Integrating genomic selection into dairy cattle breeding programmes: a review.

Science.gov (United States)

Bouquet, A; Juga, J

2013-05-01

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

Accuracy of genomic selection in European maize elite breeding populations.

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Zhao, Yusheng; Gowda, Manje; Liu, Wenxin; Würschum, Tobias; Maurer, Hans P; Longin, Friedrich H; Ranc, Nicolas; Reif, Jochen C

2012-03-01

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.

Integration of genomic information into sport horse breeding programs for optimization of accuracy of selection.

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Haberland, A M; König von Borstel, U; Simianer, H; König, S

2012-09-01

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.

Genomic analyses of modern dog breeds.

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Parker, Heidi G

2012-02-01

A rose may be a rose by any other name, but when you call a dog a poodle it becomes a very different animal than if you call it a bulldog. Both the poodle and the bulldog are examples of dog breeds of which there are >400 recognized worldwide. Breed creation has played a significant role in shaping the modern dog from the length of his leg to the cadence of his bark. The selection and line-breeding required to maintain a breed has also reshaped the genome of the dog, resulting in a unique genetic pattern for each breed. The breed-based population structure combined with extensive morphologic variation and shared human environments have made the dog a popular model for mapping both simple and complex traits and diseases. In order to obtain the most benefit from the dog as a genetic system, it is necessary to understand the effect structured breeding has had on the genome of the species. That is best achieved by looking at genomic analyses of the breeds, their histories, and their relationships to each other.

Preliminary investigation on reliability of genomic estimated breeding values in the Danish and Swedish Holstein Population

DEFF Research Database (Denmark)

Su, G; Guldbrandtsen, B; Gregersen, V R

2010-01-01

or no effects, and a single prior distribution common for all SNP. It was found that, in general, the model with a common prior distribution of scaling factors had better predictive ability than any mixture prior models. Therefore, a common prior model was used to estimate SNP effects and breeding values......Abstract This study investigated the reliability of genomic estimated breeding values (GEBV) in the Danish Holstein population. The data in the analysis included 3,330 bulls with both published conventional EBV and single nucleotide polymorphism (SNP) markers. After data editing, 38,134 SNP markers...... were available. In the analysis, all SNP were fitted simultaneously as random effects in a Bayesian variable selection model, which allows heterogeneous variances for different SNP markers. The response variables were the official EBV. Direct GEBV were calculated as the sum of individual SNP effects...

Genomic selection accuracy using multi-family prediction models in a wheat breeding program

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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...

Genome-Enabled Prediction of Breeding Values for Feedlot Average Daily Weight Gain in Nelore Cattle

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Adriana L. Somavilla

2017-06-01

Full Text Available Nelore is the most economically important cattle breed in Brazil, and the use of genetically improved animals has contributed to increased beef production efficiency. The Brazilian beef feedlot industry has grown considerably in the last decade, so the selection of animals with higher growth rates on feedlot has become quite important. Genomic selection (GS could be used to reduce generation intervals and improve the rate of genetic gains. The aim of this study was to evaluate the prediction of genomic-estimated breeding values (GEBV for average daily weight gain (ADG in 718 feedlot-finished Nelore steers. Analyses of three Bayesian model specifications [Bayesian GBLUP (BGBLUP, BayesA, and BayesCπ] were performed with four genotype panels [Illumina BovineHD BeadChip, TagSNPs, and GeneSeek High- and Low-density indicus (HDi and LDi, respectively]. Estimates of Pearson correlations, regression coefficients, and mean squared errors were used to assess accuracy and bias of predictions. Overall, the BayesCπ model resulted in less biased predictions. Accuracies ranged from 0.18 to 0.27, which are reasonable values given the heritability estimates (from 0.40 to 0.44 and sample size (568 animals in the training population. Furthermore, results from Bos taurus indicus panels were as informative as those from Illumina BovineHD, indicating that they could be used to implement GS at lower costs.

CASSAVA BREEDING I: THE VALUE OF BREEDING VALUE

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Hernán Ceballos

2016-08-01

Full Text Available Breeding cassava relies on several selection stages (single row trial-SRT; preliminary; advanced; and uniform yield trials - UYT. This study uses data from 14 years of evaluations. From more than 20,000 genotypes initially evaluated only 114 reached the last stage. The objective was to assess how the data at SRT could be used to predict the probabilities of genotypes reaching the UYT. Phenotypic data from each genotype at SRT was integrated into the selection index (SIN used by the cassava breeding program. Average SIN from all the progenies derived from each progenitor was then obtained. Average SIN is an approximation of the breeding value of each progenitor. Data clearly suggested that some genotypes were better progenitors than others (e.g. high number of their progenies reaching the UYT, suggesting important variation in breeding values of progenitors. However, regression of average SIN of each parental genotype on the number of their respective progenies reaching UYT resulted in a negligible coefficient of determination (r2 = 0.05. Breeding value (e.g. average SIN at SRT was not efficient predicting which genotypes were more likely to reach the UYT stage. Number of families and progenies derived from a given progenitor were more efficient predicting the probabilities of the progeny from a given parent reaching the UYT stage. Large within-family genetic variation tends to mask the true breeding value of each progenitor. The use of partially inbred progenitors (e.g. S1 or S2 genotypes would reduce the within-family genetic variation thus making the assessment of breeding value more accurate. Moreover, partial inbreeding of progenitors can improve the breeding value of the original (S0 parental material and sharply accelerate genetic gains. For instance, homozygous S1 genotypes for the dominant resistance to cassava mosaic disease could be generated and selected. All gametes from these selected S1 genotypes would carry the desirable allele

Genomic selection for crossbred performance accounting for breed-specific effects.

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Lopes, Marcos S; Bovenhuis, Henk; Hidalgo, André M; van Arendonk, Johan A M; Knol, Egbert F; Bastiaansen, John W M

2017-06-26

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

A two step Bayesian approach for genomic prediction of breeding values.

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Shariati, Mohammad M; Sørensen, Peter; Janss, Luc

2012-05-21

In genomic models that assign an individual variance to each marker, the contribution of one marker to the posterior distribution of the marker variance is only one degree of freedom (df), which introduces many variance parameters with only little information per variance parameter. A better alternative could be to form clusters of markers with similar effects where markers in a cluster have a common variance. Therefore, the influence of each marker group of size p on the posterior distribution of the marker variances will be p df. The simulated data from the 15th QTL-MAS workshop were analyzed such that SNP markers were ranked based on their effects and markers with similar estimated effects were grouped together. In step 1, all markers with minor allele frequency more than 0.01 were included in a SNP-BLUP prediction model. In step 2, markers were ranked based on their estimated variance on the trait in step 1 and each 150 markers were assigned to one group with a common variance. In further analyses, subsets of 1500 and 450 markers with largest effects in step 2 were kept in the prediction model. Grouping markers outperformed SNP-BLUP model in terms of accuracy of predicted breeding values. However, the accuracies of predicted breeding values were lower than Bayesian methods with marker specific variances. Grouping markers is less flexible than allowing each marker to have a specific marker variance but, by grouping, the power to estimate marker variances increases. A prior knowledge of the genetic architecture of the trait is necessary for clustering markers and appropriate prior parameterization.

Genomic prediction across dairy cattle populations and breeds

DEFF Research Database (Denmark)

Zhou, Lei

Genomic prediction is successful in single breed genetic evaluation. However, there is no achievement in acoress breed prediction until now. This thesis investigated genomic prediction across populations and breeds using Chinese Holsterin, Nordic Holstein, Norwgian Red, and Nordic Red. Nordic Red...

Using the Pareto principle in genome-wide breeding value estimation.

Science.gov (United States)

Yu, Xijiang; Meuwissen, Theo H E

2011-11-01

Genome-wide breeding value (GWEBV) estimation methods can be classified based on the prior distribution assumptions of marker effects. Genome-wide BLUP methods assume a normal prior distribution for all markers with a constant variance, and are computationally fast. In Bayesian methods, more flexible prior distributions of SNP effects are applied that allow for very large SNP effects although most are small or even zero, but these prior distributions are often also computationally demanding as they rely on Monte Carlo Markov chain sampling. In this study, we adopted the Pareto principle to weight available marker loci, i.e., we consider that x% of the loci explain (100 - x)% of the total genetic variance. Assuming this principle, it is also possible to define the variances of the prior distribution of the 'big' and 'small' SNP. The relatively few large SNP explain a large proportion of the genetic variance and the majority of the SNP show small effects and explain a minor proportion of the genetic variance. We name this method MixP, where the prior distribution is a mixture of two normal distributions, i.e. one with a big variance and one with a small variance. Simulation results, using a real Norwegian Red cattle pedigree, show that MixP is at least as accurate as the other methods in all studied cases. This method also reduces the hyper-parameters of the prior distribution from 2 (proportion and variance of SNP with big effects) to 1 (proportion of SNP with big effects), assuming the overall genetic variance is known. The mixture of normal distribution prior made it possible to solve the equations iteratively, which greatly reduced computation loads by two orders of magnitude. In the era of marker density reaching million(s) and whole-genome sequence data, MixP provides a computationally feasible Bayesian method of analysis.

Hot topic: Definition and implementation of a breeding value for feed efficiency in dairy cows.

Science.gov (United States)

Pryce, J E; Gonzalez-Recio, O; Nieuwhof, G; Wales, W J; Coffey, M P; Hayes, B J; Goddard, M E

2015-10-01

A new breeding value that combines the amount of feed saved through improved metabolic efficiency with predicted maintenance requirements is described. The breeding value includes a genomic component for residual feed intake (RFI) combined with maintenance requirements calculated from either a genomic or pedigree estimated breeding value (EBV) for body weight (BW) predicted using conformation traits. Residual feed intake is only available for genotyped Holsteins; however, BW is available for all breeds. The RFI component of the "feed saved" EBV has 2 parts: Australian calf RFI and Australian lactating cow RFI. Genomic breeding values for RFI were estimated from a reference population of 2,036 individuals in a multi-trait analysis including Australian calf RFI (n=843), Australian lactating cow RFI (n=234), and UK and Dutch lactating cow RFI (n=958). In all cases, the RFI phenotypes were deviations from a mean of 0, calculated by correcting dry matter intake for BW, growth, and milk yield (in the case of lactating cows). Single nucleotide polymorphism effects were calculated from the output of genomic BLUP and used to predict breeding values of 4,106 Holstein sires that were genotyped but did not have RFI phenotypes themselves. These bulls already had BW breeding values calculated from type traits, from which maintenance requirements in kilograms of feed per year were inferred. Finally, RFI and the feed required for maintenance (through BW) were used to calculate a feed saved breeding value and expressed as the predicted amount of feed saved per year. Animals that were 1 standard deviation above the mean were predicted to eat 66 kg dry matter less per year at the same level of milk production. In a data set of genotyped Holstein sires, the mean reliability of the feed saved breeding value was 0.37. For Holsteins that are not genotyped and for breeds other than Holsteins, feed saved is calculated using BW only. From April 2015, feed saved has been included as part of

Genomic dairy cattle breeding

DEFF Research Database (Denmark)

Mark, Thomas; Sandøe, Peter

2010-01-01

the thoughts of breeders and other stakeholders on how to best make use of genomic breeding in the future. Intensive breeding has played a major role in securing dramatic increases in milk yield since the Second World War. Until recently, the main focus in dairy cattle breeding was on production traits...... it less accountable to the concern of private farmers for the welfare of their animals. It is argued that there is a need to mobilise a wide range of stakeholders to monitor developments and maintain pressure on breeding companies so that they are aware of the need to take precautionary measures to avoid...

Application of genomic tools in plant breeding.

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Pérez-de-Castro, A M; Vilanova, S; Cañizares, J; Pascual, L; Blanca, J M; Díez, M J; Prohens, J; Picó, B

2012-05-01

Plant breeding has been very successful in developing improved varieties using conventional tools and methodologies. Nowadays, the availability of genomic tools and resources is leading to a new revolution of plant breeding, as they facilitate the study of the genotype and its relationship with the phenotype, in particular for complex traits. Next Generation Sequencing (NGS) technologies are allowing the mass sequencing of genomes and transcriptomes, which is producing a vast array of genomic information. The analysis of NGS data by means of bioinformatics developments allows discovering new genes and regulatory sequences and their positions, and makes available large collections of molecular markers. Genome-wide expression studies provide breeders with an understanding of the molecular basis of complex traits. Genomic approaches include TILLING and EcoTILLING, which make possible to screen mutant and germplasm collections for allelic variants in target genes. Re-sequencing of genomes is very useful for the genome-wide discovery of markers amenable for high-throughput genotyping platforms, like SSRs and SNPs, or the construction of high density genetic maps. All these tools and resources facilitate studying the genetic diversity, which is important for germplasm management, enhancement and use. Also, they allow the identification of markers linked to genes and QTLs, using a diversity of techniques like bulked segregant analysis (BSA), fine genetic mapping, or association mapping. These new markers are used for marker assisted selection, including marker assisted backcross selection, 'breeding by design', or new strategies, like genomic selection. In conclusion, advances in genomics are providing breeders with new tools and methodologies that allow a great leap forward in plant breeding, including the 'superdomestication' of crops and the genetic dissection and breeding for complex traits.

Effects of the number of markers per haplotype and clustering of haplotypes on the accuracy of QTL mapping and prediction of genomic breeding values

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Schrooten Chris

2009-01-01

Full Text Available Abstract The aim of this paper was to compare the effect of haplotype definition on the precision of QTL-mapping and on the accuracy of predicted genomic breeding values. In a multiple QTL model using identity-by-descent (IBD probabilities between haplotypes, various haplotype definitions were tested i.e. including 2, 6, 12 or 20 marker alleles and clustering base haplotypes related with an IBD probability of > 0.55, 0.75 or 0.95. Simulated data contained 1100 animals with known genotypes and phenotypes and 1000 animals with known genotypes and unknown phenotypes. Genomes comprising 3 Morgan were simulated and contained 74 polymorphic QTL and 383 polymorphic SNP markers with an average r2 value of 0.14 between adjacent markers. The total number of haplotypes decreased up to 50% when the window size was increased from two to 20 markers and decreased by at least 50% when haplotypes related with an IBD probability of > 0.55 instead of > 0.95 were clustered. An intermediate window size led to more precise QTL mapping. Window size and clustering had a limited effect on the accuracy of predicted total breeding values, ranging from 0.79 to 0.81. Our conclusion is that different optimal window sizes should be used in QTL-mapping versus genome-wide breeding value prediction.

A Bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits

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Hayashi Takeshi

2013-01-01

Full Text Available Abstract Background Genomic selection is an effective tool for animal and plant breeding, allowing effective individual selection without phenotypic records through the prediction of genomic breeding value (GBV. To date, genomic selection has focused on a single trait. However, actual breeding often targets multiple correlated traits, and, therefore, joint analysis taking into consideration the correlation between traits, which might result in more accurate GBV prediction than analyzing each trait separately, is suitable for multi-trait genomic selection. This would require an extension of the prediction model for single-trait GBV to multi-trait case. As the computational burden of multi-trait analysis is even higher than that of single-trait analysis, an effective computational method for constructing a multi-trait prediction model is also needed. Results We described a Bayesian regression model incorporating variable selection for jointly predicting GBVs of multiple traits and devised both an MCMC iteration and variational approximation for Bayesian estimation of parameters in this multi-trait model. The proposed Bayesian procedures with MCMC iteration and variational approximation were referred to as MCBayes and varBayes, respectively. Using simulated datasets of SNP genotypes and phenotypes for three traits with high and low heritabilities, we compared the accuracy in predicting GBVs between multi-trait and single-trait analyses as well as between MCBayes and varBayes. The results showed that, compared to single-trait analysis, multi-trait analysis enabled much more accurate GBV prediction for low-heritability traits correlated with high-heritability traits, by utilizing the correlation structure between traits, while the prediction accuracy for uncorrelated low-heritability traits was comparable or less with multi-trait analysis in comparison with single-trait analysis depending on the setting for prior probability that a SNP has zero

Comparison of analyses of the XVth QTLMAS common dataset III: Genomic Estimations of Breeding Values

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Demeure Olivier

2012-05-01

Full Text Available Abstract Background The QTLMAS XVth dataset consisted of pedigree, marker genotypes and quantitative trait performances of animals with a sib family structure. Pedigree and genotypes concerned 3,000 progenies among those 2,000 were phenotyped. The trait was regulated by 8 QTLs which displayed additive, imprinting or epistatic effects. The 1,000 unphenotyped progenies were considered as candidates to selection and their Genomic Estimated Breeding Values (GEBV were evaluated by participants of the XVth QTLMAS workshop. This paper aims at comparing the GEBV estimation results obtained by seven participants to the workshop. Methods From the known QTL genotypes of each candidate, two "true" genomic values (TV were estimated by organizers: the genotypic value of the candidate (TGV and the expectation of its progeny genotypic values (TBV. GEBV were computed by the participants following different statistical methods: random linear models (including BLUP and Ridge Regression, selection variable techniques (LASSO, Elastic Net and Bayesian methods. Accuracy was evaluated by the correlation between TV (TGV or TBV and GEBV presented by participants. Rank correlation of the best 10% of individuals and error in predictions were also evaluated. Bias was tested by regression of TV on GEBV. Results Large differences between methods were found for all criteria and type of genetic values (TGV, TBV. In general, the criteria ranked consistently methods belonging to the same family. Conclusions Bayesian methods - A

Review. Promises, pitfalls and challenges of genomic selection in breeding programs

Energy Technology Data Exchange (ETDEWEB)

Ibanez-Escriche, N.; Gonzalez-Recio, O.

2011-07-01

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.

Application of Genomic Tools in Plant Breeding

OpenAIRE

Pérez-de-Castro, A.M.; Vilanova, S.; Cañizares, J.; Pascual, L.; Blanca, J.M.; Díez, M.J.; Prohens, J.; Picó, B.

2012-01-01

Plant breeding has been very successful in developing improved varieties using conventional tools and methodologies. Nowadays, the availability of genomic tools and resources is leading to a new revolution of plant breeding, as they facilitate the study of the genotype and its relationship with the phenotype, in particular for complex traits. Next Generation Sequencing (NGS) technologies are allowing the mass sequencing of genomes and transcriptomes, which is producing a vast array of genomic...

Strategies for use of reproductive technologies in genomic dairy cattle breeding programs

DEFF Research Database (Denmark)

Thomasen, Jørn Rind; Sørensen, Anders Christian

A simulation study was performed for testing the effect of using reproductive technologies in a genomic dairy cattle young bull breeding scheme. The breeding scheme parameters: 1) number of donors, 2) number of progeny per donor, 3) age of the donor, 4) number of sires, and 5) reliability...... of genomic breeding values. The breeding schemes were evaluated according to genetic gain and rate of inbreeding. The relative gain by use of reproductive technologies is 11 to 84 percent points depending on the choice of other breeding scheme parameters. A large donor program with high selection intensity...... of sires provides the highest genetic gain. A relatively higher genetic gain is obtained for higher reliability of GEBV. Extending the donor program and number of selected bulls has a major effect of reducing the rate of inbreeding without compromising genetic gain....

Mitigation of inbreeding while preserving genetic gain in genomic breeding programs for outbred plants.

Science.gov (United States)

Lin, Zibei; Shi, Fan; Hayes, Ben J; Daetwyler, Hans D

2017-05-01

Heuristic genomic inbreeding controls reduce inbreeding in genomic breeding schemes without reducing genetic gain. Genomic selection is increasingly being implemented in plant breeding programs to accelerate genetic gain of economically important traits. However, it may cause significant loss of genetic diversity when compared with traditional schemes using phenotypic selection. We propose heuristic strategies to control the rate of inbreeding in outbred plants, which can be categorised into three types: controls during mate allocation, during selection, and simultaneous selection and mate allocation. The proposed mate allocation measure GminF allocates two or more parents for mating in mating groups that minimise coancestry using a genomic relationship matrix. Two types of relationship-adjusted genomic breeding values for parent selection candidates ([Formula: see text]) and potential offspring ([Formula: see text]) are devised to control inbreeding during selection and even enabling simultaneous selection and mate allocation. These strategies were tested in a case study using a simulated perennial ryegrass breeding scheme. As compared to the genomic selection scheme without controls, all proposed strategies could significantly decrease inbreeding while achieving comparable genetic gain. In particular, the scenario using [Formula: see text] in simultaneous selection and mate allocation reduced inbreeding to one-third of the original genomic selection scheme. The proposed strategies are readily applicable in any outbred plant breeding program.

Accelerating the Switchgrass (Panicum virgatum L.) Breeding Cycle Using Genomic Selection Approaches

Science.gov (United States)

Lipka, Alexander E.; Lu, Fei; Cherney, Jerome H.; Buckler, Edward S.; Casler, Michael D.; Costich, Denise E.

2014-01-01

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

Applied Genetics and Genomics in Alfalfa Breeding

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E. Charles Brummer

2012-03-01

Full Text Available Alfalfa (Medicago sativa L., a perennial and outcrossing species, is a widely planted forage legume for hay, pasture and silage throughout the world. Currently, alfalfa breeding relies on recurrent phenotypic selection, but alternatives incorporating molecular marker assisted breeding could enhance genetic gain per unit time and per unit cost, and accelerate alfalfa improvement. Many major quantitative trait loci (QTL related to agronomic traits have been identified by family-based QTL mapping, but in relatively large genomic regions. Candidate genes elucidated from model species have helped to identify some potential causal loci in alfalfa mapping and breeding population for specific traits. Recently, high throughput sequencing technologies, coupled with advanced bioinformatics tools, have been used to identify large numbers of single nucleotide polymorphisms (SNP in alfalfa, which are being developed into markers. These markers will facilitate fine mapping of quantitative traits and genome wide association mapping of agronomic traits and further advanced breeding strategies for alfalfa, such as marker-assisted selection and genomic selection. Based on ideas from the literature, we suggest several ways to improve selection in alfalfa including (1 diversity selection and paternity testing, (2 introgression of QTL and (3 genomic selection.

Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery

DEFF Research Database (Denmark)

Hickey, John M.; Chiurugwi, Tinashe; Mackay, Ian

2017-01-01

The rate of annual yield increases for major staple crops must more than double relative to current levels in order to feed a predicted global population of 9 billion by 2050. Controlled hybridization and selective breeding have been used for centuries to adapt plant and animal species for human...... that unifies breeding approaches, biological discovery, and tools and methods. Here we compare and contrast some animal and plant breeding approaches to make a case for bringing the two together through the application of genomic selection. We propose a strategy for the use of genomic selection as a unifying...... use. However, achieving higher, sustainable rates of improvement in yields in various species will require renewed genetic interventions and dramatic improvement of agricultural practices. Genomic prediction of breeding values has the potential to improve selection, reduce costs and provide a platform...

Accuracy of Igenity genomically estimated breeding values for predicting Australian Angus BREEDPLAN traits.

Science.gov (United States)

Boerner, V; Johnston, D; Wu, X-L; Bauck, S

2015-02-01

Genomically estimated breeding values (GEBV) for Angus beef cattle are available from at least 2 commercial suppliers (Igenity [http://www.igenity.com] and Zoetis [http://www.zoetis.com]). The utility of these GEBV for improving genetic evaluation depends on their accuracies, which can be estimated by the genetic correlation with phenotypic target traits. Genomically estimated breeding values of 1,032 Angus bulls calculated from prediction equations (PE) derived by 2 different procedures in the U.S. Angus population were supplied by Igenity. Both procedures were based on Illuminia BovineSNP50 BeadChip genotypes. In procedure sg, GEBV were calculated from PE that used subsets of only 392 SNP, where these subsets were individually selected for each trait by BayesCπ. In procedure rg GEBV were calculated from PE derived in a ridge regression approach using all available SNP. Because the total set of 1,032 bulls with GEBV contained 732 individuals used in the Igenity training population, GEBV subsets were formed characterized by a decreasing average relationship between individuals in the subsets and individuals in the training population. Accuracies of GEBV were estimated as genetic correlations between GEBV and their phenotypic target traits modeling GEBV as trait observations in a bivariate REML approach, in which phenotypic observations were those recorded in the commercial Australian Angus seed stock sector. Using results from the GEBV subset excluding all training individuals as a reference, estimated accuracies were generally in agreement with those already published, with both types of GEBV (sg and rg) yielding similar results. Accuracies for growth traits ranged from 0.29 to 0.45, for reproductive traits from 0.11 to 0.53, and for carcass traits from 0.3 to 0.75. Accuracies generally decreased with an increasing genetic distance between the training and the validation population. However, for some carcass traits characterized by a low number of phenotypic

Genomic resources in mungbean for future breeding programs

Directory of Open Access Journals (Sweden)

Sue K Kim

2015-08-01

Full Text Available Among the legume family, mungbean (Vigna radiata has become one of the important crops in Asia, showing a steady increase in global production. It provides a good source of protein and contains most notably folate and iron. Beyond the nutritional value of mungbean, certain features make it a well-suited model organism among legume plants because of its small genome size, short life-cycle, self-pollinating, and close genetic relationship to other legumes. In the past, there have been several efforts to develop molecular markers and linkage maps associated with agronomic traits for the genetic improvement of mungbean and, ultimately, breeding for cultivar development to increase the average yields of mungbean. The recent release of a reference genome of the cultivated mungbean (V. radiata var. radiata VC1973A and an additional de novo sequencing of a wild relative mungbean (V. radiata var. sublobata has provided a framework for mungbean genetic and genome research, that can further be used for genome-wide association and functional studies to identify genes related to specific agronomic traits. Moreover, the diverse gene pool of wild mungbean comprises valuable genetic resources of beneficial genes that may be helpful in widening the genetic diversity of cultivated mungbean. This review paper covers the research progress on molecular and genomics approaches and the current status of breeding programs that have developed to move toward the ultimate goal of mungbean improvement.

Does genomic selection have a future in plant breeding?

Science.gov (United States)

Jonas, Elisabeth; de Koning, Dirk-Jan

2013-09-01

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.

Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery.

Science.gov (United States)

Hickey, John M; Chiurugwi, Tinashe; Mackay, Ian; Powell, Wayne

2017-08-30

The rate of annual yield increases for major staple crops must more than double relative to current levels in order to feed a predicted global population of 9 billion by 2050. Controlled hybridization and selective breeding have been used for centuries to adapt plant and animal species for human use. However, achieving higher, sustainable rates of improvement in yields in various species will require renewed genetic interventions and dramatic improvement of agricultural practices. Genomic prediction of breeding values has the potential to improve selection, reduce costs and provide a platform that unifies breeding approaches, biological discovery, and tools and methods. Here we compare and contrast some animal and plant breeding approaches to make a case for bringing the two together through the application of genomic selection. We propose a strategy for the use of genomic selection as a unifying approach to deliver innovative 'step changes' in the rate of genetic gain at scale.

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines

Science.gov (United States)

Nielsen, Nanna Hellum; Jahoor, Ahmed; Jensen, Jens Due; Orabi, Jihad; Cericola, Fabio; Edriss, Vahid; Jensen, Just

2016-01-01

Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines) are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families. PMID:27783639

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines.

Directory of Open Access Journals (Sweden)

Nanna Hellum Nielsen

Full Text Available Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families.

Prediction of genetic values of quantitative traits with epistatic effects in plant breeding populations.

Science.gov (United States)

Wang, D; Salah El-Basyoni, I; Stephen Baenziger, P; Crossa, J; Eskridge, K M; Dweikat, I

2012-11-01

Though epistasis has long been postulated to have a critical role in genetic regulation of important pathways as well as provide a major source of variation in the process of speciation, the importance of epistasis for genomic selection in the context of plant breeding is still being debated. In this paper, we report the results on the prediction of genetic values with epistatic effects for 280 accessions in the Nebraska Wheat Breeding Program using adaptive mixed least absolute shrinkage and selection operator (LASSO). The development of adaptive mixed LASSO, originally designed for association mapping, for the context of genomic selection is reported. The results show that adaptive mixed LASSO can be successfully applied to the prediction of genetic values while incorporating both marker main effects and epistatic effects. Especially, the prediction accuracy is substantially improved by the inclusion of two-locus epistatic effects (more than onefold in some cases as measured by cross-validation correlation coefficient), which is observed for multiple traits and planting locations. This points to significant potential in using non-additive genetic effects for genomic selection in crop breeding practices.

Across Breed QTL Detection and Genomic Prediction in French and Danish Dairy Cattle Breeds

DEFF Research Database (Denmark)

van den Berg, Irene; Guldbrandtsen, Bernt; Hozé, C

Our objective was to investigate the potential benefits of using sequence data to improve across breed genomic prediction, using data from five French and Danish dairy cattle breeds. First, QTL for protein yield were detected using high density genotypes. Part of the QTL detected within breed was...

Effects of the number of markers per haplotype and clustering of haplotypes on the accuracy of QTL mapping and prediction of genomic breeding values

NARCIS (Netherlands)

Calus, M.P.L.; Meuwissen, T.H.E.; Windig, J.J.; Knol, E.F.; Schrooten, C.; Vereijken, A.L.J.; Veerkamp, R.F.

2009-01-01

The aim of this paper was to compare the effect of haplotype definition on the precision of QTL-mapping and on the accuracy of predicted genomic breeding values. In a multiple QTL model using identity-by-descent (IBD) probabilities between haplotypes, various haplotype definitions were tested i.e.

Impact of Genomic Technologies on Chickpea Breeding Strategies

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Rajeev K. Varshney

2012-08-01

Full Text Available The major abiotic and biotic stresses that adversely affect yield of chickpea (Cicer arietinum L. include drought, heat, fusarium wilt, ascochyta blight and pod borer. Excellent progress has been made in developing short-duration varieties with high resistance to fusarium wilt. The early maturity helps in escaping terminal drought and heat stresses and the adaptation of chickpea to short-season environments. Ascochyta blight continues to be a major challenge to chickpea productivity in areas where chickpea is exposed to cool and wet conditions. Limited variability for pod borer resistance has been a major bottleneck in the development of pod borer resistant cultivars. The use of genomics technologies in chickpea breeding programs has been limited, since available genomic resources were not adequate and limited polymorphism was observed in the cultivated chickpea for the available molecular markers. Remarkable progress has been made in the development of genetic and genomic resources in recent years and integration of genomic technologies in chickpea breeding has now started. Marker-assisted breeding is currently being used for improving drought tolerance and combining resistance to diseases. The integration of genomic technologies is expected to improve the precision and efficiency of chickpea breeding in the development of improved cultivars with enhanced resistance to abiotic and biotic stresses, better adaptation to existing and evolving agro-ecologies and traits preferred by farmers, industries and consumers.

Influence of model specifications on the reliabilities of genomic prediction in a Swedish-Finnish red breed cattle population

DEFF Research Database (Denmark)

Rius-Vilarrasa, E; Strandberg, E; Fikse, W F

2012-01-01

Using a combined multi-breed reference population, this study explored the influence of model specification and the effect of including a polygenic effect on the reliability of genomic breeding values (DGV and GEBV). The combined reference population consisted of 2986 Swedish Red Breed (SRB) and ...

Genomics and the Instrinsic Value of Plants

NARCIS (Netherlands)

Gremmen, H.G.J.

2005-01-01

In discussions on genetic engineering and plant breeding, the intrinsic value of plants and crops is used as an argument against this technology. This paper focuses on the new field of plant genomics, which, according to some, is almost the same as genetic engineering. This raises the question

Genomic-based-breeding tools for tropical maize improvement.

