50 BeadChip was used to investigate genetic diversity and genome selection among Suffolk, Rambouillet, Columbia, Polypay, and Targhee sheep breeds from the United States. After quality-control filtering of SNPs (single nucleotide polymorphisms, we used 48,026 SNPs, including 46,850 SNPs on autosomes that were in Hardy-Weinberg equilibrium and 1,176 SNPs on chromosome × for analysis. Phylogenetic analysis based on all 46,850 SNPs clearly separated Suffolk from Rambouillet, Columbia, Polypay, and Targhee, which was not surprising as Rambouillet contributed to the synthesis of the later three breeds. Based on pair-wise estimates of F(ST, significant genetic differentiation appeared between Suffolk and Rambouillet (F(ST = 0.1621, while Rambouillet and Targhee had the closest relationship (F(ST = 0.0681. A scan of the genome revealed 45 and 41 differentially selected regions (DSRs between Suffolk and Rambouillet and among Rambouillet-related breed populations, respectively. Our data indicated that regions 13 and 24 between Suffolk and Rambouillet might be good candidates for evaluating breed differences. Furthermore, ovine genome v3.1 assembly was used as reference to link functionally known homologous genes to economically important traits covered by these differentially selected regions. In brief, our present study provides a comprehensive genome-wide view on within- and between-breed genetic differentiation, biodiversity, and evolution among Suffolk, Rambouillet, Columbia, Polypay, and Targhee sheep breeds. These results may provide new guidance for the synthesis of new breeds with different breeding objectives.
Background Machine learning techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis as they are good for handling noisy and incomplete data, and significant results can be attained despite a small sample size. Traditionally, clinicians make prognostic decisions based on clinicopathologic markers. However, it is not easy for the most skilful clinician to come out with an accurate prognosis by using these markers alone. Thus, there is a need to use genomic markers to improve the accuracy of prognosis. The main aim of this research is to apply a hybrid of feature selection and machine learning methods in oral cancer prognosis based on the parameters of the correlation of clinicopathologic and genomic markers. Results In the first stage of this research, five feature selection methods have been proposed and experimented on the oral cancer prognosis dataset. In the second stage, the model with the features selected from each feature selection methods are tested on the proposed classifiers. Four types of classifiers are chosen; these are namely, ANFIS, artificial neural network, support vector machine and logistic regression. A k-fold cross-validation is implemented on all types of classifiers due to the small sample size. The hybrid model of ReliefF-GA-ANFIS with 3-input features of drink, invasion and p63 achieved the best accuracy (accuracy = 93.81%; AUC = 0.90) for the oral cancer prognosis. Conclusions The results revealed that the prognosis is superior with the presence of both clinicopathologic and genomic markers. The selected features can be investigated further to validate the potential of becoming as significant prognostic signature in the oral cancer studies. PMID:23725313
Zayed, Amro; Whitfield, Charles W
Apis mellifera originated in Africa and extended its range into Eurasia in two or more ancient expansions. In 1956, honey bees of African origin were introduced into South America, their descendents admixing with previously introduced European bees, giving rise to the highly invasive and economically devastating "Africanized" honey bee. Here we ask whether the honey bee's out-of-Africa expansions, both ancient and recent (invasive), were associated with a genome-wide signature of positive selection, detected by contrasting genetic differentiation estimates (F(ST)) between coding and noncoding SNPs. In native populations, SNPs in protein-coding regions had significantly higher F(ST) estimates than those in noncoding regions, indicating adaptive evolution in the genome driven by positive selection. This signal of selection was associated with the expansion of honey bees from Africa into Western and Northern Europe, perhaps reflecting adaptation to temperate environments. We estimate that positive selection acted on a minimum of 852-1,371 genes or approximately 10% of the bee's coding genome. We also detected positive selection associated with the invasion of African-derived honey bees in the New World. We found that introgression of European-derived alleles into Africanized bees was significantly greater for coding than noncoding regions. Our findings demonstrate that Africanized bees exploited the genetic diversity present from preexisting introductions in an adaptive way. Finally, we found a significant negative correlation between F(ST) estimates and the local GC content surrounding coding SNPs, suggesting that AT-rich genes play an important role in adaptive evolution in the honey bee.
The three stop codons UAA, UAG, and UGA signal the termination of mRNA translation. As a result of a mechanism that is not adequately understood, they are normally used with unequal frequencies. In this work, we showed that selective forces and mutational biases drive stop codon usage in the human genome. We found that, in respect to sense codons, stop codon usage was affected by stronger selective forces but was less influenced by neutral mutational biases. UGA is the most frequent termination codon in human genome. However, UAA was the preferred stop codon in genes with high breadth of expression, high level of expression, AT-rich coding sequences, housekeeping functions, and in gene ontology categories with the largest deviation from expected stop codon usage. Selective forces associated with the breadth and the level of expression favoured AT-rich sequences in the mRNA region including the stop site and its proximal 3'-UTR, but acted with scarce effects on sense codons, generating two regions, upstream and downstream of the stop codon, with strongly different base composition. By favouring low levels of GC-content, selection promoted labile local secondary structures at the stop site and its proximal 3'-UTR. The compositional and structural context favoured by selection was surprisingly emphasized in the class of ribosomal proteins and was consistent with sequence elements that increase the efficiency of translational termination. Stop codons were also heterogeneously distributed among chromosomes by a mechanism that was strongly correlated with the GC-content of coding sequences. In human genome, the nucleotide composition and the thermodynamic stability of stop codon site and its proximal 3'-UTR are correlated with the GC-content of coding sequences and with the breadth and the level of gene expression. In highly expressed genes stop codon usage is compositionally and structurally consistent with highly efficient translation termination signals.
Jesse M Engreitz
Full Text Available Chromosomal translocations are frequent features of cancer genomes that contribute to disease progression. These rearrangements result from formation and illegitimate repair of DNA double-strand breaks (DSBs, a process that requires spatial colocalization of chromosomal breakpoints. The "contact first" hypothesis suggests that translocation partners colocalize in the nuclei of normal cells, prior to rearrangement. It is unclear, however, the extent to which spatial interactions based on three-dimensional genome architecture contribute to chromosomal rearrangements in human disease. Here we intersect Hi-C maps of three-dimensional chromosome conformation with collections of 1,533 chromosomal translocations from cancer and germline genomes. We show that many translocation-prone pairs of regions genome-wide, including the cancer translocation partners BCR-ABL and MYC-IGH, display elevated Hi-C contact frequencies in normal human cells. Considering tissue specificity, we find that translocation breakpoints reported in human hematologic malignancies have higher Hi-C contact frequencies in lymphoid cells than those reported in sarcomas and epithelial tumors. However, translocations from multiple tissue types show significant correlation with Hi-C contact frequencies, suggesting that both tissue-specific and universal features of chromatin structure contribute to chromosomal alterations. Our results demonstrate that three-dimensional genome architecture shapes the landscape of rearrangements directly observed in human disease and establish Hi-C as a key method for dissecting these effects.
Belfield, E.J.; Gan, X.; Mithani, A.; Brown, C.; Jiang, C.; Franklin, K.; Alvey, E.; Wibowo, A.; Jung, M.; Bailey, K.; Kalwani, S.; Ragoussis, J.; Mott, R.; Harberd, N.P.
Ionizing radiation has long been known to induce heritable mutagenic change in DNA sequence. However, the genome-wide effect of radiation is not well understood. Here we report the molecular properties and frequency of mutations in phenotypically selected mutant lines isolated following exposure of the genetic model flowering plant Arabidopsis thaliana to fast neutrons (FNs). Previous studies suggested that FNs predominantly induce deletions longer than a kilobase in A. thaliana. However, we found a higher frequency of single base substitution than deletion mutations. While the overall frequency and molecular spectrum of fast-neutron (FN)-induced single base substitutions differed substantially from those of "background" mutations arising spontaneously in laboratory-grown plants, G:C>A:T transitions were favored in both. We found that FN-induced G:C>A:T transitions were concentrated at pyrimidine dinucleotide sites, suggesting that FNs promote the formation of mutational covalent linkages between adjacent pyrimidine residues. In addition, we found that FNs induced more single base than large deletions, and that these single base deletions were possibly caused by replication slippage. Our observations provide an initial picture of the genome-wide molecular profile of mutations induced in A. thaliana by FN irradiation and are particularly informative of the nature and extent of genome-wide mutation in lines selected on the basis of mutant phenotypes from FN-mutagenized A. thaliana populations.
Full Text Available Certain environmental microorganisms can cause severe human infections, even in the absence of an obvious requirement for transition through an animal host for replication ("accidental virulence". To understand this process, we compared eleven isolate genomes of Burkholderia pseudomallei (Bp, a tropical soil microbe and causative agent of the human and animal disease melioidosis. We found evidence for the existence of several new genes in the Bp reference genome, identifying 282 novel genes supported by at least two independent lines of supporting evidence (mRNA transcripts, database homologs, and presence of ribosomal binding sites and 81 novel genes supported by all three lines. Within the Bp core genome, 211 genes exhibited significant levels of positive selection (4.5%, distributed across many cellular pathways including carbohydrate and secondary metabolism. Functional experiments revealed that certain positively selected genes might enhance mammalian virulence by interacting with host cellular pathways or utilizing host nutrients. Evolutionary modifications improving Bp environmental fitness may thus have indirectly facilitated the ability of Bp to colonize and survive in mammalian hosts. These findings improve our understanding of the pathogenesis of melioidosis, and establish Bp as a model system for studying the genetics of accidental virulence.
Wang, Quanchao; Yu, Yang; Li, Fuhua; Zhang, Xiaojun; Xiang, Jianhai
Genomic selection (GS) can be used to accelerate genetic improvement by shortening the selection interval. The successful application of GS depends largely on the accuracy of the prediction of genomic estimated breeding value (GEBV). This study is a first attempt to understand the practicality of GS in Litopenaeus vannamei and aims to evaluate models for GS on growth traits. The performance of GS models in L. vannamei was evaluated in a population consisting of 205 individuals, which were genotyped for 6 359 single nucleotide polymorphism (SNP) markers by specific length amplified fragment sequencing (SLAF-seq) and phenotyped for body length and body weight. Three GS models (RR-BLUP, BayesA, and Bayesian LASSO) were used to obtain the GEBV, and their predictive ability was assessed by the reliability of the GEBV and the bias of the predicted phenotypes. The mean reliability of the GEBVs for body length and body weight predicted by the different models was 0.296 and 0.411, respectively. For each trait, the performances of the three models were very similar to each other with respect to predictability. The regression coefficients estimated by the three models were close to one, suggesting near to zero bias for the predictions. Therefore, when GS was applied in a L. vannamei population for the studied scenarios, all three models appeared practicable. Further analyses suggested that improved estimation of the genomic prediction could be realized by increasing the size of the training population as well as the density of SNPs.
Ionizing radiation has long been known to induce heritable mutagenic change in DNA sequence. However, the genome-wide effect of radiation is not well understood. Here we report the molecular properties and frequency of mutations in phenotypically selected mutant lines isolated following exposure of the genetic model flowering plant Arabidopsis thaliana to fast neutrons (FNs). Previous studies suggested that FNs predominantly induce deletions longer than a kilobase in A. thaliana. However, we found a higher frequency of single base substitution than deletion mutations. While the overall frequency and molecular spectrum of fast-neutron (FN)-induced single base substitutions differed substantially from those of "background" mutations arising spontaneously in laboratory-grown plants, G:C>A:T transitions were favored in both. We found that FN-induced G:C>A:T transitions were concentrated at pyrimidine dinucleotide sites, suggesting that FNs promote the formation of mutational covalent linkages between adjacent pyrimidine residues. In addition, we found that FNs induced more single base than large deletions, and that these single base deletions were possibly caused by replication slippage. Our observations provide an initial picture of the genome-wide molecular profile of mutations induced in A. thaliana by FN irradiation and are particularly informative of the nature and extent of genome-wide mutation in lines selected on the basis of mutant phenotypes from FN-mutagenized A. thaliana populations.
Valentina E Schneeberger
Full Text Available Patient-derived xenograft (PDX mouse models are increasingly used for preclinical therapeutic testing of human cancer. A limitation in molecular and genetic characterization of PDX tumors is the presence of integral murine stroma. This is particularly problematic for genomic sequencing of PDX models. Rapid and dependable approaches for quantitating stromal content and purifying the malignant human component of these tumors are needed. We used a recently developed technique exploiting species-specific polymerase chain reaction (PCR amplicon length (ssPAL differences to define the fractional composition of murine and human DNA, which was proportional to the fractional composition of cells in a series of lung cancer PDX lines. We compared four methods of human cancer cell isolation: fluorescence-activated cell sorting (FACS, an immunomagnetic mouse cell depletion (MCD approach, and two distinct EpCAM-based immunomagnetic positive selection methods. We further analyzed DNA extracted from the resulting enriched human cancer cells by targeted sequencing using a clinically validated multi-gene panel. Stromal content varied widely among tumors of similar histology, but appeared stable over multiple serial tumor passages of an individual model. FACS and MCD were superior to either positive selection approach, especially in cases of high stromal content, and consistently allowed high quality human-specific genomic profiling. ssPAL is a dependable approach to quantitation of murine stromal content, and MCD is a simple, efficient, and high yield approach to human cancer cell isolation for genomic analysis of PDX tumors.