Science.gov (United States)

Chakradhar, Thammineni; Hindu, Vemuri; Reddy, Palakolanu Sudhakar

2017-12-01

Maize has traditionally been the main staple diet in the Southern Asia and Sub-Saharan Africa and widely grown by millions of resource poor small scale farmers. Approximately, 35.4 million hectares are sown to tropical maize, constituting around 59% of the developing worlds. Tropical maize encounters tremendous challenges besides poor agro-climatic situations with average yields recorded <3 tones/hectare that is far less than the average of developed countries. On the contrary to poor yields, the demand for maize as food, feed, and fuel is continuously increasing in these regions. Heterosis breeding introduced in early 90 s improved maize yields significantly, but genetic gains is still a mirage, particularly for crop growing under marginal environments. Application of molecular markers has accelerated the pace of maize breeding to some extent. The availability of array of sequencing and genotyping technologies offers unrivalled service to improve precision in maize-breeding programs through modern approaches such as genomic selection, genome-wide association studies, bulk segregant analysis-based sequencing approaches, etc. Superior alleles underlying complex traits can easily be identified and introgressed efficiently using these sequence-based approaches. Integration of genomic tools and techniques with advanced genetic resources such as nested association mapping and backcross nested association mapping could certainly address the genetic issues in maize improvement programs in developing countries. Huge diversity in tropical maize and its inherent capacity for doubled haploid technology offers advantage to apply the next generation genomic tools for accelerating production in marginal environments of tropical and subtropical world. Precision in phenotyping is the key for success of any molecular-breeding approach. This article reviews genomic technologies and their application to improve agronomic traits in tropical maize breeding has been reviewed in

Breeding, genetic and genomic of citrus for disease resistance

Directory of Open Access Journals (Sweden)

Marcos A. Machado

2011-10-01

Full Text Available Although the citriculture is one of the most important economic activities in Brazil, it is based on a small number of varieties. This fact has contributed for the vulnerability of the culture regarding the phytosanitary problems. A higher number of varieties/genotypes with potential for commercial growing, either for the industry or fresh market, has been one of the main objectives of citrus breeding programs. The genetic breeding of citrus has improved, in the last decades, due to the possibility of an association between biotechnological tools and classical methods of breeding. The use of molecular markers for early selection of zygotic seedlings from controlled crosses resulted in the possibility of selection of a high number of new combination and, as a consequence, the establishment of a great number of hybrids in field experiments. The faster new tools are incorporated in the program, the faster is possibility to reach new genotypes that can be tested as a new variety. Good traits should be kept or incorporate, whereas bad traits have to be excluded or minimized in the new genotype. Scion and rootstock can not be considered separately, and graft compatibility, fruit quality and productivity are essential traits to be evaluated in the last stages of the program. The mapping of QTLs has favored breeding programs of several perennial species and in citrus it was possible to map several characteristics with qualitative and quantitative inheritance. The existence of linkage maps and QTLs already mapped, the development of EST and BAC library and the sequencing of the Citrus complete genome altogether make very demanding and urgent the exploration of such data to launch a wider genetic study of citrus. The rising of information on genome of several organisms has opened new approaches looking for integration between breeding, genetic and genome. Genome assisted selection (GAS involves more than gene or complete genome sequencing and is becoming

Overlap in genomic variation associated with milk fat composition in Holstein Friesian and Dutch native dual-purpose breeds.

Science.gov (United States)

Maurice-Van Eijndhoven, M H T; Bovenhuis, H; Veerkamp, R F; Calus, M P L

2015-09-01

The aim of this study was to identify if genomic variations associated with fatty acid (FA) composition are similar between the Holstein-Friesian (HF) and native dual-purpose breeds used in the Dutch dairy industry. Phenotypic and genotypic information were available for the breeds Meuse-Rhine-Yssel (MRY), Dutch Friesian (DF), Groningen White Headed (GWH), and HF. First, the reliability of genomic breeding values of the native Dutch dual-purpose cattle breeds MRY, DF, and GWH was evaluated using single nucleotide polymorphism (SNP) effects estimated in HF, including all SNP or subsets with stronger associations in HF. Second, the genomic variation of the regions associated with FA composition in HF (regions on Bos taurus autosome 5, 14, and 26), were studied in the different breeds. Finally, similarities in genotype and allele frequencies between MRY, DF, GWH, and HF breeds were assessed for specific regions associated with FA composition. On average across the traits, the highest reliabilities of genomic prediction were estimated for GWH (0.158) and DF (0.116) when the 8 to 22 SNP with the strongest association in HF were included. With the same set of SNP, GEBV for MRY were the least reliable (0.022). This indicates that on average only 2 (MRY) to 16% (GWH) of the genomic variation in HF is shared with the native Dutch dual-purpose breeds. The comparison of predicted variances of different regions associated with milk and milk fat composition showed that breeds clearly differed in genomic variation within these regions. Finally, the correlations of allele frequencies between breeds across the 8 to 22 SNP with the strongest association in HF were around 0.8 between the Dutch native dual-purpose breeds, whereas the correlations between the native breeds and HF were clearly lower and around 0.5. There was no consistent relationship between the reliabilities of genomic prediction for a specific breed and the correlation between the allele frequencies of this breed

Genomic Selection in Plant Breeding: Methods, Models, and Perspectives.

Science.gov (United States)

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

2017-11-01

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.

Genotyping cows for the reference increase reliability of genomic prediction in a small breed

DEFF Research Database (Denmark)

Thomasen, Jørn Rind; Sørensen, Anders Christian; Lund, Mogens Sandø

2013-01-01

We hypothesized that adding cows to the reference population in a breed with a small number of reference bulls would increase reliabilities of genomic breeding values and genetic gain. We tested this premise by comparing two strategies for maintaining the reference population for genetic gain......, inbreeding and reliabilities of genomic predictions: 1) Adding 60 progeny tested bulls each year (B), and 2) in addition to 60 progeny tested bulls, adding 2,000 genotyped cows per year (C). Two breeding schemes were tested: 1) A turbo scheme (T) with only genotyped young bulls used intensively, and 2...... compared to the H-B, at the same level of ∆F. T-C yielded 15% higher ∆G compared t o T-B. Changing the breeding scheme from H-B to H-C increased ∆G by 5.5%. The lowest ∆F was observed with genotyping of cows. Reliabilities of GEBV in the C schemes showed a steep increase in reliability during the first...

Applications of population genetics to animal breeding, from wright, fisher and lush to genomic prediction.

Science.gov (United States)

Hill, William G

2014-01-01

Although animal breeding was practiced long before the science of genetics and the relevant disciplines of population and quantitative genetics were known, breeding programs have mainly relied on simply selecting and mating the best individuals on their own or relatives' performance. This is based on sound quantitative genetic principles, developed and expounded by Lush, who attributed much of his understanding to Wright, and formalized in Fisher's infinitesimal model. Analysis at the level of individual loci and gene frequency distributions has had relatively little impact. Now with access to genomic data, a revolution in which molecular information is being used to enhance response with "genomic selection" is occurring. The predictions of breeding value still utilize multiple loci throughout the genome and, indeed, are largely compatible with additive and specifically infinitesimal model assumptions. I discuss some of the history and genetic issues as applied to the science of livestock improvement, which has had and continues to have major spin-offs into ideas and applications in other areas.

Genomic selection needs to be carefully assessed to meet specific requirements in livestock breeding programs

Directory of Open Access Journals (Sweden)

Elisabeth eJonas

2015-02-01

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

Use of Genomic Estimated Breeding Values Results in Rapid Genetic Gains for Drought Tolerance in Maize

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B.S. Vivek

2017-03-01

Full Text Available More than 80% of the 19 million ha of maize ( L. in tropical Asia is rainfed and prone to drought. The breeding methods for improving drought tolerance (DT, including genomic selection (GS, are geared to increase the frequency of favorable alleles. Two biparental populations (CIMMYT-Asia Population 1 [CAP1] and CAP2 were generated by crossing elite Asian-adapted yellow inbreds (CML470 and VL1012767 with an African white drought-tolerant line, CML444. Marker effects of polymorphic single-nucleotide polymorphisms (SNPs were determined from testcross (TC performance of F families under drought and optimal conditions. Cycle 1 (C1 was formed by recombining the top 10% of the F families based on TC data. Subsequently, (i C2[PerSe_PS] was derived by recombining those C1 plants that exhibited superior per se phenotypes (phenotype-only selection, and (ii C2[TC-GS] was derived by recombining a second set of C1 plants with high genomic estimated breeding values (GEBVs derived from TC phenotypes of F families (marker-only selection. All the generations and their top crosses to testers were evaluated under drought and optimal conditions. Per se grain yields (GYs of C2[PerSe_PS] and that of C2[TC-GS] were 23 to 39 and 31 to 53% better, respectively, than that of the corresponding F population. The C2[TC-GS] populations showed superiority of 10 to 20% over C2[PerSe-PS] of respective populations. Top crosses of C2[TC-GS] showed 4 to 43% superiority of GY over that of C2[PerSe_PS] of respective populations. Thus, GEBV-enabled selection of superior phenotypes (without the target stress resulted in rapid genetic gains for DT.

Genomic selection needs to be carefully assessed to meet specific requirements in livestock breeding programs.

Science.gov (United States)

Jonas, Elisabeth; de Koning, Dirk-Jan

2015-01-01

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.

Genome-wide identification of breed-informative single-nucleotide ...

African Journals Online (AJOL)

This is because the SNPs on BovineSNP50 and GGP-80K assays were ascertained as being common in European taurine breeds. Lower MAF and SNP informativeness observed in this study limits the application of these assays in breed assignment, and could have other implications for genome-wide studies in South ...

DNA Microarray as Part of a Genomic-Assisted Breeding Approach

DEFF Research Database (Denmark)

Vincze, Éva; Bowra, Steve

2010-01-01

) is the ‘umbrella' term used to describe a suite of tools now being applied to plant breeding. In the context of genomic-assisted breeding, we will briefly discuss in the second section of this chapter the molecular genetic-based tools underpinning GAB (understanding gene expression, candidate gene selection......In the struggle to achieve global food security, crop breeding retains an important role in crop production. A current trend is the diversification of the aims of crop production, to include an increased awareness of aspects and consequences of food quality. The added emphasis on food and feed...... quality made crop breeding more challenging and required a combination of new tools. We illustrate these concepts by taking examples from barley, one of the most ancient of domesticated grains with a diverse profile of utilisation (feed, brewing, new nutritional uses). Genomic-assisted breeding (GAB...

Genomics for greater efficiency in pigeonpea hybrid breeding

Directory of Open Access Journals (Sweden)

Rachit K Saxena

2015-10-01

Full Text Available Cytoplasmic genic male sterility based hybrid technology has demonstrated its immense potential in increasing the productivity of various crops, including pigeonpea. This technology has shown promise for breaking the long-standing yield stagnation in pigeonpea. There are difficulties in commercial hybrid seed production due to non-availability of field-oriented technologies such as time-bound assessment of genetic purity of hybrid seeds. Besides this, there are other routine breeding activities which are labour oriented and need more resources. These include breeding and maintenance of new fertility restorers and maintainer lines, diversification of cytoplasm, and incorporation of biotic and abiotic stress resistances. The recent progress in genomics research could accelerate the existing traditional efforts to strengthen the hybrid breeding technology. Marker based seed purity assessment, identification of heterotic groups; selection of new fertility restorers are few areas which have already been initiated. In this paper efforts have been made to identify critical areas and opportunities where genomics can play a leading role and assist breeders in accelerating various activities related to breeding and commercialization of pigeonpea hybrids.

Genomic Selection Accuracy using Multifamily Prediction Models in a Wheat Breeding Program

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Elliot L. Heffner

2011-03-01

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.

Advances in genome editing for improved animal breeding: A review

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Shakil Ahmad Bhat

2017-11-01

Full Text Available Since centuries, the traits for production and disease resistance are being targeted while improving the genetic merit of domestic animals, using conventional breeding programs such as inbreeding, outbreeding, or introduction of marker-assisted selection. The arrival of new scientific concepts, such as cloning and genome engineering, has added a new and promising research dimension to the existing animal breeding programs. Development of genome editing technologies such as transcription activator-like effector nuclease, zinc finger nuclease, and clustered regularly interspaced short palindromic repeats systems begun a fresh era of genome editing, through which any change in the genome, including specific DNA sequence or indels, can be made with unprecedented precision and specificity. Furthermore, it offers an opportunity of intensification in the frequency of desirable alleles in an animal population through gene-edited individuals more rapidly than conventional breeding. The specific research is evolving swiftly with a focus on improvement of economically important animal species or their traits all of which form an important subject of this review. It also discusses the hurdles to commercialization of these techniques despite several patent applications owing to the ambiguous legal status of genome-editing methods on account of their disputed classification. Nonetheless, barring ethical concerns gene-editing entailing economically important genes offers a tremendous potential for breeding animals with desirable traits.

Genetic Gain and Inbreeding from Genomic Selection in a Simulated Commercial Breeding Program for Perennial Ryegrass

Directory of Open Access Journals (Sweden)

Zibei Lin

2016-03-01

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.

Genetic Gain and Inbreeding from Genomic Selection in a Simulated Commercial Breeding Program for Perennial Ryegrass.

Science.gov (United States)

Lin, Zibei; Cogan, Noel O I; Pembleton, Luke W; Spangenberg, German C; Forster, John W; Hayes, Ben J; Daetwyler, Hans D

2016-03-01

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.

Genomic Tools in Pea Breeding Programs: Status and Perspectives

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Tayeh, Nadim; Aubert, Grégoire; Pilet-Nayel, Marie-Laure; Lejeune-Hénaut, Isabelle; Warkentin, Thomas D.; Burstin, Judith

2015-01-01

Pea (Pisum sativum L.) is an annual cool-season legume and one of the oldest domesticated crops. Dry pea seeds contain 22–25% protein, complex starch and fiber constituents, and a rich array of vitamins, minerals, and phytochemicals which make them a valuable source for human consumption and livestock feed. Dry pea ranks third to common bean and chickpea as the most widely grown pulse in the world with more than 11 million tons produced in 2013. Pea breeding has achieved great success since the time of Mendel's experiments in the mid-1800s. However, several traits still require significant improvement for better yield stability in a larger growing area. Key breeding objectives in pea include improving biotic and abiotic stress resistance and enhancing yield components and seed quality. Taking advantage of the diversity present in the pea genepool, many mapping populations have been constructed in the last decades and efforts have been deployed to identify loci involved in the control of target traits and further introgress them into elite breeding materials. Pea now benefits from next-generation sequencing and high-throughput genotyping technologies that are paving the way for genome-wide association studies and genomic selection approaches. This review covers the significant development and deployment of genomic tools for pea breeding in recent years. Future prospects are discussed especially in light of current progress toward deciphering the pea genome. PMID:26640470

Genomic tools in pea breeding programs: status and perspectives

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Nadim eTAYEH

2015-11-01

Full Text Available Pea (Pisum sativum L. is an annual cool-season legume and one of the oldest domesticated crops. Dry pea seeds contain 22-25 percent protein, complex starch and fibre constituents and a rich array of vitamins, minerals, and phytochemicals which make them a valuable source for human consumption and livestock feed. Dry pea ranks third to common bean and chickpea as the most widely grown pulse in the world with more than 11 million tonnes produced in 2013. Pea breeding has achieved great success since the time of Mendel’s experiments in the mid-1800s. However, several traits still require significant improvement for better yield stability in a larger growing area. Key breeding objectives in pea include improving biotic and abiotic stress resistance and enhancing yield components and seed quality. Taking advantage of the diversity present in the pea genepool, many mapping populations have been constructed in the last decades and efforts have been deployed to identify loci involved in the control of target traits and further introgress them into elite breeding materials. Pea now benefits from next-generation sequencing and high-throughput genotyping technologies that are paving the way for genome-wide association studies and genomic selection approaches. This review covers the significant development and deployment of genomic tools for pea breeding in recent years. Future prospects are discussed especially in light of current progress towards deciphering the pea genome.

Short communication: Genotyping of cows to speed up availability of genomic estimated breeding values for direct health traits in Austrian Fleckvieh (Simmental) cattle--genetic and economic aspects.

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Egger-Danner, C; Schwarzenbacher, H; Willam, A

2014-07-01

The aim of this study was to quantify the impact of genotyping cows with reliable phenotypes for direct health traits on annual monetary genetic gain (AMGG) and discounted profit. The calculations were based on a deterministic approach using ZPLAN software (University of Hohenheim, Stuttgart, Germany). It was assumed that increases in reliability of the total merit index (TMI) of 5, 15, and 25 percentage points were achieved through genotyping 5,000, 25,000, and 50,000 cows, respectively. Costs for phenotyping, genotyping, and genomic estimated breeding values vary between €150 and €20 per cow. The gain in genotyping cows for traits with medium to high heritability is more than for direct health traits with low heritability. The AMGG is increased by 1.5% if the reliability of TMI is 5 percentage points higher (i.e., 5,000 cows genotyped) and 6.53% higher AMGG can be expected when the reliability of TMI is increased by 25 percentage points (i.e., 50,000 cows genotyped). The discounted profit depends not only on the costs of genotyping but also on the population size. This study indicates that genotyping cows with reliable phenotypes is feasible to speed up the availability of genomic estimated breeding values for direct health traits. But, because of the huge amount of valid phenotypes and genotypes needed to establish an efficient genomic evaluation, it is likely that financial constraints will be the main limiting factor for implementation into breeding program such as Fleckvieh Austria. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Bayesian methods for jointly estimating genomic breeding values of one continuous and one threshold trait.

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Chonglong Wang

Full Text Available Genomic selection has become a useful tool for animal and plant breeding. Currently, genomic evaluation is usually carried out using a single-trait model. However, a multi-trait model has the advantage of using information on the correlated traits, leading to more accurate genomic prediction. To date, joint genomic prediction for a continuous and a threshold trait using a multi-trait model is scarce and needs more attention. Based on the previously proposed methods BayesCπ for single continuous trait and BayesTCπ for single threshold trait, we developed a novel method based on a linear-threshold model, i.e., LT-BayesCπ, for joint genomic prediction of a continuous trait and a threshold trait. Computing procedures of LT-BayesCπ using Markov Chain Monte Carlo algorithm were derived. A simulation study was performed to investigate the advantages of LT-BayesCπ over BayesCπ and BayesTCπ with regard to the accuracy of genomic prediction on both traits. Factors affecting the performance of LT-BayesCπ were addressed. The results showed that, in all scenarios, the accuracy of genomic prediction obtained from LT-BayesCπ was significantly increased for the threshold trait compared to that from single trait prediction using BayesTCπ, while the accuracy for the continuous trait was comparable with that from single trait prediction using BayesCπ. The proposed LT-BayesCπ could be a method of choice for joint genomic prediction of one continuous and one threshold trait.

Twenty years of artificial directional selection have shaped the genome of the Italian Large White pig breed.

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Schiavo, G; Galimberti, G; Calò, D G; Samorè, A B; Bertolini, F; Russo, V; Gallo, M; Buttazzoni, L; Fontanesi, L

2016-04-01

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.

Implementation of genomic prediction in Lolium perenne (L. breeding populations

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Nastasiya F Grinberg

2016-02-01

Full Text Available Perennial ryegrass (Lolium perenne L. is one of the most widely grown forage grasses in temperate agriculture. In order to maintain and increase its usage as forage in livestock agriculture, there is a continued need for improvement in biomass yield, quality, disease resistance and seed yield. Genetic gain for traits such as biomass yield has been relatively modest. This has been attributed to its long breeding cycle, and the necessity to use population based breeding methods. Thanks to recent advances in genotyping techniques there is increasing interest in genomic selection from which genomically estimated breeding values (GEBV are derived. In this paper we compare the classical RRBLUP model with state-of-the-art machine learning (ML techniques that should yield themselves easily to use in GS and demonstrate their application to predicting quantitative traits in a breeding population of L. perenne. Prediction accuracies varied from 0 to 0.59 depending on trait, prediction model and composition of the training population. The BLUP model produced the highest prediction accuracies for most traits and training populations. Forage quality traits had the highest accuracies compared to yield related traits. There appeared to be no clear pattern to the effect of the training population composition on the prediction accuracies. The heritability of the forage quality traits was generally higher than for the yield related traits, and could partly explain the difference in accuracy. Some population structure was evident in the breeding populations, and probably contributed to the varying effects of training population on the predictions. The average linkage disequilibrium (LD between adjacent markers ranged from 0.121 to 0.215. Higher marker density and larger training population closely related with the test population are likely to improve the prediction accuracy.

Genome-editing technologies and their potential application in horticultural crop breeding

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Xiong, Jin-Song; Ding, Jing; Li, Yi

2015-01-01

Plant breeding, one of the oldest agricultural activities, parallels human civilization. Many crops have been domesticated to satisfy human's food and aesthetical needs, including numerous specialty horticultural crops such as fruits, vegetables, ornamental flowers, shrubs, and trees. Crop varieties originated through selection during early human civilization. Other technologies, such as various forms of hybridization, mutation, and transgenics, have also been invented and applied to crop breeding over the past centuries. The progress made in these breeding technologies, especially the modern biotechnology-based breeding technologies, has had a great impact on crop breeding as well as on our lives. Here, we first review the developmental process and applications of these technologies in horticultural crop breeding. Then, we mainly describe the principles of the latest genome-editing technologies and discuss their potential applications in the genetic improvement of horticultural crops. The advantages and challenges of genome-editing technologies in horticultural crop breeding are also discussed. PMID:26504570

Population Structure and Genomic Breed Composition in an Angus-Brahman Crossbred Cattle Population.

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Gobena, Mesfin; Elzo, Mauricio A; Mateescu, Raluca G

2018-01-01

Crossbreeding is a common strategy used in tropical and subtropical regions to enhance beef production, and having accurate knowledge of breed composition is essential for the success of a crossbreeding program. Although pedigree records have been traditionally used to obtain the breed composition of crossbred cattle, the accuracy of pedigree-based breed composition can be reduced by inaccurate and/or incomplete records and Mendelian sampling. Breed composition estimation from genomic data has multiple advantages including higher accuracy without being affected by missing, incomplete, or inaccurate records and the ability to be used as independent authentication of breed in breed-labeled beef products. The present study was conducted with 676 Angus-Brahman crossbred cattle with genotype and pedigree information to evaluate the feasibility and accuracy of using genomic data to determine breed composition. We used genomic data in parametric and non-parametric methods to detect population structure due to differences in breed composition while accounting for the confounding effect of close familial relationships. By applying principal component analysis (PCA) and the maximum likelihood method of ADMIXTURE to genomic data, it was possible to successfully characterize population structure resulting from heterogeneous breed ancestry, while accounting for close familial relationships. PCA results offered additional insight into the different hierarchies of genetic variation structuring. The first principal component was strongly correlated with Angus-Brahman proportions, and the second represented variation within animals that have a relatively more extended Brangus lineage-indicating the presence of a distinct pattern of genetic variation in these cattle. Although there was strong agreement between breed proportions estimated from pedigree and genetic information, there were significant discrepancies between these two methods for certain animals. This was most likely due

Population Structure and Genomic Breed Composition in an Angus–Brahman Crossbred Cattle Population

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Mesfin Gobena

2018-03-01

Full Text Available Crossbreeding is a common strategy used in tropical and subtropical regions to enhance beef production, and having accurate knowledge of breed composition is essential for the success of a crossbreeding program. Although pedigree records have been traditionally used to obtain the breed composition of crossbred cattle, the accuracy of pedigree-based breed composition can be reduced by inaccurate and/or incomplete records and Mendelian sampling. Breed composition estimation from genomic data has multiple advantages including higher accuracy without being affected by missing, incomplete, or inaccurate records and the ability to be used as independent authentication of breed in breed-labeled beef products. The present study was conducted with 676 Angus–Brahman crossbred cattle with genotype and pedigree information to evaluate the feasibility and accuracy of using genomic data to determine breed composition. We used genomic data in parametric and non-parametric methods to detect population structure due to differences in breed composition while accounting for the confounding effect of close familial relationships. By applying principal component analysis (PCA and the maximum likelihood method of ADMIXTURE to genomic data, it was possible to successfully characterize population structure resulting from heterogeneous breed ancestry, while accounting for close familial relationships. PCA results offered additional insight into the different hierarchies of genetic variation structuring. The first principal component was strongly correlated with Angus–Brahman proportions, and the second represented variation within animals that have a relatively more extended Brangus lineage—indicating the presence of a distinct pattern of genetic variation in these cattle. Although there was strong agreement between breed proportions estimated from pedigree and genetic information, there were significant discrepancies between these two methods for certain animals

Genomics-assisted breeding for boosting crop improvement in pigeonpea (Cajanus cajan

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Lekha ePazhamala

2015-02-01

Full Text Available Pigeonpea is an important pulse crop grown predominantly in the tropical and sub-tropical regions of the world. Although pigeonpea growing area has considerably increased, yield has remained stagnant for the last six decades mainly due to the exposure of the crop to various biotic and abiotic constraints. In addition, low level of genetic variability and limited genomic resources have been serious impediments to pigeonpea crop improvement through modern breeding approaches. In recent years, however, due to the availability of next generation sequencing and high-throughput genotyping technologies, the scenario has changed tremendously. The reduced sequencing costs resulting in the decoding of the pigeonpea genome has led to the development of various genomic resources including molecular markers, transcript sequences and comprehensive genetic maps. Mapping of some important traits including resistance to Fusarium wilt and sterility mosaic disease, fertility restoration, determinacy with other agronomically important traits have paved the way for applying genomics-assisted breeding (GAB through marker assisted selection as well as genomic selection. This would lead to accelerate the development and improvement of both varieties and hybrids in pigeonpea. Particularly for hybrid breeding programme, mitochondrial genomes of cytoplasmic male sterile lines, maintainers and hybrids have also been sequenced to identify genes responsible for cytoplasmic male sterility. Furthermore, several diagnostic molecular markers have been developed to assess the purity of commercial hybrids. In summary, pigeonpea has become a genomic resources-rich crop and efforts have already been initiated to integrate these resources in pigeonpea breeding.

Toward Genomics-Based Breeding in C3 Cool-Season Perennial Grasses

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Talukder, Shyamal K.; Saha, Malay C.

2017-01-01

Most important food and feed crops in the world belong to the C3 grass family. The future of food security is highly reliant on achieving genetic gains of those grasses. Conventional breeding methods have already reached a plateau for improving major crops. Genomics tools and resources have opened an avenue to explore genome-wide variability and make use of the variation for enhancing genetic gains in breeding programs. Major C3 annual cereal breeding programs are well equipped with genomic tools; however, genomic research of C3 cool-season perennial grasses is lagging behind. In this review, we discuss the currently available genomics tools and approaches useful for C3 cool-season perennial grass breeding. Along with a general review, we emphasize the discussion focusing on forage grasses that were considered orphan and have little or no genetic information available. Transcriptome sequencing and genotype-by-sequencing technology for genome-wide marker detection using next-generation sequencing (NGS) are very promising as genomics tools. Most C3 cool-season perennial grass members have no prior genetic information; thus NGS technology will enhance collinear study with other C3 model grasses like Brachypodium and rice. Transcriptomics data can be used for identification of functional genes and molecular markers, i.e., polymorphism markers and simple sequence repeats (SSRs). Genome-wide association study with NGS-based markers will facilitate marker identification for marker-assisted selection. With limited genetic information, genomic selection holds great promise to breeders for attaining maximum genetic gain of the cool-season C3 perennial grasses. Application of all these tools can ensure better genetic gains, reduce length of selection cycles, and facilitate cultivar development to meet the future demand for food and fodder. PMID:28798766

Toward Genomics-Based Breeding in C3 Cool-Season Perennial Grasses

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Shyamal K. Talukder

2017-07-01

Full Text Available Most important food and feed crops in the world belong to the C3 grass family. The future of food security is highly reliant on achieving genetic gains of those grasses. Conventional breeding methods have already reached a plateau for improving major crops. Genomics tools and resources have opened an avenue to explore genome-wide variability and make use of the variation for enhancing genetic gains in breeding programs. Major C3 annual cereal breeding programs are well equipped with genomic tools; however, genomic research of C3 cool-season perennial grasses is lagging behind. In this review, we discuss the currently available genomics tools and approaches useful for C3 cool-season perennial grass breeding. Along with a general review, we emphasize the discussion focusing on forage grasses that were considered orphan and have little or no genetic information available. Transcriptome sequencing and genotype-by-sequencing technology for genome-wide marker detection using next-generation sequencing (NGS are very promising as genomics tools. Most C3 cool-season perennial grass members have no prior genetic information; thus NGS technology will enhance collinear study with other C3 model grasses like Brachypodium and rice. Transcriptomics data can be used for identification of functional genes and molecular markers, i.e., polymorphism markers and simple sequence repeats (SSRs. Genome-wide association study with NGS-based markers will facilitate marker identification for marker-assisted selection. With limited genetic information, genomic selection holds great promise to breeders for attaining maximum genetic gain of the cool-season C3 perennial grasses. Application of all these tools can ensure better genetic gains, reduce length of selection cycles, and facilitate cultivar development to meet the future demand for food and fodder.

Genomic prediction in a breeding program of perennial ryegrass

DEFF Research Database (Denmark)

Fé, Dario; Ashraf, Bilal; Greve-Pedersen, Morten

2015-01-01

We present a genomic selection study performed on 1918 rye grass families (Lolium perenne L.), which were derived from a commercial breeding program at DLF-Trifolium, Denmark. Phenotypes were recorded on standard plots, across 13 years and in 6 different countries. Variants were identified...... this set. Estimated Breeding Value and prediction accuracies were calculated trough two different cross-validation schemes: (i) k-fold (k=10); (ii) leaving out one parent combination at the time, in order to test for accuracy of predicting new families. Accuracies ranged between 0.56 and 0.97 for scheme (i....... A larger set of 1791 F2s were used as training set to predict EBVs of 127 synthetic families (originated from poly-crosses between 5-11 single plants) for heading date and crown rust resistance. Prediction accuracies were 0.93 and 0.57 respectively. Results clearly demonstrate considerable potential...

Selection on Optimal Haploid Value Increases Genetic Gain and Preserves More Genetic Diversity Relative to Genomic Selection.

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Daetwyler, Hans D; Hayden, Matthew J; Spangenberg, German C; Hayes, Ben J

2015-08-01

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.

A comparison of phenotypic traits related to trypanotolerance in five west african cattle breeds highlights the value of shorthorn taurine breeds.

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David Berthier

Full Text Available Animal African Trypanosomosis particularly affects cattle and dramatically impairs livestock development in sub-Saharan Africa. African Zebu (AFZ or European taurine breeds usually die of the disease in the absence of treatment, whereas West African taurine breeds (AFT, considered trypanotolerant, are able to control the pathogenic effects of trypanosomosis. Up to now, only one AFT breed, the longhorn N'Dama (NDA, has been largely studied and is considered as the reference trypanotolerant breed. Shorthorn taurine trypanotolerance has never been properly assessed and compared to NDA and AFZ breeds.This study compared the trypanotolerant/susceptible phenotype of five West African local breeds that differ in their demographic history. Thirty-six individuals belonging to the longhorn taurine NDA breed, two shorthorn taurine Lagune (LAG and Baoulé (BAO breeds, the Zebu Fulani (ZFU and the Borgou (BOR, an admixed breed between AFT and AFZ, were infected by Trypanosoma congolense IL1180. All the cattle were genetically characterized using dense SNP markers, and parameters linked to parasitaemia, anaemia and leukocytes were analysed using synthetic variables and mixed models. We showed that LAG, followed by NDA and BAO, displayed the best control of anaemia. ZFU showed the greatest anaemia and the BOR breed had an intermediate value, as expected from its admixed origin. Large differences in leukocyte counts were also observed, with higher leukocytosis for AFT. Nevertheless, no differences in parasitaemia were found, except a tendency to take longer to display detectable parasites in ZFU.We demonstrated that LAG and BAO are as trypanotolerant as NDA. This study highlights the value of shorthorn taurine breeds, which display strong local adaptation to trypanosomosis. Thanks to further analyses based on comparisons of the genome or transcriptome of the breeds, these results open up the way for better knowledge of host-pathogen interactions and

Emerging Genomic Tools for Legume Breeding: Current Status and Future Prospects

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Pandey, Manish K.; Roorkiwal, Manish; Singh, Vikas K.; Ramalingam, Abirami; Kudapa, Himabindu; Thudi, Mahendar; Chitikineni, Anu; Rathore, Abhishek; Varshney, Rajeev K.

2016-01-01

Legumes play a vital role in ensuring global nutritional food security and improving soil quality through nitrogen fixation. Accelerated higher genetic gains is required to meet the demand of ever increasing global population. In recent years, speedy developments have been witnessed in legume genomics due to advancements in next-generation sequencing (NGS) and high-throughput genotyping technologies. Reference genome sequences for many legume crops have been reported in the last 5 years. The availability of the draft genome sequences and re-sequencing of elite genotypes for several important legume crops have made it possible to identify structural variations at large scale. Availability of large-scale genomic resources and low-cost and high-throughput genotyping technologies are enhancing the efficiency and resolution of genetic mapping and marker-trait association studies. Most importantly, deployment of molecular breeding approaches has resulted in development of improved lines in some legume crops such as chickpea and groundnut. In order to support genomics-driven crop improvement at a fast pace, the deployment of breeder-friendly genomics and decision support tools seems appear to be critical in breeding programs in developing countries. This review provides an overview of emerging genomics and informatics tools/approaches that will be the key driving force for accelerating genomics-assisted breeding and ultimately ensuring nutritional and food security in developing countries. PMID:27199998

Approximation of reliability of direct genomic breeding values

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Two methods to efficiently approximate theoretical genomic reliabilities are presented. The first method is based on the direct inverse of the left hand side (LHS) of mixed model equations. It uses the genomic relationship matrix for a small subset of individuals with the highest genomic relationshi...

Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.).

Science.gov (United States)

Auinger, Hans-Jürgen; Schönleben, Manfred; Lehermeier, Christina; Schmidt, Malthe; Korzun, Viktor; Geiger, Hartwig H; Piepho, Hans-Peter; Gordillo, Andres; Wilde, Peer; Bauer, Eva; Schön, Chris-Carolin

2016-11-01

Genomic prediction accuracy can be significantly increased by model calibration across multiple breeding cycles as long as selection cycles are connected by common ancestors. In hybrid rye breeding, application of genome-based prediction is expected to increase selection gain because of long selection cycles in population improvement and development of hybrid components. Essentially two prediction scenarios arise: (1) prediction of the genetic value of lines from the same breeding cycle in which model training is performed and (2) prediction of lines from subsequent cycles. It is the latter from which a reduction in cycle length and consequently the strongest impact on selection gain is expected. We empirically investigated genome-based prediction of grain yield, plant height and thousand kernel weight within and across four selection cycles of a hybrid rye breeding program. Prediction performance was assessed using genomic and pedigree-based best linear unbiased prediction (GBLUP and PBLUP). A total of 1040 S 2 lines were genotyped with 16 k SNPs and each year testcrosses of 260 S 2 lines were phenotyped in seven or eight locations. The performance gap between GBLUP and PBLUP increased significantly for all traits when model calibration was performed on aggregated data from several cycles. Prediction accuracies obtained from cross-validation were in the order of 0.70 for all traits when data from all cycles (N CS  = 832) were used for model training and exceeded within-cycle accuracies in all cases. As long as selection cycles are connected by a sufficient number of common ancestors and prediction accuracy has not reached a plateau when increasing sample size, aggregating data from several preceding cycles is recommended for predicting genetic values in subsequent cycles despite decreasing relatedness over time.

Genomic value prediction for quantitative traits under the epistatic model

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Xu Shizhong

2011-01-01

Full Text Available Abstract Background Most quantitative traits are controlled by multiple quantitative trait loci (QTL. The contribution of each locus may be negligible but the collective contribution of all loci is usually significant. Genome selection that uses markers of the entire genome to predict the genomic values of individual plants or animals can be more efficient than selection on phenotypic values and pedigree information alone for genetic improvement. When a quantitative trait is contributed by epistatic effects, using all markers (main effects and marker pairs (epistatic effects to predict the genomic values of plants can achieve the maximum efficiency for genetic improvement. Results In this study, we created 126 recombinant inbred lines of soybean and genotyped 80 makers across the genome. We applied the genome selection technique to predict the genomic value of somatic embryo number (a quantitative trait for each line. Cross validation analysis showed that the squared correlation coefficient between the observed and predicted embryo numbers was 0.33 when only main (additive effects were used for prediction. When the interaction (epistatic effects were also included in the model, the squared correlation coefficient reached 0.78. Conclusions This study provided an excellent example for the application of genome selection to plant breeding.

Prospects for genomic selection in cassava breeding

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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...

Accuracy of Genomic Selection in a Rice Synthetic Population Developed for Recurrent Selection Breeding.

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Grenier, Cécile; Cao, Tuong-Vi; Ospina, Yolima; Quintero, Constanza; Châtel, Marc Henri; Tohme, Joe; Courtois, Brigitte; Ahmadi, Nourollah

2015-01-01

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.

Evaluation of approaches for estimating the accuracy of genomic prediction in plant breeding.

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Ould Estaghvirou, Sidi Boubacar; Ogutu, Joseph O; Schulz-Streeck, Torben; Knaak, Carsten; Ouzunova, Milena; Gordillo, Andres; Piepho, Hans-Peter

2013-12-06

In genomic prediction, an important measure of accuracy is the correlation between the predicted and the true breeding values. Direct computation of this quantity for real datasets is not possible, because the true breeding value is unknown. Instead, the correlation between the predicted breeding values and the observed phenotypic values, called predictive ability, is often computed. In order to indirectly estimate predictive accuracy, this latter correlation is usually divided by an estimate of the square root of heritability. In this study we use simulation to evaluate estimates of predictive accuracy for seven methods, four (1 to 4) of which use an estimate of heritability to divide predictive ability computed by cross-validation. Between them the seven methods cover balanced and unbalanced datasets as well as correlated and uncorrelated genotypes. We propose one new indirect method (4) and two direct methods (5 and 6) for estimating predictive accuracy and compare their performances and those of four other existing approaches (three indirect (1 to 3) and one direct (7)) with simulated true predictive accuracy as the benchmark and with each other. The size of the estimated genetic variance and hence heritability exerted the strongest influence on the variation in the estimated predictive accuracy. Increasing the number of genotypes considerably increases the time required to compute predictive accuracy by all the seven methods, most notably for the five methods that require cross-validation (Methods 1, 2, 3, 4 and 6). A new method that we propose (Method 5) and an existing method (Method 7) used in animal breeding programs were the fastest and gave the least biased, most precise and stable estimates of predictive accuracy. Of the methods that use cross-validation Methods 4 and 6 were often the best. The estimated genetic variance and the number of genotypes had the greatest influence on predictive accuracy. Methods 5 and 7 were the fastest and produced the least

Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus.

Science.gov (United States)

Müller, Bárbara S F; Neves, Leandro G; de Almeida Filho, Janeo E; Resende, Márcio F R; Muñoz, Patricio R; Dos Santos, Paulo E T; Filho, Estefano Paludzyszyn; Kirst, Matias; Grattapaglia, Dario

2017-07-11

capture large proportions of trait heritability and predict growth traits in trees with accuracies equal or better than those attainable by phenotypic selection. Additionally, our results document the superiority of the whole-genome regression approach in accounting for large proportions of the heritability of complex traits such as growth in contrast to the limited value of the local GWAS approach toward breeding applications in forest trees.

Genomic Selection in the Era of Next Generation Sequencing for Complex Traits in Plant Breeding.

Science.gov (United States)

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

2016-01-01

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.

Strategies for Selecting Crosses Using Genomic Prediction in Two Wheat Breeding Programs.

Science.gov (United States)

Lado, Bettina; Battenfield, Sarah; Guzmán, Carlos; Quincke, Martín; Singh, Ravi P; Dreisigacker, Susanne; Peña, R Javier; Fritz, Allan; Silva, Paula; Poland, Jesse; Gutiérrez, Lucía

2017-07-01

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.

Allele coding in genomic evaluation

DEFF Research Database (Denmark)

Standen, Ismo; Christensen, Ole Fredslund

2011-01-01

Genomic data are used in animal breeding to assist genetic evaluation. Several models to estimate genomic breeding values have been studied. In general, two approaches have been used. One approach estimates the marker effects first and then, genomic breeding values are obtained by summing marker...... effects. In the second approach, genomic breeding values are estimated directly using an equivalent model with a genomic relationship matrix. Allele coding is the method chosen to assign values to the regression coefficients in the statistical model. A common allele coding is zero for the homozygous...... genotype of the first allele, one for the heterozygote, and two for the homozygous genotype for the other allele. Another common allele coding changes these regression coefficients by subtracting a value from each marker such that the mean of regression coefficients is zero within each marker. We call...

Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix.

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Zhe Zhang

2010-09-01

Full Text Available With the availability of high density whole-genome single nucleotide polymorphism chips, genomic selection has become a promising method to estimate genetic merit with potentially high accuracy for animal, plant and aquaculture species of economic importance. With markers covering the entire genome, genetic merit of genotyped individuals can be predicted directly within the framework of mixed model equations, by using a matrix of relationships among individuals that is derived from the markers. Here we extend that approach by deriving a marker-based relationship matrix specifically for the trait of interest.In the framework of mixed model equations, a new best linear unbiased prediction (BLUP method including a trait-specific relationship matrix (TA was presented and termed TABLUP. The TA matrix was constructed on the basis of marker genotypes and their weights in relation to the trait of interest. A simulation study with 1,000 individuals as the training population and five successive generations as candidate population was carried out to validate the proposed method. The proposed TABLUP method outperformed the ridge regression BLUP (RRBLUP and BLUP with realized relationship matrix (GBLUP. It performed slightly worse than BayesB with an accuracy of 0.79 in the standard scenario.The proposed TABLUP method is an improvement of the RRBLUP and GBLUP method. It might be equivalent to the BayesB method but it has additional benefits like the calculation of accuracies for individual breeding values. The results also showed that the TA-matrix performs better in predicting ability than the classical numerator relationship matrix and the realized relationship matrix which are derived solely from pedigree or markers without regard to the trait. This is because the TA-matrix not only accounts for the Mendelian sampling term, but also puts the greater emphasis on those markers that explain more of the genetic variance in the trait.

Sunflower Hybrid Breeding: From Markers to Genomic Selection.

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Dimitrijevic, Aleksandra; Horn, Renate

2017-01-01

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

Sunflower Hybrid Breeding: From Markers to Genomic Selection

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Aleksandra Dimitrijevic

2018-01-01

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

Sunflower Hybrid Breeding: From Markers to Genomic Selection

Science.gov (United States)

Dimitrijevic, Aleksandra; Horn, Renate

2018-01-01

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

Genetic diversity analysis of two commercial breeds of pigs using genomic and pedigree data.

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Zanella, Ricardo; Peixoto, Jane O; Cardoso, Fernando F; Cardoso, Leandro L; Biegelmeyer, Patrícia; Cantão, Maurício E; Otaviano, Antonio; Freitas, Marcelo S; Caetano, Alexandre R; Ledur, Mônica C

2016-03-30

Genetic improvement in livestock populations can be achieved without significantly affecting genetic diversity if mating systems and selection decisions take genetic relationships among individuals into consideration. The objective of this study was to examine the genetic diversity of two commercial breeds of pigs. Genotypes from 1168 Landrace (LA) and 1094 Large White (LW) animals from a commercial breeding program in Brazil were obtained using the Illumina PorcineSNP60 Beadchip. Inbreeding estimates based on pedigree (F x) and genomic information using runs of homozygosity (F ROH) and the single nucleotide polymorphisms (SNP) by SNP inbreeding coefficient (F SNP) were obtained. Linkage disequilibrium (LD), correlation of linkage phase (r) and effective population size (N e ) were also estimated. Estimates of inbreeding obtained with pedigree information were lower than those obtained with genomic data in both breeds. We observed that the extent of LD was slightly larger at shorter distances between SNPs in the LW population than in the LA population, which indicates that the LW population was derived from a smaller N e . Estimates of N e based on genomic data were equal to 53 and 40 for the current populations of LA and LW, respectively. The correlation of linkage phase between the two breeds was equal to 0.77 at distances up to 50 kb, which suggests that genome-wide association and selection should be performed within breed. Although selection intensities have been stronger in the LA breed than in the LW breed, levels of genomic and pedigree inbreeding were lower for the LA than for the LW breed. The use of genomic data to evaluate population diversity in livestock animals can provide new and more precise insights about the effects of intense selection for production traits. Resulting information and knowledge can be used to effectively increase response to selection by appropriately managing the rate of inbreeding, minimizing negative effects of inbreeding

Genomic Tools in Cowpea Breeding Programs: Status and Perspectives

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Boukar, Ousmane; Fatokun, Christian A.; Huynh, Bao-Lam; Roberts, Philip A.; Close, Timothy J.

2016-01-01

Cowpea is one of the most important grain legumes in sub-Saharan Africa (SSA). It provides strong support to the livelihood of small-scale farmers through its contributions to their nutritional security, income generation and soil fertility enhancement. Worldwide about 6.5 million metric tons of cowpea are produced annually on about 14.5 million hectares. The low productivity of cowpea is attributable to numerous abiotic and biotic constraints. The abiotic stress factors comprise drought, low soil fertility, and heat while biotic constraints include insects, diseases, parasitic weeds, and nematodes. Cowpea farmers also have limited access to quality seeds of improved varieties for planting. Some progress has been made through conventional breeding at international and national research institutions in the last three decades. Cowpea improvement could also benefit from modern breeding methods based on molecular genetic tools. A number of advances in cowpea genetic linkage maps, and quantitative trait loci associated with some desirable traits such as resistance to Striga, Macrophomina, Fusarium wilt, bacterial blight, root-knot nematodes, aphids, and foliar thrips have been reported. An improved consensus genetic linkage map has been developed and used to identify QTLs of additional traits. In order to take advantage of these developments single nucleotide polymorphism (SNP) genotyping is being streamlined to establish an efficient workflow supported by genotyping support service (GSS)-client interactions. About 1100 SNPs mapped on the cowpea genome were converted by LGC Genomics to KASP assays. Several cowpea breeding programs have been exploiting these resources to implement molecular breeding, especially for MARS and MABC, to accelerate cowpea variety improvement. The combination of conventional breeding and molecular breeding strategies, with workflow managed through the CGIAR breeding management system (BMS), promises an increase in the number of improved

Genomic tools in cowpea breeding programs: status and perspectives

Directory of Open Access Journals (Sweden)

Ousmane eBoukar

2016-06-01

Full Text Available Cowpea is one of the most important grain legumes in sub-Saharan Africa (SSA. It provides strong support to the livelihood of small-scale farmers through its contributions to their nutritional security, income generation and soil fertility enhancement. Worldwide about 6.5 million metric tons of cowpea are produced annually on about 14.5 million hectares. The low productivity of cowpea is attributable to numerous abiotic and biotic constraints. The abiotic stress factors comprise drought, low soil fertility, and heat while biotic constraints include insects, diseases, parasitic weeds and nematodes. Cowpea farmers also have limited access to quality seeds of improved varieties for planting. Some progress has been made through conventional breeding at international and national research institutions in the last three decades. Cowpea improvement could also benefit from modern breeding methods based on molecular genetic tools. A number of advances in cowpea genetic linkage maps, and quantitative trait loci associated with some desirable traits such as resistance to Striga, Macrophomina, Fusarium wilt, bacterial blight, root-knot nematodes, aphids and foliar thrips have been reported. An improved consensus genetic linkage map has been developed and used to identify QTLs of additional traits. In order to take advantage of these developments single nucleotide polymorphism (SNP genotyping is being streamlined to establish an efficient workflow supported by genotyping support service (GSS-client interactions. About 1100 SNPs mapped on the cowpea genome were converted by LGC Genomics to KASP assays. Several cowpea breeding programs have been exploiting these resources to implement molecular breeding, especially for MARS and MABC, to accelerate cowpea variety improvement. The combination of conventional breeding and molecular breeding strategies, with workflow managed through the CGIAR breeding management system (BMS, promises an increase in the number of

Alfalfa breeding benefits from genomics of Medicago truncatula

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Žilije Bernadet

2010-01-01

Full Text Available International programs aim at developing knowledge and tools in the model species Medicago truncatula. Genetic resources, DNA sequences, markers, genetic and physical maps are now publicly available. These efforts contribute to improve breeding schemes of crop species such as alfalfa. However, transfer of information from M. truncatula to alfalfa is not straightforward. The article reviews the gain given by the model species to better analyze genetic determinism of breeding traits in alfalfa. It also shows that investments in alfalfa genomics (DNA sequences, SNP development are needed to benefit from the model species.

Genomic prediction when some animals are not genotyped

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Lund Mogens S

2010-01-01

Full Text Available Abstract Background The use of genomic selection in breeding programs may increase the rate of genetic improvement, reduce the generation time, and provide higher accuracy of estimated breeding values (EBVs. A number of different methods have been developed for genomic prediction of breeding values, but many of them assume that all animals have been genotyped. In practice, not all animals are genotyped, and the methods have to be adapted to this situation. Results In this paper we provide an extension of a linear mixed model method for genomic prediction to the situation with non-genotyped animals. The model specifies that a breeding value is the sum of a genomic and a polygenic genetic random effect, where genomic genetic random effects are correlated with a genomic relationship matrix constructed from markers and the polygenic genetic random effects are correlated with the usual relationship matrix. The extension of the model to non-genotyped animals is made by using the pedigree to derive an extension of the genomic relationship matrix to non-genotyped animals. As a result, in the extended model the estimated breeding values are obtained by blending the information used to compute traditional EBVs and the information used to compute purely genomic EBVs. Parameters in the model are estimated using average information REML and estimated breeding values are best linear unbiased predictions (BLUPs. The method is illustrated using a simulated data set. Conclusions The extension of the method to non-genotyped animals presented in this paper makes it possible to integrate all the genomic, pedigree and phenotype information into a one-step procedure for genomic prediction. Such a one-step procedure results in more accurate estimated breeding values and has the potential to become the standard tool for genomic prediction of breeding values in future practical evaluations in pig and cattle breeding.

Genomic Selection for Predicting Fusarium Head Blight Resistance in a Wheat Breeding Program

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Marcio P. Arruda

2015-11-01

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.

Accuracy of Genomic Selection in a Rice Synthetic Population Developed for Recurrent Selection Breeding.

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Cécile Grenier

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.

Whole-genome sequencing reveals mutational landscape underlying phenotypic differences between two widespread Chinese cattle breeds.

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Yao Xu

Full Text Available Whole-genome sequencing provides a powerful tool to obtain more genetic variability that could produce a range of benefits for cattle breeding industry. Nanyang (Bos indicus and Qinchuan (Bos taurus are two important Chinese indigenous cattle breeds with distinct phenotypes. To identify the genetic characteristics responsible for variation in phenotypes between the two breeds, in the present study, we for the first time sequenced the genomes of four Nanyang and four Qinchuan cattle with 10 to 12 fold on average of 97.86% and 98.98% coverage of genomes, respectively. Comparison with the Bos_taurus_UMD_3.1 reference assembly yielded 9,010,096 SNPs for Nanyang, and 6,965,062 for Qinchuan cattle, 51% and 29% of which were novel SNPs, respectively. A total of 154,934 and 115,032 small indels (1 to 3 bp were found in the Nanyang and Qinchuan genomes, respectively. The SNP and indel distribution revealed that Nanyang showed a genetically high diversity as compared to Qinchuan cattle. Furthermore, a total of 2,907 putative cases of copy number variation (CNV were identified by aligning Nanyang to Qinchuan genome, 783 of which (27% encompassed the coding regions of 495 functional genes. The gene ontology (GO analysis revealed that many CNV genes were enriched in the immune system and environment adaptability. Among several CNV genes related to lipid transport and fat metabolism, Lepin receptor gene (LEPR overlapping with CNV_1815 showed remarkably higher copy number in Qinchuan than Nanyang (log2 (ratio = -2.34988; P value = 1.53E-102. Further qPCR and association analysis investigated that the copy number of the LEPR gene presented positive correlations with transcriptional expression and phenotypic traits, suggesting the LEPR CNV may contribute to the higher fat deposition in muscles of Qinchuan cattle. Our findings provide evidence that the distinct phenotypes of Nanyang and Qinchuan breeds may be due to the different genetic variations including SNPs

Signatures of Selection in the Genomes of Commercial and Non-Commercial Chicken Breeds

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Elferink, Martin G.; Megens, Hendrik-Jan; Vereijken, Addie; Hu, Xiaoxiang; Crooijmans, Richard P. M. A.; Groenen, Martien A. M.

2012-01-01

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

Compression distance can discriminate animals by genetic profile, build relationship matrices and estimate breeding values.

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Hudson, Nicholas J; Porto-Neto, Laercio; Kijas, James W; Reverter, Antonio

2015-10-13

Genetic relatedness is currently estimated by a combination of traditional pedigree-based approaches (i.e. numerator relationship matrices, NRM) and, given the recent availability of molecular information, using marker genotypes (via genomic relationship matrices, GRM). To date, GRM are computed by genome-wide pair-wise SNP (single nucleotide polymorphism) correlations. We describe a new estimate of genetic relatedness using the concept of normalised compression distance (NCD) that is borrowed from Information Theory. Analogous to GRM, the resultant compression relationship matrix (CRM) exploits numerical patterns in genome-wide allele order and proportion, which are known to vary systematically with relatedness. We explored properties of the CRM in two industry cattle datasets by analysing the genetic basis of yearling weight, a phenotype of moderate heritability. In both Brahman (Bos indicus) and Tropical Composite (Bos taurus by Bos indicus) populations, the clustering inferred by NCD was comparable to that based on SNP correlations using standard principal component analysis approaches. One of the versions of the CRM modestly increased the amount of explained genetic variance, slightly reduced the 'missing heritability' and tended to improve the prediction accuracy of breeding values in both populations when compared to both NRM and GRM. Finally, a sliding window-based application of the compression approach on these populations identified genomic regions influenced by introgression of taurine haplotypes. For these two bovine populations, CRM reduced the missing heritability and increased the amount of explained genetic variation for a moderately heritable complex trait. Given that NCD can sensitively discriminate closely related individuals, we foresee CRM having possible value for estimating breeding values in highly inbred populations.

Application of Genomic Technologies to the Breeding of Trees.

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Badenes, Maria L; Fernández I Martí, Angel; Ríos, Gabino; Rubio-Cabetas, María J

2016-01-01

The recent introduction of next generation sequencing (NGS) technologies represents a major revolution in providing new tools for identifying the genes and/or genomic intervals controlling important traits for selection in breeding programs. In perennial fruit trees with long generation times and large sizes of adult plants, the impact of these techniques is even more important. High-throughput DNA sequencing technologies have provided complete annotated sequences in many important tree species. Most of the high-throughput genotyping platforms described are being used for studies of genetic diversity and population structure. Dissection of complex traits became possible through the availability of genome sequences along with phenotypic variation data, which allow to elucidate the causative genetic differences that give rise to observed phenotypic variation. Association mapping facilitates the association between genetic markers and phenotype in unstructured and complex populations, identifying molecular markers for assisted selection and breeding. Also, genomic data provide in silico identification and characterization of genes and gene families related to important traits, enabling new tools for molecular marker assisted selection in tree breeding. Deep sequencing of transcriptomes is also a powerful tool for the analysis of precise expression levels of each gene in a sample. It consists in quantifying short cDNA reads, obtained by NGS technologies, in order to compare the entire transcriptomes between genotypes and environmental conditions. The miRNAs are non-coding short RNAs involved in the regulation of different physiological processes, which can be identified by high-throughput sequencing of RNA libraries obtained by reverse transcription of purified short RNAs, and by in silico comparison with known miRNAs from other species. All together, NGS techniques and their applications have increased the resources for plant breeding in tree species, closing the

Whole-genome sequencing reveals mutational landscape underlying phenotypic differences between two widespread Chinese cattle breeds

OpenAIRE

Xu, Yao; Jiang, Yu; Shi, Tao; Cai, Hanfang; Lan, Xianyong; Zhao, Xin; Plath, Martin; Chen, Hong

2017-01-01

Whole-genome sequencing provides a powerful tool to obtain more genetic variability that could produce a range of benefits for cattle breeding industry. Nanyang (Bos indicus) and Qinchuan (Bos taurus) are two important Chinese indigenous cattle breeds with distinct phenotypes. To identify the genetic characteristics responsible for variation in phenotypes between the two breeds, in the present study, we for the first time sequenced the genomes of four Nanyang and four Qinchuan cattle with 10 ...

Goals and hurdles for a successful implementation of genomic selection in breeding programme for selected annual and perennial crops.

Science.gov (United States)

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 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.

Science.gov (United States)

Spindel, Jennifer; Begum, Hasina; Akdemir, Deniz; Virk, Parminder; Collard, Bertrand; Redoña, Edilberto; Atlin, Gary; Jannink, Jean-Luc; McCouch, Susan R

2015-02-01

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.

Directory of Open Access Journals (Sweden)

Jennifer Spindel

2015-02-01

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

Science.gov (United States)

Spindel, Jennifer; Begum, Hasina; Akdemir, Deniz; Virk, Parminder; Collard, Bertrand; Redoña, Edilberto; Atlin, Gary; Jannink, Jean-Luc; McCouch, Susan R.

2015-01-01

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

Genomic tools in pearl millet breeding for drought tolerance: Status and prospects

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Desalegn Debelo Serba

2016-11-01

Full Text Available Pearl millet (Penisetum glaucum (L R. Br. is a hardy cereal crop grown in the arid and semiarid tropics where other cereals are likely to fail to produce economic yields due to drought and heat stresses. Adaptive evolution, a form of natural selection shaped the crop to grow and yield satisfactorily with limited moisture supply or under periodic water deficits in the soil. Drought tolerance is a complex polygenic trait that various morphological and physiological responses are controlled by hundreds of genes and significantly influenced by the environment. The development of genomic tools will have enormous potential to improve the efficiency and precision of conventional breeding. The apparent independent domestication events, highly outcrossing nature and traditional cultivation in stressful environments maintained tremendous amount of polymorphism in pearl millet. This high polymorphism of the crop has been revealed by genome mapping that in turn stimulated the mapping and tagging of genomic regions controlling important traits such as drought tolerance. Mapping of a major QTL for terminal drought tolerance in independent populations envisaged the prospect for the development of molecular breeding in pearl millet. To accelerate genetic gains for drought tolerance targeted novel approaches such as establishment of marker-trait associations, genomic selection tools, genome sequence and genotyping-by-sequencing are still limited. Development and application of high throughput genomic tools need to be intensified to improve the breeding efficiency of pearl millet to minimize the impact of climate change on its production.

Identification of genomic regions associated with phenotypic variation between dog breeds using selection mapping.

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Amaury Vaysse

2011-10-01

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.

Allele coding in genomic evaluation

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Christensen Ole F

2011-06-01

Full Text Available Abstract Background Genomic data are used in animal breeding to assist genetic evaluation. Several models to estimate genomic breeding values have been studied. In general, two approaches have been used. One approach estimates the marker effects first and then, genomic breeding values are obtained by summing marker effects. In the second approach, genomic breeding values are estimated directly using an equivalent model with a genomic relationship matrix. Allele coding is the method chosen to assign values to the regression coefficients in the statistical model. A common allele coding is zero for the homozygous genotype of the first allele, one for the heterozygote, and two for the homozygous genotype for the other allele. Another common allele coding changes these regression coefficients by subtracting a value from each marker such that the mean of regression coefficients is zero within each marker. We call this centered allele coding. This study considered effects of different allele coding methods on inference. Both marker-based and equivalent models were considered, and restricted maximum likelihood and Bayesian methods were used in inference. Results Theoretical derivations showed that parameter estimates and estimated marker effects in marker-based models are the same irrespective of the allele coding, provided that the model has a fixed general mean. For the equivalent models, the same results hold, even though different allele coding methods lead to different genomic relationship matrices. Calculated genomic breeding values are independent of allele coding when the estimate of the general mean is included into the values. Reliabilities of estimated genomic breeding values calculated using elements of the inverse of the coefficient matrix depend on the allele coding because different allele coding methods imply different models. Finally, allele coding affects the mixing of Markov chain Monte Carlo algorithms, with the centered coding being

Invited review: Breeding and ethical perspectives on genetically modified and genome edited cattle.

Science.gov (United States)

Eriksson, S; Jonas, E; Rydhmer, L; Röcklinsberg, H

2018-01-01

The hot topic of genetic modification and genome editing is sometimes presented as a rapid solution to various problems in the field of animal breeding and genetics. These technologies hold potential for future use in agriculture but we need to be aware of difficulties in large-scale application and integration in breeding schemes. In this review, we discuss applications of both classical genetic modifications (GM) using vectors and genome editing in dairy cattle breeding. We use an interdisciplinary approach considering both ethical and animal breeding perspectives. Decisions on how to make use of these techniques need to be made based not only on what is possible, but on what is reasonable to do. Principles of animal integrity, naturalness, risk perception, and animal welfare issues are examples of ethically relevant factors to consider. These factors also influence public perception and decisions about regulations by authorities. We need to acknowledge that we lack complete understanding of the genetic background of complex traits. It may be difficult, therefore, to predict the full effect of certain modifications in large-scale breeding programs. We present 2 potential applications: genome editing to dispense with dehorning, and insertion of human genes in bovine genomes to improve udder health as an example of classical GM. Both of these cases could be seen as beneficial for animal welfare but they differ in other aspects. In the former case, a genetic variant already present within the species is introduced, whereas in the latter case, transgenic animals are generated-this difference may influence how society regards the applications. We underline that the use of GM, as well as genome editing, of farm animals such as cattle is not independent of the context, and should be considered as part of an entire process, including, for example, the assisted reproduction technology that needs to be used. We propose that breeding organizations and breeding companies

Predictive ability of genomic selection models for breeding value estimation on growth traits of Pacific white shrimp Litopenaeus vannamei

Science.gov (United States)

Wang, Quanchao; Yu, Yang; Li, Fuhua; Zhang, Xiaojun; Xiang, Jianhai

2017-09-01

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.

Genomic Prediction from Whole Genome Sequence in Livestock: The 1000 Bull Genomes Project

DEFF Research Database (Denmark)

Hayes, Benjamin J; MacLeod, Iona M; Daetwyler, Hans D

Advantages of using whole genome sequence data to predict genomic estimated breeding values (GEBV) include better persistence of accuracy of GEBV across generations and more accurate GEBV across breeds. The 1000 Bull Genomes Project provides a database of whole genome sequenced key ancestor bulls....... In a dairy data set, predictions using BayesRC and imputed sequence data from 1000 Bull Genomes were 2% more accurate than with 800k data. We could demonstrate the method identified causal mutations in some cases. Further improvements will come from more accurate imputation of sequence variant genotypes...

Whole-genome regression and prediction methods applied to plant and animal breeding

NARCIS (Netherlands)

Los Campos, De G.; Hickey, J.M.; Pong-Wong, R.; Daetwyler, H.D.; Calus, M.P.L.

2013-01-01

Genomic-enabled prediction is becoming increasingly important in animal and plant breeding, and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of

Be-Breeder – an application for analysis of genomic data in plant breeding

Directory of Open Access Journals (Sweden)

Filipe Inácio Matias

2016-12-01

Full Text Available Be-Breeder is an application directed toward genetic breeding of plants, developed through the Shiny package of the R software, which allows different phenotype and molecular (marker analysis to be undertaken. The section for analysis of molecular data of the Be-Breeder application makes it possible to achieve quality control of genotyping data, to obtain genomic kinship matrices, and to analyze genomic selection, genome association, and genetic diversity in a simple manner on line. This application is available for use in a network through the site of the Allogamous Plant Breeding Laboratory of ESALQ-USP (http://www.genetica.esalq.usp.br/alogamas/R.html.

Characterization of the complete mitochondrial genome of the king pigeon (Columba livia breed king).

Science.gov (United States)

Zhang, Rui-Hua; He, Wen-Xiao; Xu, Tong

2015-06-01

The king pigeon is a breed of pigeon developed over many years of selective breeding primarily as a utility breed. In the present work, we report the complete mitochondrial genome sequence of king pigeon for the first time. The total length of the mitogenome was 17,221 bp with the base composition of 30.14% for A, 24.05% for T, 31.82% for C, and 13.99% for G and an A-T (54.22 %)-rich feature was detected. It harbored 13 protein-coding genes, two ribosomal RNA genes, 22 transfer RNA genes, and one non-coding control region (D-loop region). The arrangement of all genes was identical to the typical mitochondrial genomes of pigeon. The complete mitochondrial genome sequence of king pigeon would serve as an important data set of the germplasm resources for further study.

The complete mitochondrial genome of the Jacobin pigeon (Columba livia breed Jacobin).

Science.gov (United States)

He, Wen-Xiao; Jia, Jin-Feng

2015-06-01

The Jacobin is a breed of fancy pigeon developed over many years of selective breeding that originated in Asia. In the present work, we report the complete mitochondrial genome sequence of Jacobin pigeon for the first time. The total length of the mitogenome was 17,245 bp with the base composition of 30.18% for A, 23.98% for T, 31.88% for C, and 13.96% for G and an A-T (54.17 %)-rich feature was detected. It harbored 13 protein-coding genes, 2 ribosomal RNA genes, 22 transfer RNA genes and 1 non-coding control region. The arrangement of all genes was identical to the typical mitochondrial genomes of pigeon. The complete mitochondrial genome sequence of Jacobin pigeon would serve as an important data set of the germplasm resources for further study.

Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding

Science.gov (United States)

de los Campos, Gustavo; Hickey, John M.; Pong-Wong, Ricardo; Daetwyler, Hans D.; Calus, Mario P. L.

2013-01-01

Genomic-enabled prediction is becoming increasingly important in animal and plant breeding and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of markers concurrently. Methods exist that allow implementing these large-p with small-n regressions, and genome-enabled selection (GS) is being implemented in several plant and animal breeding programs. The list of available methods is long, and the relationships between them have not been fully addressed. In this article we provide an overview of available methods for implementing parametric WGR models, discuss selected topics that emerge in applications, and present a general discussion of lessons learned from simulation and empirical data analysis in the last decade. PMID:22745228

Genomic Prediction of Single Crosses in the Early Stages of a Maize Hybrid Breeding Pipeline

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Dnyaneshwar C. Kadam

2016-11-01

Full Text Available Prediction of single-cross performance has been a major goal of plant breeders since the beginning of hybrid breeding. Recently, genomic prediction has shown to be a promising approach, but only limited studies have examined the accuracy of predicting single-cross performance. Moreover, no studies have examined the potential of predicting single crosses among random inbreds derived from a series of biparental families, which resembles the structure of germplasm comprising the initial stages of a hybrid maize breeding pipeline. The main objectives of this study were to evaluate the potential of genomic prediction for identifying superior single crosses early in the hybrid breeding pipeline and optimize its application. To accomplish these objectives, we designed and analyzed a novel population of single crosses representing the Iowa Stiff Stalk synthetic/non-Stiff Stalk heterotic pattern commonly used in the development of North American commercial maize hybrids. The performance of single crosses was predicted using parental combining ability and covariance among single crosses. Prediction accuracies were estimated using cross-validation and ranged from 0.28 to 0.77 for grain yield, 0.53 to 0.91 for plant height, and 0.49 to 0.94 for staygreen, depending on the number of tested parents of the single cross and genomic prediction method used. The genomic estimated general and specific combining abilities showed an advantage over genomic covariances among single crosses when one or both parents of the single cross were untested. Overall, our results suggest that genomic prediction of single crosses in the early stages of a hybrid breeding pipeline holds great potential to redesign hybrid breeding and increase its efficiency.

The importance of identity-by-state information for the accuracy of genomic selection

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Luan Tu

2012-08-01

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

Accuracy of Genomic Prediction in a Commercial Perennial Ryegrass Breeding Program

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Dario Fè

2016-11-01

Full Text Available The implementation of genomic selection (GS in plant breeding, so far, has been mainly evaluated in crops farmed as homogeneous varieties, and the results have been generally positive. Fewer results are available for species, such as forage grasses, that are grown as heterogenous families (developed from multiparent crosses in which the control of the genetic variation is far more complex. Here we test the potential for implementing GS in the breeding of perennial ryegrass ( L. using empirical data from a commercial forage breeding program. Biparental F and multiparental synthetic (SYN families of diploid perennial ryegrass were genotyped using genotyping-by-sequencing, and phenotypes for five different traits were analyzed. Genotypes were expressed as family allele frequencies, and phenotypes were recorded as family means. Different models for genomic prediction were compared by using practically relevant cross-validation strategies. All traits showed a highly significant level of genetic variance, which could be traced using the genotyping assay. While there was significant genotype × environment (G × E interaction for some traits, accuracies were high among F families and between biparental F and multiparental SYN families. We have demonstrated that the implementation of GS in grass breeding is now possible and presents an opportunity to make significant gains for various traits.

The complete mitochondrial genome of the ice pigeon (Columba livia breed ice).

Science.gov (United States)

Zhang, Rui-Hua; He, Wen-Xiao

2015-02-01

The ice pigeon is a breed of fancy pigeon developed over many years of selective breeding. In the present work, we report the complete mitochondrial genome sequence of ice pigeon for the first time. The total length of the mitogenome was 17,236 bp with the base composition of 30.2% for A, 24.0% for T, 31.9% for C, and 13.9% for G and an A-T (54.2 %)-rich feature was detected. It harbored 13 protein-coding genes, 2 ribosomal RNA genes, 22 transfer RNA genes and 1 non-coding control region (D-loop region). The arrangement of all genes was identical to the typical mitochondrial genomes of pigeon. The complete mitochondrial genome sequence of ice pigeon would serve as an important data set of the germplasm resources for further study.

Analysis of Genome-Wide Copy Number Variations in Chinese Indigenous and Western Pig Breeds by 60 K SNP Genotyping Arrays

Science.gov (United States)

Sun, Yaqi; Wang, Hongyang; Wang, Chao; Yu, Shaobo; Liu, Jing; Zhang, Yu; Fan, Bin; Li, Kui; Liu, Bang

2014-01-01

Copy number variations (CNVs) represent a substantial source of structural variants in mammals and contribute to both normal phenotypic variability and disease susceptibility. Although low-resolution CNV maps are produced in many domestic animals, and several reports have been published about the CNVs of porcine genome, the differences between Chinese and western pigs still remain to be elucidated. In this study, we used Porcine SNP60 BeadChip and PennCNV algorithm to perform a genome-wide CNV detection in 302 individuals from six Chinese indigenous breeds (Tongcheng, Laiwu, Luchuan, Bama, Wuzhishan and Ningxiang pigs), three western breeds (Yorkshire, Landrace and Duroc) and one hybrid (Tongcheng×Duroc). A total of 348 CNV Regions (CNVRs) across genome were identified, covering 150.49 Mb of the pig genome or 6.14% of the autosomal genome sequence. In these CNVRs, 213 CNVRs were found to exist only in the six Chinese indigenous breeds, and 60 CNVRs only in the three western breeds. The characters of CNVs in four Chinese normal size breeds (Luchuan, Tongcheng and Laiwu pigs) and two minipig breeds (Bama and Wuzhishan pigs) were also analyzed in this study. Functional annotation suggested that these CNVRs possess a great variety of molecular function and may play important roles in phenotypic and production traits between Chinese and western breeds. Our results are important complementary to the CNV map in pig genome, which provide new information about the diversity of Chinese and western pig breeds, and facilitate further research on porcine genome CNVs. PMID:25198154

Genome-Wide Association Studies and Comparison of Models and Cross-Validation Strategies for Genomic Prediction of Quality Traits in Advanced Winter Wheat Breeding Lines

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Peter S. Kristensen

2018-02-01

Full Text Available The aim of the this study was to identify SNP markers associated with five important wheat quality traits (grain protein content, Zeleny sedimentation, test weight, thousand-kernel weight, and falling number, and to investigate the predictive abilities of GBLUP and Bayesian Power Lasso models for genomic prediction of these traits. In total, 635 winter wheat lines from two breeding cycles in the Danish plant breeding company Nordic Seed A/S were phenotyped for the quality traits and genotyped for 10,802 SNPs. GWAS were performed using single marker regression and Bayesian Power Lasso models. SNPs with large effects on Zeleny sedimentation were found on chromosome 1B, 1D, and 5D. However, GWAS failed to identify single SNPs with significant effects on the other traits, indicating that these traits were controlled by many QTL with small effects. The predictive abilities of the models for genomic prediction were studied using different cross-validation strategies. Leave-One-Out cross-validations resulted in correlations between observed phenotypes corrected for fixed effects and genomic estimated breeding values of 0.50 for grain protein content, 0.66 for thousand-kernel weight, 0.70 for falling number, 0.71 for test weight, and 0.79 for Zeleny sedimentation. Alternative cross-validations showed that the genetic relationship between lines in training and validation sets had a bigger impact on predictive abilities than the number of lines included in the training set. Using Bayesian Power Lasso instead of GBLUP models, gave similar or slightly higher predictive abilities. Genomic prediction based on all SNPs was more effective than prediction based on few associated SNPs.

Breeding value estimation for somatic cell score in South African ...

African Journals Online (AJOL)

Breeding value estimation for somatic cell score in South African dairy cattle. ... are not unity, the RM-model estimates more competitive variances and requires ... are therefore recommended for breeding value estimation on a national basis.

The effect of using cow genomic information on accuracy and bias of genomic breeding values in a simulated Holstein dairy cattle population.

Science.gov (United States)

Dehnavi, E; Mahyari, S Ansari; Schenkel, F S; Sargolzaei, M

2018-06-01

Using cow data in the training population is attractive as a way to mitigate bias due to highly selected training bulls and to implement genomic selection for countries with no or limited proven bull data. However, one potential issue with cow data is a bias due to the preferential treatment. The objectives of this study were to (1) investigate the effect of including cow genotype and phenotype data into the training population on accuracy and bias of genomic predictions and (2) assess the effect of preferential treatment for different proportions of elite cows. First, a 4-pathway Holstein dairy cattle population was simulated for 2 traits with low (0.05) and moderate (0.3) heritability. Then different numbers of cows (0, 2,500, 5,000, 10,000, 15,000, or 20,000) were randomly selected and added to the training group composed of different numbers of top bulls (0, 2,500, 5,000, 10,000, or 15,000). Reliability levels of de-regressed estimated breeding values for training cows and bulls were 30 and 75% for traits with low heritability and were 60 and 90% for traits with moderate heritability, respectively. Preferential treatment was simulated by introducing upward bias equal to 35% of phenotypic variance to 5, 10, and 20% of elite bull dams in each scenario. Two different validation data sets were considered: (1) all animals in the last generation of both elite and commercial tiers (n = 42,000) and (2) only animals in the last generation of the elite tier (n = 12,000). Adding cow data into the training population led to an increase in accuracy (r) and decrease in bias of genomic predictions in all considered scenarios without preferential treatment. The gain in r was higher for the low heritable trait (from 0.004 to 0.166 r points) compared with the moderate heritable trait (from 0.004 to 0.116 r points). The gain in accuracy in scenarios with a lower number of training bulls was relatively higher (from 0.093 to 0.166 r points) than with a higher number of training

Be-Breeder - an application for analysis of genomic data in plant breeding

OpenAIRE

Matias,Filipe Inácio; Granato,Italo Stefanine Correa; Dequigiovanni,Gabriel; Fritsche-Neto,Roberto

2017-01-01

Abstract Be-Breeder is an application directed toward genetic breeding of plants, developed through the Shiny package of the R software, which allows different phenotype and molecular (marker) analysis to be undertaken. The section for analysis of molecular data of the Be-Breeder application makes it possible to achieve quality control of genotyping data, to obtain genomic kinship matrices, and to analyze genome selection, genome association, and genetic diversity in a simple manner on line. ...

Predicting Breeding Values in Animals by Kalman Filter

DEFF Research Database (Denmark)

Karacaoren, B; Janss, L L G; Kadarmideen, H N

2012-01-01

The aim of this study was to investigate usefulness of Kalman Filter (KF) Random Walk methodology (KF-RW) for prediction of breeding values in animals. We used body condition score (BCS) from dairy cattle for illustrating use of KF-RW. BCS was measured by Swiss Holstein Breeding Association during...

Genomic Footprints in Selected and Unselected Beef Cattle Breeds in Korea.

Directory of Open Access Journals (Sweden)

Dajeong Lim

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.

Genomic Footprints in Selected and Unselected Beef Cattle Breeds in Korea.

Science.gov (United States)

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

2016-01-01

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.

A genome wide survey of SNP variation reveals the genetic structure of sheep breeds.

Directory of Open Access Journals (Sweden)

James W Kijas

Full Text Available The genetic structure of sheep reflects their domestication and subsequent formation into discrete breeds. Understanding genetic structure is essential for achieving genetic improvement through genome-wide association studies, genomic selection and the dissection of quantitative traits. After identifying the first genome-wide set of SNP for sheep, we report on levels of genetic variability both within and between a diverse sample of ovine populations. Then, using cluster analysis and the partitioning of genetic variation, we demonstrate sheep are characterised by weak phylogeographic structure, overlapping genetic similarity and generally low differentiation which is consistent with their short evolutionary history. The degree of population substructure was, however, sufficient to cluster individuals based on geographic origin and known breed history. Specifically, African and Asian populations clustered separately from breeds of European origin sampled from Australia, New Zealand, Europe and North America. Furthermore, we demonstrate the presence of stratification within some, but not all, ovine breeds. The results emphasize that careful documentation of genetic structure will be an essential prerequisite when mapping the genetic basis of complex traits. Furthermore, the identification of a subset of SNP able to assign individuals into broad groupings demonstrates even a small panel of markers may be suitable for applications such as traceability.

Genome-Wide Specific Selection in Three Domestic Sheep Breeds.

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Huihua Wang

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.

Genome-Wide Specific Selection in Three Domestic Sheep Breeds.

Science.gov (United States)

Wang, Huihua; Zhang, Li; Cao, Jiaxve; Wu, Mingming; Ma, Xiaomeng; Liu, Zhen; Liu, Ruizao; Zhao, Fuping; Wei, Caihong; Du, Lixin

2015-01-01

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.

Whole-genome sequence, SNP chips and pedigree structure: building demographic profiles in domestic dog breeds to optimize genetic-trait mapping

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Dreger, Dayna L.; Rimbault, Maud; Davis, Brian W.; Bhatnagar, Adrienne; Parker, Heidi G.

2016-01-01

ABSTRACT In the decade following publication of the draft genome sequence of the domestic dog, extraordinary advances with application to several fields have been credited to the canine genetic system. Taking advantage of closed breeding populations and the subsequent selection for aesthetic and behavioral characteristics, researchers have leveraged the dog as an effective natural model for the study of complex traits, such as disease susceptibility, behavior and morphology, generating unique contributions to human health and biology. When designing genetic studies using purebred dogs, it is essential to consider the unique demography of each population, including estimation of effective population size and timing of population bottlenecks. The analytical design approach for genome-wide association studies (GWAS) and analysis of whole-genome sequence (WGS) experiments are inextricable from demographic data. We have performed a comprehensive study of genomic homozygosity, using high-depth WGS data for 90 individuals, and Illumina HD SNP data from 800 individuals representing 80 breeds. These data were coupled with extensive pedigree data analyses for 11 breeds that, together, allowed us to compute breed structure, demography, and molecular measures of genome diversity. Our comparative analyses characterize the extent, formation and implication of breed-specific diversity as it relates to population structure. These data demonstrate the relationship between breed-specific genome dynamics and population architecture, and provide important considerations influencing the technological and cohort design of association and other genomic studies. PMID:27874836

High-throughput phenotyping and genomic selection: the frontiers of crop breeding converge.

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Cabrera-Bosquet, Llorenç; Crossa, José; von Zitzewitz, Jarislav; Serret, María Dolors; Araus, José Luis

2012-05-01

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.

Breeding in peach, cherry and plum: from a tissue culture, genetic, transcriptomic and genomic perspective

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Basilio Carrasco

2013-01-01

Full Text Available This review is an overview of traditional and modern breeding methodologies being used to develop new Prunus cultivars (stone fruits with major emphasis on peach, sweet cherry and Japanese plum. To this end, common breeding tools used to produce seedlings, including in vitro culture tools, are discussed. Additionally, the mechanisms of inheritance of many important agronomical traits are described. Recent advances in stone fruit transcriptomics and genomic resources are providing an understanding of the molecular basis of phenotypic variability as well as the identification of allelic variants and molecular markers. These have potential applications for understanding the genetic diversity of the Prunus species, molecular marker-assisted selection and transgenesis. Simple Sequence Repeat (SSR and Single Nucleotide Polymorphism (SNPs molecular markers are described as useful tools to describe genetic diversity in peach, sweet cherry and Japanese plum. Additionally, the recently sequenced peach genome and the public release of the sweet cherry genome are discussed in terms of their applicability to breeding programs

Unique cultural values of Madura cattle: is cross-breeding a threat?

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Tri Satya Mastuti Widi, Tri; Udo, H.M.J.; Oldenbroek, J.K.; Budisatria, I.G.S.; Baliarti, E.; Zijpp, van der A.J.

2013-01-01

In Indonesia, cross-breeding local cattle with European beef breeds is widely promoted to stimulate beef production. This cross-breeding is threatening local breeds that have often different functions, including cultural roles. This study analysed the cultural values of Madura cattle and the effects

Whole genome sequencing of Gyeongbuk Araucana, a newly developed blue-egg laying chicken breed, reveals its origin and genetic characteristics.

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Jeong, Hyeonsoo; Kim, Kwondo; Caetano-Anollés, Kelsey; Kim, Heebal; Kim, Byung-Ki; Yi, Jun-Koo; Ha, Jae-Jung; Cho, Seoae; Oh, Dong Yep

2016-05-24

Chicken, Gallus gallus, is a valuable species both as a food source and as a model organism for scientific research. Here, we sequenced the genome of Gyeongbuk Araucana, a rare chicken breed with unique phenotypic characteristics including flight ability, large body size, and laying blue-shelled eggs, to identify its genomic features. We generated genomes of Gyeongbuk Araucana, Leghorn, and Korean Native Chicken at a total of 33.5, 35.82, and 33.23 coverage depth, respectively. Along with the genomes of 12 Chinese breeds, we identified genomic variants of 16.3 million SNVs and 2.3 million InDels in mapped regions. Additionally, through assembly of unmapped reads and selective sweep, we identified candidate genes that fall into heart, vasculature and muscle development and body growth categories, which provided insight into Gyeongbuk Araucana's phenotypic traits. Finally, genetic variation based on the transposable element insertion pattern was investigated to elucidate the features of transposable elements related to blue egg shell formation. This study presents results of the first genomic study on the Gyeongbuk Araucana breed; it has potential to serve as an invaluable resource for future research on the genomic characteristics of this chicken breed as well as others.

Updates to the Cool Season Food Legume Genome Database: Resources for pea, lentil, faba bean and chickpea genetics, genomics and breeding

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The Cool Season Food Legume Genome database (CSFL, www.coolseasonfoodlegume.org) is an online resource for genomics, genetics, and breeding research for chickpea, lentil,pea, and faba bean. The user-friendly and curated website allows for all publicly available map,marker,trait, gene,transcript, ger...

Genomic prediction in early selection stages using multi-year data in a hybrid rye breeding program.

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Bernal-Vasquez, Angela-Maria; Gordillo, Andres; Schmidt, Malthe; Piepho, Hans-Peter

2017-05-31

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

Refining QTL with high-density SNP genotyping and whole genome sequence in three cattle breeds

DEFF Research Database (Denmark)

Sahana, Goutam; Guldbrandtsen, Bernt; Lund, Mogens Sandø

2012-01-01

Genome-wide association study was carried out in Nordic Holsteins, Nordic Red and Jersey breeds for functional traits using BovineHD Genotyping BreadChip (Illumina, San Diego, CA). The association analyses were carried out using both linear mixed model approach and a Bayesian variable selection...... method. Principal components were used to account for population structure. The QTL segregating in all three breeds were selected and a few of the most significant ones were followed in further analyses. The polymorphisms in the identified QTL regions were imputed using 90 whole genome sequences...

Commonalities in Development of Pure Breeds and Population Isolates Revealed in the Genome of the Sardinian Fonni's Dog

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Dreger, Dayna L.; Davis, Brian W.; Cocco, Raffaella; Sechi, Sara; Di Cerbo, Alessandro; Parker, Heidi G.; Polli, Michele; Marelli, Stefano P.; Crepaldi, Paola; Ostrander, Elaine A.

2016-01-01

The island inhabitants of Sardinia have long been a focus for studies of complex human traits due to their unique ancestral background and population isolation reflecting geographic and cultural restriction. Population isolates share decreased genomic diversity, increased linkage disequilibrium, and increased inbreeding coefficients. In many regions, dogs and humans have been exposed to the same natural and artificial forces of environment, growth, and migration. Distinct dog breeds have arisen through human-driven selection of characteristics to meet an ideal standard of appearance and function. The Fonni’s Dog, an endemic dog population on Sardinia, has not been subjected to an intensive system of artificial selection, but rather has developed alongside the human population of Sardinia, influenced by geographic isolation and unregulated selection based on its environmental adaptation and aptitude for owner-desired behaviors. Through analysis of 28 dog breeds, represented with whole-genome sequences from 13 dogs and ∼170,000 genome-wide single nucleotide variants from 155 dogs, we have produced a genomic illustration of the Fonni’s Dog. Genomic patterns confirm within-breed similarity, while population and demographic analyses provide spatial identity of Fonni’s Dog to other Mediterranean breeds. Investigation of admixture and fixation indices reveals insights into the involvement of Fonni’s Dogs in breed development throughout the Mediterranean. We describe how characteristics of population isolates are reflected in dog breeds that have undergone artificial selection, and are mirrored in the Fonni’s Dog through traditional isolating factors that affect human populations. Lastly, we show that the genetic history of Fonni’s Dog parallels demographic events in local human populations. PMID:27519604

Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.).

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Bassi, Filippo M; Bentley, Alison R; Charmet, Gilles; Ortiz, Rodomiro; Crossa, Jose

2016-01-01

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.

Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers.

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Crossa, José; Campos, Gustavo de Los; Pérez, Paulino; Gianola, Daniel; Burgueño, Juan; Araus, José Luis; Makumbi, Dan; Singh, Ravi P; Dreisigacker, Susanne; Yan, Jianbing; Arief, Vivi; Banziger, Marianne; Braun, Hans-Joachim

2010-10-01

The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype × environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed.

Genome-wide association mapping for yield and other agronomic traits in an elite breeding population of tropical rice (Oryza sativa).

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Begum, Hasina; Spindel, Jennifer E; Lalusin, Antonio; Borromeo, Teresita; Gregorio, Glenn; Hernandez, Jose; Virk, Parminder; Collard, Bertrand; McCouch, Susan R

2015-01-01

Genome-wide association mapping studies (GWAS) are frequently used to detect QTL in diverse collections of crop germplasm, based on historic recombination events and linkage disequilibrium across the genome. Generally, diversity panels genotyped with high density SNP panels are utilized in order to assay a wide range of alleles and haplotypes and to monitor recombination breakpoints across the genome. By contrast, GWAS have not generally been performed in breeding populations. In this study we performed association mapping for 19 agronomic traits including yield and yield components in a breeding population of elite irrigated tropical rice breeding lines so that the results would be more directly applicable to breeding than those from a diversity panel. The population was genotyped with 71,710 SNPs using genotyping-by-sequencing (GBS), and GWAS performed with the explicit goal of expediting selection in the breeding program. Using this breeding panel we identified 52 QTL for 11 agronomic traits, including large effect QTLs for flowering time and grain length/grain width/grain-length-breadth ratio. We also identified haplotypes that can be used to select plants in our population for short stature (plant height), early flowering time, and high yield, and thus demonstrate the utility of association mapping in breeding populations for informing breeding decisions. We conclude by exploring how the newly identified significant SNPs and insights into the genetic architecture of these quantitative traits can be leveraged to build genomic-assisted selection models.

Genome-enabled prediction models for yield related traits in chickpea

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Genomic selection (GS) unlike marker-assisted backcrossing (MABC) predicts breeding values of lines using genome-wide marker profiling and allows selection of lines prior to field-phenotyping, thereby shortening the breeding cycle. A collection of 320 elite breeding lines was selected and phenotyped...

Transgenesis and genomics in molecular breeding of pasture grasses and legumes for forage quality and other traits

International Nuclear Information System (INIS)

Spangenberg, G.

2005-01-01

Significant advances in the establishment of the methodologies required for the molecular breeding of temperate forage grasses (Lolium and Festuca species) and legumes (Trifolium and Medicago species) are reviewed. Examples of current products and approaches for the application of these methodologies to forage grass and legume improvement are outlined. The plethora of new technologies and tools now available for high-throughput gene discovery and genome-wide expression analysis have opened up opportunities for innovative applications in the identification, functional characterization and use of genes of value in forage production systems and beyond. Selected examples of current work in pasture plant genomics, xenogenomics, symbiogenomics and micro-array-based molecular phenotyping are discussed. (author)

Impact of selective genotyping in the training population on accuracy and bias of genomic selection.

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Zhao, Yusheng; Gowda, Manje; Longin, Friedrich H; Würschum, Tobias; Ranc, Nicolas; Reif, Jochen C

2012-08-01

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.

Application of genomics-assisted breeding for generation of climate resilient crops: Progress and prospects

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Chittaranjan eKole

2015-08-01

Full Text Available Climate change affects agricultural productivity worldwide. Increased prices of food commodities are the initial indication of drastic edible yield loss, which is expected to surge further due to global warming. This situation has compelled plant scientists to develop climate change-resilient crops, which can withstand broad-spectrum stresses such as drought, heat, cold, salinity, flood and submergence, and pests along with increased productivity. Genomics appears to be a promising tool for deciphering the stress responsiveness of crop species with adaptation traits or in wild relatives towards identifying underlying genes, alleles or quantitative trait loci. Molecular breeding approaches have been proven helpful in enhancing the stress adaptation of crop plants, and recent advancement in next-generation sequencing along with high-throughput sequencing and phenotyping platforms have transformed molecular breeding to genomics-assisted breeding (GAB. In view of this, the present review elaborates the progress and prospects of GAB in improving climate change resilience in crop plants towards circumventing global food insecurity.

Exploiting Genomic Knowledge in Optimising Molecular Breeding Programmes: Algorithms from Evolutionary Computing

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O'Hagan, Steve; Knowles, Joshua; Kell, Douglas B.

2012-01-01

Comparatively few studies have addressed directly the question of quantifying the benefits to be had from using molecular genetic markers in experimental breeding programmes (e.g. for improved crops and livestock), nor the question of which organisms should be mated with each other to best effect. We argue that this requires in silico modelling, an approach for which there is a large literature in the field of evolutionary computation (EC), but which has not really been applied in this way to experimental breeding programmes. EC seeks to optimise measurable outcomes (phenotypic fitnesses) by optimising in silico the mutation, recombination and selection regimes that are used. We review some of the approaches from EC, and compare experimentally, using a biologically relevant in silico landscape, some algorithms that have knowledge of where they are in the (genotypic) search space (G-algorithms) with some (albeit well-tuned ones) that do not (F-algorithms). For the present kinds of landscapes, F- and G-algorithms were broadly comparable in quality and effectiveness, although we recognise that the G-algorithms were not equipped with any ‘prior knowledge’ of epistatic pathway interactions. This use of algorithms based on machine learning has important implications for the optimisation of experimental breeding programmes in the post-genomic era when we shall potentially have access to the full genome sequence of every organism in a breeding population. The non-proprietary code that we have used is made freely available (via Supplementary information). PMID:23185279

Genomic heritabilities and genomic estimated breeding values for methane traits in Angus cattle.

Science.gov (United States)

Hayes, B J; Donoghue, K A; Reich, C M; Mason, B A; Bird-Gardiner, T; Herd, R M; Arthur, P F

2016-03-01

Enteric methane emissions from beef cattle are a significant component of total greenhouse gas emissions from agriculture. The variation between beef cattle in methane emissions is partly genetic, whether measured as methane production, methane yield (methane production/DMI), or residual methane production (observed methane production - expected methane production), with heritabilities ranging from 0.19 to 0.29. This suggests methane emissions could be reduced by selection. Given the high cost of measuring methane production from individual beef cattle, genomic selection is the most feasible approach to achieve this reduction in emissions. We derived genomic EBV (GEBV) for methane traits from a reference set of 747 Angus animals phenotyped for methane traits and genotyped for 630,000 SNP. The accuracy of GEBV was tested in a validation set of 273 Angus animals phenotyped for the same traits. Accuracies of GEBV ranged from 0.29 ± 0.06 for methane yield and 0.35 ± 0.06 for residual methane production. Selection on GEBV using the genomic prediction equations derived here could reduce emissions for Angus cattle by roughly 5% over 10 yr.

Whole-Genome Analyses of Korean Native and Holstein Cattle Breeds by Massively Parallel Sequencing

Science.gov (United States)

Stothard, Paul; Chung, Won-Hyong; Jeon, Heoyn-Jeong; Miller, Stephen P.; Choi, So-Young; Lee, Jeong-Koo; Yang, Bokyoung; Lee, Kyung-Tai; Han, Kwang-Jin; Kim, Hyeong-Cheol; Jeong, Dongkee; Oh, Jae-Don; Kim, Namshin; Kim, Tae-Hun; Lee, Hak-Kyo; Lee, Sung-Jin

2014-01-01

A main goal of cattle genomics is to identify DNA differences that account for variations in economically important traits. In this study, we performed whole-genome analyses of three important cattle breeds in Korea—Hanwoo, Jeju Heugu, and Korean Holstein—using the Illumina HiSeq 2000 sequencing platform. We achieved 25.5-, 29.6-, and 29.5-fold coverage of the Hanwoo, Jeju Heugu, and Korean Holstein genomes, respectively, and identified a total of 10.4 million single nucleotide polymorphisms (SNPs), of which 54.12% were found to be novel. We also detected 1,063,267 insertions–deletions (InDels) across the genomes (78.92% novel). Annotations of the datasets identified a total of 31,503 nonsynonymous SNPs and 859 frameshift InDels that could affect phenotypic variations in traits of interest. Furthermore, genome-wide copy number variation regions (CNVRs) were detected by comparing the Hanwoo, Jeju Heugu, and previously published Chikso genomes against that of Korean Holstein. A total of 992, 284, and 1881 CNVRs, respectively, were detected throughout the genome. Moreover, 53, 65, 45, and 82 putative regions of homozygosity (ROH) were identified in Hanwoo, Jeju Heugu, Chikso, and Korean Holstein respectively. The results of this study provide a valuable foundation for further investigations to dissect the molecular mechanisms underlying variation in economically important traits in cattle and to develop genetic markers for use in cattle breeding. PMID:24992012

Accuracy and responses of genomic selection on key traits in apple breeding

NARCIS (Netherlands)

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.

2015-01-01

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

Impact of reduced marker set estimation of genomic relationship matrices on genomic selection for feed efficiency in Angus cattle

Directory of Open Access Journals (Sweden)

Northcutt Sally L

2010-04-01

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.

Mitochondrial genome sequence of Egyptian swift Rock Pigeon (Columba livia breed Egyptian swift).

Science.gov (United States)

Li, Chun-Hong; Shi, Wei; Shi, Wan-Yu

2015-06-01

The Egyptian swift Rock Pigeon is a breed of fancy pigeon developed over many years of selective breeding. In this work, we report the complete mitochondrial genome sequence of Egyptian swift Rock Pigeon. The total length of the mitogenome was 17,239 bp and its overall base composition was estimated to be 30.2% for A, 24.0% for T, 31.9% for C and 13.9% for G, indicating an A-T (54.2%)-rich feature in the mitogenome. It contained the typical structure of 13 protein-coding genes, 2 ribosomal RNA genes, 22 transfer RNA genes and a non-coding control region (D-loop region). The complete mitochondrial genome sequence of Egyptian swift Rock Pigeon would serve as an important data set of the germplasm resources for further study.

Economic values for production traits for different sheep breeds in Kosovo

Directory of Open Access Journals (Sweden)

Hysen Bytyqi

2015-10-01

Full Text Available Generally, all traits of economic importance should be included in the breeding goal of livestock breeding programmes. The main aim of this study was to examine economic values for a combination of dairy and meat traits of the dual purpose Bardhoka (BAR, Balusha (BAL, Sharri (SHA and Kosova (KOS sheep breeds in Kosovo. The underlying herd model is based on a deterministic approach considering a sheep flock with milk production, lamb fattening and replacement stock. In order to examine the economic impact for different sheep breeds, the production traits considered were milk (milk yield and fattening (daily gain. For each lactation cycle among the breeds the main revenues resulted from selling milk (€ 61.82/46.03/26.37/27.22 and fattened lambs (€ 34.06/39.88/35.42/33.92 for BAR/BAL/ SHA/KOS, respectively. For BAR and BAL the economic value per average ewe place and year was € 37.7 for both breeds, while for SHA and KOS it was € 35.1 and € 32.5, respectively. For lamb fattening, daily gain was found to have the highest economic value at € 7.00 for BAL, SHA and KOS, while for BAR it was € 6.67. The relative economic values for milk yield and daily gain for BAR, BAL, SHA and KOS were 84.96:15.04, 84.42:15.58, 83.45:16.55, and 82.36:17.64, respectively.

Multiple-Trait Genomic Selection Methods Increase Genetic Value Prediction Accuracy

Science.gov (United States)

Jia, Yi; Jannink, Jean-Luc

2012-01-01

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

Right-hand-side updating for fast computing of genomic breeding values

NARCIS (Netherlands)

Calus, M.P.L.