Nguyen, Thanh-Tung; Huang, Joshua; Wu, Qingyao; Nguyen, Thuy; Li, Mark
Single-nucleotide polymorphisms (SNPs) selection and identification are the most important tasks in Genome-wide association data analysis. The problem is difficult because genome-wide association data is very high dimensional and a large portion of SNPs in the data is irrelevant to the disease. Advanced machine learning methods have been successfully used in Genome-wide association studies (GWAS) for identification of genetic variants that have relatively big effects in some common, complex diseases. Among them, the most successful one is Random Forests (RF). Despite of performing well in terms of prediction accuracy in some data sets with moderate size, RF still suffers from working in GWAS for selecting informative SNPs and building accurate prediction models. In this paper, we propose to use a new two-stage quality-based sampling method in random forests, named ts-RF, for SNP subspace selection for GWAS. The method first applies p-value assessment to find a cut-off point that separates informative and irrelevant SNPs in two groups. The informative SNPs group is further divided into two sub-groups: highly informative and weak informative SNPs. When sampling the SNP subspace for building trees for the forest, only those SNPs from the two sub-groups are taken into account. The feature subspaces always contain highly informative SNPs when used to split a node at a tree. This approach enables one to generate more accurate trees with a lower prediction error, meanwhile possibly avoiding overfitting. It allows one to detect interactions of multiple SNPs with the diseases, and to reduce the dimensionality and the amount of Genome-wide association data needed for learning the RF model. Extensive experiments on two genome-wide SNP data sets (Parkinson case-control data comprised of 408,803 SNPs and Alzheimer case-control data comprised of 380,157 SNPs) and 10 gene data sets have demonstrated that the proposed model significantly reduced prediction errors and outperformed
Washietl, Stefan; Pedersen, Jakob Skou; Korbel, Jan O
Functional RNA structures play an important role both in the context of noncoding RNA transcripts as well as regulatory elements in mRNAs. Here we present a computational study to detect functional RNA structures within the ENCODE regions of the human genome. Since structural RNAs in general lack...... with the GENCODE annotation points to functional RNAs in all genomic contexts, with a slightly increased density in 3'-UTRs. While we estimate a significant false discovery rate of approximately 50%-70% many of the predictions can be further substantiated by additional criteria: 248 loci are predicted by both RNAz...
Full Text Available Selective sweep can cause genetic differentiation across populations, which allows for the identification of possible causative regions/genes underlying important traits. The pig has experienced a long history of allele frequency changes through artificial selection in the domestication process. We obtained an average of 329,482,871 sequence reads for 24 pigs from three pig breeds: Yorkshire (n = 5, Landrace (n = 13, and Duroc (n = 6. An average read depth of 11.7 was obtained using whole-genome resequencing on an Illumina HiSeq2000 platform. In this study, cross-population extended haplotype homozygosity and cross-population composite likelihood ratio tests were implemented to detect genes experiencing positive selection for the genome-wide resequencing data generated from three commercial pig breeds. In our results, 26, 7, and 14 genes from Yorkshire, Landrace, and Duroc, respectively were detected by two kinds of statistical tests. Significant evidence for positive selection was identified on genes ST6GALNAC2 and EPHX1 in Yorkshire, PARK2 in Landrace, and BMP6, SLA-DQA1, and PRKG1 in Duroc.These genes are reportedly relevant to lactation, reproduction, meat quality, and growth traits. To understand how these single nucleotide polymorphisms (SNPs related positive selection affect protein function, we analyzed the effect of non-synonymous SNPs. Three SNPs (rs324509622, rs80931851, and rs80937718 in the SLA-DQA1 gene were significant in the enrichment tests, indicating strong evidence for positive selection in Duroc. Our analyses identified genes under positive selection for lactation, reproduction, and meat-quality and growth traits in Yorkshire, Landrace, and Duroc, respectively.
Zheng, Hong-Xiang; Li, Lei; Jiang, Xiao-Yan; Yan, Shi; Qin, Zhendong; Wang, Xiaofeng; Jin, Li
Considerable attention has been focused on the effect of deleterious mutations caused by the recent relaxation of selective constraints on human health, including the prevalence of obesity, which might represent an adaptive response of energy-conserving metabolism under the conditions of modern society. Mitochondrial DNA (mtDNA) encoding 13 core subunits of oxidative phosphorylation plays an important role in metabolism. Therefore, we hypothesized that a relaxation of selection constraints on mtDNA and an increase in the proportion of deleterious mutations have played a role in obesity prevalence. In this study, we collected and sequenced the mtDNA genomes of 722 Uyghurs, a typical population with a high prevalence of obesity. We identified the variants that occurred in the Uyghur population for each sample and found that the number of nonsynonymous mutations carried by Uyghur individuals declined with elevation of their BMI (P = 0.015). We further calculated the nonsynonymous and synonymous ratio (N/S) of the high-BMI and low-BMI haplogroups, and the results showed that a significantly higher N/S occurred in the whole mtDNA genomes of the low-BMI haplogroups (0.64) than in that of the high-BMI haplogroups (0.35, P = 0.030) and ancestor haplotypes (0.41, P = 0.032); these findings indicated that low-BMI individuals showed a recent relaxation of selective constraints. In addition, we investigated six clinical characteristics and found that fasting plasma glucose might be correlated with the N/S and selective pressures. We hypothesized that a higher proportion of deleterious mutations led to mild mitochondrial dysfunction, which helps to drive glucose consumption and thereby prevents obesity. Our results provide new insights into the relationship between obesity predisposition and mitochondrial genome evolution.
Thomasen, Jørn Rind; Egger-Danner, C; Willam, A
progeny testing. Strong positive interaction effects between increased reliability of genomic predictions and more intensive use of young bulls exist. From an economic perspective a juvenile scheme is always advantageous. The main future focus area for the smaller dairy cattle breeds is to join forces...
Rice association mapping panels are collections of rice (Oryza sativa L.) accessions developed for genome-wide association (GWA) studies. One of these panels, the Rice Diversity Panel 1 (RDP1) was phenotyped by various research groups for several traits of interest, and more recently, genotyped with...
Yuri Tani Utsunomiya
Full Text Available As the methodologies available for the detection of positive selection from genomic data vary in terms of assumptions and execution, weak correlations are expected among them. However, if there is any given signal that is consistently supported across different methodologies, it is strong evidence that the locus has been under past selection. In this paper, a straightforward frequentist approach based on the Stouffer Method to combine P-values across different tests for evidence of recent positive selection in common variations, as well as strategies for extracting biological information from the detected signals, were described and applied to high density single nucleotide polymorphism (SNP data generated from dairy and beef cattle (taurine and indicine. The ancestral Bovinae allele state of over 440,000 SNP is also reported. Using this combination of methods, highly significant (P<3.17×10(-7 population-specific sweeps pointing out to candidate genes and pathways that may be involved in beef and dairy production were identified. The most significant signal was found in the Cornichon homolog 3 gene (CNIH3 in Brown Swiss (P = 3.82×10(-12, and may be involved in the regulation of pre-ovulatory luteinizing hormone surge. Other putative pathways under selection are the glucolysis/gluconeogenesis, transcription machinery and chemokine/cytokine activity in Angus; calpain-calpastatin system and ribosome biogenesis in Brown Swiss; and gangliosides deposition in milk fat globules in Gyr. The composite method, combined with the strategies applied to retrieve functional information, may be a useful tool for surveying genome-wide selective sweeps and providing insights in to the source of selection.
Nasrullah, Izza; Butt, Azeem M; Tahir, Shifa; Idrees, Muhammad; Tong, Yigang
The Marburg virus (MARV) has a negative-sense single-stranded RNA genome, belongs to the family Filoviridae, and is responsible for several outbreaks of highly fatal hemorrhagic fever. Codon usage patterns of viruses reflect a series of evolutionary changes that enable viruses to shape their survival rates and fitness toward the external environment and, most importantly, their hosts. To understand the evolution of MARV at the codon level, we report a comprehensive analysis of synonymous codon usage patterns in MARV genomes. Multiple codon analysis approaches and statistical methods were performed to determine overall codon usage patterns, biases in codon usage, and influence of various factors, including mutation pressure, natural selection, and its two hosts, Homo sapiens and Rousettus aegyptiacus. Nucleotide composition and relative synonymous codon usage (RSCU) analysis revealed that MARV shows mutation bias and prefers U- and A-ended codons to code amino acids. Effective number of codons analysis indicated that overall codon usage among MARV genomes is slightly biased. The Parity Rule 2 plot analysis showed that GC and AU nucleotides were not used proportionally which accounts for the presence of natural selection. Codon usage patterns of MARV were also found to be influenced by its hosts. This indicates that MARV have evolved codon usage patterns that are specific to both of its hosts. Moreover, selection pressure from R. aegyptiacus on the MARV RSCU patterns was found to be dominant compared with that from H. sapiens. Overall, mutation pressure was found to be the most important and dominant force that shapes codon usage patterns in MARV. To our knowledge, this is the first detailed codon usage analysis of MARV and extends our understanding of the mechanisms that contribute to codon usage and evolution of MARV.
Alm, Eric; Shapiro, B. Jesse
Different microbial species are thought to occupy distinct ecological niches, subjecting each species to unique selective constraints, which may leave a recognizable signal in their genomes. Thus, it may be possible to extract insight into the genetic basis of ecological differences among lineages by identifying unusual patterns of substitutions in orthologous gene or protein sequences. We use the ratio of substitutions in slow versus fast-evolving sites (nucleotides in DNA, or amino acids in protein sequence) to quantify deviations from the typical pattern of selective constraint observed across bacterial lineages. We propose that elevated S:F in one branch (an excess of slow-site substitutions) can indicate a functionally-relevant change, due to either positive selection or relaxed evolutionary constraint. In a genome-wide comparative study of gamma-proteobacterial proteins, we find that cell-surface proteins involved with motility and secretion functions often have high S:F ratios, while information-processing genes do not. Change in evolutionary constraints in some species is evidenced by increased S:F ratios within functionally-related sets of genes (e.g., energy production in Pseudomonas fluorescens), while other species apparently evolve mostly by drift (e.g., uniformly elevated S:F across most genes in Buchnera spp.). Overall, S:F reveals several species-specific, protein-level changes with potential functional/ecological importance. As microbial genome projects yield more species-rich gene-trees, the S:F ratio will become an increasingly powerful tool for uncovering functional genetic differences among species.
Wendt, Toni; Holm, Preben Bach; Starker, Colby G
, and their broad targeting range. Here we report the assembly of several TALENs for a specific genomic locus in barley. The cleavage activity of individual TALENs was first tested in vivo using a yeast-based, single-strand annealing assay. The most efficient TALEN was then selected for barley transformation....... Analysis of the resulting transformants showed that TALEN-induced double strand breaks led to the introduction of short deletions at the target site. Additional analysis revealed that each barley transformant contained a range of different mutations, indicating that mutations occurred independently...
Moon, S.; Kim, T.H.; Lee, K.T.; Kwak, W.; Lee, T.; Lee, S.W.; Kim, M.J.; Cho, K.; Kim, N.; Chung, W.H.; Sung, S.; Park, T.; Cho, S.; Groenen, M.A.M.; Nielsen, R.; Kim, Y.; Kim, H.
Background: Animal domestication involved drastic phenotypic changes driven by strong artificial selection and also resulted in new populations of breeds, established by humans. This study aims to identify genes that show evidence of recent artificial selection during pig domestication. Results:
Background The accessibility of high-throughput genotyping technologies has contributed greatly to the development of genomic resources in non-model organisms. High-density genotyping arrays have only recently been developed for some economically important species such as conifers. The potential for using genomic technologies in association mapping and breeding depends largely on the genome wide patterns of diversity and linkage disequilibrium in current breeding populations. This study aims to deepen our knowledge regarding these issues in maritime pine, the first species used for reforestation in south western Europe. Results Using a new map merging algorithm, we first established a 1,712 cM composite linkage map (comprising 1,838 SNP markers in 12 linkage groups) by bringing together three already available genetic maps. Using rigorous statistical testing based on kernel density estimation and resampling we identified cold and hot spots of recombination. In parallel, 186 unrelated trees of a mass-selected population were genotyped using a 12k-SNP array. A total of 2,600 informative SNPs allowed to describe historical recombination, genetic diversity and genetic structure of this recently domesticated breeding pool that forms the basis of much of the current and future breeding of this species. We observe very low levels of population genetic structure and find no evidence that artificial selection has caused a reduction in genetic diversity. By combining these two pieces of information, we provided the map position of 1,671 SNPs corresponding to 1,192 different loci. This made it possible to analyze the spatial pattern of genetic diversity (H e ) and long distance linkage disequilibrium (LD) along the chromosomes. We found no particular pattern in the empirical variogram of H e across the 12 linkage groups and, as expected for an outcrossing species with large effective population size, we observed an almost complete lack of long distance LD. Conclusions These
Full Text Available Newcastle disease (ND and avian influenza (AI are the most feared diseases in the poultry industry worldwide. They can cause flock mortality up to 100%, resulting in a catastrophic economic loss. This is the first study to investigate the feasibility of genomic selection for antibody response to Newcastle disease virus (Ab-NDV and antibody response to Avian Influenza virus (Ab-AIV in chickens. The data were collected from a crossbred population. Breeding values for Ab-NDV and Ab-AIV were estimated using a pedigree-based best linear unbiased prediction model (BLUP and a genomic best linear unbiased prediction model (GBLUP. Single-trait and multiple-trait analyses were implemented. According to the analysis using the pedigree-based model, the heritability for Ab-NDV estimated from the single-trait and multiple-trait models was 0.478 and 0.487, respectively. The heritability for Ab-AIV estimated from the two models was 0.301 and 0.291, respectively. The estimated genetic correlation between the two traits was 0.438. A four-fold cross-validation was used to assess the accuracy of the estimated breeding values (EBV in the two validation scenarios. In the family sample scenario each half-sib family is randomly allocated to one of four subsets and in the random sample scenario the individuals are randomly divided into four subsets. In the family sample scenario, compared with the pedigree-based model, the accuracy of the genomic prediction increased from 0.086 to 0.237 for Ab-NDV and from 0.080 to 0.347 for Ab-AIV. In the random sample scenario, the accuracy was improved from 0.389 to 0.427 for Ab-NDV and from 0.281 to 0.367 for Ab-AIV. The multiple-trait GBLUP model led to a slightly higher accuracy of genomic prediction for both traits. These results indicate that genomic selection for antibody response to ND and AI in chickens is promising.
Gao, Hongding; Su, Guosheng; Janss, Luc
This study compared genomic predictions based on imputed high-density markers (~777,000) in the Nordic Holstein population using a genomic BLUP (GBLUP) model, 4 Bayesian exponential power models with different shape parameters (0.3, 0.5, 0.8, and 1.0) for the exponential power distribution...... relationship with the training population. Groupsmgs had both the sire and the maternal grandsire (MGS), Groupsire only had the sire, Groupmgs only had the MGS, and Groupnon had neither the sire nor the MGS in the training population. Reliability of DGV was measured as the squared correlation between DGV...... and DRP divided by the reliability of DRP for the bulls in validation data set. Unbiasedness of DGV was measured as the regression of DRP on DGV. The results indicated that DGV were more accurate and less biased for animals that were more related to the training population. In general, the Bayesian...
Kakioka, Ryo; Kokita, Tomoyuki; Kumada, Hiroki; Watanabe, Katsutoshi; Okuda, Noboru
Evolution of ecomorphologically relevant traits such as body shapes is important to colonize and persist in a novel environment. Habitat-related adaptive divergence of these traits is therefore common among animals. We studied the genomic architecture of habitat-related divergence in the body shape of Gnathopogon fishes, a novel example of lake-stream ecomorphological divergence, and tested for the action of directional selection on body shape differentiation. Compared to stream-dwelling Gnathopogon elongatus, the sister species Gnathopogon caerulescens, exclusively inhabiting a large ancient lake, had an elongated body, increased proportion of the caudal region and small head, which would be advantageous in the limnetic environment. Using an F2 interspecific cross between the two Gnathopogon species (195 individuals), quantitative trait locus (QTL) analysis with geometric morphometric quantification of body shape and restriction-site associated DNA sequencing-derived markers (1622 loci) identified 26 significant QTLs associated with the interspecific differences of body shape-related traits. These QTLs had small to moderate effects, supporting polygenic inheritance of the body shape-related traits. Each QTL was mostly located on different genomic regions, while colocalized QTLs were detected for some ecomorphologically relevant traits that are proxy of body and caudal peduncle depths, suggesting different degree of modularity among traits. The directions of the body shape QTLs were mostly consistent with the interspecific difference, and QTL sign test suggested a genetic signature of directional selection in the body shape divergence. Thus, we successfully elucidated the genomic architecture underlying the adaptive changes of the quantitative and complex morphological trait in a novel system. © 2015 John Wiley & Sons Ltd.
Background Through the wealth of information contained within them, genome-wide association studies (GWAS) have the potential to provide researchers with a systematic means of associating genetic variants with a wide variety of disease phenotypes. Due to the limitations of approaches that have analyzed single variants one at a time, it has been proposed that the genetic basis of these disorders could be determined through detailed analysis of the genetic variants themselves and in conjunction with one another. The construction of models that account for these subsets of variants requires methodologies that generate predictions based on the total risk of a particular group of polymorphisms. However, due to the excessive number of variants, constructing these types of models has so far been computationally infeasible. Results We have implemented an algorithm, known as greedy RLS, that we use to perform the first known wrapper-based feature selection on the genome-wide level. The running time of greedy RLS grows linearly in the number of training examples, the number of features in the original data set, and the number of selected features. This speed is achieved through computational short-cuts based on matrix calculus. Since the memory consumption in present-day computers can form an even tighter bottleneck than running time, we also developed a space efficient variation of greedy RLS which trades running time for memory. These approaches are then compared to traditional wrapper-based feature selection implementations based on support vector machines (SVM) to reveal the relative speed-up and to assess the feasibility of the new algorithm. As a proof of concept, we apply greedy RLS to the Hypertension – UK National Blood Service WTCCC dataset and select the most predictive variants using 3-fold external cross-validation in less than 26 minutes on a high-end desktop. On this dataset, we also show that greedy RLS has a better classification performance on independent
Bernal-Vasquez, Angela-Maria; Gordillo, Andres; Schmidt, Malthe; Piepho, Hans-Peter
The use of multiple genetic backgrounds across years is appealing for genomic prediction (GP) because past years' data provide valuable information on marker effects. Nonetheless, single-year GP models are less complex and computationally less demanding than multi-year GP models. In devising a suitable analysis strategy for multi-year data, we may exploit the fact that even if there is no replication of genotypes across years, there is plenty of replication at the level of marker loci. Our principal aim was to evaluate different GP approaches to simultaneously model genotype-by-year (GY) effects and breeding values using multi-year data in terms of predictive ability. The models were evaluated under different scenarios reflecting common practice in plant breeding programs, such as different degrees of relatedness between training and validation sets, and using a selected fraction of genotypes in the training set. We used empirical grain yield data of a rye hybrid breeding program. A detailed description of the prediction approaches highlighting the use of kinship for modeling GY is presented. Using the kinship to model GY was advantageous in particular for datasets disconnected across years. On average, predictive abilities were 5% higher for models using kinship to model GY over models without kinship. We confirmed that using data from multiple selection stages provides valuable GY information and helps increasing predictive ability. This increase is on average 30% higher when the predicted genotypes are closely related with the genotypes in the training set. A selection of top-yielding genotypes together with the use of kinship to model GY improves the predictive ability in datasets composed of single years of several selection cycles. Our results clearly demonstrate that the use of multi-year data and appropriate modeling is beneficial for GP because it allows dissecting GY effects from genomic estimated breeding values. The model choice, as well as ensuring
Full Text Available Abstract Background Marine fishes have been shown to display low levels of genetic structuring and associated high levels of gene flow, suggesting shallow evolutionary trajectories and, possibly, limited or lacking adaptive divergence among local populations. We investigated variation in 98 gene-associated single nucleotide polymorphisms (SNPs for evidence of selection in local populations of Atlantic cod (Gadus morhua L. across the species distribution. Results Our global genome scan analysis identified eight outlier gene loci with very high statistical support, likely to be subject to directional selection in local demes, or closely linked to loci under selection. Likewise, on a regional south/north transect of central and eastern Atlantic populations, seven loci displayed strongly elevated levels of genetic differentiation. Selection patterns among populations appeared to be relatively widespread and complex, i.e. outlier loci were generally not only associated with one of a few divergent local populations. Even on a limited geographical scale between the proximate North Sea and Baltic Sea populations four loci displayed evidence of adaptive evolution. Temporal genome scan analysis applied to DNA from archived otoliths from a Faeroese population demonstrated stability of the intra-population variation over 24 years. An exploratory landscape genetic analysis was used to elucidate potential effects of the most likely environmental factors responsible for the signatures of local adaptation. We found that genetic variation at several of the outlier loci was better correlated with temperature and/or salinity conditions at spawning grounds at spawning time than with geographic distance per se. Conclusion These findings illustrate that adaptive population divergence may indeed be prevalent despite seemingly high levels of gene flow, as found in most marine fishes. Thus, results have important implications for our understanding of the interplay of
Katukiza, A Y; Temanu, H; Chung, J W; Foppen, J W A; Lens, P N L
The presence of viruses in a slum environment where sanitation is poor is a major concern. However, little is known of their occurrence and genomic copy concentration in the slum environment. The main objective of this study was to determine the genomic copy concentrations of human adenoviruses F and G, Rotavirus (RV), Hepatitis A virus (HAV), Hepatitis E virus (HEV) and human adenovirus species A,C,D,E, and F (HAdV-ACDEF) in Bwaise III, a typical slum in Kampala, Uganda. Forty-one samples from surface water, grey water and ground water were collected from 30 sampling locations. The virus particles were recovered by glass wool filtration with elution using beef extract. DNA and RNA viruses were detected by the real time quantitative polymerase chain reaction (qPCR) and the reverse transcription-qPCR (RT-qPCR), respectively. HAdV-F and G were detected in 70.7% of the samples with concentrations up to 2.65 × 10(1) genomic copies per mL (gc mL(-1)). RV and HAV were detected in 60.9% and 17.1% of the samples, respectively. The maximum concentration of RV was 1.87 × 10(2)gc mL(-1). In addition, 78% of the samples tested positive for the HAdV-ACDEF, but all samples tested negative for HEV. These new data are essential for quantitative microbial risk assessment, and for understanding the effects of environmental pollution in slums.
Vaysse, Amaury; Ratnakumar, Abhirami; Derrien, Thomas
The extraordinary phenotypic diversity of dog breeds has been sculpted by a unique population history accompanied by selection for novel and desirable traits. Here we perform a comprehensive analysis using multiple test statistics to identify regions under selection in 509 dogs from 46 diverse br...
Buch, Line Hjortø; Sørensen, Morten Kargo; Berg, Peer
We tested the following hypotheses: (i) breeding schemes with genomic selection are superior to breeding schemes without genomic selection regarding annual genetic gain of the aggregate genotype (ΔGAG), annual genetic gain of the functional traits and rate of inbreeding per generation (ΔF), (ii......) a positive interaction exists between the use of genotypic information and a short generation interval on ΔGAG and (iii) the inclusion of an indicator trait in the selection index will only result in a negligible increase in ΔGAG if genotypic information about the breeding goal trait is known. We examined......, greater contributions of the functional trait to ΔGAG and lower ΔF than the two breeding schemes without genomic selection. Thus, the use of genotypic information may lead to more sustainable breeding schemes. In addition, a short generation interval increases the effect of using genotypic information...
Williamson, Scott H.; Hernandez, Ryan; Fledel-Alon, Adi
Natural selection and demographic forces can have similar effects on patterns of DNA polymorphism. Therefore, to infer selection from samples of DNA sequences, one must simultaneously account for demographic effects. Here we take a model-based approach to this problem by developing predictions fo......-specific methods, and (iii) strong evidence for very recent population growth....... for patterns of polymorphism in the presence of both population size change and natural selection. If data are available from different functional classes of variation, and a priori information suggests that mutations in one of those classes are selectively neutral, then the putatively neutral class can...... this method to a large polymorphism data set from 301 human genes and find (i) widespread negative selection acting on standing nonsynonymous variation, (ii) that the fitness effects of nonsynonymous mutations are well predicted by several measures of amino acid exchangeability, especially site...
Xia, Jun Hong; Li, Hong Lian; Zhang, Yong; Meng, Zi Ning; Lin, Hao Ran
Fish species inhabitating seawater (SW) or freshwater (FW) habitats have to develop genetic adaptations to alternative environment factors, especially salinity. Functional consequences of the protein variations associated with habitat environments in fish mitochondrial genomes have not yet received much attention. We analyzed 829 complete fish mitochondrial genomes and compared the amino acid differences of 13 mitochondrial protein families between FW and SW fish groups. We identified 47 specificity determining sites (SDS) that associated with FW or SW environments from 12 mitochondrial protein families. Thirty-two (68%) of the SDS sites are hydrophobic, 13 (28%) are neutral, and the remaining sites are acidic or basic. Seven of those SDS from ND1, ND2 and ND5 were scored as probably damaging to the protein structures. Furthermore, phylogenetic tree based Bayes Empirical Bayes analysis also detected 63 positive sites associated with alternative habitat environments across ten mtDNA proteins. These signatures could be important for studying mitochondrial genetic variation relevant to fish physiology and ecology.
Polyadenylation of pre-mRNAs, a critical step in eukaryotic gene expression, is mediated by cis elements collectively called the polyadenylation signal. Genome-wide analysis of such polyadenylation signals was missing in fission yeast, even though it is an important model organism. We demonstrate that the canonical AATAAA motif is the most frequent and functional polyadenylation signal in Schizosaccharomyces pombe. Using analysis of RNA-Seq data sets from cells grown under various physiological conditions, we identify 3\\' UTRs for nearly 90% of the yeast genes. Heterogeneity of cleavage sites is common, as is alternative polyadenylation within and between conditions. We validated the computationally identified sequence elements likely to promote polyadenylation by functional assays, including qRT-PCR and 3\\'RACE analysis. The biological importance of the AATAAA motif is underlined by functional analysis of the genes containing it. Furthermore, it has been shown that convergent genes require trans elements, like cohesin for efficient transcription termination. Here we show that convergent genes lacking cohesin (on chromosome 2) are generally associated with longer overlapping mRNA transcripts. Our bioinformatic and experimental genome-wide results are summarized and can be accessed and customized in a user-friendly database Pomb(A).
Schlackow, M.; Marguerat, S.; Proudfoot, N. J.; Bahler, J.; Erban, R.; Gullerova, M.