2014-01-01

Since both the number of SNPs (single nucleotide polymorphisms) used in genomic prediction and the number of individuals used in training datasets are rapidly increasing, there is an increasing need to improve the efficiency of genomic prediction models in terms of computing time and memory (RAM)

Value-based genomics.

Science.gov (United States)

Gong, Jun; Pan, Kathy; Fakih, Marwan; Pal, Sumanta; Salgia, Ravi

2018-03-20

Advancements in next-generation sequencing have greatly enhanced the development of biomarker-driven cancer therapies. The affordability and availability of next-generation sequencers have allowed for the commercialization of next-generation sequencing platforms that have found widespread use for clinical-decision making and research purposes. Despite the greater availability of tumor molecular profiling by next-generation sequencing at our doorsteps, the achievement of value-based care, or improving patient outcomes while reducing overall costs or risks, in the era of precision oncology remains a looming challenge. In this review, we highlight available data through a pre-established and conceptualized framework for evaluating value-based medicine to assess the cost (efficiency), clinical benefit (effectiveness), and toxicity (safety) of genomic profiling in cancer care. We also provide perspectives on future directions of next-generation sequencing from targeted panels to whole-exome or whole-genome sequencing and describe potential strategies needed to attain value-based genomics.

Population genomic structure and linkage disequilibrium analysis of South African goat breeds using genome-wide SNP data.

Science.gov (United States)

Mdladla, K; Dzomba, E F; Huson, H J; Muchadeyi, F C

2016-08-01

The sustainability of goat farming in marginal areas of southern Africa depends on local breeds that are adapted to specific agro-ecological conditions. Unimproved non-descript goats are the main genetic resources used for the development of commercial meat-type breeds of South Africa. Little is known about genetic diversity and the genetics of adaptation of these indigenous goat populations. This study investigated the genetic diversity, population structure and breed relations, linkage disequilibrium, effective population size and persistence of gametic phase in goat populations of South Africa. Three locally developed meat-type breeds of the Boer (n = 33), Savanna (n = 31), Kalahari Red (n = 40), a feral breed of Tankwa (n = 25) and unimproved non-descript village ecotypes (n = 110) from four goat-producing provinces of the Eastern Cape, KwaZulu-Natal, Limpopo and North West were assessed using the Illumina Goat 50K SNP Bead Chip assay. The proportion of SNPs with minor allele frequencies >0.05 ranged from 84.22% in the Tankwa to 97.58% in the Xhosa ecotype, with a mean of 0.32 ± 0.13 across populations. Principal components analysis, admixture and pairwise FST identified Tankwa as a genetically distinct population and supported clustering of the populations according to their historical origins. Genome-wide FST identified 101 markers potentially under positive selection in the Tankwa. Average linkage disequilibrium was highest in the Tankwa (r(2)  = 0.25 ± 0.26) and lowest in the village ecotypes (r(2) range = 0.09 ± 0.12 to 0.11 ± 0.14). We observed an effective population size of 100 kb with the exception of those in Savanna and Tswana populations. This study highlights the high level of genetic diversity in South African indigenous goats as well as the utility of the genome-wide SNP marker panels in genetic studies of these populations. © 2016 Stichting International Foundation for Animal Genetics.

1000 Bull Genomes - Toward genomic Selectionf from whole genome sequence Data in Dairy and Beef Cattle

NARCIS (Netherlands)

Hayes, B.; Daetwyler, H.D.; Fries, R.; Guldbrandtsen, B.; Mogens Sando Lund, M.; Didier A. Boichard, D.A.; Stothard, P.; Veerkamp, R.F.; Hulsegge, B.; Rocha, D.; Tassell, C.; Mullaart, E.; Gredler, B.; Druet, T.; Bagnato, A.; Goddard, M.E.; Chamberlain, H.L.

2013-01-01

Genomic prediction of breeding values is now used as the basis for selection of dairy cattle, and in some cases beef cattle, in a number of countries. When genomic prediction was introduced most of the information was to thought to be derived from linkage disequilibrium between markers and causative

Development of Highly Informative Genome-Wide Single Sequence Repeat Markers for Breeding Applications in Sesame and Construction of a Web Resource: SisatBase

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Komivi Dossa

2017-08-01

Full Text Available The sequencing of the full nuclear genome of sesame (Sesamum indicum L. provides the platform for functional analyses of genome components and their application in breeding programs. Although the importance of microsatellites markers or simple sequence repeats (SSR in crop genotyping, genetics, and breeding applications is well established, only a little information exist concerning SSRs at the whole genome level in sesame. In addition, SSRs represent a suitable marker type for sesame molecular breeding in developing countries where it is mainly grown. In this study, we identified 138,194 genome-wide SSRs of which 76.5% were physically mapped onto the 13 pseudo-chromosomes. Among these SSRs, up to three primers pairs were supplied for 101,930 SSRs and used to in silico amplify the reference genome together with two newly sequenced sesame accessions. A total of 79,957 SSRs (78% were polymorphic between the three genomes thereby suggesting their promising use in different genomics-assisted breeding applications. From these polymorphic SSRs, 23 were selected and validated to have high polymorphic potential in 48 sesame accessions from different growing areas of Africa. Furthermore, we have developed an online user-friendly database, SisatBase (http://www.sesame-bioinfo.org/SisatBase/, which provides free access to SSRs data as well as an integrated platform for functional analyses. Altogether, the reference SSR and SisatBase would serve as useful resources for genetic assessment, genomic studies, and breeding advancement in sesame, especially in developing countries.

Genomic Analyses Reveal the Influence of Geographic Origin, Migration, and Hybridization on Modern Dog Breed Development

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Heidi G. Parker

2017-04-01

Full Text Available There are nearly 400 modern domestic dog breeds with a unique histories and genetic profiles. To track the genetic signatures of breed development, we have assembled the most diverse dataset of dog breeds, reflecting their extensive phenotypic variation and heritage. Combining genetic distance, migration, and genome-wide haplotype sharing analyses, we uncover geographic patterns of development and independent origins of common traits. Our analyses reveal the hybrid history of breeds and elucidate the effects of immigration, revealing for the first time a suggestion of New World dog within some modern breeds. Finally, we used cladistics and haplotype sharing to show that some common traits have arisen more than once in the history of the dog. These analyses characterize the complexities of breed development, resolving longstanding questions regarding individual breed origination, the effect of migration on geographically distinct breeds, and, by inference, transfer of trait and disease alleles among dog breeds.

Neural networks for predicting breeding values and genetic gains

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Gabi Nunes Silva

2014-12-01

Full Text Available Analysis using Artificial Neural Networks has been described as an approach in the decision-making process that, although incipient, has been reported as presenting high potential for use in animal and plant breeding. In this study, we introduce the procedure of using the expanded data set for training the network. Wealso proposed using statistical parameters to estimate the breeding value of genotypes in simulated scenarios, in addition to the mean phenotypic value in a feed-forward back propagation multilayer perceptron network. After evaluating artificial neural network configurations, our results showed its superiority to estimates based on linear models, as well as its applicability in the genetic value prediction process. The results further indicated the good generalization performance of the neural network model in several additional validation experiments.

Genomic Prediction of Manganese Efficiency in Winter Barley

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Florian Leplat

2016-07-01

Full Text Available Manganese efficiency is a quantitative abiotic stress trait controlled by several genes each with a small effect. Manganese deficiency leads to yield reduction in winter barley ( L.. Breeding new cultivars for this trait remains difficult because of the lack of visual symptoms and the polygenic features of the trait. Hence, Mn efficiency is a potential suitable trait for a genomic selection (GS approach. A collection of 248 winter barley varieties was screened for Mn efficiency using Chlorophyll (Chl fluorescence in six environments prone to induce Mn deficiency. Two models for genomic prediction were implemented to predict future performance and breeding value of untested varieties. Predictions were obtained using multivariate mixed models: best linear unbiased predictor (BLUP and genomic best linear unbiased predictor (G-BLUP. In the first model, predictions were based on the phenotypic evaluation, whereas both phenotypic and genomic marker data were included in the second model. Accuracy of predicting future phenotype, , and accuracy of predicting true breeding values, , were calculated and compared for both models using six cross-validation (CV schemes; these were designed to mimic plant breeding programs. Overall, the CVs showed that prediction accuracies increased when using the G-BLUP model compared with the prediction accuracies using the BLUP model. Furthermore, the accuracies [] of predicting breeding values were more accurate than accuracy of predicting future phenotypes []. The study confirms that genomic data may enhance the prediction accuracy. Moreover it indicates that GS is a suitable breeding approach for quantitative abiotic stress traits.

Single nucleotide variants and InDels identified from whole-genome re-sequencing of Guzerat, Gyr, Girolando and Holstein cattle breeds.

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Nedenia Bonvino Stafuzza

Full Text Available Whole-genome re-sequencing, alignment and annotation analyses were undertaken for 12 sires representing four important cattle breeds in Brazil: Guzerat (multi-purpose, Gyr, Girolando and Holstein (dairy production. A total of approximately 4.3 billion reads from an Illumina HiSeq 2000 sequencer generated for each animal 10.7 to 16.4-fold genome coverage. A total of 27,441,279 single nucleotide variations (SNVs and 3,828,041 insertions/deletions (InDels were detected in the samples, of which 2,557,670 SNVs and 883,219 InDels were novel. The submission of these genetic variants to the dbSNP database significantly increased the number of known variants, particularly for the indicine genome. The concordance rate between genotypes obtained using the Bovine HD BeadChip array and the same variants identified by sequencing was about 99.05%. The annotation of variants identified numerous non-synonymous SNVs and frameshift InDels which could affect phenotypic variation. Functional enrichment analysis was performed and revealed that variants in the olfactory transduction pathway was over represented in all four cattle breeds, while the ECM-receptor interaction pathway was over represented in Girolando and Guzerat breeds, the ABC transporters pathway was over represented only in Holstein breed, and the metabolic pathways was over represented only in Gyr breed. The genetic variants discovered here provide a rich resource to help identify potential genomic markers and their associated molecular mechanisms that impact economically important traits for Gyr, Girolando, Guzerat and Holstein breeding programs.

Optimizing the creation of base populations for aquaculture breeding programs using phenotypic and genomic data and its consequences on genetic progress.

Science.gov (United States)

Fernández, Jesús; Toro, Miguel Á; Sonesson, Anna K; Villanueva, Beatriz

2014-01-01

The success of an aquaculture breeding program critically depends on the way in which the base population of breeders is constructed since all the genetic variability for the traits included originally in the breeding goal as well as those to be included in the future is contained in the initial founders. Traditionally, base populations were created from a number of wild strains by sampling equal numbers from each strain. However, for some aquaculture species improved strains are already available and, therefore, mean phenotypic values for economically important traits can be used as a criterion to optimize the sampling when creating base populations. Also, the increasing availability of genome-wide genotype information in aquaculture species could help to refine the estimation of relationships within and between candidate strains and, thus, to optimize the percentage of individuals to be sampled from each strain. This study explores the advantages of using phenotypic and genome-wide information when constructing base populations for aquaculture breeding programs in terms of initial and subsequent trait performance and genetic diversity level. Results show that a compromise solution between diversity and performance can be found when creating base populations. Up to 6% higher levels of phenotypic performance can be achieved at the same level of global diversity in the base population by optimizing the selection of breeders instead of sampling equal numbers from each strain. The higher performance observed in the base population persisted during 10 generations of phenotypic selection applied in the subsequent breeding program.

Genomic selection strategies in breeding programs: Strong positive interaction between application of genotypic information and intensive use of young bulls on genetic gain

DEFF Research Database (Denmark)

Buch, Line Hjortø; Sørensen, Morten Kargo; Berg, Peer

2012-01-01

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...

Overlap in genomic variation associated with milk fat composition in Holstein Friesian and Dutch native dual-purpose breeds

NARCIS (Netherlands)

Maurice - Van Eijndhoven, M.H.T.; Bovenhuis, H.; Veerkamp, R.F.; Calus, M.P.L.

2015-01-01

The aim of this study was to identify if genomic variations associated with fatty acid (FA) composition are similar between the Holstein-Friesian (HF) and native dual-purpose breeds used in the Dutch dairy industry. Phenotypic and genotypic information were available for the breeds Meuse-Rhine-Yssel

Light whole genome sequence for SNP discovery across domestic cat breeds

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Driscoll Carlos

2010-06-01

Full Text Available Abstract Background The domestic cat has offered enormous genomic potential in the veterinary description of over 250 hereditary disease models as well as the occurrence of several deadly feline viruses (feline leukemia virus -- FeLV, feline coronavirus -- FECV, feline immunodeficiency virus - FIV that are homologues to human scourges (cancer, SARS, and AIDS respectively. However, to realize this bio-medical potential, a high density single nucleotide polymorphism (SNP map is required in order to accomplish disease and phenotype association discovery. Description To remedy this, we generated 3,178,297 paired fosmid-end Sanger sequence reads from seven cats, and combined these data with the publicly available 2X cat whole genome sequence. All sequence reads were assembled together to form a 3X whole genome assembly allowing the discovery of over three million SNPs. To reduce potential false positive SNPs due to the low coverage assembly, a low upper-limit was placed on sequence coverage and a high lower-limit on the quality of the discrepant bases at a potential variant site. In all domestic cats of different breeds: female Abyssinian, female American shorthair, male Cornish Rex, female European Burmese, female Persian, female Siamese, a male Ragdoll and a female African wildcat were sequenced lightly. We report a total of 964 k common SNPs suitable for a domestic cat SNP genotyping array and an additional 900 k SNPs detected between African wildcat and domestic cats breeds. An empirical sampling of 94 discovered SNPs were tested in the sequenced cats resulting in a SNP validation rate of 99%. Conclusions These data provide a large collection of mapped feline SNPs across the cat genome that will allow for the development of SNP genotyping platforms for mapping feline diseases.

Avian Polyomavirus Genome Sequences Recovered from Parrots in Captive Breeding Facilities in Poland

OpenAIRE

Dayaram, Anisha; Piasecki, Tomasz; Chrząstek, Klaudia; White, Robyn; Julian, Laurel; van Bysterveldt, Katherine; Varsani, Arvind

2015-01-01

Eight genomes of avian polyomaviruses (APVs) were recovered and sequenced from deceased Psittacula eupatria, Psittacula krameri, and Melopsittacus undulatus from various breeding facilities in Poland. Of these APV-positive samples, six had previously tested positive for beak and feather disease virus (BFDV) and/or parrot hepatitis B virus (PHBV).

Consumers & plant genomics : the positioning and acceptance of a new plant breeding practice

NARCIS (Netherlands)

Heuvel, van den T.

2008-01-01

Innovative developments in technology, such as the emergence of genomics as a plant breeding practice, hold the potential to change the supply side of the market. The success of these practices not only depends on the improved efficiency and effectiveness it brings, but also on how well they are

[Genomic selection and its application].

Science.gov (United States)

Li, Heng-De; Bao, Zhen-Min; Sun, Xiao-Wen

2011-12-01

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.

Selection on Optimal Haploid Value Increases Genetic Gain and Preserves More Genetic Diversity Relative to Genomic Selection

OpenAIRE

Daetwyler, Hans D.; Hayden, Matthew J.; Spangenberg, German C.; Hayes, Ben J.

2015-01-01

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...

Determination of non-market values to inform conservation strategies for the threatened Alistana-Sanabresa cattle breed.

Science.gov (United States)

Martin-Collado, D; Diaz, C; Drucker, A G; Carabaño, M J; Zander, K K

2014-08-01

Livestock breed-related public good functions are often used to justify support for endangered breed conservation despite the fact that little is known about such non-market values. We show how stated preference techniques can be used to assess the non-market values that people place on livestock breeds. Through the application of a case study choice experiment survey in Zamora province, Spain, the total economic value (TEV) of the threatened Alistana-Sanabresa (AS) cattle breed was investigated. An analysis of the relative importance of the non-market components of its TEV and an assessment of the socio-economic variables that influence people's valuation of such components is used to inform conservation strategy design. Overall, the findings reveal that the AS breed had significant non-market values associated with it and that the value that respondents placed on each specific public good function also varied significantly. Functions related with indirect use cultural and existence values were much more highly valued than landscape maintenance values. These high cultural and existence values (totalling over 80% of TEV) suggest that an AS in situ conservation strategy will be required to secure such values. As part of such a strategy, incentive mechanisms will be needed to permit farmers to capture some of these public good values and thus be able to afford to maintain breed population numbers at socially desirable levels. One such mechanism could be related to the development of breed-related agritourism initiatives, with a view to enhancing private good values and providing an important addition to continued direct support. Where linked with cultural dimensions, niche product market development, including through improving AS breed-related product quality and brand recognition may also have a role to play as part of such an overall conservation and use strategy. We conclude that livestock breed conservation strategies with the highest potential to maximise

Genome-wide detection of copy number variations among diverse horse breeds by array CGH.

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Wei Wang

Full Text Available Recent studies have found that copy number variations (CNVs are widespread in human and animal genomes. CNVs are a significant source of genetic variation, and have been shown to be associated with phenotypic diversity. However, the effect of CNVs on genetic variation in horses is not well understood. In the present study, CNVs in 6 different breeds of mare horses, Mongolia horse, Abaga horse, Hequ horse and Kazakh horse (all plateau breeds and Debao pony and Thoroughbred, were determined using aCGH. In total, seven hundred CNVs were identified ranging in size from 6.1 Kb to 0.57 Mb across all autosomes, with an average size of 43.08 Kb and a median size of 15.11 Kb. By merging overlapping CNVs, we found a total of three hundred and fifty-three CNV regions (CNVRs. The length of the CNVRs ranged from 6.1 Kb to 1.45 Mb with average and median sizes of 38.49 Kb and 13.1 Kb. Collectively, 13.59 Mb of copy number variation was identified among the horses investigated and accounted for approximately 0.61% of the horse genome sequence. Five hundred and eighteen annotated genes were affected by CNVs, which corresponded to about 2.26% of all horse genes. Through the gene ontology (GO, genetic pathway analysis and comparison of CNV genes among different breeds, we found evidence that CNVs involving 7 genes may be related to the adaptation to severe environment of these plateau horses. This study is the first report of copy number variations in Chinese horses, which indicates that CNVs are ubiquitous in the horse genome and influence many biological processes of the horse. These results will be helpful not only in mapping the horse whole-genome CNVs, but also to further research for the adaption to the high altitude severe environment for plateau horses.

Physiological values of some blood indicators in selected dwarf rabbit breeds

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V. Šimek

2017-03-01

Full Text Available The aim of the present study was to evaluate the effect of breed on haematological and biochemical indicators in 3 dwarf rabbit breeds. In the experiment, 30 sexually intact dwarf rabbit females aged 6 mo were used. With the sole exception of white blood cells and haematocrit value, breed had the most significant effect on the majority of haematological indicators monitored. The red blood cell count was higher in the Dwarf Lop compared to the Netherland Dwarf (+1.91×1012 cells/L; P<0.05 and also the Teddy Dwarf (+1.32×1012 cells/L; P<0.05. For haemoglobin concentration, a higher value was found in the Netherland Dwarf than in the Teddy Dwarf (+39.29 g/L; P<0.05 and the Dwarf Lop (+26.36 g/L; P<0.05. For erythrocytic indicators, the highest values of mean corpuscular volume, mean corpuscular haemoglobin and mean corpuscular haemoglobin concentration were found in the Netherland Dwarf. The breed had a significant effect on the urea and potassium values. A higher value of urea was recorded in the Dwarf Lop compared to the Teddy Dwarf (+1.56 mmol/L; P<0.05. For potassium, a higher value was found in the Netherland Dwarf compared to the Teddy Dwarf (+0.85 mmol/L; P<0.05. In addition, a significantly positive correlation (P<0.05 was found between the live weight of dwarf females and values of haematocrit (0.49, albumin (0.54, alanine aminotransferase (0.51, and aspartate aminotransferase (0.41, while a significantly negative correlation (P<0.05 was found between their live weight and values of triacylglycerols (–0.44, alkaline phosphatase (–0.38 and inorganic phosphorus (–0.52.

Recent and historical recombination in the admixed Norwegian Red cattle breed

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Grove Harald

2011-01-01

Full Text Available Abstract Background Comparison of recent patterns of recombination derived from linkage maps to historical patterns of recombination from linkage disequilibrium (LD could help identify genomic regions affected by strong artificial selection, appearing as reduced recent recombination. Norwegian Red cattle (NRF make an interesting case study for investigating these patterns as it is an admixed breed with an extensively recorded pedigree. NRF have been under strong artificial selection for traits such as milk and meat production, fertility and health. While measures of LD is also crucial for determining the number of markers required for association mapping studies, estimates of recombination rate can be used to assess quality of genomic assemblies. Results A dataset containing more than 17,000 genome-wide distributed SNPs and 2600 animals was used to assess recombination rates and LD in NRF. Although low LD measured by r2 was observed in NRF relative to some of the breeds from which this breed originates, reports from breeds other than those assessed in this study have described more rapid decline in r2 at short distances than what was found in NRF. Rate of decline in r2 for NRF suggested that to obtain an expected r2 between markers and a causal polymorphism of at least 0.5 for genome-wide association studies, approximately one SNP every 15 kb or a total of 200,000 SNPs would be required. For well known quantitative trait loci (QTLs for milk production traits on Bos Taurus chromosomes 1, 6 and 20, map length based on historic recombination was greater than map length based on recent recombination in NRF. Further, positions for 130 previously unpositioned contigs from assembly of the bovine genome sequence (Btau_4.0 found using comparative sequence analysis were validated by linkage analysis, and 28% of these positions corresponded to extreme values of population recombination rate. Conclusion While LD is reduced in NRF compared to some of the

Non-additive Effects in Genomic Selection

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Luis Varona

2018-03-01

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.

Methods to estimate breeding values in honey bees

NARCIS (Netherlands)

Brascamp, E.W.; Bijma, P.

2014-01-01

Background Efficient methodologies based on animal models are widely used to estimate breeding values in farm animals. These methods are not applicable in honey bees because of their mode of reproduction. Observations are recorded on colonies, which consist of a single queen and thousands of workers

Avian Polyomavirus Genome Sequences Recovered from Parrots in Captive Breeding Facilities in Poland.

Science.gov (United States)

Dayaram, Anisha; Piasecki, Tomasz; Chrząstek, Klaudia; White, Robyn; Julian, Laurel; van Bysterveldt, Katherine; Varsani, Arvind

2015-09-24

Eight genomes of avian polyomaviruses (APVs) were recovered and sequenced from deceased Psittacula eupatria, Psittacula krameri, and Melopsittacus undulatus from various breeding facilities in Poland. Of these APV-positive samples, six had previously tested positive for beak and feather disease virus (BFDV) and/or parrot hepatitis B virus (PHBV). Copyright © 2015 Dayaram et al.

RosBREED: Enabling marker-assisted breeding in Rosaceae

NARCIS (Netherlands)

Iezzoni, A.F.; Weebadde, C.; Luby, J.; Yue, C.; Weg, van de W.E.; Fazio, G.; Main, D.; Peace, C.P.; Bassil, N.V.; McFerson, J.

2010-01-01

Genomics research has not yet been translated into routine practical application in breeding Rosaceae fruit crops (peach, apple, strawberry, cherry, apricot, pear, raspberry, etc.). Through dedicated efforts of many researchers worldwide, a wealth of genomics resources has accumulated, including EST

The research progress of genomic selection in livestock.

Science.gov (United States)

Li, Hong-wei; Wang, Rui-jun; Wang, Zhi-ying; Li, Xue-wu; Wang, Zhen-yu; Yanjun, Zhang; Rui, Su; Zhihong, Liu; Jinquan, Li

2017-05-20

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.

RESEARCH ON THE BREEDING VALUE ESTIMATION FOR BEEF TRAITS BY A SIMPLIFIED MIXED MODEL

Directory of Open Access Journals (Sweden)

Agatha POPESCU

2014-10-01

Full Text Available The paper purpose was to apply a simplified mixed model BLUP for estimating bulls' breeding value for meat production in terms of weight daily gain and establish their hierarchy, Also, it aimed to compare the bulls' ranging obtained by a simplified BLUP mixed model with their hierarchy set up by contemporary comparison. A sample of 1,705 half sibs steers, offspring of 106 Friesian bulls were used as biological material. Bulls' breeding value varied between + 244.5 g for the best bull and -204.7 g for the bull with the weakest records. A number of 57 bulls ( 53.77% registered positive breeding values. The accuracy of the breeding value estimation varied between 80, the highest precision, in case of the bull number 21 and 53, the lowest precision, in case of the bull number 38. A number of 7 bulls of the total of 57 with a positive breeding value were situated aproximately on the same positions at a difference of 0 to 1 points on the both lists established by BLUP and contemporary comparison. As a conclusion, BLUP could be largely and easily applied in bull evaluation for meat production traits in term of weight daily gain, considered the key parameter during the fattening period and its precision is very high, a guarantee that the bulls' hierarchy is a correct one. If a farmer would chose a high breeding value bull from a catalogue, he could be sure of the improvement of beef production by genetic gain.

Establishing the basis for Genomic Prediction in Perennial Ryegrass

DEFF Research Database (Denmark)

Fé, Dario

2015-01-01

Genomic Selection (GS) is a relatively new technology, which has already revolutionized animal breeding and which is expected to have a high impact on plant breeding. In contrast to traditional marker assisted breeding, which only focuses on specific genes. GS estimates the genetic value...

Strategies for implementing genomic selection in family-based aquaculture breeding schemes: double haploid sib test populations

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Nirea Kahsay G

2012-10-01

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

Textbook animal breeding : animal breeding andgenetics for BSc students

NARCIS (Netherlands)

Oldenbroek, Kor; Waaij, van der Liesbeth

2014-01-01

This textbook contains teaching material on animal breeding and genetics for BSc students. The text book started as an initiative of the Dutch Universities for Applied (Agricultural) Sciences. The textbook is made available by the Animal Breeding and Genomics Centre (ABGC) of Wageningen UR

Comparison of analyses of the QTLMAS XIII common dataset. I: genomic selection

NARCIS (Netherlands)

Bastiaansen, J.W.M.; Bink, M.C.A.M.; Coster, A.; Maliepaard, C.A.; Calus, M.P.L.

2010-01-01

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

Most of the benefits from genomic selection can be realised by genotyping a proportion of selection candidates

DEFF Research Database (Denmark)

Henryon, Mark; Berg, Peer; Sørensen, Anders Christian

2012-01-01

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...

Genome wide selection in Citrus breeding.

Science.gov (United States)

Gois, I B; Borém, A; Cristofani-Yaly, M; de Resende, M D V; Azevedo, C F; Bastianel, M; Novelli, V M; Machado, M A

2016-10-17

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.

Genomics meets ethology: a new route to understanding domestication, behavior, and sustainability in animal breeding.

Science.gov (United States)

Jensen, Per; Andersson, Leif

2005-06-01

Animal behavior is a central part of animal welfare, a keystone in sustainable animal breeding. During domestication, animals have adapted with respect to behavior and an array of other traits. We compared the behavior of junglefowl and White Leghorn layers, selected for egg production (and indirectly for growth). Jungle-fowl had a more active behavior in social, exploratory, anti-predatory, and feeding tests. A genome scan for Quantitative Trait Loci (QTLs) in a junglefowl x White Leghorn intercross revealed several significant or suggestive QTLs for different traits. Some production QTLs coincided with QTLs for behavior, suggesting that pleiotropic effects may be important for the development of domestication phenotypes. One gene has been located, which has a strong effect on the risk of being a victim of feather pecking, a detrimental behavior disorder. Modern genomics paired with analysis of behavior may help in designing more sustainable and robust breeding in the future.

Genetic distinctiveness of the Herdwick sheep breed and two other locally adapted hill breeds of the UK.

Science.gov (United States)

Bowles, Dianna; Carson, Amanda; Isaac, Peter

2014-01-01

There is considerable interest in locally adapted breeds of livestock as reservoirs of genetic diversity that may provide important fitness traits for future use in agriculture. In marginal areas, these animals contribute to food security and extract value from land unsuitable for other systems of farming. In England, close to 50% of the national sheep flock is farmed on grassland designated as disadvantaged areas for agricultural production. Many of these areas are in the uplands, where some native breeds of sheep continue to be commercially farmed only in highly localised geographical regions to which they are adapted. This study focuses on three of these breeds, selected for their adaptation to near identical environments and their geographical concentration in regions close to one another. Our objective has been to use retrotyping, microsatellites and single nucleotide polymorphisms to explore the origins of the breeds and whether, despite their similar adaptations and proximity, they are genetically distinctive. We find the three breeds each have a surprisingly different pattern of retrovirus insertions into their genomes compared with one another and with other UK breeds. Uniquely, there is a high incidence of the R0 retrotype in the Herdwick population, characteristic of a primitive genome found previously in very few breeds worldwide and none in the UK mainland. The Herdwick and Rough Fells carry two rare retroviral insertion events, common only in Texels, suggesting sheep populations in the northern uplands have a historical association with the original pin-tail sheep of Texel Island. Microsatellite data and analyses of SNPs associated with RXFP2 (horn traits) and PRLR (reproductive performance traits) also distinguished the three breeds. Significantly, an SNP linked to TMEM154, a locus controlling susceptibility to infection by Maedi-Visna, indicated that all three native hill breeds have a lower than average risk of infection to the lentivirus.

Genetic distinctiveness of the Herdwick sheep breed and two other locally adapted hill breeds of the UK.

Directory of Open Access Journals (Sweden)

Dianna Bowles

Full Text Available There is considerable interest in locally adapted breeds of livestock as reservoirs of genetic diversity that may provide important fitness traits for future use in agriculture. In marginal areas, these animals contribute to food security and extract value from land unsuitable for other systems of farming. In England, close to 50% of the national sheep flock is farmed on grassland designated as disadvantaged areas for agricultural production. Many of these areas are in the uplands, where some native breeds of sheep continue to be commercially farmed only in highly localised geographical regions to which they are adapted. This study focuses on three of these breeds, selected for their adaptation to near identical environments and their geographical concentration in regions close to one another. Our objective has been to use retrotyping, microsatellites and single nucleotide polymorphisms to explore the origins of the breeds and whether, despite their similar adaptations and proximity, they are genetically distinctive. We find the three breeds each have a surprisingly different pattern of retrovirus insertions into their genomes compared with one another and with other UK breeds. Uniquely, there is a high incidence of the R0 retrotype in the Herdwick population, characteristic of a primitive genome found previously in very few breeds worldwide and none in the UK mainland. The Herdwick and Rough Fells carry two rare retroviral insertion events, common only in Texels, suggesting sheep populations in the northern uplands have a historical association with the original pin-tail sheep of Texel Island. Microsatellite data and analyses of SNPs associated with RXFP2 (horn traits and PRLR (reproductive performance traits also distinguished the three breeds. Significantly, an SNP linked to TMEM154, a locus controlling susceptibility to infection by Maedi-Visna, indicated that all three native hill breeds have a lower than average risk of infection to the

Marker-assisted selection in fish and shellfish breeding schemes

International Nuclear Information System (INIS)

Martinez, V.

2007-01-01

The main goals of breeding programmes for fish and shellfish are to increase the profitability and sustainability of aquaculture. Traditionally, these have been carried out successfully using pedigree information by selecting individuals based on breeding values predicted for traits measured on candidates using an 'animal model'. This methodology assumes that phenotypes are explained by a large number of genes with small effects and random environmental deviations. However, information on individual genes with medium or large effects cannot be used in this manner. In selective breeding programmes using pedigree information, molecular markers have been used primarily for parentage assignment when tagging individual fish is difficult and to avoid causing common environmental effects from rearing families in separate tanks. The use of these techniques in such conventional breeding programmes is discussed in detail. Exploiting the great biological diversity of many fish and shellfish species, different experimental designs may use either chromosomal manipulations or large family sizes to increase the likelihood of finding the loci affecting quantitative traits, the so-called QTL, by screening the segregation of molecular markers. Using information on identified loci in breeding schemes in aquaculture is expected to be cost-effective compared with traditional breeding methods only when the accuracy of predicting breeding values is rather low, e.g. for traits with low heritability such as disease resistance or carcass quality. One of the problems facing aquaculture is that some of the resources required to locate QTL accurately, such as dense linkage maps, are not yet available for the many species. Recently, however, information from expressed sequence tag (EST) databases has been used for developing molecular markers such as microsatellites and single nucleotide polymorphisms (SNPs). Marker-assisted selection (MAS) or genome-wide marker-assisted selection (G-MAS) using

Use of modern tomato breeding germplasm for deciphering the genetic control of agronomical traits by Genome Wide Association study.