Polyadenylation of pre-mRNAs, a critical step in eukaryotic gene expression, is mediated by cis elements collectively called the polyadenylation signal. Genome-wide analysis of such polyadenylation signals was missing in fission yeast, even though it is an important model organism. We demonstrate that the canonical AATAAA motif is the most frequent and functional polyadenylation signal in Schizosaccharomyces pombe. Using analysis of RNA-Seq data sets from cells grown under various physiological conditions, we identify 3' UTRs for nearly 90% of the yeast genes. Heterogeneity of cleavage sites is common, as is alternative polyadenylation within and between conditions. We validated the computationally identified sequence elements likely to promote polyadenylation by functional assays, including qRT-PCR and 3'RACE analysis. The biological importance of the AATAAA motif is underlined by functional analysis of the genes containing it. Furthermore, it has been shown that convergent genes require trans elements, like cohesin for efficient transcription termination. Here we show that convergent genes lacking cohesin (on chromosome 2) are generally associated with longer overlapping mRNA transcripts. Our bioinformatic and experimental genome-wide results are summarized and can be accessed and customized in a user-friendly database Pomb(A).
Price, Morgan N; Arkin, Adam P
Free-living bacteria are usually thought to have large effective population sizes, and so tiny selective differences can drive their evolution. However, because recombination is infrequent, "background selection" against slightly deleterious alleles should reduce the effective population size (Ne) by orders of magnitude. For example, for a well-mixed population with 10(12) individuals and a typical level of homologous recombination (r/m = 3, i.e., nucleotide changes due to recombination [r] occur at 3 times the mutation rate [m]), we predict that Ne is selection should be sufficient to drive evolution if Ne × s is >1, where s is the selection coefficient. We found that this remains approximately correct if background selection is occurring or when population structure is present. Overall, we predict that even for free-living bacteria with enormous populations, natural selection is only a significant force if s is above 10(-7) or so. Because bacteria form huge populations with trillions of individuals, the simplest theoretical prediction is that the better allele at a site would predominate even if its advantage was just 10(-9) per generation. In other words, virtually every nucleotide would be at the local optimum in most individuals. A more sophisticated theory considers that bacterial genomes have millions of sites each and selection events on these many sites could interfere with each other, so that only larger effects would be important. However, bacteria can exchange genetic material, and in principle, this exchange could eliminate the interference between the evolution of the sites. We used simulations to confirm that during multisite evolution with realistic levels of recombination, only larger effects are important. We propose that advantages of less than 10(-7) are effectively neutral. Copyright © 2015 Price and Arkin.
Full Text Available VKORC1 (vitamin K epoxide reductase complex subunit 1, 16p11.2 is the main genetic determinant of human response to oral anticoagulants of antivitamin K type (AVK. This gene was recently suggested to be a putative target of positive selection in East Asian populations. In this study, we genotyped the HGDP-CEPH Panel for six VKORC1 SNPs and downloaded chromosome 16 genotypes from the HGDP-CEPH database in order to characterize the geographic distribution of footprints of positive selection within and around this locus. A unique VKORC1 haplotype carrying the promoter mutation associated with AVK sensitivity showed especially high frequencies in all the 17 HGDP-CEPH East Asian population samples. VKORC1 and 24 neighboring genes were found to lie in a 505 kb region of strong linkage disequilibrium in these populations. Patterns of allele frequency differentiation and haplotype structure suggest that this genomic region has been submitted to a near complete selective sweep in all East Asian populations and only in this geographic area. The most extreme scores of the different selection tests are found within a smaller 45 kb region that contains VKORC1 and three other genes (BCKDK, MYST1 (KAT8, and PRSS8 with different functions. Because of the strong linkage disequilibrium, it is not possible to determine if VKORC1 or one of the three other genes is the target of this strong positive selection that could explain present-day differences among human populations in AVK dose requirement. Our results show that the extended region surrounding a presumable single target of positive selection should be analyzed for genetic variation in a wide range of genetically diverse populations in order to account for other neighboring and confounding selective events and the hitchhiking effect.
Kijas, James W.; Lenstra, Johannes A.; Hayes, Ben; Boitard, Simon; Porto Neto, Laercio R.; San Cristobal, Magali; Servin, Bertrand; McCulloch, Russell; Whan, Vicki; Gietzen, Kimberly; Paiva, Samuel; Barendse, William; Ciani, Elena; Raadsma, Herman; McEwan, John; Dalrymple, Brian
Through their domestication and subsequent selection, sheep have been adapted to thrive in a diverse range of environments. To characterise the genetic consequence of both domestication and selection, we genotyped 49,034 SNP in 2,819 animals from a diverse collection of 74 sheep breeds. We find the majority of sheep populations contain high SNP diversity and have retained an effective population size much higher than most cattle or dog breeds, suggesting domestication occurred from a broad genetic base. Extensive haplotype sharing and generally low divergence time between breeds reveal frequent genetic exchange has occurred during the development of modern breeds. A scan of the genome for selection signals revealed 31 regions containing genes for coat pigmentation, skeletal morphology, body size, growth, and reproduction. We demonstrate the strongest selection signal has occurred in response to breeding for the absence of horns. The high density map of genetic variability provides an in-depth view of the genetic history for this important livestock species. PMID:22346734
James W Kijas
Full Text Available Through their domestication and subsequent selection, sheep have been adapted to thrive in a diverse range of environments. To characterise the genetic consequence of both domestication and selection, we genotyped 49,034 SNP in 2,819 animals from a diverse collection of 74 sheep breeds. We find the majority of sheep populations contain high SNP diversity and have retained an effective population size much higher than most cattle or dog breeds, suggesting domestication occurred from a broad genetic base. Extensive haplotype sharing and generally low divergence time between breeds reveal frequent genetic exchange has occurred during the development of modern breeds. A scan of the genome for selection signals revealed 31 regions containing genes for coat pigmentation, skeletal morphology, body size, growth, and reproduction. We demonstrate the strongest selection signal has occurred in response to breeding for the absence of horns. The high density map of genetic variability provides an in-depth view of the genetic history for this important livestock species.
Bustamente, Carlos D.; Fledel-Alon, Adi; Williamson, Scott
, showing an excess of deleterious variation within local populations 9, 10 . Here we contrast patterns of coding sequence polymorphism identified by direct sequencing of 39 humans for over 11,000 genes to divergence between humans and chimpanzees, and find strong evidence that natural selection has shaped......Comparisons of DNA polymorphism within species to divergence between species enables the discovery of molecular adaptation in evolutionarily constrained genes as well as the differentiation of weak from strong purifying selection 1, 2, 3, 4 . The extent to which weak negative and positive darwinian...... selection have driven the molecular evolution of different species varies greatly 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 , with some species, such as Drosophila melanogaster, showing strong evidence of pervasive positive selection 6, 7, 8, 9 , and others, such as the selfing weed Arabidopsis thaliana...
Cericola, Fabio; Fé, Dario; Janss, Luc
the diagonal elements by estimating the amount of genetic variance caused by the reduction of the coverage depth. Secondly we developed a method to scale the relationship matrix by taking into account the overall amount of pairwise non-missing loci between all families. Rust resistance and heading date were......Genotyping by sequencing (GBS) allows generating up to millions of molecular markers with a cost per sample which is proportional to the level of multiplexing. Increasing the sample multiplexing decreases the genotyping price but also reduces the numbers of reads per marker. In this work we...... investigated how this reduction of the coverage depth affects the genomic relationship matrices used to estimated breeding value of F2 family pools in perennial ryegrass. A total of 995 families were genotyped via GBS providing more than 1.8M allele frequency estimates for each family with an average coverage...
Garlapow, Megan E.; Everett, Logan J.; Zhou, Shanshan; Gearhart, Alexander W.; Fay, Kairsten A.; Huang, Wen; Morozova, Tatiana V.; Arya, Gunjan H.; Turlapati, Lavanya; Armour, Genevieve St.; Hussain, Yasmeen N.; McAdams, Sarah E.; Fochler, Sophia; Mackay, Trudy F. C.
Food consumption is an essential component of animal fitness; however, excessive food intake in humans increases risk for many diseases. The roles of neuroendocrine feedback loops, food sensing modalities, and physiological state in regulating food intake are well understood, but not the genetic basis underlying variation in food consumption. Here, we applied ten generations of artificial selection for high and low food consumption in replicate populations of Drosophila melanogaster. The phenotypic response to selection was highly asymmetric, with significant responses only for increased food consumption and minimal correlated responses in body mass and composition. We assessed the molecular correlates of selection responses by DNA and RNA sequencing of the selection lines. The high and low selection lines had variants with significantly divergent allele frequencies within or near 2,081 genes and 3,526 differentially expressed genes in one or both sexes. A total of 519 genes were both genetically divergent and differentially expressed between the divergent selection lines. We performed functional analyses of the effects of RNAi suppression of gene expression and induced mutations for 27 of these candidate genes that have human orthologs and the strongest statistical support, and confirmed that 25 (93%) affected the mean and/or variance of food consumption. PMID:27704301
Full Text Available Some mammals breed throughout the year, while others breed only at certain times of year. These differences in reproductive behavior can be explained by evolution. We identified positively-selected genes in two sets of species with different degrees of relatedness including seasonal and non-seasonal breeding species, using branch-site models. After stringent filtering by sum of pairs scoring, we revealed that more genes underwent positive selection in seasonal compared with non-seasonal breeding species. Positively-selected genes were verified by cDNA mapping of the positive sites with the corresponding cDNA sequences. The design of the evolutionary analysis can effectively lower the false-positive rate and thus identify valid positive genes. Validated, positively-selected genes, including CGA, DNAH1, INVS, and CD151, were related to reproductive behaviors such as spermatogenesis and cell proliferation in non-seasonal breeding species. Genes in seasonal breeding species, including THRAP3, TH1L, and CMTM6, may be related to the evolution of sperm and the circadian rhythm system. Identification of these positively-selected genes might help to identify the molecular mechanisms underlying seasonal and non-seasonal reproductive behaviors.
Jonci N Wolff
Full Text Available Numts are an integral component of many eukaryote genomes offering a snapshot of the evolutionary process that led from the incorporation of an α-proteobacterium into a larger eukaryotic cell some 1.8 billion years ago. Although numt sequence can be harnessed as molecular marker, these sequences often remain unidentified and are mistaken for genuine mtDNA leading to erroneous interpretation of mtDNA data sets. It is therefore indispensable that during the process of amplifying and sequencing mitochondrial genes, preventive measures are taken to ensure the exclusion of numts to guarantee the recovery of genuine mtDNA. This applies to mtDNA analyses in general but especially to studies where mtDNAs are sequenced de novo as the launch pad for subsequent mtDNA-based research. By using a combination of dilution series and nested rolling circle amplification (RCA, we present a novel strategy to selectively amplify mtDNA and exclude the amplification of numt sequence. We have successfully applied this strategy to de novo sequence the mtDNA of the Black Field Cricket Teleogryllus commodus, a species known to contain numts. Aligning our assembled sequence to the reference genome of Teleogryllus emma (GenBank EU557269.1 led to the identification of a numt sequence in the reference sequence. This unexpected result further highlights the need of a reliable and accessible strategy to eliminate this source of error.
Gorjanc, G.; Bijma, P.; Hickey, J.M.
Background: Reliability is an important parameter in breeding. It measures the precision of estimated breeding values (EBV) and, thus, potential response to selection on those EBV. The precision of EBV is commonly measured by relating the prediction error variance (PEV) of EBV to the base population
Metspalu, Mait; Romero, Irene Gallego; Yunusbayev, Bayazit; Chaubey, Gyaneshwer; Mallick, Chandana Basu; Hudjashov, Georgi; Nelis, Mari; Mägi, Reedik; Metspalu, Ene; Remm, Maido; Pitchappan, Ramasamy; Singh, Lalji; Thangaraj, Kumarasamy; Villems, Richard; Kivisild, Toomas
South Asia harbors one of the highest levels genetic diversity in Eurasia, which could be interpreted as a result of its long-term large effective population size and of admixture during its complex demographic history. In contrast to Pakistani populations, populations of Indian origin have been underrepresented in previous genomic scans of positive selection and population structure. Here we report data for more than 600,000 SNP markers genotyped in 142 samples from 30 ethnic groups in India. Combining our results with other available genome-wide data, we show that Indian populations are characterized by two major ancestry components, one of which is spread at comparable frequency and haplotype diversity in populations of South and West Asia and the Caucasus. The second component is more restricted to South Asia and accounts for more than 50% of the ancestry in Indian populations. Haplotype diversity associated with these South Asian ancestry components is significantly higher than that of the components dominating the West Eurasian ancestry palette. Modeling of the observed haplotype diversities suggests that both Indian ancestry components are older than the purported Indo-Aryan invasion 3,500 YBP. Consistent with the results of pairwise genetic distances among world regions, Indians share more ancestry signals with West than with East Eurasians. However, compared to Pakistani populations, a higher proportion of their genes show regionally specific signals of high haplotype homozygosity. Among such candidates of positive selection in India are MSTN and DOK5, both of which have potential implications in lipid metabolism and the etiology of type 2 diabetes. Copyright © 2011 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
López-Wilchis, Ricardo; Del Río-Portilla, Miguel Ángel; Guevara-Chumacero, Luis Manuel
We described the complete mitochondrial genome (mitogenome) of the Wagner's mustached bat, Pteronotus personatus, a species belonging to the family Mormoopidae, and compared it with other published mitogenomes of bats (Chiroptera). The mitogenome of P. personatus was 16,570 bp long and contained a typically conserved structure including 13 protein-coding genes, 22 transfer RNA genes, two ribosomal RNA genes, and one control region (D-loop). Most of the genes were encoded on the H-strand, except for eight tRNA and the ND6 genes. The order of protein-coding and rRNA genes was highly conserved in all mitogenomes. All protein-coding genes started with an ATG codon, except for ND2, ND3, and ND5, which initiated with ATA, and terminated with the typical stop codon TAA/TAG or the codon AGA. Phylogenetic trees constructed using Maximum Parsimony, Maximum Likelihood, and Bayesian inference methods showed an identical topology and indicated the monophyly of different families of bats (Mormoopidae, Phyllostomidae, Vespertilionidae, Rhinolophidae, and Pteropopidae) and the existence of two major clades corresponding to the suborders Yangochiroptera and Yinpterochiroptera. The mitogenome sequence provided here will be useful for further phylogenetic analyses and population genetic studies in mormoopid bats.