Science.gov (United States)

Bauchet, Guillaume; Grenier, Stéphane; Samson, Nicolas; Bonnet, Julien; Grivet, Laurent; Causse, Mathilde

2017-05-01

A panel of 300 tomato accessions including breeding materials was built and characterized with >11,000 SNP. A population structure in six subgroups was identified. Strong heterogeneity in linkage disequilibrium and recombination landscape among groups and chromosomes was shown. GWAS identified several associations for fruit weight, earliness and plant growth. Genome-wide association studies (GWAS) have become a method of choice in quantitative trait dissection. First limited to highly polymorphic and outcrossing species, it is now applied in horticultural crops, notably in tomato. Until now GWAS in tomato has been performed on panels of heirloom and wild accessions. Using modern breeding materials would be of direct interest for breeding purpose. To implement GWAS on a large panel of 300 tomato accessions including 168 breeding lines, this study assessed the genetic diversity and linkage disequilibrium decay and revealed the population structure and performed GWA experiment. Genetic diversity and population structure analyses were based on molecular markers (>11,000 SNP) covering the whole genome. Six genetic subgroups were revealed and associated to traits of agronomical interest, such as fruit weight and disease resistance. Estimates of linkage disequilibrium highlighted the heterogeneity of its decay among genetic subgroups. Haplotype definition allowed a fine characterization of the groups and their recombination landscape revealing the patterns of admixture along the genome. Selection footprints showed results in congruence with introgressions. Taken together, all these elements refined our knowledge of the genetic material included in this panel and allowed the identification of several associations for fruit weight, plant growth and earliness, deciphering the genetic architecture of these complex traits and identifying several new loci useful for tomato breeding.

Significance of pH-value for meat quality of broilers: Influence of breed lines

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Ristic M.

2013-01-01

Full Text Available For determination of poultry quality shortly after slaughtering, physical criteria (pH-value, conductivity, colour, juice retention are of importance. However, they are affected by breeding, transport, cooling and the storage period. PH-values of breast meat (genetically structured material were recorded shortly after slaughtering (15 min p.m. and differences between breeding line and gender were found (n=5109. The pH1-values ranged from 5.50 to 6.79. Male broilers showed significantly lower pH1-values than female ones (6.02:6.10. There were also significant differences concerning breeding line and gender. Meat quality (PSE, DFD of broilers can be recorded quickly and accurately determining the pH1-value of breast meat. Threshold ranges to be considered are ≤ 5.8 (PSE, 5.9-6.2 (standard meat properties and ≥ 6.3 (DFD. This classification is not to be compared to the deviation of pork.

A Ranking Approach to Genomic Selection.

Science.gov (United States)

Blondel, Mathieu; Onogi, Akio; Iwata, Hiroyoshi; Ueda, Naonori

2015-01-01

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.

Trends in genome-wide and region-specific genetic diversity in the Dutch-Flemish Holstein-Friesian breeding program from 1986 to 2015.

Science.gov (United States)

Doekes, Harmen P; Veerkamp, Roel F; Bijma, Piter; Hiemstra, Sipke J; Windig, Jack J

2018-04-11

In recent decades, Holstein-Friesian (HF) selection schemes have undergone profound changes, including the introduction of optimal contribution selection (OCS; around 2000), a major shift in breeding goal composition (around 2000) and the implementation of genomic selection (GS; around 2010). These changes are expected to have influenced genetic diversity trends. Our aim was to evaluate genome-wide and region-specific diversity in HF artificial insemination (AI) bulls in the Dutch-Flemish breeding program from 1986 to 2015. Pedigree and genotype data (~ 75.5 k) of 6280 AI-bulls were used to estimate rates of genome-wide inbreeding and kinship and corresponding effective population sizes. Region-specific inbreeding trends were evaluated using regions of homozygosity (ROH). Changes in observed allele frequencies were compared to those expected under pure drift to identify putative regions under selection. We also investigated the direction of changes in allele frequency over time. Effective population size estimates for the 1986-2015 period ranged from 69 to 102. Two major breakpoints were observed in genome-wide inbreeding and kinship trends. Around 2000, inbreeding and kinship levels temporarily dropped. From 2010 onwards, they steeply increased, with pedigree-based, ROH-based and marker-based inbreeding rates as high as 1.8, 2.1 and 2.8% per generation, respectively. Accumulation of inbreeding varied substantially across the genome. A considerable fraction of markers showed changes in allele frequency that were greater than expected under pure drift. Putative selected regions harboured many quantitative trait loci (QTL) associated to a wide range of traits. In consecutive 5-year periods, allele frequencies changed more often in the same direction than in opposite directions, except when comparing the 1996-2000 and 2001-2005 periods. Genome-wide and region-specific diversity trends reflect major changes in the Dutch-Flemish HF breeding program. Introduction of

[Preface for genome editing special issue].

Science.gov (United States)

Gu, Feng; Gao, Caixia

2017-10-25

Genome editing technology, as an innovative biotechnology, has been widely used for editing the genome from model organisms, animals, plants and microbes. CRISPR/Cas9-based genome editing technology shows its great value and potential in the dissection of functional genomics, improved breeding and genetic disease treatment. In the present special issue, the principle and application of genome editing techniques has been summarized. The advantages and disadvantages of the current genome editing technology and future prospects would also be highlighted.

Impact of Molecular Technologies on Faba Bean (Vicia faba L. Breeding Strategies

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Tao Yang

2012-07-01

Full Text Available Faba bean (Vicia faba L. is a major food and feed legume because of the high nutritional value of its seeds. The main objectives of faba bean breeding are to improve yield, disease resistance, abiotic stress tolerance, seed quality and other agronomic traits. The partial cross-pollinated nature of faba bean introduces both challenges and opportunities for population development and breeding. Breeding methods that are applicable to self-pollinated crops or open-pollinated crops are not highly suitable for faba bean. However, traditional breeding methods such as recurrent mass selection have been established in faba bean and used successfully in breeding for resistance to diseases. Molecular breeding strategies that integrate the latest innovations in genetics and genomics with traditional breeding strategies have many potential applications for future faba bean cultivar development. Hence, considerable efforts have been undertaken in identifying molecular markers, enriching genetic and genomic resources using high-throughput sequencing technologies and improving genetic transformation techniques in faba bean. However, the impact of research on practical faba bean breeding and cultivar release to farmers has been limited due to disconnects between research and breeding objectives and the high costs of research and implementation. The situation with faba bean is similar to other small crops and highlights the need for coordinated, collaborative research programs that interact closely with commercially focused breeding programs to ensure that technologies are implemented effectively.

Assessing Genomic Selection Prediction Accuracy in a Dynamic Barley Breeding Population

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A. H. Sallam

2015-03-01

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.

Aquaculture genomics, genetics and breeding in the United States: Current status, challenges, and priorities for future research

Science.gov (United States)

Advancing the production efficiency and profitability of aquaculture is dependent upon the ability to utilize a diverse array of genetic resources. The ultimate goals of aquaculture genomics, genetics and breeding research are to enhance aquaculture production efficiency, sustainability, product qua...

Optimization of a genomic breeding program for a moderately sized dairy cattle population.

Science.gov (United States)

Reiner-Benaim, A; Ezra, E; Weller, J I

2017-04-01

Although it now standard practice to genotype thousands of female calves, genotyping of bull calves is generally limited to progeny of elite cows. In addition to genotyping costs, increasing the pool of candidate sires requires purchase, isolation, and identification of calves until selection decisions are made. We economically optimized via simulation a genomic breeding program for a population of approximately 120,000 milk-recorded cows, corresponding to the Israeli Holstein population. All 30,000 heifers and 60,000 older cows of parities 1 to 3 were potential bull dams. Animals were assumed to have genetic evaluations for a trait with heritability of 0.25 derived by an animal model evaluation of the population. Only bull calves were assumed to be genotyped. A pseudo-phenotype corresponding to each animal's genetic evaluation was generated, consisting of the animal's genetic value plus a residual with variance set to obtain the assumed reliability for each group of animals. Between 4 and 15 bulls and between 200 and 27,000 cows with the highest pseudo-phenotypes were selected as candidate bull parents. For all progeny of the founder animals, genetic values were simulated as the mean of the parental values plus a Mendelian sampling effect with variance of 0.5. A probability of 0.3 for a healthy bull calf per mating, and a genomic reliability of 0.43 were assumed. The 40 bull calves with the highest genomic evaluations were selected for general service for 1 yr. Costs included genotyping of candidate bulls and their dams, purchase of the calves from the farmers, and identification. Costs of raising culled calves were partially recovered by resale for beef. Annual costs were estimated as $10,922 + $305 × candidate bulls. Nominal profit per cow per genetic standard deviation was $106. Economic optimum with a discount rate of 5%, first returns after 4 yr, and a profit horizon of 15 yr were obtained with genotyping 1,620 to 1,750 calves for all numbers of bull sires

Performance comparison of two efficient genomic selection methods (gsbay & MixP) applied in aquacultural organisms

Science.gov (United States)

Su, Hailin; Li, Hengde; Wang, Shi; Wang, Yangfan; Bao, Zhenmin

2017-02-01

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.

The origin and evolution of fibromelanosis in domesticated chickens: Genomic comparison of Indonesian Cemani and Chinese Silkie breeds.

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Anik Budhi Dharmayanthi

Full Text Available Like Chinese Silkie, Indonesian Ayam Cemani exhibits fibromelanosis or dermal hyperpigmentation and possesses complex segmental duplications on chromosome 20 that involve the endothelin 3 gene, EDN3. A genomic region, DR1 of 127 kb, together with another region, DR2 of 171 kb, was duplicated by unequal crossing over, accompanied by inversion of one DR2. Quantitative PCR and copy number variation analyses on the Cemani genome sequence confirmed the duplication of EDN3. These genetic arrangements are identical in Cemani and Silkie, indicating a single origin of the genetic cause of Fm. The two DR1s harbor two distinct EDN3 haplotypes in a form of permanent heterozygosity, although they remain allelic in the ancestral Red Jungle Fowl population and some domesticated chicken breeds, with their allelic divergence time being as recent as 0.3 million years ago. In Cemani and Silkie breeds, artificial selection favoring the Fm phenotype has left an unambiguous record for selective sweep that extends in both directions from tandemly duplicated EDN3 loci. This highly homozygous tract is different in length between Cemani and Silkie, reflecting their distinct breeding histories. It is estimated that the Fm phenotype came into existence at least 6600-9100 years ago, prior to domestication of Cemani and Silkie, and that throughout domestication there has been intense artificial selection with strength s > 50% in each breed.

Variable selection models for genomic selection using whole-genome sequence data and singular value decomposition.

Science.gov (United States)

Meuwissen, Theo H E; Indahl, Ulf G; Ødegård, Jørgen

2017-12-27

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

Genome-Wide Analysis of the World's Sheep Breeds Reveals High Levels of Historic Mixture and Strong Recent Selection

Science.gov (United States)

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

2012-01-01

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

Genome-wide analysis of the world's sheep breeds reveals high levels of historic mixture and strong recent selection.

Directory of Open Access Journals (Sweden)

James W Kijas

2012-02-01

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.

Changes in sunflower breeding over the last fifty years

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Vear Felicity

2016-03-01

Full Text Available This article discusses changes in sunflower breeding objectives since the introduction of hybrid varieties 50 years ago. After a reminder of the importance of some early programmes, Canadian in particular, the present situation for each breeding objective is compared with those encountered earlier. Breeding for yield has changed from maximum possible yield under intensive agriculture to yield with resistance to abiotic stresses, moderate droughts and shallow soils in particular, helped by collaboration with agronomists to produce crop models. Breeding for oil has changed from quantity to quality and the value of seed meal is again becoming economically important. Necessary disease resistances vary with agronomic practises and selection pressure on pathogens according to varietal genetics. The possibilities of new types of sunflower are also discussed. Advances in genomics will change breeding procedures, but with rapidly changing molecular techniques, international collaboration is particularly important.

Validating the breeding value for maternal preweaning gain in beef ...

African Journals Online (AJOL)

The objective of this research was to validate the maternal breeding value for preweaning gain as a predictor of genetic differences in milk production. Phenotypic variation in preweaning gain and in milk production measured by the weigh-suckle-weigh method was partitioned into genetic and non-genetic components.

Accuracies of breeding values for dry matter intake using nongenotyped animals and predictor traits in different lactations.

Science.gov (United States)

Manzanilla-Pech, C I V; Veerkamp, R F; de Haas, Y; Calus, M P L; Ten Napel, J

2017-11-01

Given the interest of including dry matter intake (DMI) in the breeding goal, accurate estimated breeding values (EBV) for DMI are needed, preferably for separate lactations. Due to the limited amount of records available on DMI, 2 main approaches have been suggested to compute those EBV: (1) the inclusion of predictor traits, such as fat- and protein-corrected milk (FPCM) and live weight (LW), and (2) the addition of genomic information of animals using what is called genomic prediction. Recently, several methodologies to estimate EBV utilizing genomic information (EBV) have become available. In this study, a new method known as single-step ridge-regression BLUP (SSRR-BLUP) is suggested. The SSRR-BLUP method does not have an imposed limit on the number of genotyped animals, as the commonly used methods do. The objective of this study was to estimate genetic parameters using a relatively large data set with DMI records, as well as compare the accuracies of the EBV for DMI. These accuracies were obtained using 4 different methods: BLUP (using pedigree for all animals with phenotypes), genomic BLUP (GBLUP; only for genotyped animals), single-step GBLUP (SS-GBLUP), and SSRR-BLUP (for genotyped and nongenotyped animals). Records from different lactations, with or without predictor traits (FPCM and LW), were used in the model. Accuracies of EBV for DMI (defined as the correlation between the EBV and pre-adjusted DMI phenotypes divided by the average accuracy of those phenotypes) ranged between 0.21 and 0.38 across methods and scenarios. Accuracies of EBV for DMI using BLUP were the lowest accuracies obtained across methods. Meanwhile, accuracies of EBV for DMI were similar in SS-GBLUP and SSRR-BLUP, and lower for the GBLUP method. Hence, SSRR-BLUP could be used when the number of genotyped animals is large, avoiding the construction of the inverse genomic relationship matrix. Adding information on DMI from different lactations in the reference population gave higher

Plant Breeding Goes Microbial

NARCIS (Netherlands)

Wei, Zhong; Jousset, Alexandre

Plant breeding has traditionally improved traits encoded in the plant genome. Here we propose an alternative framework reaching novel phenotypes by modifying together genomic information and plant-associated microbiota. This concept is made possible by a novel technology that enables the

Molecular Breeding Algae For Improved Traits For The Conversion Of Waste To Fuels And Commodities.

Energy Technology Data Exchange (ETDEWEB)

Bagwell, C. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)

2015-10-14

This Exploratory LDRD aimed to develop molecular breeding methodology for biofuel algal strain improvement for applications in waste to energy / commodity conversion technologies. Genome shuffling technologies, specifically protoplast fusion, are readily available for the rapid production of genetic hybrids for trait improvement and have been used successfully in bacteria, yeast, plants and animals. However, genome fusion has not been developed for exploiting the remarkable untapped potential of eukaryotic microalgae for large scale integrated bio-conversion and upgrading of waste components to valued commodities, fuel and energy. The proposed molecular breeding technology is effectively sexual reproduction in algae; though compared to traditional breeding, the molecular route is rapid, high-throughput and permits selection / improvement of complex traits which cannot be accomplished by traditional genetics. Genome fusion technologies are the cutting edge of applied biotechnology. The goals of this Exploratory LDRD were to 1) establish reliable methodology for protoplast production among diverse microalgal strains, and 2) demonstrate genome fusion for hybrid strain production using a single gene encoded trait as a proof of the concept.

The value of cows in reference populations for genomic selection of new functional traits

DEFF Research Database (Denmark)

Buch, Line Hjortø; Kargo, Morten; Berg, Peer

2012-01-01

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....

Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery

OpenAIRE

Hickey, John M; Chiurugwi, Tinashe; Mackay, Ian; Powell, Wayne; Implementing Genomic Selection in CGIAR Breeding Programs Workshop Participants

2017-01-01

The rate of annual yield increases for major staple crops must more than double relative to current levels in order to feed a predicted global population of 9 billion by 2050. Controlled hybridization and selective breeding have been used for centuries to adapt plant and animal species for human use. However, achieving higher, sustainable rates of improvement in yields in various species will require renewed genetic interventions and dramatic improvement of agricultural practices. Genomic pre...

An innovative procedure of genome-wide association analysis fits studies on germplasm population and plant breeding.

Science.gov (United States)

He, Jianbo; Meng, Shan; Zhao, Tuanjie; Xing, Guangnan; Yang, Shouping; Li, Yan; Guan, Rongzhan; Lu, Jiangjie; Wang, Yufeng; Xia, Qiuju; Yang, Bing; Gai, Junyi

2017-11-01

The innovative RTM-GWAS procedure provides a relatively thorough detection of QTL and their multiple alleles for germplasm population characterization, gene network identification, and genomic selection strategy innovation in plant breeding. The previous genome-wide association studies (GWAS) have been concentrated on finding a handful of major quantitative trait loci (QTL), but plant breeders are interested in revealing the whole-genome QTL-allele constitution in breeding materials/germplasm (in which tremendous historical allelic variation has been accumulated) for genome-wide improvement. To match this requirement, two innovations were suggested for GWAS: first grouping tightly linked sequential SNPs into linkage disequilibrium blocks (SNPLDBs) to form markers with multi-allelic haplotypes, and second utilizing two-stage association analysis for QTL identification, where the markers were preselected by single-locus model followed by multi-locus multi-allele model stepwise regression. Our proposed GWAS procedure is characterized as a novel restricted two-stage multi-locus multi-allele GWAS (RTM-GWAS, https://github.com/njau-sri/rtm-gwas ). The Chinese soybean germplasm population (CSGP) composed of 1024 accessions with 36,952 SNPLDBs (generated from 145,558 SNPs, with reduced linkage disequilibrium decay distance) was used to demonstrate the power and efficiency of RTM-GWAS. Using the CSGP marker information, simulation studies demonstrated that RTM-GWAS achieved the highest QTL detection power and efficiency compared with the previous procedures, especially under large sample size and high trait heritability conditions. A relatively thorough detection of QTL with their multiple alleles was achieved by RTM-GWAS compared with the linear mixed model method on 100-seed weight in CSGP. A QTL-allele matrix (402 alleles of 139 QTL × 1024 accessions) was established as a compact form of the population genetic constitution. The 100-seed weight QTL-allele matrix was

Genomic predictions across Nordic Holstein and Nordic Red using the genomic best linear unbiased prediction model with different genomic relationship matrices.

Science.gov (United States)

Zhou, L; Lund, M S; Wang, Y; Su, G

2014-08-01

This study investigated genomic predictions across Nordic Holstein and Nordic Red using various genomic relationship matrices. Different sources of information, such as consistencies of linkage disequilibrium (LD) phase and marker effects, were used to construct the genomic relationship matrices (G-matrices) across these two breeds. Single-trait genomic best linear unbiased prediction (GBLUP) model and two-trait GBLUP model were used for single-breed and two-breed genomic predictions. The data included 5215 Nordic Holstein bulls and 4361 Nordic Red bulls, which was composed of three populations: Danish Red, Swedish Red and Finnish Ayrshire. The bulls were genotyped with 50 000 SNP chip. Using the two-breed predictions with a joint Nordic Holstein and Nordic Red reference population, accuracies increased slightly for all traits in Nordic Red, but only for some traits in Nordic Holstein. Among the three subpopulations of Nordic Red, accuracies increased more for Danish Red than for Swedish Red and Finnish Ayrshire. This is because closer genetic relationships exist between Danish Red and Nordic Holstein. Among Danish Red, individuals with higher genomic relationship coefficients with Nordic Holstein showed more increased accuracies in the two-breed predictions. Weighting the two-breed G-matrices by LD phase consistencies, marker effects or both did not further improve accuracies of the two-breed predictions. © 2014 Blackwell Verlag GmbH.

Accounting for Genotype-by-Environment Interactions and Residual Genetic Variation in Genomic Selection for Water-Soluble Carbohydrate Concentration in Wheat.

Science.gov (United States)

Ovenden, Ben; Milgate, Andrew; Wade, Len J; Rebetzke, Greg J; Holland, James B

2018-05-31

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.

Factors affecting the accuracy of genomic selection for growth and wood quality traits in an advanced-breeding population of black spruce (Picea mariana).

Science.gov (United States)

Lenz, Patrick R N; Beaulieu, Jean; Mansfield, Shawn D; Clément, Sébastien; Desponts, Mireille; Bousquet, Jean

2017-04-28

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

[Genomic selection of milk cattle. The practical application over five years].

Science.gov (United States)

Smaragdov, M G

2013-11-01

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.

Transcriptional Profiling Identifies Location-Specific and Breed-Specific Differentially Expressed Genes in Embryonic Myogenesis in Anas Platyrhynchos.

Directory of Open Access Journals (Sweden)

Rong-Ping Zhang

Full Text Available Skeletal muscle growth and development are highly orchestrated processes involving significant changes in gene expressions. Differences in the location-specific and breed-specific genes and pathways involved have important implications for meat productions and meat quality. Here, RNA-Seq was performed to identify differences in the muscle deposition between two muscle locations and two duck breeds for functional genomics studies. To achieve those goals, skeletal muscle samples were collected from the leg muscle (LM and the pectoral muscle (PM of two genetically different duck breeds, Heiwu duck (H and Peking duck (P, at embryonic 15 days. Functional genomics studies were performed in two experiments: Experiment 1 directly compared the location-specific genes between PM and LM, and Experiment 2 compared the two breeds (H and P at the same developmental stage (embryonic 15 days. Almost 13 million clean reads were generated using Illumina technology (Novogene, Beijing, China on each library, and more than 70% of the reads mapped to the Peking duck (Anas platyrhynchos genome. A total of 168 genes were differentially expressed between the two locations analyzed in Experiment 1, whereas only 8 genes were differentially expressed when comparing the same location between two breeds in Experiment 2. Gene Ontology (GO and the Kyoto Encyclopedia of Genes and Genomes pathways (KEGG were used to functionally annotate DEGs (differentially expression genes. The DEGs identified in Experiment 1 were mainly involved in focal adhesion, the PI3K-Akt signaling pathway and ECM-receptor interaction pathways (corrected P-value<0.05. In Experiment 2, the DEGs were associated with only the ribosome signaling pathway (corrected P-value<0.05. In addition, quantitative real-time PCR was used to confirm 15 of the differentially expressed genes originally detected by RNA-Seq. A comparative transcript analysis of the leg and pectoral muscles of two duck breeds not only

Value of a newly sequenced bacterial genome

DEFF Research Database (Denmark)

Barbosa, Eudes; Aburjaile, Flavia F; Ramos, Rommel Tj

2014-01-01

and annotation will not be undertaken. It is important to know what is lost when we settle for a draft genome and to determine the "scientific value" of a newly sequenced genome. This review addresses the expected impact of newly sequenced genomes on antibacterial discovery and vaccinology. Also, it discusses...... heightened expectations that NGS would boost antibacterial discovery and vaccine development. Although many possible drug and vaccine targets have been discovered, the success rate of genome-based analysis has remained below expectations. Furthermore, NGS has had consequences for genome quality, resulting...

Best Linear Unbiased Prediction of Genomic Breeding Values Using a Trait-Specific Marker-Derived Relationship Matrix

NARCIS (Netherlands)

Zhe Zhang, Z.; Liu, J.F.; Ding, Z.; Bijma, P.; Koning, de D.J.

2010-01-01

With the availability of high density whole-genome single nucleotide polymorphism chips, genomic selection has become a promising method to estimate genetic merit with potentially high accuracy for animal, plant and aquaculture species of economic importance. With markers covering the entire genome,

Genomic diversity and introgression in O. sativa reveal the impact of domestication and breeding on the rice genome.

Directory of Open Access Journals (Sweden)

Keyan Zhao

2010-05-01

Full Text Available The domestication of Asian rice (Oryza sativa was a complex process punctuated by episodes of introgressive hybridization among and between subpopulations. Deep genetic divergence between the two main varietal groups (Indica and Japonica suggests domestication from at least two distinct wild populations. However, genetic uniformity surrounding key domestication genes across divergent subpopulations suggests cultural exchange of genetic material among ancient farmers.In this study, we utilize a novel 1,536 SNP panel genotyped across 395 diverse accessions of O. sativa to study genome-wide patterns of polymorphism, to characterize population structure, and to infer the introgression history of domesticated Asian rice. Our population structure analyses support the existence of five major subpopulations (indica, aus, tropical japonica, temperate japonica and GroupV consistent with previous analyses. Our introgression analysis shows that most accessions exhibit some degree of admixture, with many individuals within a population sharing the same introgressed segment due to artificial selection. Admixture mapping and association analysis of amylose content and grain length illustrate the potential for dissecting the genetic basis of complex traits in domesticated plant populations.Genes in these regions control a myriad of traits including plant stature, blast resistance, and amylose content. These analyses highlight the power of population genomics in agricultural systems to identify functionally important regions of the genome and to decipher the role of human-directed breeding in refashioning the genomes of a domesticated species.

Genomic selection in plant breeding: from theory to practice.

Science.gov (United States)

Jannink, Jean-Luc; Lorenz, Aaron J; Iwata, Hiroyoshi

2010-03-01

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.

Genomic prediction applied to high-biomass sorghum for bioenergy production.

Science.gov (United States)

de Oliveira, Amanda Avelar; Pastina, Maria Marta; de Souza, Vander Filipe; da Costa Parrella, Rafael Augusto; Noda, Roberto Willians; Simeone, Maria Lúcia Ferreira; Schaffert, Robert Eugene; de Magalhães, Jurandir Vieira; Damasceno, Cynthia Maria Borges; Margarido, Gabriel Rodrigues Alves

2018-01-01

The increasing cost of energy and finite oil and gas reserves have created a need to develop alternative fuels from renewable sources. Due to its abiotic stress tolerance and annual cultivation, high-biomass sorghum ( Sorghum bicolor L. Moench) shows potential as a bioenergy crop. Genomic selection is a useful tool for accelerating genetic gains and could restructure plant breeding programs by enabling early selection and reducing breeding cycle duration. This work aimed at predicting breeding values via genomic selection models for 200 sorghum genotypes comprising landrace accessions and breeding lines from biomass and saccharine groups. These genotypes were divided into two sub-panels, according to breeding purpose. We evaluated the following phenotypic biomass traits: days to flowering, plant height, fresh and dry matter yield, and fiber, cellulose, hemicellulose, and lignin proportions. Genotyping by sequencing yielded more than 258,000 single-nucleotide polymorphism markers, which revealed population structure between subpanels. We then fitted and compared genomic selection models BayesA, BayesB, BayesCπ, BayesLasso, Bayes Ridge Regression and random regression best linear unbiased predictor. The resulting predictive abilities varied little between the different models, but substantially between traits. Different scenarios of prediction showed the potential of using genomic selection results between sub-panels and years, although the genotype by environment interaction negatively affected accuracies. Functional enrichment analyses performed with the marker-predicted effects suggested several interesting associations, with potential for revealing biological processes relevant to the studied quantitative traits. This work shows that genomic selection can be successfully applied in biomass sorghum breeding programs.

Breeding Ruminants that Emit Less Methane – The Role of International Collaboration

DEFF Research Database (Denmark)

Oddy, V H; de Haas, Y; Basarab, J

production of 40-45%. If a methane-related trait were to be implemented by a livestock industry it will most likely be via genomic breeding values, which demand large numbers of measured animals in the reference population. Given the size of the reference population required for methane traits...

Milk protein concentration, estimated breeding value for fertility, and reproductive performance in lactating dairy cows.

Science.gov (United States)

Morton, John M; Auldist, Martin J; Douglas, Meaghan L; Macmillan, Keith L

2017-07-01

Milk protein concentration in dairy cows has been positively associated with a range of measures of reproductive performance, and genetic factors affecting both milk protein concentration and reproductive performance may contribute to the observed phenotypic associations. It was of interest to assess whether these beneficial phenotypic associations are accounted for or interact with the effects of estimated breeding values for fertility. The effects of a multitrait estimated breeding value for fertility [the Australian breeding value for daughter fertility (ABV fertility)] on reproductive performance were also of interest. Interactions of milk protein concentration and ABV fertility with the interval from calving date to the start of the herd's seasonally concentrated breeding period were also assessed. A retrospective single cohort study was conducted using data collected from 74 Australian seasonally and split calving dairy herds. Associations between milk protein concentration, ABV fertility, and reproductive performance in Holstein cows were assessed using random effects logistic regression. Between 52,438 and 61,939 lactations were used for analyses of 4 reproductive performance measures. Milk protein concentration was strongly and positively associated with reproductive performance in dairy cows, and this effect was not accounted for by the effects of ABV fertility. Increases in ABV fertility had important additional beneficial effects on the probability of pregnancy by wk 6 and 21 of the herd's breeding period. For cows calved before the start of the breeding period, the effects of increases in both milk protein concentration and ABV fertility were beneficial regardless of their interval from calving to the start of the breeding period. These findings demonstrate the potential for increasing reproductive performance through identifying the causes of the association between milk protein concentration and reproductive performance and then devising management

Biotechnology and apple breeding in Japan

Science.gov (United States)

Igarashi, Megumi; Hatsuyama, Yoshimichi; Harada, Takeo; Fukasawa-Akada, Tomoko

2016-01-01

Apple is a fruit crop of significant economic importance, and breeders world wide continue to develop novel cultivars with improved characteristics. The lengthy juvenile period and the large field space required to grow apple populations have imposed major limitations on breeding. Various molecular biological techniques have been employed to make apple breeding easier. Transgenic technology has facilitated the development of apples with resistance to fungal or bacterial diseases, improved fruit quality, or root stocks with better rooting or dwarfing ability. DNA markers for disease resistance (scab, powdery mildew, fire-blight, Alternaria blotch) and fruit skin color have also been developed, and marker-assisted selection (MAS) has been employed in breeding programs. In the last decade, genomic sequences and chromosome maps of various cultivars have become available, allowing the development of large SNP arrays, enabling efficient QTL mapping and genomic selection (GS). In recent years, new technologies for genetic improvement, such as trans-grafting, virus vectors, and genome-editing, have emerged. Using these techniques, no foreign genes are present in the final product, and some of them show considerable promise for application to apple breeding. PMID:27069388

Advances and Challenges in Genomic Selection for Disease Resistance.

Science.gov (United States)

Poland, Jesse; Rutkoski, Jessica

2016-08-04

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

Initiating genomic selection in tetraploid potato

DEFF Research Database (Denmark)

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....

Optimizing the allocation of resources for genomic selection in one breeding cycle.

Science.gov (United States)

Riedelsheimer, Christian; Melchinger, Albrecht E

2013-11-01

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

Breeding Jatropha curcas by genomic selection: A pilot assessment of the accuracy of predictive models.

Science.gov (United States)

Azevedo Peixoto, Leonardo de; Laviola, Bruno Galvêas; Alves, Alexandre Alonso; Rosado, Tatiana Barbosa; Bhering, Leonardo Lopes

2017-01-01

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.

Breeding Jatropha curcas by genomic selection: A pilot assessment of the accuracy of predictive models.

Directory of Open Access Journals (Sweden)

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.

Diversifying Selection Between Pure-Breed and Free-Breeding Dogs Inferred from Genome-Wide SNP Analysis

Directory of Open Access Journals (Sweden)

Małgorzata Pilot

2016-08-01

Full Text Available Domesticated species are often composed of distinct populations differing in the character and strength of artificial and natural selection pressures, providing a valuable model to study adaptation. In contrast to pure-breed dogs that constitute artificially maintained inbred lines, free-ranging dogs are typically free-breeding, i.e., unrestrained in mate choice. Many traits in free-breeding dogs (FBDs may be under similar natural and sexual selection conditions to wild canids, while relaxation of sexual selection is expected in pure-breed dogs. We used a Bayesian approach with strict false-positive control criteria to identify FST-outlier SNPs between FBDs and either European or East Asian breeds, based on 167,989 autosomal SNPs. By identifying outlier SNPs located within coding genes, we found four candidate genes under diversifying selection shared by these two comparisons. Three of them are associated with the Hedgehog (HH signaling pathway regulating vertebrate morphogenesis. A comparison between FBDs and East Asian breeds also revealed diversifying selection on the BBS6 gene, which was earlier shown to cause snout shortening and dental crowding via disrupted HH signaling. Our results suggest that relaxation of natural and sexual selection in pure-breed dogs as opposed to FBDs could have led to mild changes in regulation of the HH signaling pathway. HH inhibits adhesion and the migration of neural crest cells from the neural tube, and minor deficits of these cells during embryonic development have been proposed as the underlying cause of “domestication syndrome.” This suggests that the process of breed formation involved the same genetic and developmental pathways as the process of domestication.