Lehmann, Jason S.; Corey, Victoria C.; Ricaldi, Jessica N.; Vinetz, Joseph M.; Winzeler, Elizabeth A.; Matthias, Michael A.
Leptospirosis is the most common zoonotic disease worldwide with an estimated 500,000 severe cases reported annually, and case fatality rates of 12–25%, due primarily to acute kidney and lung injuries. Despite its prevalence, the molecular mechanisms underlying leptospirosis pathogenesis remain poorly understood. To identify virulence-related genes in Leptospira interrogans, we delineated cumulative genome changes that occurred during serial in vitro passage of a highly virulent strain of L. interrogans serovar Lai into a nearly avirulent isogenic derivative. Comparison of protein coding and computationally predicted noncoding RNA (ncRNA) genes between these two polyclonal strains identified 15 nonsynonymous single nucleotide variant (nsSNV) alleles that increased in frequency and 19 that decreased, whereas no changes in allelic frequency were observed among the ncRNA genes. Some of the nsSNV alleles were in six genes shown previously to be transcriptionally upregulated during exposure to in vivo-like conditions. Five of these nsSNVs were in evolutionarily conserved positions in genes related to signal transduction and metabolism. Frequency changes of minor nsSNV alleles identified in this study likely contributed to the loss of virulence during serial in vitro culture. The identification of new virulence-associated genes should spur additional experimental inquiry into their potential role in Leptospira pathogenesis. PMID:26711524
Full Text Available The objective of this study was to evaluate the usefulness of comprehensive chromosome screening (CCS using array comparative genomic hybridization (aCGH. The study included 1420 CCS cycles for recurrent miscarriage (n=203; repetitive implantation failure (n=188; severe male factor (n=116; previous trisomic pregnancy (n=33; and advanced maternal age (n=880. CCS was performed in cycles with fresh oocytes and embryos (n=774; mixed cycles with fresh and vitrified oocytes (n=320; mixed cycles with fresh and vitrified day-2 embryos (n=235; and mixed cycles with fresh and vitrified day-3 embryos (n=91. Day-3 embryo biopsy was performed and analyzed by aCGH followed by day-5 embryo transfer. Consistent implantation (range: 40.5–54.2% and pregnancy rates per transfer (range: 46.0–62.9% were obtained for all the indications and independently of the origin of the oocytes or embryos. However, a lower delivery rate per cycle was achieved in women aged over 40 years (18.1% due to the higher percentage of aneuploid embryos (85.3% and lower number of cycles with at least one euploid embryo available per transfer (40.3%. We concluded that aneuploidy is one of the major factors which affect embryo implantation.
Background Discerning the traits evolving under neutral conditions from those traits evolving rapidly because of various selection pressures is a great challenge. We propose a new method, composite selection signals (CSS), which unifies the multiple pieces of selection evidence from the rank distribution of its diverse constituent tests. The extreme CSS scores capture highly differentiated loci and underlying common variants hauling excess haplotype homozygosity in the samples of a target population. Results The data on high-density genotypes were analyzed for evidence of an association with either polledness or double muscling in various cohorts of cattle and sheep. In cattle, extreme CSS scores were found in the candidate regions on autosome BTA-1 and BTA-2, flanking the POLL locus and MSTN gene, for polledness and double muscling, respectively. In sheep, the regions with extreme scores were localized on autosome OAR-2 harbouring the MSTN gene for double muscling and on OAR-10 harbouring the RXFP2 gene for polledness. In comparison to the constituent tests, there was a partial agreement between the signals at the four candidate loci; however, they consistently identified additional genomic regions harbouring no known genes. Persuasively, our list of all the additional significant CSS regions contains genes that have been successfully implicated to secondary phenotypic diversity among several subpopulations in our data. For example, the method identified a strong selection signature for stature in cattle capturing selective sweeps harbouring UQCC-GDF5 and PLAG1-CHCHD7 gene regions on BTA-13 and BTA-14, respectively. Both gene pairs have been previously associated with height in humans, while PLAG1-CHCHD7 has also been reported for stature in cattle. In the additional analysis, CSS identified significant regions harbouring multiple genes for various traits under selection in European cattle including polledness, adaptation, metabolism, growth rate, stature
Gapare, Washington; Liu, Shiming; Conaty, Warren; Zhu, Qian-Hao; Gillespie, Vanessa; Llewellyn, Danny; Stiller, Warwick; Wilson, Iain
Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there has not been a study to evaluate GS prediction models that may be used for predicting cotton breeding lines across multiple environments. In this study, we evaluated the performance of Bayes Ridge Regression, BayesA, BayesB, BayesC and Reproducing Kernel Hilbert Spaces regression models. We then extended the single-site GS model to accommodate genotype × environment interaction (G×E) in order to assess the merits of multi- over single-environment models in a practical breeding and selection context in cotton, a crop for which this has not previously been evaluated. Our study was based on a population of 215 upland cotton ( Gossypium hirsutum ) breeding lines which were evaluated for fiber length and strength at multiple locations in Australia and genotyped with 13,330 single nucleotide polymorphic (SNP) markers. BayesB, which assumes unique variance for each marker and a proportion of markers to have large effects, while most other markers have zero effect, was the preferred model. GS accuracy for fiber length based on a single-site model varied across sites, ranging from 0.27 to 0.77 (mean = 0.38), while that of fiber strength ranged from 0.19 to 0.58 (mean = 0.35) using randomly selected sub-populations as the training population. Prediction accuracies from the M×E model were higher than those for single-site and across-site models, with an average accuracy of 0.71 and 0.59 for fiber length and strength, respectively. The use of the M×E model could therefore identify which breeding lines have effects that are stable across environments and which ones are responsible for G×E and so reduce the amount of phenotypic screening required in cotton breeding programs to identify adaptable genotypes. Copyright © 2018, G3: Genes, Genomes, Genetics.
Battlay, Paul; Schmidt, Joshua M; Fournier-Level, Alexandre; Robin, Charles
Scans of the Drosophila melanogaster genome have identified organophosphate resistance loci among those with the most pronounced signature of positive selection. In this study, the molecular basis of resistance to the organophosphate insecticide azinphos-methyl was investigated using the Drosophila Genetic Reference Panel, and genome-wide association. Recently released full transcriptome data were used to extend the utility of the Drosophila Genetic Reference Panel resource beyond traditional genome-wide association studies to allow systems genetics analyses of phenotypes. We found that both genomic and transcriptomic associations independently identified Cyp6g1, a gene involved in resistance to DDT and neonicotinoid insecticides, as the top candidate for azinphos-methyl resistance. This was verified by transgenically overexpressing Cyp6g1 using natural regulatory elements from a resistant allele, resulting in a 6.5-fold increase in resistance. We also identified four novel candidate genes associated with azinphos-methyl resistance, all of which are involved in either regulation of fat storage, or nervous system development. In Cyp6g1, we find a demonstrable resistance locus, a verification that transcriptome data can be used to identify variants associated with insecticide resistance, and an overlap between peaks of a genome-wide association study, and a genome-wide selective sweep analysis. Copyright © 2016 Battlay et al.
Full Text Available Abstract Background Understanding how genes are expressed specifically in particular tissues is a fundamental question in developmental biology. Many tissue-specific genes are involved in the pathogenesis of complex human diseases. However, experimental identification of tissue-specific genes is time consuming and difficult. The accurate predictions of tissue-specific gene targets could provide useful information for biomarker development and drug target identification. Results In this study, we have developed a machine learning approach for predicting the human tissue-specific genes using microarray expression data. The lists of known tissue-specific genes for different tissues were collected from UniProt database, and the expression data retrieved from the previously compiled dataset according to the lists were used for input vector encoding. Random Forests (RFs and Support Vector Machines (SVMs were used to construct accurate classifiers. The RF classifiers were found to outperform SVM models for tissue-specific gene prediction. The results suggest that the candidate genes for brain or liver specific expression can provide valuable information for further experimental studies. Our approach was also applied for identifying tissue-selective gene targets for different types of tissues. Conclusions A machine learning approach has been developed for accurately identifying the candidate genes for tissue specific/selective expression. The approach provides an efficient way to select some interesting genes for developing new biomedical markers and improve our knowledge of tissue-specific expression.
Zeng, Zhaoqing; Zhao, Peng; Luo, Jing; Zhuang, Wenying; Yu, Zhihe
A DNA barcode is a short segment of sequence that is able to distinguish species. A barcode must ideally contain enough variation to distinguish every individual species and be easily obtained. Fungi of Nectriaceae are economically important and show high species diversity. To establish a standard DNA barcode for this group of fungi, the genomes of Neurospora crassa and 30 other filamentous fungi were compared. The expect value was treated as a criterion to recognize homologous sequences. Four candidate markers, Hsp90, AAC, CDC48, and EF3, were tested for their feasibility as barcodes in the identification of 34 well-established species belonging to 13 genera of Nectriaceae. Two hundred and fifteen sequences were analyzed. Intra- and inter-specific variations and the success rate of PCR amplification and sequencing were considered as important criteria for estimation of the candidate markers. Ultimately, the partial EF3 gene met the requirements for a good DNA barcode: No overlap was found between the intra- and inter-specific pairwise distances. The smallest inter-specific distance of EF3 gene was 3.19%, while the largest intra-specific distance was 1.79%. In addition, there was a high success rate in PCR and sequencing for this gene (96.3%). CDC48 showed sufficiently high sequence variation among species, but the PCR and sequencing success rate was 84% using a single pair of primers. Although the Hsp90 and AAC genes had higher PCR and sequencing success rates (96.3% and 97.5%, respectively), overlapping occurred between the intra- and inter-specific variations, which could lead to misidentification. Therefore, we propose the EF3 gene as a possible DNA barcode for the nectriaceous fungi.
Ovenden, Ben; Milgate, Andrew; Wade, Len J; Rebetzke, Greg J; Holland, James B
Abiotic stress tolerance traits are often complex and recalcitrant targets for conventional breeding improvement in many crop species. This study evaluated the potential of genomic selection to predict water-soluble carbohydrate concentration (WSCC), an important drought tolerance trait, in wheat under field conditions. A panel of 358 varieties and breeding lines constrained for maturity was evaluated under rainfed and irrigated treatments across two locations and two years. Whole-genome marker profiles and factor analytic mixed models were used to generate genomic estimated breeding values (GEBVs) for specific environments and environment groups. Additive genetic variance was smaller than residual genetic variance for WSCC, such that genotypic values were dominated by residual genetic effects rather than additive breeding values. As a result, GEBVs were not accurate predictors of genotypic values of the extant lines, but GEBVs should be reliable selection criteria to choose parents for intermating to produce new populations. The accuracy of GEBVs for untested lines was sufficient to increase predicted genetic gain from genomic selection per unit time compared to phenotypic selection if the breeding cycle is reduced by half by the use of GEBVs in off-season generations. Further, genomic prediction accuracy depended on having phenotypic data from environments with strong correlations with target production environments to build prediction models. By combining high-density marker genotypes, stress-managed field evaluations, and mixed models that model simultaneously covariances among genotypes and covariances of complex trait performance between pairs of environments, we were able to train models with good accuracy to facilitate genetic gain from genomic selection. Copyright © 2018 Ovenden et al.
Full Text Available Genomic selection (GS is a methodology that can improve crop breeding efficiency. To implement GS, a training population (TP with phenotypic and genotypic data is required to train a statistical model used to predict genotyped selection candidates (SCs. A key factor impacting prediction accuracy is the relationship between the TP and the SCs. This study used empirical data for quantitative adult plant resistance to stem rust of wheat ( L. to investigate the utility of a historical TP (TP compared with a population-specific TP (TP, the potential for TP optimization, and the utility of TP data when close relative data is available for training. We found that, depending on the population size, a TP was 1.5 to 4.4 times more accurate than a TP, and TP optimization based on the mean of the generalized coefficient of determination or prediction error variance enabled the selection of subsets that led to significantly higher accuracy than randomly selected subsets. Retaining historical data when data on close relatives were available lead to a 11.9% increase in accuracy, at best, and a 12% decrease in accuracy, at worst, depending on the heritability. We conclude that historical data could be used successfully to initiate a GS program, especially if the dataset is very large and of high heritability. Training population optimization would be useful for the identification of TP subsets to phenotype additional traits. However, after model updating, discarding historical data may be warranted. More studies are needed to determine if these observations represent general trends.
Full Text Available The Lohmann Selected Leghorn (LSL and Lohmann Brown (LB layer lines have been selected for high egg production since more than 50 years and belong to the worldwide leading commercial layer lines. The objectives of the present study were to characterize the molecular processes that are different among these two layer lines using whole genome RNA expression profiles. The hens were kept in the newly developed small group housing system Eurovent German with two different group sizes. Differential expression was observed for 6,276 microarray probes (FDR adjusted P-value <0.05 among the two layer lines LSL and LB. A 2-fold or greater change in gene expression was identified on 151 probe sets. In LSL, 72 of the 151 probe sets were up- and 79 of them were down-regulated. Gene ontology (GO enrichment analysis accounting for biological processes evinced 18 GO-terms for the 72 probe sets with higher expression in LSL, especially those taking part in immune system processes and membrane organization. A total of 32 enriched GO-terms were determined among the 79 down-regulated probe sets of LSL. Particularly, these terms included phosphorus metabolic processes and signaling pathways. In conclusion, the phenotypic differences among the two layer lines LSL and LB are clearly reflected in their gene expression profiles of the cerebrum. These novel findings provide clues for genes involved in economically important line characteristics of commercial laying hens.
Calvin, W. M.; Titus, T. N.; Mahoney, S. A.
There is a long history of telescopic and spacecraft observations of the polar regions of Mars. The finely laminated ice deposits and surrounding layered terrains are commonly thought to contain a record of past climate conditions and change. Understanding the basic nature of the deposits and their mineral and ice constituents is a continued focus of current and future orbited missions. Unresolved issues in Martian polar science include a) the unusual nature of the CO2 ice deposits ("Swiss Cheese", "slab ice" etc.) b) the relationship of the ice deposits to underlying layered units (which differs from the north to the south), c) understanding the seasonal variations and their connections to the finely laminated units observed in high-resolution images and d) the relationship of dark materials in the wind-swept lanes and reentrant valleys to the surrounding dark dune and surface materials. Our work focuses on understanding these issues in relationship to the north residual ice cap. Recent work using Mars Global Surveyor (MGS) data sets have described evolution of the seasonal CO2 frost deposits. In addition, the north polar residual ice cap exhibits albedo variations between Mars years and within the summer season. The Thermal Emission Spectrometer (TES) data set can augment these observations providing additional constraints such as temperature evolution and spectral properties associated with ice and rocky materials. Exploration of these properties is the subject of our current study.
Full Text Available Domestication and selection for important performance traits can impact the genome, which is most often reflected by reduced heterozygosity in and surrounding genes related to traits affected by selection. In this study, analysis of the genomic impact caused by domestication and artificial selection was conducted by investigating the signatures of selection using single nucleotide polymorphisms (SNPs in channel catfish (Ictalurus punctatus. A total of 8.4 million candidate SNPs were identified by using next generation sequencing. On average, the channel catfish genome harbors one SNP per 116 bp. Approximately 6.6 million, 5.3 million, 4.9 million, 7.1 million and 6.7 million SNPs were detected in the Marion, Thompson, USDA103, Hatchery strain, and wild population, respectively. The allele frequencies of 407,861 SNPs differed significantly between the domestic and wild populations. With these SNPs, 23 genomic regions with putative selective sweeps were identified that included 11 genes. Although the function for the majority of the genes remain unknown in catfish, several genes with known function related to aquaculture performance traits were included in the regions with selective sweeps. These included hypoxia-inducible factor 1β. HIFιβ.. and the transporter gene ATP-binding cassette sub-family B member 5 (ABCB5. HIF1β. is important for response to hypoxia and tolerance to low oxygen levels is a critical aquaculture trait. The large numbers of SNPs identified from this study are valuable for the development of high-density SNP arrays for genetic and genomic studies of performance traits in catfish.
Full Text Available We have performed a metabolite quantitative trait locus (mQTL study of the (1H nuclear magnetic resonance spectroscopy ((1H NMR metabolome in humans, building on recent targeted knowledge of genetic drivers of metabolic regulation. Urine and plasma samples were collected from two cohorts of individuals of European descent, with one cohort comprised of female twins donating samples longitudinally. Sample metabolite concentrations were quantified by (1H NMR and tested for association with genome-wide single-nucleotide polymorphisms (SNPs. Four metabolites' concentrations exhibited significant, replicable association with SNP variation (8.6×10(-11
genomic regions. Two of the three hit regions lie within haplotype blocks (at 2p13.1 and 10q24.2 that carry the genetic signature of strong, recent, positive selection in European populations. Genes NAT8 and PYROXD2, both with relatively uncharacterized functional roles, are good candidates for mediating the corresponding mQTL associations. The study's longitudinal twin design allowed detailed variance-components analysis of the sources of population variation in metabolite levels. The mQTLs explained 40%-64% of biological population variation in the corresponding metabolites' concentrations. These effect sizes are stronger than those reported in a recent, targeted mQTL study of metabolites in serum using the targeted-metabolomics Biocrates platform. By re-analysing our plasma samples using the Biocrates platform, we replicated the mQTL findings of the previous study and discovered a previously uncharacterized yet substantial familial component of variation in metabolite levels in addition to the heritability contribution from
Wang, Le; Wan, Zi Yi; Lim, Huan Sein; Yue, Gen Hua
Genomewide analysis of genetic divergence is critically important in understanding the genetic processes of allopatric speciation. We sequenced RAD tags of 131 Asian seabass individuals of six populations from South-East Asia and Australia/Papua New Guinea. Using 32 433 SNPs, we examined the genetic diversity and patterns of population differentiation across all the populations. We found significant evidence of genetic heterogeneity between South-East Asian and Australian/Papua New Guinean populations. The Australian/Papua New Guinean populations showed a rather lower level of genetic diversity. FST and principal components analysis revealed striking divergence between South-East Asian and Australian/Papua New Guinean populations. Interestingly, no evidence of contemporary gene flow was observed. The demographic history was further tested based on the folded joint site frequency spectrum. The scenario of ancient migration with historical population size changes was suggested to be the best fit model to explain the genetic divergence of Asian seabass between South-East Asia and Australia/Papua New Guinea. This scenario also revealed that Australian/Papua New Guinean populations were founded by ancestors from South-East Asia during mid-Pleistocene and were completely isolated from the ancestral population after the last glacial retreat. We also detected footprints of local selection, which might be related to differential ecological adaptation. The ancient gene flow was examined and deemed likely insufficient to counteract the genetic differentiation caused by genetic drift. The observed genomic pattern of divergence conflicted with the 'genomic islands' scenario. Altogether, Asian seabass have likely been evolving towards allopatric speciation since the split from the ancestral population during mid-Pleistocene. © 2016 John Wiley & Sons Ltd.
Abiotic stress tolerance traits are often complex and recalcitrant targets for conventional breeding improvement in many crop species. This study evaluated the potential of genomic selection to predict water-soluble carbohydrate concentration (WSCC), an important drought tolerance trait, in wheat un...
Ratcliffe, B; El-Dien, O G; Klápště, J; Porth, I; Chen, C; Jaquish, B; El-Kassaby, Y A
Genomic selection (GS) potentially offers an unparalleled advantage over traditional pedigree-based selection (TS) methods by reducing the time commitment required to carry out a single cycle of tree improvement. This quality is particularly appealing to tree breeders, where lengthy improvement cycles are the norm. We explored the prospect of implementing GS for interior spruce (Picea engelmannii × glauca) utilizing a genotyped population of 769 trees belonging to 25 open-pollinated families. A series of repeated tree height measurements through ages 3-40 years permitted the testing of GS methods temporally. The genotyping-by-sequencing (GBS) platform was used for single nucleotide polymorphism (SNP) discovery in conjunction with three unordered imputation methods applied to a data set with 60% missing information. Further, three diverse GS models were evaluated based on predictive accuracy (PA), and their marker effects. Moderate levels of PA (0.31-0.55) were observed and were of sufficient capacity to deliver improved selection response over TS. Additionally, PA varied substantially through time accordingly with spatial competition among trees. As expected, temporal PA was well correlated with age-age genetic correlation (r=0.99), and decreased substantially with increasing difference in age between the training and validation populations (0.04-0.47). Moreover, our imputation comparisons indicate that k-nearest neighbor and singular value decomposition yielded a greater number of SNPs and gave higher predictive accuracies than imputing with the mean. Furthermore, the ridge regression (rrBLUP) and BayesCπ (BCπ) models both yielded equal, and better PA than the generalized ridge regression heteroscedastic effect model for the traits evaluated.
Sonia E. Eynard
Full Text Available Genomic selection (GS is commonly used in livestock and increasingly in plant breeding. Relying on phenotypes and genotypes of a reference population, GS allows performance prediction for young individuals having only genotypes. This is expected to achieve fast high genetic gain but with a potential loss of genetic diversity. Existing methods to conserve genetic diversity depend mostly on the choice of the breeding individuals. In this study, we propose a modification of the reference population composition to mitigate diversity loss. Since the high cost of phenotyping is the limiting factor for GS, our findings are of major economic interest. This study aims to answer the following questions: how would decisions on the reference population affect the breeding population, and how to best select individuals to update the reference population and balance maximizing genetic gain and minimizing loss of genetic diversity? We investigated three updating strategies for the reference population: random, truncation, and optimal contribution (OC strategies. OC maximizes genetic merit for a fixed loss of genetic diversity. A French Montbéliarde dairy cattle population with 50K SNP chip genotypes and simulations over 10 generations were used to compare these different strategies using milk production as the trait of interest. Candidates were selected to update the reference population. Prediction bias and both genetic merit and diversity were measured. Changes in the reference population composition slightly affected the breeding population. Optimal contribution strategy appeared to be an acceptable compromise to maintain both genetic gain and diversity in the reference and the breeding populations.
Osthushenrich, Tanja; Frisch, Matthias; Herzog, Eva
In a line or a hybrid breeding program superior lines are selected from a breeding pool as parental lines for the next breeding cycle. From a cross of two parental lines, new lines are derived by single-seed descent (SSD) or doubled haploid (DH) technology. However, not all possible crosses between the parental lines can be carried out due to limited resources. Our objectives were to present formulas to characterize a cross by the mean and variance of the genotypic values of the lines derived from the cross, and to apply the formulas to predict means and variances of flowering time traits in recombinant inbred line families of a publicly available data set in maize. We derived formulas which are based on the expected linkage disequilibrium (LD) between two loci and which can be used for arbitrary mating systems. Results were worked out for SSD and DH lines derived from a cross after an arbitrary number of intermating generations. The means and variances were highly correlated with results obtained by the simulation software PopVar. Compared with these simulations, computation time for our closed formulas was about ten times faster. The means and variances for flowering time traits observed in the recombinant inbred line families of the investigated data set showed correlations of around 0.9 for the means and of 0.46 and 0.65 for the standard deviations with the estimated values. We conclude that our results provide a framework that can be exploited to increase the efficiency of hybrid and line breeding programs by extending genomic selection approaches to the selection of crossing partners.
Eynard, Sonia E; Croiseau, Pascal; Laloë, Denis; Fritz, Sebastien; Calus, Mario P L; Restoux, Gwendal
Genomic selection (GS) is commonly used in livestock and increasingly in plant breeding. Relying on phenotypes and genotypes of a reference population, GS allows performance prediction for young individuals having only genotypes. This is expected to achieve fast high genetic gain but with a potential loss of genetic diversity. Existing methods to conserve genetic diversity depend mostly on the choice of the breeding individuals. In this study, we propose a modification of the reference population composition to mitigate diversity loss. Since the high cost of phenotyping is the limiting factor for GS, our findings are of major economic interest. This study aims to answer the following questions: how would decisions on the reference population affect the breeding population, and how to best select individuals to update the reference population and balance maximizing genetic gain and minimizing loss of genetic diversity? We investigated three updating strategies for the reference population: random, truncation, and optimal contribution (OC) strategies. OC maximizes genetic merit for a fixed loss of genetic diversity. A French Montbéliarde dairy cattle population with 50K SNP chip genotypes and simulations over 10 generations were used to compare these different strategies using milk production as the trait of interest. Candidates were selected to update the reference population. Prediction bias and both genetic merit and diversity were measured. Changes in the reference population composition slightly affected the breeding population. Optimal contribution strategy appeared to be an acceptable compromise to maintain both genetic gain and diversity in the reference and the breeding populations. Copyright © 2018 Eynard et al.
Osthushenrich, Tanja; Frisch, Matthias
In a line or a hybrid breeding program superior lines are selected from a breeding pool as parental lines for the next breeding cycle. From a cross of two parental lines, new lines are derived by single-seed descent (SSD) or doubled haploid (DH) technology. However, not all possible crosses between the parental lines can be carried out due to limited resources. Our objectives were to present formulas to characterize a cross by the mean and variance of the genotypic values of the lines derived from the cross, and to apply the formulas to predict means and variances of flowering time traits in recombinant inbred line families of a publicly available data set in maize. We derived formulas which are based on the expected linkage disequilibrium (LD) between two loci and which can be used for arbitrary mating systems. Results were worked out for SSD and DH lines derived from a cross after an arbitrary number of intermating generations. The means and variances were highly correlated with results obtained by the simulation software PopVar. Compared with these simulations, computation time for our closed formulas was about ten times faster. The means and variances for flowering time traits observed in the recombinant inbred line families of the investigated data set showed correlations of around 0.9 for the means and of 0.46 and 0.65 for the standard deviations with the estimated values. We conclude that our results provide a framework that can be exploited to increase the efficiency of hybrid and line breeding programs by extending genomic selection approaches to the selection of crossing partners. PMID:29200436
Cohn Zachary A
Full Text Available Abstract Background Cartilage plays a fundamental role in the development of the human skeleton. Early in embryogenesis, mesenchymal cells condense and differentiate into chondrocytes to shape the early skeleton. Subsequently, the cartilage anlagen differentiate to form the growth plates, which are responsible for linear bone growth, and the articular chondrocytes, which facilitate joint function. However, despite the multiplicity of roles of cartilage during human fetal life, surprisingly little is known about its transcriptome. To address this, a whole genome microarray expression profile was generated using RNA isolated from 18–22 week human distal femur fetal cartilage and compared with a database of control normal human tissues aggregated at UCLA, termed Celsius. Results 161 cartilage-selective genes were identified, defined as genes significantly expressed in cartilage with low expression and little variation across a panel of 34 non-cartilage tissues. Among these 161 genes were cartilage-specific genes such as cartilage collagen genes and 25 genes which have been associated with skeletal phenotypes in humans and/or mice. Many of the other cartilage-selective genes do not have established roles in cartilage or are novel, unannotated genes. Quantitative RT-PCR confirmed the unique pattern of gene expression observed by microarray analysis. Conclusion Defining the gene expression pattern for cartilage has identified new genes that may contribute to human skeletogenesis as well as provided further candidate genes for skeletal dysplasias. The data suggest that fetal cartilage is a complex and transcriptionally active tissue and demonstrate that the set of genes selectively expressed in the tissue has been greatly underestimated.
Patricia A Thompson
Full Text Available A number of studies of copy number imbalances (CNIs in breast tumors support associations between individual CNIs and patient outcomes. However, no pattern or signature of CNIs has emerged for clinical use. We determined copy number (CN gains and losses using high-density molecular inversion probe (MIP arrays for 971 stage I/II breast tumors and applied a boosting strategy to fit hazards models for CN and recurrence, treating chromosomal segments in a dose-specific fashion (-1 [loss], 0 [no change] and +1 [gain]. The concordance index (C-Index was used to compare prognostic accuracy between a training (n = 728 and test (n = 243 set and across models. Twelve novel prognostic CNIs were identified: losses at 1p12, 12q13.13, 13q12.3, 22q11, and Xp21, and gains at 2p11.1, 3q13.12, 10p11.21, 10q23.1, 11p15, 14q13.2-q13.3, and 17q21.33. In addition, seven CNIs previously implicated as prognostic markers were selected: losses at 8p22 and 16p11.2 and gains at 10p13, 11q13.5, 12p13, 20q13, and Xq28. For all breast cancers combined, the final full model including 19 CNIs, clinical covariates, and tumor marker-approximated subtypes (estrogen receptor [ER], progesterone receptor, ERBB2 amplification, and Ki67 significantly outperformed a model containing only clinical covariates and tumor subtypes (C-Index(full model, train[test] = 0.72[0.71] ± 0.02 vs. C-Index(clinical + subtype model, train[test] = 0.62[0.62] ± 0.02; p<10(-6. In addition, the full model containing 19 CNIs significantly improved prognostication separately for ER-, HER2+, luminal B, and triple negative tumors over clinical variables alone. In summary, we show that a set of 19 CNIs discriminates risk of recurrence among early-stage breast tumors, independent of ER status. Further, our data suggest the presence of specific CNIs that promote and, in some cases, limit tumor spread.
Hartmann, Willian K.; Berman, Daniel C.; Betts, Bruce H.
We have examined a number of potential landing sites to study effects associated with impact crater populations. We used Mars Global Surveyor high resolution MOC images, and emphasized "ground truth" by calibrating with the MOC images of Viking 1 and Pathfinder sites. An interesting result is that most of Mars (all surfaces with model ages older than 100 My) have small crater populations in saturation equilibrium below diameters D approx. = 60 meters (and down to the smallest resolvable, countable sizes, approx. = 15 m). This may have consequences for preservation of surface bedrock exposures accessible to rovers. In the lunar maria, a similar saturation equilibrium is reached for crater diameters below about 300 meters, and this has produced a regolith depth of about 10-20 meters in those areas. Assuming linear scaling, we infer that saturation at D approx. = 60 m would produce gardening and Martian regolith, or fragmental layers, about 2 to 4 meters deep over all but extremely young surfaces (such as the very fresh thin surface flows in southern Elysium Planitia, which have model ages around 10 My or less). This result may explain the global production of ubiquitous dust and fragmental material on Mars. Removal of fines may leave the boulders that have been seen at all three of the first landing sites. Accumulation of the fines elsewhere produces dunes. Due to these effects, it may be difficult to set down rovers in areas where bedrock is well preserved at depths of centimeters, unless we find cliff sides or areas of deflation where wind has exposed clean surfaces (among residual boulders?) We have also surveyed the PHOBOS 2 Termoskan data to look for regions of thermal anomalies that might produce interesting landing sites. For landing site selection, two of the more interesting types of features are thermally distinct ejecta blankets and thermally distinct channels and valleys. Martian "thermal features" such as these that correlate closely with nonaeolian
Rosli, Rozana; Amiruddin, Nadzirah; Ab Halim, Mohd Amin; Chan, Pek-Lan; Chan, Kuang-Lim; Azizi, Norazah; Morris, Priscilla E.; Leslie Low, Eng-Ti; Ong-Abdullah, Meilina; Sambanthamurthi, Ravigadevi; Singh, Rajinder
Comparative genomics and transcriptomic analyses were performed on two agronomically important groups of genes from oil palm versus other major crop species and the model organism, Arabidopsis thaliana. The first analysis was of two gene families with key roles in regulation of oil quality and in particular the accumulation of oleic acid, namely stearoyl ACP desaturases (SAD) and acyl-acyl carrier protein (ACP) thioesterases (FAT). In both cases, these were found to be large gene families with complex expression profiles across a wide range of tissue types and developmental stages. The detailed classification of the oil palm SAD and FAT genes has enabled the updating of the latest version of the oil palm gene model. The second analysis focused on disease resistance (R) genes in order to elucidate possible candidates for breeding of pathogen tolerance/resistance. Ortholog analysis showed that 141 out of the 210 putative oil palm R genes had homologs in banana and rice. These genes formed 37 clusters with 634 orthologous genes. Classification of the 141 oil palm R genes showed that the genes belong to the Kinase (7), CNL (95), MLO-like (8), RLK (3) and Others (28) categories. The CNL R genes formed eight clusters. Expression data for selected R genes also identified potential candidates for breeding of disease resistance traits. Furthermore, these findings can provide information about the species evolution as well as the identification of agronomically important genes in oil palm and other major crops. PMID:29672525
Full Text Available Two methods of SNPs pre-selection based on single marker regression for the estimation of genomic breeding values (G-EBVs were compared using simulated data provided by the XII QTL-MAS workshop: i Bonferroni correction of the significance threshold and ii Permutation test to obtain the reference distribution of the null hypothesis and identify significant markers at P<0.01 and P<0.001 significance thresholds. From the set of markers significant at P<0.001, random subsets of 50% and 25% markers were extracted, to evaluate the effect of further reducing the number of significant SNPs on G-EBV predictions. The Bonferroni correction method allowed the identification of 595 significant SNPs that gave the best G-EBV accuracies in prediction generations (82.80%. The permutation methods gave slightly lower G-EBV accuracies even if a larger number of SNPs resulted significant (2,053 and 1,352 for 0.01 and 0.001 significance thresholds, respectively. Interestingly, halving or dividing by four the number of SNPs significant at P<0.001 resulted in an only slightly decrease of G-EBV accuracies. The genetic structure of the simulated population with few QTL carrying large effects, might have favoured the Bonferroni method.
Shaw, C D; Lonchamp, J; Downing, T; Imamura, H; Freeman, T M; Cotton, J A; Sanders, M; Blackburn, G; Dujardin, J C; Rijal, S; Khanal, B; Illingworth, C J R; Coombs, G H; Carter, K C
In this study, we followed the genomic, lipidomic and metabolomic changes associated with the selection of miltefosine (MIL) resistance in two clinically derived Leishmania donovani strains with different inherent resistance to antimonial drugs (antimony sensitive strain Sb-S; and antimony resistant Sb-R). MIL-R was easily induced in both strains using the promastigote-stage, but a significant increase in MIL-R in the intracellular amastigote compared to the corresponding wild-type did not occur until promastigotes had adapted to 12.2 μM MIL. A variety of common and strain-specific genetic changes were discovered in MIL-adapted parasites, including deletions at the LdMT transporter gene, single-base mutations and changes in somy. The most obvious lipid changes in MIL-R promastigotes occurred to phosphatidylcholines and lysophosphatidylcholines and results indicate that the Kennedy pathway is involved in MIL resistance. The inherent Sb resistance of the parasite had an impact on the changes that occurred in MIL-R parasites, with more genetic changes occurring in Sb-R compared with Sb-S parasites. Initial interpretation of the changes identified in this study does not support synergies with Sb-R in the mechanisms of MIL resistance, though this requires an enhanced understanding of the parasite's biochemical pathways and how they are genetically regulated to be verified fully. © 2015 The Authors. Molecular Microbiology published by John Wiley & Sons Ltd.
Rosli, Rozana; Amiruddin, Nadzirah; Ab Halim, Mohd Amin; Chan, Pek-Lan; Chan, Kuang-Lim; Azizi, Norazah; Morris, Priscilla E; Leslie Low, Eng-Ti; Ong-Abdullah, Meilina; Sambanthamurthi, Ravigadevi; Singh, Rajinder; Murphy, Denis J
Comparative genomics and transcriptomic analyses were performed on two agronomically important groups of genes from oil palm versus other major crop species and the model organism, Arabidopsis thaliana. The first analysis was of two gene families with key roles in regulation of oil quality and in particular the accumulation of oleic acid, namely stearoyl ACP desaturases (SAD) and acyl-acyl carrier protein (ACP) thioesterases (FAT). In both cases, these were found to be large gene families with complex expression profiles across a wide range of tissue types and developmental stages. The detailed classification of the oil palm SAD and FAT genes has enabled the updating of the latest version of the oil palm gene model. The second analysis focused on disease resistance (R) genes in order to elucidate possible candidates for breeding of pathogen tolerance/resistance. Ortholog analysis showed that 141 out of the 210 putative oil palm R genes had homologs in banana and rice. These genes formed 37 clusters with 634 orthologous genes. Classification of the 141 oil palm R genes showed that the genes belong to the Kinase (7), CNL (95), MLO-like (8), RLK (3) and Others (28) categories. The CNL R genes formed eight clusters. Expression data for selected R genes also identified potential candidates for breeding of disease resistance traits. Furthermore, these findings can provide information about the species evolution as well as the identification of agronomically important genes in oil palm and other major crops.
Lia Carolina Soares Medeiros
Full Text Available Trypanosomatids (order Kinetoplastida, including the human pathogens Trypanosoma cruzi (agent of Chagas disease, Trypanosoma brucei, (African sleeping sickness, and Leishmania (leishmaniasis, affect millions of people and animals globally. T. cruzi is considered one of the least studied and most poorly understood tropical disease-causing parasites, in part because of the relative lack of facile genetic engineering tools. This situation has improved recently through the application of clustered regularly interspaced short palindromic repeats–CRISPR-associated protein 9 (CRISPR-Cas9 technology, but a number of limitations remain, including the toxicity of continuous Cas9 expression and the long drug marker selection times. In this study, we show that the delivery of ribonucleoprotein (RNP complexes composed of recombinant Cas9 from Staphylococcus aureus (SaCas9, but not from the more routinely used Streptococcus pyogenes Cas9 (SpCas9, and in vitro-transcribed single guide RNAs (sgRNAs results in rapid gene edits in T. cruzi and other kinetoplastids at frequencies approaching 100%. The highly efficient genome editing via SaCas9/sgRNA RNPs was obtained for both reporter and endogenous genes and observed in multiple parasite life cycle stages in various strains of T. cruzi, as well as in T. brucei and Leishmania major. RNP complex delivery was also used to successfully tag proteins at endogenous loci and to assess the biological functions of essential genes. Thus, the use of SaCas9 RNP complexes for gene editing in kinetoplastids provides a simple, rapid, and cloning- and selection-free method to assess gene function in these important human pathogens.
Pierella Karlusich, Juan J; Ceccoli, Romina D; Graña, Martín; Romero, Héctor; Carrillo, Néstor
Oxidative stress and iron limitation represent the grim side of life in an oxygen-rich atmosphere. The versatile electron transfer shuttle ferredoxin, an iron-sulfur protein, is particularly sensitive to these hardships, and its downregulation under adverse conditions severely compromises survival of phototrophs. Replacement of ferredoxin by a stress-resistant isofunctional carrier, flavin-containing flavodoxin, is a widespread strategy employed by photosynthetic microorganisms to overcome environmental adversities. The flavodoxin gene was lost in the course of plant evolution, but its reintroduction in transgenic plants confers increased tolerance to environmental stress and iron starvation, raising the question as to why a genetic asset with obvious adaptive value was not kept by natural selection. Phylogenetic analyses reveal that the evolutionary history of flavodoxin is intricate, with several horizontal gene transfer events between distant organisms, including Eukarya, Bacteria, and Archaea. The flavodoxin gene is unevenly distributed in most algal lineages, with flavodoxin-containing species being overrepresented in iron-limited regions and scarce or absent in iron-rich environments. Evaluation of cyanobacterial genomic and metagenomic data yielded essentially the same results, indicating that there was little selection pressure to retain flavodoxin in iron-rich coastal/freshwater phototrophs. Our results show a highly dynamic evolution pattern of flavodoxin tightly connected to the bioavailability of iron. Evidence presented here also indicates that the high concentration of iron in coastal and freshwater habitats may have facilitated the loss of flavodoxin in the freshwater ancestor of modern plants during the transition of photosynthetic organisms from the open oceans to the firm land. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
Yoder, C. F.; Konopliv, A. S.; Yuan, D. N.; Standish, E. M.; Folkner, W. M.
The solar tidal deformation of Mars, measured by its k2 potential Love number, has been obtained from analysis of MGS radio tracking. The observed k2 =0.164+-0.016 is large enough to rule out a solid iron core. The inferred core radius Rc (1600km
Aslam, Roohi; Williams, Lorraine E; Bhatti, Muhammad Faraz; Virk, Nasar
P 2 - type calcium ATPases (ACAs-auto inhibited calcium ATPases and ECAs-endoplasmic reticulum calcium ATPases) belong to the P- type ATPase family of active membrane transporters and are significantly involved in maintaining accurate levels of Ca 2+ , Mn 2+ and Zn 2+ in the cytosol as well as playing a very important role in stress signaling, stomatal opening and closing and pollen tube growth. Here we report the identification and possible role of some of these ATPases from wheat. In this study, ACA and ECA sequences of six species (belonging to Poaceae) were retrieved from different databases and a phylogenetic tree was constructed. A high degree of evolutionary relatedness was observed among P 2 sequences characterized in this study. Members of the respective groups from different plant species were observed to fall under the same clade. This pattern highlights the common ancestry of P 2- type calcium ATPases. Furthermore, qRT-PCR was used to analyse the expression of selected ACAs and ECAs from Triticum aestivum (wheat) under calcium toxicity and calcium deficiency. The data indicated that expression of ECAs is enhanced under calcium stress, suggesting possible roles of these ATPases in calcium homeostasis in wheat. Similarly, the expression of ACAs was significantly different in plants grown under calcium stress as compared to plants grown under control conditions. This gives clues to the role of ACAs in signal transduction during calcium stress in wheat. Here we concluded that wheat genome consists of nine P 2B and three P 2A -type calcium ATPases. Moreover, gene loss events in wheat ancestors lead to the loss of a particular homoeolog of a gene in wheat. To elaborate the role of these wheat ATPases, qRT-PCR was performed. The results indicated that when plants are exposed to calcium stress, both P 2A and P 2B gene expression get enhanced. This further gives clues about the possible role of these ATPases in wheat in calcium management. These findings can be
Lenz, Patrick R N; Beaulieu, Jean; Mansfield, Shawn D; Clément, Sébastien; Desponts, Mireille; Bousquet, Jean
Genomic selection (GS) uses information from genomic signatures consisting of thousands of genetic markers to predict complex traits. As such, GS represents a promising approach to accelerate tree breeding, which is especially relevant for the genetic improvement of boreal conifers characterized by long breeding cycles. In the present study, we tested GS in an advanced-breeding population of the boreal black spruce (Picea mariana [Mill.] BSP) for growth and wood quality traits, and concurrently examined factors affecting GS model accuracy. The study relied on 734 25-year-old trees belonging to 34 full-sib families derived from 27 parents and that were established on two contrasting sites. Genomic profiles were obtained from 4993 Single Nucleotide Polymorphisms (SNPs) representative of as many gene loci distributed among the 12 linkage groups common to spruce. GS models were obtained for four growth and wood traits. Validation using independent sets of trees showed that GS model accuracy was high, related to trait heritability and equivalent to that of conventional pedigree-based models. In forward selection, gains per unit of time were three times higher with the GS approach than with conventional selection. In addition, models were also accurate across sites, indicating little genotype-by-environment interaction in the area investigated. Using information from half-sibs instead of full-sibs led to a significant reduction in model accuracy, indicating that the inclusion of relatedness in the model contributed to its higher accuracies. About 500 to 1000 markers were sufficient to obtain GS model accuracy almost equivalent to that obtained with all markers, whether they were well spread across the genome or from a single linkage group, further confirming the implication of relatedness and potential long-range linkage disequilibrium (LD) in the high accuracy estimates obtained. Only slightly higher model accuracy was obtained when using marker subsets that were
Chen, Ze-Hui; Zhang, Min; Lv, Feng-Hua; Ren, Xue; Li, Wen-Rong; Liu, Ming-Jun; Nam, Kiwoong; Bruford, Michael W; Li, Meng-Hua
Analyses of genomic diversity along the X chromosome and of its correlation with autosomal diversity can facilitate understanding of evolutionary forces in shaping sex-linked genomic architecture. Strong selective sweeps and accelerated genetic drift on the X-chromosome have been inferred in primates and other model species, but no such insight has yet been gained in domestic animals compared with their wild relatives. Here, we analyzed X-chromosome variability in a large ovine data set, including a BeadChip array for 943 ewes from the world's sheep populations and 110 whole genomes of wild and domestic sheep. Analyzing whole-genome sequences, we observed a substantially reduced X-to-autosome diversity ratio (∼0.6) compared with the value expected under a neutral model (0.75). In particular, one large X-linked segment (43.05-79.25 Mb) was found to show extremely low diversity, most likely due to a high density of coding genes, featuring highly conserved regions. In general, we observed higher nucleotide diversity on the autosomes, but a flat diversity gradient in X-linked segments, as a function of increasing distance from the nearest genes, leading to a decreased X: autosome (X/A) diversity ratio and contrasting to the positive correlation detected in primates and other model animals. Our evidence suggests that accelerated genetic drift but reduced directional selection on X chromosome, as well as sex-biased demographic events, explain low X-chromosome diversity in sheep species. The distinct patterns of X-linked and X/A diversity we observed between Middle Eastern and non-Middle Eastern sheep populations can be explained by multiple migrations, selection, and admixture during the domestic sheep's recent postdomestication demographic expansion, coupled with natural selection for adaptation to new environments. In addition, we identify important novel genes involved in abnormal behavioral phenotypes, metabolism, and immunity, under selection on the sheep X-chromosome.
Bwogi, Josephine; Jere, Khuzwayo C; Karamagi, Charles; Byarugaba, Denis K; Namuwulya, Prossy; Baliraine, Frederick N; Desselberger, Ulrich; Iturriza-Gomara, Miren
Rotaviruses of species A (RVA) are a common cause of diarrhoea in children and the young of various other mammals and birds worldwide. To investigate possible interspecies transmission of RVAs, whole genomes of 18 human and 6 domestic animal RVA strains identified in Uganda between 2012 and 2014 were sequenced using the Illumina HiSeq platform. The backbone of the human RVA strains had either a Wa- or a DS-1-like genetic constellation. One human strain was a Wa-like mono-reassortant containing a DS-1-like VP2 gene of possible animal origin. All eleven genes of one bovine RVA strain were closely related to those of human RVAs. One caprine strain had a mixed genotype backbone, suggesting that it emerged from multiple reassortment events involving different host species. The porcine RVA strains had mixed genotype backbones with possible multiple reassortant events with strains of human and bovine origin.Overall, whole genome characterisation of rotaviruses found in domestic animals in Uganda strongly suggested the presence of human-to animal RVA transmission, with concomitant circulation of multi-reassortant strains potentially derived from complex interspecies transmission events. However, whole genome data from the human RVA strains causing moderate and severe diarrhoea in under-fives in Uganda indicated that they were primarily transmitted from person-to-person.
Full Text Available Abstract Background For most organisms, developing hundreds of genetic markers spanning the whole genome still requires excessive if not unrealistic efforts. In this context, there is an obvious need for methodologies allowing the low-cost, fast and high-throughput genotyping of virtually any species, such as the Diversity Arrays Technology (DArT. One of the crucial steps of the DArT technique is the genome complexity reduction, which allows obtaining a genomic representation characteristic of the studied DNA sample and necessary for subsequent genotyping. In this article, using the mosquito Aedes aegypti as a study model, we describe a new genome complexity reduction method taking advantage of the abundance of miniature inverted repeat transposable elements (MITEs in the genome of this species. Results Ae. aegypti genomic representations were produced following a two-step procedure: (1 restriction digestion of the genomic DNA and simultaneous ligation of a specific adaptor to compatible ends, and (2 amplification of restriction fragments containing a particular MITE element called Pony using two primers, one annealing to the adaptor sequence and one annealing to a conserved sequence motif of the Pony element. Using this protocol, we constructed a library comprising more than 6,000 DArT clones, of which at least 5.70% were highly reliable polymorphic markers for two closely related mosquito strains separated by only a few generations of artificial selection. Within this dataset, linkage disequilibrium was low, and marker redundancy was evaluated at 2.86% only. Most of the detected genetic variability was observed between the two studied mosquito strains, but individuals of the same strain could still be clearly distinguished. Conclusion The new complexity reduction method was particularly efficient to reveal genetic polymorphisms in Ae. egypti. Overall, our results testify of the flexibility of the DArT genotyping technique and open new
Stölting, Kai N; Paris, Margot; Meier, Cécile; Heinze, Berthold; Castiglione, Stefano; Bartha, Denes; Lexer, Christian
Studying the divergence continuum in plants is relevant to fundamental and applied biology because of the potential to reveal functionally important genetic variation. In this context, whole-genome sequencing (WGS) provides the necessary rigour for uncovering footprints of selection. We resequenced populations of two divergent phylogeographic lineages of Populus alba (n = 48), thoroughly characterized by microsatellites (n = 317), and scanned their genomes for regions of unusually high allelic differentiation and reduced diversity using > 1.7 million single nucleotide polymorphisms (SNPs) from WGS. Results were confirmed by Sanger sequencing. On average, 9134 high-differentiation (≥ 4 standard deviations) outlier SNPs were uncovered between populations, 848 of which were shared by ≥ three replicate comparisons. Annotation revealed that 545 of these were located in 437 predicted genes. Twelve percent of differentiation outlier genome regions exhibited significantly reduced genetic diversity. Gene ontology (GO) searches were successful for 327 high-differentiation genes, and these were enriched for 63 GO terms. Our results provide a snapshot of the roles of 'hard selective sweeps' vs divergent selection of standing genetic variation in distinct postglacial recolonization lineages of P. alba. Thus, this study adds to our understanding of the mechanisms responsible for the origin of functionally relevant variation in temperate trees. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.
Fuller, Zachary L; Niño, Elina L; Patch, Harland M; Bedoya-Reina, Oscar C; Baumgarten, Tracey; Muli, Elliud; Mumoki, Fiona; Ratan, Aakrosh; McGraw, John; Frazier, Maryann; Masiga, Daniel; Schuster, Stephen; Grozinger, Christina M; Miller, Webb
With the development of inexpensive, high-throughput sequencing technologies, it has become feasible to examine questions related to population genetics and molecular evolution of non-model species in their ecological contexts on a genome-wide scale. Here, we employed a newly developed suite of integrated, web-based programs to examine population dynamics and signatures of selection across the genome using several well-established tests, including F ST, pN/pS, and McDonald-Kreitman. We applied these techniques to study populations of honey bees (Apis mellifera) in East Africa. In Kenya, there are several described A. mellifera subspecies, which are thought to be localized to distinct ecological regions. We performed whole genome sequencing of 11 worker honey bees from apiaries distributed throughout Kenya and identified 3.6 million putative single-nucleotide polymorphisms. The dense coverage allowed us to apply several computational procedures to study population structure and the evolutionary relationships among the populations, and to detect signs of adaptive evolution across the genome. While there is considerable gene flow among the sampled populations, there are clear distinctions between populations from the northern desert region and those from the temperate, savannah region. We identified several genes showing population genetic patterns consistent with positive selection within African bee populations, and between these populations and European A. mellifera or Asian Apis florea. These results lay the groundwork for future studies of adaptive ecological evolution in honey bees, and demonstrate the use of new, freely available web-based tools and workflows ( http://usegalaxy.org/r/kenyanbee ) that can be applied to any model system with genomic information.
Guillot, Gilles; Vitalis, Renaud; Rouzic, Arnaud le
to disentangle the potential effect of environmental variables from the confounding effect of population history. For the routine analysis of genome-wide datasets, one also needs fast inference and model selection algorithms. We propose a method based on an explicit spatial model which is an instance of spatial...... for the most common types of genetic markers, obtained either at the individual or at the population level. Analyzing the simulated data produced under a geostatistical model then under an explicit model of selection, we show that the method is efficient. We also re-analyze a dataset relative to nineteen pine...
Wang, Haohan; Aragam, Bryon; Xing, Eric P
A fundamental and important challenge in modern datasets of ever increasing dimensionality is variable selection, which has taken on renewed interest recently due to the growth of biological and medical datasets with complex, non-i.i.d. structures. Naïvely applying classical variable selection methods such as the Lasso to such datasets may lead to a large number of false discoveries. Motivated by genome-wide association studies in genetics, we study the problem of variable selection for datasets arising from multiple subpopulations, when this underlying population structure is unknown to the researcher. We propose a unified framework for sparse variable selection that adaptively corrects for population structure via a low-rank linear mixed model. Most importantly, the proposed method does not require prior knowledge of sample structure in the data and adaptively selects a covariance structure of the correct complexity. Through extensive experiments, we illustrate the effectiveness of this framework over existing methods. Further, we test our method on three different genomic datasets from plants, mice, and human, and discuss the knowledge we discover with our method. Copyright © 2018. Published by Elsevier Inc.