Plant Breeding by Using Radiation Mutation

International Nuclear Information System (INIS)

Kang, Si Yong; Kim, Dong Sub; Lee, Geung Joo

2007-06-01

A mutation breeding is to use physical or chemical mutagens to induce mutagenesis, followed by individual selections with favorable traits. The mutation breeding has many advantages over other breeding methods, which include the usefulness for improving one or two inferior characteristics, applications to broad species with different reproductive systems or to diverse plant materials, native or plant introduction with narrow genetic background, time and cost-effectiveness, and valuable mutant resources for genomic researches. Recent applications of the radiation breeding techniques to developments of flowering plants or food crops with improved functional constituents heightened the public's interests in agriculture and in our genetic resources and seed industries. The goals of this project, therefore, include achieving advances in domestic seed industries and agricultural productivities by developing and using new radiation mutants with favored traits, protecting an intellectual property right of domestic seeds or germplasm, and sharing the valuable mutants and mutated gene information for the genomic and biotech researches that eventually leads to economic benefits

Plant Breeding by Using Radiation Mutation

Energy Technology Data Exchange (ETDEWEB)

Kang, Si Yong; Kim, Dong Sub; Lee, Geung Joo (and others)

2007-06-15

A mutation breeding is to use physical or chemical mutagens to induce mutagenesis, followed by individual selections with favorable traits. The mutation breeding has many advantages over other breeding methods, which include the usefulness for improving one or two inferior characteristics, applications to broad species with different reproductive systems or to diverse plant materials, native or plant introduction with narrow genetic background, time and cost-effectiveness, and valuable mutant resources for genomic researches. Recent applications of the radiation breeding techniques to developments of flowering plants or food crops with improved functional constituents heightened the public's interests in agriculture and in our genetic resources and seed industries. The goals of this project, therefore, include achieving advances in domestic seed industries and agricultural productivities by developing and using new radiation mutants with favored traits, protecting an intellectual property right of domestic seeds or germplasm, and sharing the valuable mutants and mutated gene information for the genomic and biotech researches that eventually leads to economic benefits.

Breed-Predispositions to Cancer in Pedigree Dogs

Science.gov (United States)

Dobson, Jane M.

2013-01-01

Cancer is a common problem in dogs and although all breeds of dog and crossbred dogs may be affected, it is notable that some breeds of pedigree dogs appear to be at increased risk of certain types of cancer suggesting underlying genetic predisposition to cancer susceptibility. Although the aetiology of most cancers is likely to be multifactorial, the limited genetic diversity seen in purebred dogs facilitates genetic linkage or association studies on relatively small populations as compared to humans, and by using newly developed resources, genome-wide association studies in dog breeds are proving to be a powerful tool for unravelling complex disorders. This paper will review the literature on canine breed susceptibility to histiocytic sarcoma, osteosarcoma, haemangiosarcoma, mast cell tumours, lymphoma, melanoma, and mammary tumours including the recent advances in knowledge through molecular genetic, cytogenetic, and genome wide association studies. PMID:23738139

Genomic analysis for managing small and endangered populations: A case study in Tyrol Grey cattle

Directory of Open Access Journals (Sweden)

Gábor eMészáros

2015-05-01

Full Text Available Analysis of genomic data is increasingly becoming part of the livestock industry. Therefore the routine collection of genomic information would be an invaluable resource for management of breeding programs in small, endangered populations. The objectives of this project were to analyse 1. linkage disequlibrium decay and the effective population size; 2. Inbreeding level and effective population size (NeROH based on runs of homozygosity (ROH; 3. Prediction of genomic breeding values (GEBV within and across breeds. In addition, the use of genomic information for breed management is discussed. The study was based on all available genotypes of Tyrol Grey AI bulls. ROHs were derived based on regions covering at least 4 Mb, 8 Mb and 16 Mb regions, with the corresponding mean inbreeding coefficients 4.0%, 2.9% and 1.6%, respectively. The NeROH was 125 (NeROH>16Mb, 186 (NeROH>8Mb and 370 (NeROH>4Mb, indicating strict avoidance of close inbreeding in the population.The genomic selection was developed for and is working well in large breeds. Contrary to the expectations, the accuracy of GEBVs with very small within breed reference populations were very high, between 0.13-0.91 and 0.12-0.63, when EBVs and dEBVs were used as pseudo-phenotypes, respectively. Subsequent analyses confirmed the high accuracies being heavily influenced by parent averages. Multi-breed and across breed reference sets gave inconsistent and lower accuracies. Genomic information may have a crucial role in management of small breeds. It allows to assess relatedness between individuals, trends in inbreeding and to take decisions accordingly. These decisions would be based on the real genome architecture, rather than conventional pedigree information, which can be missing or incomplete. We strongly suggest the routine genotyping of all individuals that belong to a small breed in order to facilitate the effective management of endangered livestock populations.

Genomic Selection for Drought Tolerance Using Genome-Wide SNPs in Maize

Directory of Open Access Journals (Sweden)

Thirunavukkarasu Nepolean

2017-04-01

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.

Next generation breeding.

Science.gov (United States)

Barabaschi, Delfina; Tondelli, Alessandro; Desiderio, Francesca; Volante, Andrea; Vaccino, Patrizia; Valè, Giampiero; Cattivelli, Luigi

2016-01-01

The genomic revolution of the past decade has greatly improved our understanding of the genetic make-up of living organisms. The sequencing of crop genomes has completely changed our vision and interpretation of genome organization and evolution. Re-sequencing allows the identification of an unlimited number of markers as well as the analysis of germplasm allelic diversity based on allele mining approaches. High throughput marker technologies coupled with advanced phenotyping platforms provide new opportunities for discovering marker-trait associations which can sustain genomic-assisted breeding. The availability of genome sequencing information is enabling genome editing (site-specific mutagenesis), to obtain gene sequences desired by breeders. This review illustrates how next generation sequencing-derived information can be used to tailor genomic tools for different breeders' needs to revolutionize crop improvement. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Trends in genome-wide and region-specific genetic diversity in the Dutch-Flemish Holstein-Friesian breeding program from 1986 to 2015

NARCIS (Netherlands)

Doekes, Harmen P.; Veerkamp, Roel F.; Bijma, Piter; Hiemstra, Sipke J.; Windig, Jack J.

2018-01-01

Background: In recent decades, Holstein-Friesian (HF) selection schemes have undergone profound changes, including the introduction of optimal contribution selection (OCS; around 2000), a major shift in breeding goal composition (around 2000) and the implementation of genomic selection (GS;

Genetic Diversity of Seven Cattle Breeds Inferred Using Copy Number Variations

Directory of Open Access Journals (Sweden)

Magretha D. Pierce

2018-05-01

Full Text Available Copy number variations (CNVs comprise deletions, duplications, and insertions found within the genome larger than 50 bp in size. CNVs are thought to be primary role-players in breed formation and adaptation. South Africa boasts a diverse ecology with harsh environmental conditions and a broad spectrum of parasites and diseases that pose challenges to livestock production. This has led to the development of composite cattle breeds which combine the hardiness of Sanga breeds and the production potential of the Taurine breeds. The prevalence of CNVs within these respective breeds of cattle and the prevalence of CNV regions (CNVRs in their diversity, adaptation and production is however not understood. This study therefore aimed to ascertain the prevalence, diversity, and correlations of CNVRs within cattle breeds used in South Africa. Illumina Bovine SNP50 data and PennCNV were utilized to identify CNVRs within the genome of 287 animals from seven cattle breeds representing Sanga, Taurine, Composite, and cross breeds. Three hundred and fifty six CNVRs of between 36 kb to 4.1 Mb in size were identified. The null hypothesis that one CNVR loci is independent of another was tested using the GENEPOP software. One hunded and two and seven of the CNVRs in the Taurine and Sanga/Composite cattle breeds demonstrated a significant (p ≤ 0.05 association. PANTHER overrepresentation analyses of correlated CNVRs demonstrated significant enrichment of a number of biological processes, molecular functions, cellular components, and protein classes. CNVR genetic variation between and within breed group was measured using phiPT which allows intra-individual variation to be suppressed and hence proved suitable for measuring binary CNVR presence/absence data. Estimate PhiPT within and between breed variance was 2.722 and 0.518 respectively. Pairwise population PhiPT values corresponded with breed type, with Taurine Holstein and Angus breeds demonstrating no between

The business value and cost-effectiveness of genomic medicine.

Science.gov (United States)

Crawford, James M; Aspinall, Mara G

2012-05-01

Genomic medicine offers the promise of more effective diagnosis and treatment of human diseases. Genome sequencing early in the course of disease may enable more timely and informed intervention, with reduced healthcare costs and improved long-term outcomes. However, genomic medicine strains current models for demonstrating value, challenging efforts to achieve fair payment for services delivered, both for laboratory diagnostics and for use of molecular information in clinical management. Current models of healthcare reform stipulate that care must be delivered at equal or lower cost, with better patient and population outcomes. To achieve demonstrated value, genomic medicine must overcome many uncertainties: the clinical relevance of genomic variation; potential variation in technical performance and/or computational analysis; management of massive information sets; and must have available clinical interventions that can be informed by genomic analysis, so as to attain more favorable cost management of healthcare delivery and demonstrate improvements in cost-effectiveness.

Localization of canine brachycephaly using an across breed mapping approach.

Directory of Open Access Journals (Sweden)

Danika Bannasch

2010-03-01

Full Text Available The domestic dog, Canis familiaris, exhibits profound phenotypic diversity and is an ideal model organism for the genetic dissection of simple and complex traits. However, some of the most interesting phenotypes are fixed in particular breeds and are therefore less tractable to genetic analysis using classical segregation-based mapping approaches. We implemented an across breed mapping approach using a moderately dense SNP array, a low number of animals and breeds carefully selected for the phenotypes of interest to identify genetic variants responsible for breed-defining characteristics. Using a modest number of affected (10-30 and control (20-60 samples from multiple breeds, the correct chromosomal assignment was identified in a proof of concept experiment using three previously defined loci; hyperuricosuria, white spotting and chondrodysplasia. Genome-wide association was performed in a similar manner for one of the most striking morphological traits in dogs: brachycephalic head type. Although candidate gene approaches based on comparable phenotypes in mice and humans have been utilized for this trait, the causative gene has remained elusive using this method. Samples from nine affected breeds and thirteen control breeds identified strong genome-wide associations for brachycephalic head type on Cfa 1. Two independent datasets identified the same genomic region. Levels of relative heterozygosity in the associated region indicate that it has been subjected to a selective sweep, consistent with it being a breed defining morphological characteristic. Genotyping additional dogs in the region confirmed the association. To date, the genetic structure of dog breeds has primarily been exploited for genome wide association for segregating traits. These results demonstrate that non-segregating traits under strong selection are equally tractable to genetic analysis using small sample numbers.

Development of breeding objectives for beef cattle breeding ...

African Journals Online (AJOL)

Mnr J F Kluyts

However, to solve the simultaneous equations the ... The aggregate breeding value represents a fundamental concept, the breeding objective, which is ..... Two properties characterise a linear programming problem. The first is additivity, ...

Genetic improvement of Pacific white shrimp (Penaeus (Litopenaeus vannamei: perspectives for genomic selection

Directory of Open Access Journals (Sweden)

Héctor eCastillo-Juárez

2015-03-01

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.

Effect of Trait Heritability, Training Population Size and Marker Density on Genomic Prediction Accuracy Estimation in 22 bi-parental Tropical Maize Populations.

Science.gov (United States)

Zhang, Ao; Wang, Hongwu; Beyene, Yoseph; Semagn, Kassa; Liu, Yubo; Cao, Shiliang; Cui, Zhenhai; Ruan, Yanye; Burgueño, Juan; San Vicente, Felix; Olsen, Michael; Prasanna, Boddupalli M; Crossa, José; Yu, Haiqiu; Zhang, Xuecai

2017-01-01

Genomic selection is being used increasingly in plant breeding to accelerate genetic gain per unit time. One of the most important applications of genomic selection in maize breeding is to predict and select the best un-phenotyped lines in bi-parental populations based on genomic estimated breeding values. In the present study, 22 bi-parental tropical maize populations genotyped with low density SNPs were used to evaluate the genomic prediction accuracy ( r MG ) of the six trait-environment combinations under various levels of training population size (TPS) and marker density (MD), and assess the effect of trait heritability ( h 2 ), TPS and MD on r MG estimation. Our results showed that: (1) moderate r MG values were obtained for different trait-environment combinations, when 50% of the total genotypes was used as training population and ~200 SNPs were used for prediction; (2) r MG increased with an increase in h 2 , TPS and MD, both correlation and variance analyses showed that h 2 is the most important factor and MD is the least important factor on r MG estimation for most of the trait-environment combinations; (3) predictions between pairwise half-sib populations showed that the r MG values for all the six trait-environment combinations were centered around zero, 49% predictions had r MG values above zero; (4) the trend observed in r MG differed with the trend observed in r MG / h , and h is the square root of heritability of the predicted trait, it indicated that both r MG and r MG / h values should be presented in GS study to show the accuracy of genomic selection and the relative accuracy of genomic selection compared with phenotypic selection, respectively. This study provides useful information to maize breeders to design genomic selection workflow in their breeding programs.

Effect of Trait Heritability, Training Population Size and Marker Density on Genomic Prediction Accuracy Estimation in 22 bi-parental Tropical Maize Populations

Directory of Open Access Journals (Sweden)

Ao Zhang

2017-11-01

Full Text Available Genomic selection is being used increasingly in plant breeding to accelerate genetic gain per unit time. One of the most important applications of genomic selection in maize breeding is to predict and select the best un-phenotyped lines in bi-parental populations based on genomic estimated breeding values. In the present study, 22 bi-parental tropical maize populations genotyped with low density SNPs were used to evaluate the genomic prediction accuracy (rMG of the six trait-environment combinations under various levels of training population size (TPS and marker density (MD, and assess the effect of trait heritability (h2, TPS and MD on rMG estimation. Our results showed that: (1 moderate rMG values were obtained for different trait-environment combinations, when 50% of the total genotypes was used as training population and ~200 SNPs were used for prediction; (2 rMG increased with an increase in h2, TPS and MD, both correlation and variance analyses showed that h2 is the most important factor and MD is the least important factor on rMG estimation for most of the trait-environment combinations; (3 predictions between pairwise half-sib populations showed that the rMG values for all the six trait-environment combinations were centered around zero, 49% predictions had rMG values above zero; (4 the trend observed in rMG differed with the trend observed in rMG/h, and h is the square root of heritability of the predicted trait, it indicated that both rMG and rMG/h values should be presented in GS study to show the accuracy of genomic selection and the relative accuracy of genomic selection compared with phenotypic selection, respectively. This study provides useful information to maize breeders to design genomic selection workflow in their breeding programs.

Genomic prediction in animals and plants: simulation of data, validation, reporting, and benchmarking

NARCIS (Netherlands)

Daetwyler, H.D.; Calus, M.P.L.; Pong-Wong, R.; Los Campos, De G.; Hickey, J.M.

2013-01-01

The genomic prediction of phenotypes and breeding values in animals and plants has developed rapidly into its own research field. Results of genomic prediction studies are often difficult to compare because data simulation varies, real or simulated data are not fully described, and not all relevant

Kazusa Marker DataBase: a database for genomics, genetics, and molecular breeding in plants

Science.gov (United States)

Shirasawa, Kenta; Isobe, Sachiko; Tabata, Satoshi; Hirakawa, Hideki

2014-01-01

In order to provide useful genomic information for agronomical plants, we have established a database, the Kazusa Marker DataBase (http://marker.kazusa.or.jp). This database includes information on DNA markers, e.g., SSR and SNP markers, genetic linkage maps, and physical maps, that were developed at the Kazusa DNA Research Institute. Keyword searches for the markers, sequence data used for marker development, and experimental conditions are also available through this database. Currently, 10 plant species have been targeted: tomato (Solanum lycopersicum), pepper (Capsicum annuum), strawberry (Fragaria × ananassa), radish (Raphanus sativus), Lotus japonicus, soybean (Glycine max), peanut (Arachis hypogaea), red clover (Trifolium pratense), white clover (Trifolium repens), and eucalyptus (Eucalyptus camaldulensis). In addition, the number of plant species registered in this database will be increased as our research progresses. The Kazusa Marker DataBase will be a useful tool for both basic and applied sciences, such as genomics, genetics, and molecular breeding in crops. PMID:25320561

Relationships among and variation within rare breeds of swine.

Science.gov (United States)

Roberts, K S; Lamberson, W R

2015-08-01

Extinction of rare breeds of livestock threatens to reduce the total genetic variation available for selection in the face of the changing environment and new diseases. Swine breeds facing extinction typically share characteristics such as small size, slow growth rate, and high fat percentage, which limit them from contributing to commercial production. Compounding the risk of loss of variation is the lack of pedigree information for many rare breeds due to inadequate herd books, which increases the chance that producers are breeding closely related individuals. By making genetic data available, producers can make more educated breeding decisions to preserve genetic diversity in future generations, and conservation organizations can prioritize investments in breed preservation. The objective of this study was to characterize genetic variation within and among breeds of swine and prioritize heritage breeds for preservation. Genotypes from the Illumina PorcineSNP60 BeadChip (GeneSeek, Lincoln, NE) were obtained for Guinea, Ossabaw Island, Red Wattle, American Saddleback, Mulefoot, British Saddleback, Duroc, Landrace, Large White, Pietrain, and Tamworth pigs. A whole-genome analysis toolset was used to construct a genomic relationship matrix and to calculate inbreeding coefficients for the animals within each breed. Relatedness and average inbreeding coefficient differed among breeds, and pigs from rare breeds were generally more closely related and more inbred ( Guinea pigs. Tamworth, Duroc, and Mulefoot tended to not cluster with the other 7 breeds.

Genomic prediction for Nordic Red Cattle using one-step and selection index blending

DEFF Research Database (Denmark)

Guosheng, Su; Madsen, Per; Nielsen, Ulrik Sander

2012-01-01

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...

Piglet use of the creep area - Effects of breeding value and farrowing environment

DEFF Research Database (Denmark)

Vasdal, Guro; Andersen, Inger Lise; Pedersen, Lene Juul

2009-01-01

The objective of this study was to investigate piglet use of the creep area, comparing litters of sows with a high vs. low breeding value for piglet survival in the first 5 days postpartum, that were either housed in crates or individual pens during farrowing and lactation. Seventy-five Yorkshire...... × Danish Landrace sows were video recorded for 4 days after farrowing, and the analysis was conducted using instantaneous sampling every 10 min commencing 24 h after the birth of the first piglet for a period of 72 h. Breeding value for piglet survival had no effect on piglet use of the creep area or time spent...... in any location of the farrowing environment. Farrowing environment had significant effects on piglet location; during all days there were significantly more piglets in the creep area in the crates compared to the pens (P 

A two step Bayesian approach for genomic prediction of breeding values

DEFF Research Database (Denmark)

Mahdi Shariati, Mohammad; Sørensen, Peter; Janss, Luc

2012-01-01

. A better alternative could be to form clusters of markers with similar effects where markers in a cluster have a common variance. Therefore, the influence of each marker group of size p on the posterior distribution of the marker variances will be p df. Methods: The simulated data from the 15th QTL......Background: In genomic models that assign an individual variance to each marker, the contribution of one marker to the posterior distribution of the marker variance is only one degree of freedom (df), which introduces many variance parameters with only little information per variance parameter......-MAS workshop were analyzed such that SNP markers were ranked based on their effects and markers with similar estimated effects were grouped together. In step 1, all markers with minor allele frequency more than 0.01 were included in a SNP-BLUP prediction model. In step 2, markers were ranked based...

Nonparametric method for genomics-based prediction of performance of quantitative traits involving epistasis in plant breeding.

Directory of Open Access Journals (Sweden)

Xiaochun Sun

Full Text Available Genomic selection (GS procedures have proven useful in estimating breeding value and predicting phenotype with genome-wide molecular marker information. However, issues of high dimensionality, multicollinearity, and the inability to deal effectively with epistasis can jeopardize accuracy and predictive ability. We, therefore, propose a new nonparametric method, pRKHS, which combines the features of supervised principal component analysis (SPCA and reproducing kernel Hilbert spaces (RKHS regression, with versions for traits with no/low epistasis, pRKHS-NE, to high epistasis, pRKHS-E. Instead of assigning a specific relationship to represent the underlying epistasis, the method maps genotype to phenotype in a nonparametric way, thus requiring fewer genetic assumptions. SPCA decreases the number of markers needed for prediction by filtering out low-signal markers with the optimal marker set determined by cross-validation. Principal components are computed from reduced marker matrix (called supervised principal components, SPC and included in the smoothing spline ANOVA model as independent variables to fit the data. The new method was evaluated in comparison with current popular methods for practicing GS, specifically RR-BLUP, BayesA, BayesB, as well as a newer method by Crossa et al., RKHS-M, using both simulated and real data. Results demonstrate that pRKHS generally delivers greater predictive ability, particularly when epistasis impacts trait expression. Beyond prediction, the new method also facilitates inferences about the extent to which epistasis influences trait expression.

Nonparametric method for genomics-based prediction of performance of quantitative traits involving epistasis in plant breeding.

Science.gov (United States)

Sun, Xiaochun; Ma, Ping; Mumm, Rita H

2012-01-01

Genomic selection (GS) procedures have proven useful in estimating breeding value and predicting phenotype with genome-wide molecular marker information. However, issues of high dimensionality, multicollinearity, and the inability to deal effectively with epistasis can jeopardize accuracy and predictive ability. We, therefore, propose a new nonparametric method, pRKHS, which combines the features of supervised principal component analysis (SPCA) and reproducing kernel Hilbert spaces (RKHS) regression, with versions for traits with no/low epistasis, pRKHS-NE, to high epistasis, pRKHS-E. Instead of assigning a specific relationship to represent the underlying epistasis, the method maps genotype to phenotype in a nonparametric way, thus requiring fewer genetic assumptions. SPCA decreases the number of markers needed for prediction by filtering out low-signal markers with the optimal marker set determined by cross-validation. Principal components are computed from reduced marker matrix (called supervised principal components, SPC) and included in the smoothing spline ANOVA model as independent variables to fit the data. The new method was evaluated in comparison with current popular methods for practicing GS, specifically RR-BLUP, BayesA, BayesB, as well as a newer method by Crossa et al., RKHS-M, using both simulated and real data. Results demonstrate that pRKHS generally delivers greater predictive ability, particularly when epistasis impacts trait expression. Beyond prediction, the new method also facilitates inferences about the extent to which epistasis influences trait expression.

Estimated allele substitution effects underlying genomic evaluation models depend on the scaling of allele counts

NARCIS (Netherlands)

Bouwman, Aniek C.; Hayes, Ben J.; Calus, Mario P.L.

2017-01-01

Background: Genomic evaluation is used to predict direct genomic values (DGV) for selection candidates in breeding programs, but also to estimate allele substitution effects (ASE) of single nucleotide polymorphisms (SNPs). Scaling of allele counts influences the estimated ASE, because scaling of

A Pathway-Centered Analysis of Pig Domestication and Breeding in Eurasia

Directory of Open Access Journals (Sweden)

Jordi Leno-Colorado

2017-07-01

Full Text Available Ascertaining the molecular and physiological basis of domestication and breeding is an active area of research. Due to the current wide distribution of its wild ancestor, the wild boar, the pig (Sus scrofa is an excellent model to study these processes, which occurred independently in East Asia and Europe ca. 9000 yr ago. Analyzing genome variability patterns in terms of metabolic pathways is attractive since it considers the impact of interrelated functions of genes, in contrast to genome-wide scans that treat genes or genome windows in isolation. To that end, we studied 40 wild boars and 123 domestic pig genomes from Asia and Europe when metabolic pathway was the unit of analysis. We computed statistical significance for differentiation (Fst and linkage disequilibrium (nSL statistics at the pathway level. In terms of Fst, we found 21 and 12 pathways significantly differentiated at a q-value 10 significant pathways (in terms of Fst, comprising genes involved in the transduction of a large number of signals, like phospholipase PCLB1, which is expressed in the brain, or ITPR3, which has an important role in taste transduction. In terms of nSL, significant pathways were mainly related to reproductive performance (ovarian steroidogenesis, a similarly important target trait during domestication and modern animal breeding. Different levels of recombination cannot explain these results, since we found no correlation between Fst and recombination rate. However, we did find an increased ratio of deleterious mutations in domestic vs. wild populations, suggesting a relaxed functional constraint associated with the domestication and breeding processes. Purifying selection was, nevertheless, stronger in significantly differentiated pathways than in random pathways, mainly in Europe. We conclude that pathway analysis facilitates the biological interpretation of genome-wide studies. Notably, in the case of pig, behavior played an important role, among other

A Pathway-Centered Analysis of Pig Domestication and Breeding in Eurasia.

Science.gov (United States)

Leno-Colorado, Jordi; Hudson, Nick J; Reverter, Antonio; Pérez-Enciso, Miguel

2017-07-05

Ascertaining the molecular and physiological basis of domestication and breeding is an active area of research. Due to the current wide distribution of its wild ancestor, the wild boar, the pig ( Sus scrofa ) is an excellent model to study these processes, which occurred independently in East Asia and Europe ca. 9000 yr ago. Analyzing genome variability patterns in terms of metabolic pathways is attractive since it considers the impact of interrelated functions of genes, in contrast to genome-wide scans that treat genes or genome windows in isolation. To that end, we studied 40 wild boars and 123 domestic pig genomes from Asia and Europe when metabolic pathway was the unit of analysis. We computed statistical significance for differentiation (Fst) and linkage disequilibrium (nSL) statistics at the pathway level. In terms of Fst, we found 21 and 12 pathways significantly differentiated at a q -value 10 significant pathways (in terms of Fst), comprising genes involved in the transduction of a large number of signals, like phospholipase PCLB1, which is expressed in the brain, or ITPR3, which has an important role in taste transduction. In terms of nSL, significant pathways were mainly related to reproductive performance (ovarian steroidogenesis), a similarly important target trait during domestication and modern animal breeding. Different levels of recombination cannot explain these results, since we found no correlation between Fst and recombination rate. However, we did find an increased ratio of deleterious mutations in domestic vs. wild populations, suggesting a relaxed functional constraint associated with the domestication and breeding processes. Purifying selection was, nevertheless, stronger in significantly differentiated pathways than in random pathways, mainly in Europe. We conclude that pathway analysis facilitates the biological interpretation of genome-wide studies. Notably, in the case of pig, behavior played an important role, among other physiological

A high-quality genome assembly of quinoa provides insights into the molecular basis of salt bladder-based salinity tolerance and the exceptional nutritional value

Science.gov (United States)

Zou, Changsong; Chen, Aojun; Xiao, Lihong; Muller, Heike M; Ache, Peter; Haberer, Georg; Zhang, Meiling; Jia, Wei; Deng, Ping; Huang, Ru; Lang, Daniel; Li, Feng; Zhan, Dongliang; Wu, Xiangyun; Zhang, Hui; Bohm, Jennifer; Liu, Renyi; Shabala, Sergey; Hedrich, Rainer; Zhu, Jian-Kang; Zhang, Heng

2017-01-01

Chenopodium quinoa is a halophytic pseudocereal crop that is being cultivated in an ever-growing number of countries. Because quinoa is highly resistant to multiple abiotic stresses and its seed has a better nutritional value than any other major cereals, it is regarded as a future crop to ensure global food security. We generated a high-quality genome draft using an inbred line of the quinoa cultivar Real. The quinoa genome experienced one recent genome duplication about 4.3 million years ago, likely reflecting the genome fusion of two Chenopodium parents, in addition to the γ paleohexaploidization reported for most eudicots. The genome is highly repetitive (64.5% repeat content) and contains 54 438 protein-coding genes and 192 microRNA genes, with more than 99.3% having orthologous genes from glycophylic species. Stress tolerance in quinoa is associated with the expansion of genes involved in ion and nutrient transport, ABA homeostasis and signaling, and enhanced basal-level ABA responses. Epidermal salt bladder cells exhibit similar characteristics as trichomes, with a significantly higher expression of genes related to energy import and ABA biosynthesis compared with the leaf lamina. The quinoa genome sequence provides insights into its exceptional nutritional value and the evolution of halophytes, enabling the identification of genes involved in salinity tolerance, and providing the basis for molecular breeding in quinoa. PMID:28994416

Prediction of Breeding Values for Dairy Cattle Using Artificial Neural Networks and Neuro-Fuzzy Systems

Directory of Open Access Journals (Sweden)

Saleh Shahinfar

2012-01-01

Full Text Available Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production.

Estimation of breeding values using selected pedigree records.

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Morton, Richard; Howarth, Jordan M

2005-06-01

Fish bred in tanks or ponds cannot be easily tagged individually. The parentage of any individual may be determined by DNA fingerprinting, but is sufficiently expensive that large numbers cannot be so finger-printed. The measurement of the objective trait can be made on a much larger sample relatively cheaply. This article deals with experimental designs for selecting individuals to be finger-printed and for the estimation of the individual and family breeding values. The general setup provides estimates for both genetic effects regarded as fixed or random and for fixed effects due to known regressors. The family effects can be well estimated when even very small numbers are finger-printed, provided that they are the individuals with the most extreme phenotypes.

Blood gas and electrolyte values of (Capra hircus Canindé breed goats raised in the northeastern semiarid

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Talyta Lins Nunes

2014-09-01

Full Text Available ABSTRACT. Nunes T.L., Oliveira M.G.C., Paiva A.L.C., Bezerra T.C.G., Barrêto Júnior R.A. & Paula V.V. [Blood gas and electrolyte values of (Capra hircus Canindé breed goats raised in the northeastern semiarid.] Valores hemogasométricos de caprinos (Capra hircus da raça Canindé criados no semiárido nordestino. Revista Brasileira de Medicina Veterinária, 36(3:255-260, 2014. Departamento de Ciências Animais, Universidade Federal Rural do Semi-Árido, BR 110, Km 47, Bairro Presidente Costa e Silva, Mossoró, RN 59625-900, Brasil. E-mail: talyta_lins@hotmail.com Canindé goat breed is native of northeastern Brazil and is in danger of extinction. The objective of this research was to determine blood gas and electrolytes values in Canindé goat breed reared in the state of Rio Grande do Norte, establishing baseline data for the breed in the semi-arid conditions. Blood samples from 83 clinically healthy animals were collected and distributed in four groups: Group I comprised of 32 pregnant females, Group II, 29 non-pregnant, Group III, 10 males, and group IV consisted of 12 pups. The blood sample was submitted for blood gas analysis, determining the concentration of sodium, potassium, chloride, total carbon dioxide, pH, carbon dioxide partial pressure, sodium bicarbonate, excess base, and anion gap. Data with normal distribution were analyzed by ANOVA and Tukey test; those which did not have a normal distribution were analyzed by Kruskal-Wallis, Mann-Whitney and Spearman. The results were expressed as mean and standard deviation. The values obtained were similar to those found in other studies with different goat and sheep breeds, and thus may serve as reference for the Canindé breed. The data suggest an influence of age, sex and reproductive status in the variables analyzed.

Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement

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Spindel, J E; Begum, H; Akdemir, D; Collard, B; Redoña, E; Jannink, J-L; McCouch, S

2016-01-01

To address the multiple challenges to food security posed by global climate change, population growth and rising incomes, plant breeders are developing new crop varieties that can enhance both agricultural productivity and environmental sustainability. Current breeding practices, however, are unable to keep pace with demand. Genomic selection (GS) is a new technique that helps accelerate the rate of genetic gain in breeding by using whole-genome data to predict the breeding value of offspring. Here, we describe a new GS model that combines RR-BLUP with markers fit as fixed effects selected from the results of a genome-wide-association study (GWAS) on the RR-BLUP training data. We term this model GS + de novo GWAS. In a breeding population of tropical rice, GS + de novo GWAS outperformed six other models for a variety of traits and in multiple environments. On the basis of these results, we propose an extended, two-part breeding design that can be used to efficiently integrate novel variation into elite breeding populations, thus expanding genetic diversity and enhancing the potential for sustainable productivity gains. PMID:26860200

Assessment of expected breeding values for fertility traits of Murrah buffaloes under subtropical climate

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Soumya Dash

2015-03-01

Full Text Available Aim: The aim of the present study was to assess the influence of temperature and humidity prevalent under subtropical climate on the breeding values for fertility traits viz. service period (SP, pregnancy rate (PR and conception rate (CR of Murrah buffaloes in National Dairy Research Institute (NDRI herd. Materials and Methods: Fertility data on 1379 records of 581 Murrah buffaloes spread over four lactations and climatic parameters viz. dry bulb temperature and relative humidity (RH spanned over 20 years (1993-2012 were collected from NDRI and Central Soil and Salinity Research Institute, Karnal, India. Monthly average temperature humidity index (THI values were estimated. Threshold THI value affecting fertility traits was identified by fixed least-squares model analysis. Three zones of non-heat stress, heat stress and critical heat stress zones were developed in a year. The genetic parameters heritablility (h2 and repeatability (r of each fertility trait were estimated. Genetic evaluation of Murrah buffaloes was performed in each zone with respect to their expected breeding values (EBV for fertility traits. Results: Effect of THI was found significant (p<0.001 on all fertility traits with threshold THI value identified as 75. Based on THI values, a year was classified into three zones: Non heat stress zone(THI 56.71-73.21, HSZ (THI 75.39-81.60 and critical HSZ (THI 80.27-81.60. The EBVfor SP, PR, CR were estimated as 138.57 days, 0.362 and 69.02% in non-HSZ while in HSZ EBV were found as 139.62 days, 0.358 and 68.81%, respectively. EBV for SP was increased to 140.92 days and for PR and CR, it was declined to 0.357 and 68.71% in critical HSZ. Conclusion: The negative effect of THI was observed on EBV of fertility traits under the non-HSZ and critical HSZ Thus, the influence of THI should be adjusted before estimating the breeding values for fertility traits in Murrah buffaloes.

Future breeding for organic and low-input agriculture: integrating values and modern breeding tools for improving robustness

NARCIS (Netherlands)

Lammerts Van Bueren, E.

2010-01-01

Organic production and also the attention for plant breeding for organic agriculture is still increasing in Europe. The question often raised is how much does plant breeding for the organic sector differ from modern plant breeding and does a ban on GMO also include refraining from molecular marker

Cow genotyping strategies for genomic selection in a small dairy cattle population.

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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

2017-01-01

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

Genome-wide scan for visceral leishmaniasis in mixed-breed dogs identifies candidate genes involved in T helper cells and macrophage signaling

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We conducted a genome-wide scan for visceral leishmaniasis in mixed-breed dogs from a highly endemic area in Brazil using 149,648 single nucleotide polymorphism (SNP) markers genotyped in 20 cases and 28 controls. Using a mixed model approach, we found two candidate loci on canine autosomes 1 and 2....

Comparisons of single-stage and two-stage approaches to genomic selection.

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Schulz-Streeck, Torben; Ogutu, Joseph O; Piepho, Hans-Peter

2013-01-01

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.

Genomic selection in maritime pine.

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Isik, Fikret; Bartholomé, Jérôme; Farjat, Alfredo; Chancerel, Emilie; Raffin, Annie; Sanchez, Leopoldo; Plomion, Christophe; Bouffier, Laurent

2016-01-01

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.

DNA-informed breeding of rosaceous crops: promises, progress and prospects

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Peace, Cameron P

2017-01-01

Crops of the Rosaceae family provide valuable contributions to rural economies and human health and enjoyment. Sustained solutions to production challenges and market demands can be met with genetically improved new cultivars. Traditional rosaceous crop breeding is expensive and time-consuming and would benefit from improvements in efficiency and accuracy. Use of DNA information is becoming conventional in rosaceous crop breeding, contributing to many decisions and operations, but only after past decades of solved challenges and generation of sufficient resources. Successes in deployment of DNA-based knowledge and tools have arisen when the ‘chasm’ between genomics discoveries and practical application is bridged systematically. Key steps are establishing breeder desire for use of DNA information, adapting tools to local breeding utility, identifying efficient application schemes, accessing effective services in DNA-based diagnostics and gaining experience in integrating DNA information into breeding operations and decisions. DNA-informed germplasm characterization for revealing identity and relatedness has benefitted many programs and provides a compelling entry point to reaping benefits of genomics research. DNA-informed germplasm evaluation for predicting trait performance has enabled effective reallocation of breeding resources when applied in pioneering programs. DNA-based diagnostics is now expanding from specific loci to genome-wide considerations. Realizing the full potential of this expansion will require improved accuracy of predictions, multi-trait DNA profiling capabilities, streamlined breeding information management systems, strategies that overcome plant-based features that limit breeding progress and widespread training of current and future breeding personnel and allied scientists. PMID:28326185

A validated genome wide association study to breed cattle adapted to an environment altered by climate change.

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Ben J Hayes

Full Text Available Continued production of food in areas predicted to be most affected by climate change, such as dairy farming regions of Australia, will be a major challenge in coming decades. Along with rising temperatures and water shortages, scarcity of inputs such as high energy feeds is predicted. With the motivation of selecting cattle adapted to these changing environments, we conducted a genome wide association study to detect DNA markers (single nucleotide polymorphisms associated with the sensitivity of milk production to environmental conditions. To do this we combined historical milk production and weather records with dense marker genotypes on dairy sires with many daughters milking across a wide range of production environments in Australia. Markers associated with sensitivity of milk production to feeding level and sensitivity of milk production to temperature humidity index on chromosome nine and twenty nine respectively were validated in two independent populations, one a different breed of cattle. As the extent of linkage disequilibrium across cattle breeds is limited, the underlying causative mutations have been mapped to a small genomic interval containing two promising candidate genes. The validated marker panels we have reported here will aid selection for high milk production under anticipated climate change scenarios, for example selection of sires whose daughters will be most productive at low levels of feeding.

Accuracy of genomic selection in biparental populations of flax (Linum usitatissimum L.

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Frank M. You

2016-08-01

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.

Effects of correcting missing daily feed intake values on the genetic parameters and estimated breeding values for feeding traits in pigs.

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Ito, Tetsuya; Fukawa, Kazuo; Kamikawa, Mai; Nikaidou, Satoshi; Taniguchi, Masaaki; Arakawa, Aisaku; Tanaka, Genki; Mikawa, Satoshi; Furukawa, Tsutomu; Hirose, Kensuke

2018-01-01

Daily feed intake (DFI) is an important consideration for improving feed efficiency, but measurements using electronic feeder systems contain many missing and incorrect values. Therefore, we evaluated three methods for correcting missing DFI data (quadratic, orthogonal polynomial, and locally weighted (Loess) regression equations) and assessed the effects of these missing values on the genetic parameters and the estimated breeding values (EBV) for feeding traits. DFI records were obtained from 1622 Duroc pigs, comprising 902 individuals without missing DFI and 720 individuals with missing DFI. The Loess equation was the most suitable method for correcting the missing DFI values in 5-50% randomly deleted datasets among the three equations. Both variance components and heritability for the average DFI (ADFI) did not change because of the missing DFI proportion and Loess correction. In terms of rank correlation and information criteria, Loess correction improved the accuracy of EBV for ADFI compared to randomly deleted cases. These findings indicate that the Loess equation is useful for correcting missing DFI values for individual pigs and that the correction of missing DFI values could be effective for the estimation of breeding values and genetic improvement using EBV for feeding traits. © 2017 The Authors. Animal Science Journal published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Animal Science.

Technical note: Equivalent genomic models with a residual polygenic effect.

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Liu, Z; Goddard, M E; Hayes, B J; Reinhardt, F; Reents, R

2016-03-01

Routine genomic evaluations in animal breeding are usually based on either a BLUP with genomic relationship matrix (GBLUP) or single nucleotide polymorphism (SNP) BLUP model. For a multi-step genomic evaluation, these 2 alternative genomic models were proven to give equivalent predictions for genomic reference animals. The model equivalence was verified also for young genotyped animals without phenotypes. Due to incomplete linkage disequilibrium of SNP markers to genes or causal mutations responsible for genetic inheritance of quantitative traits, SNP markers cannot explain all the genetic variance. A residual polygenic effect is normally fitted in the genomic model to account for the incomplete linkage disequilibrium. In this study, we start by showing the proof that the multi-step GBLUP and SNP BLUP models are equivalent for the reference animals, when they have a residual polygenic effect included. Second, the equivalence of both multi-step genomic models with a residual polygenic effect was also verified for young genotyped animals without phenotypes. Additionally, we derived formulas to convert genomic estimated breeding values of the GBLUP model to its components, direct genomic values and residual polygenic effect. Third, we made a proof that the equivalence of these 2 genomic models with a residual polygenic effect holds also for single-step genomic evaluation. Both the single-step GBLUP and SNP BLUP models lead to equal prediction for genotyped animals with phenotypes (e.g., reference animals), as well as for (young) genotyped animals without phenotypes. Finally, these 2 single-step genomic models with a residual polygenic effect were proven to be equivalent for estimation of SNP effects, too. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Joint Genomic Prediction of Canine Hip Dysplasia in UK and US Labrador Retrievers

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Stefan M. Edwards

2018-03-01

Full Text Available Canine hip dysplasia, a debilitating orthopedic disorder that leads to osteoarthritis and cartilage degeneration, is common in several large-sized dog breeds and shows moderate heritability suggesting that selection can reduce prevalence. Estimating genomic breeding values require large reference populations, which are expensive to genotype for development of genomic prediction tools. Combining datasets from different countries could be an option to help build larger reference datasets without incurring extra genotyping costs. Our objective was to evaluate genomic prediction based on a combination of UK and US datasets of genotyped dogs with records of Norberg angle scores, related to canine hip dysplasia. Prediction accuracies using a single population were 0.179 and 0.290 for 1,179 and 242 UK and US Labrador Retrievers, respectively. Prediction accuracies changed to 0.189 and 0.260, with an increased bias of genomic breeding values when using a joint training set (biased upwards for the US population and downwards for the UK population. Our results show that in this study of canine hip dysplasia, little or no benefit was gained from using a joint training set as compared to using a single population as training set. We attribute this to differences in the genetic background of the two populations as well as the small sample size of the US dataset.

Genome-Wide Association Analyses Highlight the Potential for Different Genetic Mechanisms for Litter Size Among Sheep Breeds

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Xu, Song-Song; Gao, Lei; Xie, Xing-Long; Ren, Yan-Ling; Shen, Zhi-Qiang; Wang, Feng; Shen, Min; Eyϸórsdóttir, Emma; Hallsson, Jón H.; Kiseleva, Tatyana; Kantanen, Juha; Li, Meng-Hua

2018-01-01

Reproduction is an important trait in sheep breeding as well as in other livestock. However, despite its importance the genetic mechanisms of litter size in domestic sheep (Ovis aries) are still poorly understood. To explore genetic mechanisms underlying the variation in litter size, we conducted multiple independent genome-wide association studies in five sheep breeds of high prolificacy (Wadi, Hu, Icelandic, Finnsheep, and Romanov) and one low prolificacy (Texel) using the Ovine Infinium HD BeadChip, respectively. We identified different sets of candidate genes associated with litter size in different breeds: BMPR1B, FBN1, and MMP2 in Wadi; GRIA2, SMAD1, and CTNNB1 in Hu; NCOA1 in Icelandic; INHBB, NF1, FLT1, PTGS2, and PLCB3 in Finnsheep; ESR2 in Romanov and ESR1, GHR, ETS1, MMP15, FLI1, and SPP1 in Texel. Further annotation of genes and bioinformatics analyses revealed that different biological pathways could be involved in the variation in litter size of females: hormone secretion (FSH and LH) in Wadi and Hu, placenta and embryonic lethality in Icelandic, folliculogenesis and LH signaling in Finnsheep, ovulation and preovulatory follicle maturation in Romanov, and estrogen and follicular growth in Texel. Taken together, our results provide new insights into the genetic mechanisms underlying the prolificacy trait in sheep and other mammals, suggesting targets for selection where the aim is to increase prolificacy in breeding projects.

Bayesian genomic selection: the effect of haplotype lenghts and priors

DEFF Research Database (Denmark)

Villumsen, Trine Michelle; Janss, Luc

2009-01-01

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 ...

Annotation-Based Whole Genomic Prediction and Selection

DEFF Research Database (Denmark)

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...

A Multi-Breed Genome-Wide Association Analysis for Canine Hypothyroidism Identifies a Shared Major Risk Locus on CFA12.

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Matteo Bianchi

Full Text Available Hypothyroidism is a complex clinical condition found in both humans and dogs, thought to be caused by a combination of genetic and environmental factors. In this study we present a multi-breed analysis of predisposing genetic risk factors for hypothyroidism in dogs using three high-risk breeds--the Gordon Setter, Hovawart and the Rhodesian Ridgeback. Using a genome-wide association approach and meta-analysis, we identified a major hypothyroidism risk locus shared by these breeds on chromosome 12 (p = 2.1x10(-11. Further characterisation of the candidate region revealed a shared ~167 kb risk haplotype (4,915,018-5,081,823 bp, tagged by two SNPs in almost complete linkage disequilibrium. This breed-shared risk haplotype includes three genes (LHFPL5, SRPK1 and SLC26A8 and does not extend to the dog leukocyte antigen (DLA class II gene cluster located in the vicinity. These three genes have not been identified as candidate genes for hypothyroid disease previously, but have functions that could potentially contribute to the development of the disease. Our results implicate the potential involvement of novel genes and pathways for the development of canine hypothyroidism, raising new possibilities for screening, breeding programmes and treatments in dogs. This study may also contribute to our understanding of the genetic etiology of human hypothyroid disease, which is one of the most common endocrine disorders in humans.

Quick cytogenetic screening of breeding bulls using flow cytometric sperm DNA histogram analysis.

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Nagy, Szabolcs; Polgár, Péter J; Andersson, Magnus; Kovács, András

2016-09-01

The aim of the present study was to test the FXCycle PI/RNase kit for routine DNA analyses in order to detect breeding bulls and/or insemination doses carrying cytogenetic aberrations. In a series of experiments we first established basic DNA histogram parameters of cytogenetically healthy breeding bulls by measuring the intraspecific genome size variation of three animals, then we compared the histogram profiles of bulls carrying cytogenetic defects to the baseline values. With the exception of one case the test was able to identify bulls with cytogenetic defects. Therefore, we conclude that the assay could be incorporated into the laboratory routine where flow cytometry is applied for semen quality control.

Genomic testing interacts with reproductive surplus in reducing genetic lag and increasing economic net return

DEFF Research Database (Denmark)

Hjortø, Line; Ettema, Jehan Frans; Kargo, Morten

2015-01-01

Until now, genomic information has mainly been used to improve the accuracy of genomic breeding values for breeding animals at a population level. However, we hypothesize that the use of information from genotyped females also opens up the possibility of reducing genetic lag in a dairy herd......, especially if genomic tests are used in combination with sexed semen or a high management level for reproductive performance, because both factors provide the opportunity for generating a reproductive surplus in the herd. In this study, sexed semen is used in combination with beef semen to produce high......-value crossbred beef calves. Thus, on average there is no surplus of and selection among replacement heifers whether to go into the herd or to be sold. In this situation, the selection opportunities arise when deciding which cows to inseminate with sexed semen, conventional semen, or beef semen. We tested...

A roadmap for breeding orphan leafy vegetable species: a case study of Gynandropsis gynandra (Cleomaceae).

Science.gov (United States)

Sogbohossou, E O Deedi; Achigan-Dako, Enoch G; Maundu, Patrick; Solberg, Svein; Deguenon, Edgar M S; Mumm, Rita H; Hale, Iago; Van Deynze, Allen; Schranz, M Eric

2018-01-01

Despite an increasing awareness of the potential of "orphan" or unimproved crops to contribute to food security and enhanced livelihoods for farmers, coordinated research agendas to facilitate production and use of orphan crops by local communities are generally lacking. We provide an overview of the current knowledge on leafy vegetables with a focus on Gynandropsis gynandra , a highly nutritious species used in Africa and Asia, and highlight general and species-specific guidelines for participatory, genomics-assisted breeding of orphan crops. Key steps in genome-enabled orphan leafy vegetables improvement are identified and discussed in the context of Gynandropsis gynandra breeding, including: (1) germplasm collection and management; (2) product target definition and refinement; (3) characterization of the genetic control of key traits; (4) design of the 'process' for cultivar development; (5) integration of genomic data to optimize that 'process'; (6) multi-environmental participatory testing and end-user evaluation; and (7) crop value chain development. The review discusses each step in detail, with emphasis on improving leaf yield, phytonutrient content, organoleptic quality, resistance to biotic and abiotic stresses and post-harvest management.

Integration of external estimated breeding values and associated reliabilities using correlations among traits and effects

NARCIS (Netherlands)

Vandenplas, J.; Colinet, F.G.; Glorieux, G.; Bertozzi, C.; Gengler, N.

2015-01-01

Based on a Bayesian view of linear mixed models, several studies showed the possibilities to integrate estimated breeding values (EBV) and associated reliabilities (REL) provided by genetic evaluations performed outside a given evaluation system into this genetic evaluation. Hereafter, the term

Comparison of genomic-enhanced EPD systems using an external phenotypic database

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The American Angus Association (AAA) is currently evaluating two methods to incorporate genomic information into their genetic evaluation program: 1) multi-trait incorporation of an externally produced molecular breeding value as an indicator trait (MT) and 2) single-step evaluation with an unweight...

Characterizing the population structure and genetic diversity of maize breeding germplasm in Southwest China using genome-wide SNP markers.

Science.gov (United States)

Zhang, Xiao; Zhang, Hua; Li, Lujiang; Lan, Hai; Ren, Zhiyong; Liu, Dan; Wu, Ling; Liu, Hailan; Jaqueth, Jennifer; Li, Bailin; Pan, Guangtang; Gao, Shibin

2016-08-31

Maize breeding germplasm used in Southwest China has high complexity because of the diverse ecological features of this area. In this study, the population structure, genetic diversity, and linkage disequilibrium decay distance of 362 important inbred lines collected from the breeding program of Southwest China were characterized using the MaizeSNP50 BeadChip with 56,110 single nucleotide polymorphisms (SNPs). With respect to population structure, two (Tropical and Temperate), three (Tropical, Stiff Stalk and non-Stiff Stalk), four [Tropical, group A germplasm derived from modern U.S. hybrids (PA), group B germplasm derived from modern U.S. hybrids (PB) and Reid] and six (Tropical, PB, Reid, Iowa Stiff Stalk Synthetic, PA and North) subgroups were identified. With increasing K value, the Temperate group showed pronounced hierarchical structure with division into further subgroups. The Genetic Diversity of each group was also estimated, and the Tropical group was more diverse than the Temperate group. Seven low-genetic-diversity and one high-genetic-diversity regions were collectively identified in the Temperate, Tropical groups, and the entire panel. SNPs with significant variation in allele frequency between the Tropical and Temperate groups were also evaluated. Among them, a region located at 130 Mb on Chromosome 2 showed the highest genetic diversity, including both number of SNPs with significant variation and the ratio of significant SNPs to total SNPs. Linkage disequilibrium decay distance in the Temperate group was greater (2.5-3 Mb) than that in the entire panel (0.5-0.75 Mb) and the Tropical group (0.25-0.5 Mb). A large region at 30-120 Mb of Chromosome 7 was concluded to be a region conserved during the breeding process by comparison between S37, which was considered a representative tropical line in Southwest China, and its 30 most similar derived lines. For the panel covered most of widely used inbred lines in Southwest China, this work

An Evaluation Framework for Lossy Compression of Genome Sequencing Quality Values

OpenAIRE

Alberti, Claudio; Daniels, Noah; Hernaez, Mikel; Voges, Jan; Goldfeder, Rachel L.; Hernandez-Lopez, Ana A.; Mattavelli, Marco; Berger, Bonnie

2016-01-01

This paper provides the specification and an initial validation of an evaluation framework for the comparison of lossy compressors of genome sequencing quality values. The goal is to define reference data, test sets, tools and metrics that shall be used to evaluate the impact of lossy compression of quality values on human genome variant calling. The functionality of the framework is validated referring to two state-of-the-art genomic compressors. This work has been spurred by the current act...

An Evaluation Framework for Lossy Compression of Genome Sequencing Quality Values.

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Alberti, Claudio; Daniels, Noah; Hernaez, Mikel; Voges, Jan; Goldfeder, Rachel L; Hernandez-Lopez, Ana A; Mattavelli, Marco; Berger, Bonnie

2016-01-01

This paper provides the specification and an initial validation of an evaluation framework for the comparison of lossy compressors of genome sequencing quality values. The goal is to define reference data, test sets, tools and metrics that shall be used to evaluate the impact of lossy compression of quality values on human genome variant calling. The functionality of the framework is validated referring to two state-of-the-art genomic compressors. This work has been spurred by the current activity within the ISO/IEC SC29/WG11 technical committee (a.k.a. MPEG), which is investigating the possibility of starting a standardization activity for genomic information representation.

Risk aversion affects economic values of blue fox breeding scheme.

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Peura, J; Kempe, R; Strandén, I; Rydhmer, L

2016-12-01

The profit and production of an average Finnish blue fox farm was simulated using a deterministic bio-economic farm model. Risk was included using Arrow-Prat absolute risk aversion coefficient and profit variance. Risk-rated economic values were calculated for pregnancy rate, litter loss, litter size, pelt size, pelt quality, pelt colour clarity, feed efficiency and eye infection. With high absolute risk aversion, economic values were lower than with low absolute risk aversion. Economic values were highest for litter loss (18.16 and 26.42 EUR), litter size (13.27 and 19.40 EUR), pregnancy (11.99 and 18.39 EUR) and eye infection (12.39 and 13.81 EUR). Sensitivity analysis showed that selection pressure for improved eye health depended strongly on proportion of culled animals among infected animals and much less on the proportion of infected animals. The economic value of feed efficiency was lower than expected (6.06 and 8.03 EUR). However, it was almost the same magnitude as pelt quality (7.30 and 7.30 EUR) and higher than the economic value of pelt size (3.37 and 5.26 EUR). Risk factors should be considered in blue fox breeding scheme because they change the relative importance of traits. © 2016 Blackwell Verlag GmbH.

Enhancing genetic gain in the era of molecular breeding.

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Xu, Yunbi; Li, Ping; Zou, Cheng; Lu, Yanli; Xie, Chuanxiao; Zhang, Xuecai; Prasanna, Boddupalli M; Olsen, Michael S

2017-05-17

As one of the important concepts in conventional quantitative genetics and breeding, genetic gain can be defined as the amount of increase in performance that is achieved annually through artificial selection. To develop pro ducts that meet the increasing demand of mankind, especially for food and feed, in addition to various industrial uses, breeders are challenged to enhance the potential of genetic gain continuously, at ever higher rates, while they close the gaps that remain between the yield potential in breeders' demonstration trials and the actual yield in farmers' fields. Factors affecting genetic gain include genetic variation available in breeding materials, heritability for traits of interest, selection intensity, and the time required to complete a breeding cycle. Genetic gain can be improved through enhancing the potential and closing the gaps, which has been evolving and complemented with modern breeding techniques and platforms, mainly driven by molecular and genomic tools, combined with improved agronomic practice. Several key strategies are reviewed in this article. Favorable genetic variation can be unlocked and created through molecular and genomic approaches including mutation, gene mapping and discovery, and transgene and genome editing. Estimation of heritability can be improved by refining field experiments through well-controlled and precisely assayed environmental factors or envirotyping, particularly for understanding and controlling spatial heterogeneity at the field level. Selection intensity can be significantly heightened through improvements in the scale and precision of genotyping and phenotyping. The breeding cycle time can be shortened by accelerating breeding procedures through integrated breeding approaches such as marker-assisted selection and doubled haploid development. All the strategies can be integrated with other widely used conventional approaches in breeding programs to enhance genetic gain. More transdisciplinary

Development of Genomic Resources in the Species of Trifolium L. and Its Application in Forage Legume Breeding

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Leif Skøt

2012-05-01

Full Text Available Clovers (genus Trifolium are a large and widespread genus of legumes. A number of clovers are of agricultural importance as forage crops in grassland agriculture, particularly temperate areas. White clover (Trifolium repens L. is used in grazed pasture and red clover (T. pratense L. is widely cut and conserved as a winter feed. For the diploid red clover, genetic and genomic tools and resources have developed rapidly over the last five years including genetic and physical maps, BAC (bacterial artificial chromosome end sequence and transcriptome sequence information. This has paved the way for the use of genome wide selection and high throughput phenotyping in germplasm development. For the allotetraploid white clover progress has been slower although marker assisted selection is in use and relatively robust genetic maps and QTL (quantitative trait locus information now exist. For both species the sequencing of the model legume Medicago truncatula gene space is an important development to aid genomic, biological and evolutionary studies. The first genetic maps of another species, subterranean clover (Trifolium subterraneum L. have also been published and its comparative genomics with red clover and M. truncatula conducted. Next generation sequencing brings the potential to revolutionize clover genomics, but international consortia and effective use of germplasm, novel population structures and phenomics will be required to carry out effective translation into breeding. Another avenue for clover genomic and genetic improvement is interspecific hybridization. This approach has considerable potential with regard to crop improvement but also opens windows of opportunity for studies of biological and evolutionary processes.

Genomic Selection Improves Heat Tolerance in Dairy Cattle

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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.

2016-01-01

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

An integrated approach for increasing breeding efficiency in apple and peach in Europe.

Science.gov (United States)

Laurens, Francois; Aranzana, Maria José; Arus, Pere; Bassi, Daniele; Bink, Marco; Bonany, Joan; Caprera, Andrea; Corelli-Grappadelli, Luca; Costes, Evelyne; Durel, Charles-Eric; Mauroux, Jehan-Baptiste; Muranty, Hélène; Nazzicari, Nelson; Pascal, Thierry; Patocchi, Andrea; Peil, Andreas; Quilot-Turion, Bénédicte; Rossini, Laura; Stella, Alessandra; Troggio, Michela; Velasco, Riccardo; van de Weg, Eric

2018-01-01

Despite the availability of whole genome sequences of apple and peach, there has been a considerable gap between genomics and breeding. To bridge the gap, the European Union funded the FruitBreedomics project (March 2011 to August 2015) involving 28 research institutes and private companies. Three complementary approaches were pursued: (i) tool and software development, (ii) deciphering genetic control of main horticultural traits taking into account allelic diversity and (iii) developing plant materials, tools and methodologies for breeders. Decisive breakthroughs were made including the making available of ready-to-go DNA diagnostic tests for Marker Assisted Breeding, development of new, dense SNP arrays in apple and peach, new phenotypic methods for some complex traits, software for gene/QTL discovery on breeding germplasm via Pedigree Based Analysis (PBA). This resulted in the discovery of highly predictive molecular markers for traits of horticultural interest via PBA and via Genome Wide Association Studies (GWAS) on several European genebank collections. FruitBreedomics also developed pre-breeding plant materials in which multiple sources of resistance were pyramided and software that can support breeders in their selection activities. Through FruitBreedomics, significant progresses were made in the field of apple and peach breeding, genetics, genomics and bioinformatics of which advantage will be made by breeders, germplasm curators and scientists. A major part of the data collected during the project has been stored in the FruitBreedomics database and has been made available to the public. This review covers the scientific discoveries made in this major endeavour, and perspective in the apple and peach breeding and genomics in Europe and beyond.

POPULATION ANALYSIS OF THE LOCAL ENDANGERED PŘEŠTICE BLACK-PIED PIG BREED

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Emil Krupa

2015-09-01

Full Text Available The pedigree analysis of the local endangered Přeštice Black-Pied pig breed (n=19 289 was performed. Animals born within the period 2012-2014 were assumed as the reference population (n=1 374. The pedigree completeness index reached 100% for four generations back. The 100 % of the genetic pool was explained by 66 ancestors. Although all animals of the reference population were inbred, 57% of them had inbreeding less than five percent. Average inbreeding, co-ancestry coefficient and rate of inbreeding reached 4.93%, 13.48% and 1.29% in reference population, respectively. The effective population size calculated by four different methods varied from 32 to 91 animals in 2014. Average generation interval, average family size for sire and dam parents was 2.5, 17.46 and 6.5 animals, respectively. Total number of founders, effective number of founders, effective number of founders’ genomes and effective number of non-founders genomes reached values 299, 98.05, 21.92 and 28.23 founders, respectively. The average genetic diversity (GD loss was 13.71% in reference population. The GD loss has increased within the last three year period mainly due to the random genetic drift (77.6% and by unequal contribution of founders (22.4%. The Preštice Black-Pied breed is highly endangered with GD loss. Mating of closely related animals has to be prevented in breeding and mating program of this breed.

A function accounting for training set size and marker density to model the average accuracy of genomic prediction.

Science.gov (United States)

Erbe, Malena; Gredler, Birgit; Seefried, Franz Reinhold; Bapst, Beat; Simianer, Henner

2013-01-01

Prediction of genomic breeding values is of major practical relevance in dairy cattle breeding. Deterministic equations have been suggested to predict the accuracy of genomic breeding values in a given design which are based on training set size, reliability of phenotypes, and the number of independent chromosome segments ([Formula: see text]). The aim of our study was to find a general deterministic equation for the average accuracy of genomic breeding values that also accounts for marker density and can be fitted empirically. Two data sets of 5'698 Holstein Friesian bulls genotyped with 50 K SNPs and 1'332 Brown Swiss bulls genotyped with 50 K SNPs and imputed to ∼600 K SNPs were available. Different k-fold (k = 2-10, 15, 20) cross-validation scenarios (50 replicates, random assignment) were performed using a genomic BLUP approach. A maximum likelihood approach was used to estimate the parameters of different prediction equations. The highest likelihood was obtained when using a modified form of the deterministic equation of Daetwyler et al. (2010), augmented by a weighting factor (w) based on the assumption that the maximum achievable accuracy is [Formula: see text]. The proportion of genetic variance captured by the complete SNP sets ([Formula: see text]) was 0.76 to 0.82 for Holstein Friesian and 0.72 to 0.75 for Brown Swiss. When modifying the number of SNPs, w was found to be proportional to the log of the marker density up to a limit which is population and trait specific and was found to be reached with ∼20'000 SNPs in the Brown Swiss population studied.

A function accounting for training set size and marker density to model the average accuracy of genomic prediction.

Directory of Open Access Journals (Sweden)

Malena Erbe

Full Text Available Prediction of genomic breeding values is of major practical relevance in dairy cattle breeding. Deterministic equations have been suggested to predict the accuracy of genomic breeding values in a given design which are based on training set size, reliability of phenotypes, and the number of independent chromosome segments ([Formula: see text]. The aim of our study was to find a general deterministic equation for the average accuracy of genomic breeding values that also accounts for marker density and can be fitted empirically. Two data sets of 5'698 Holstein Friesian bulls genotyped with 50 K SNPs and 1'332 Brown Swiss bulls genotyped with 50 K SNPs and imputed to ∼600 K SNPs were available. Different k-fold (k = 2-10, 15, 20 cross-validation scenarios (50 replicates, random assignment were performed using a genomic BLUP approach. A maximum likelihood approach was used to estimate the parameters of different prediction equations. The highest likelihood was obtained when using a modified form of the deterministic equation of Daetwyler et al. (2010, augmented by a weighting factor (w based on the assumption that the maximum achievable accuracy is [Formula: see text]. The proportion of genetic variance captured by the complete SNP sets ([Formula: see text] was 0.76 to 0.82 for Holstein Friesian and 0.72 to 0.75 for Brown Swiss. When modifying the number of SNPs, w was found to be proportional to the log of the marker density up to a limit which is population and trait specific and was found to be reached with ∼20'000 SNPs in the Brown Swiss population studied.

Opinion piece: genomics and crop plant science in Europe.

Science.gov (United States)

Hughes, Steve

2006-01-01

Recent report reviews and funding initiatives in the field of plant genomic research are considered in the context of their translation into practical and economic value via plant breeding. It is concluded that there is a deficit in investment and that a change in working styles towards knowledge sharing and connectivity is required.

Genome-wide resequencing of KRICE_CORE reveals their potential for future breeding, as well as functional and evolutionary studies in the post-genomic era.

Science.gov (United States)

Kim, Tae-Sung; He, Qiang; Kim, Kyu-Won; Yoon, Min-Young; Ra, Won-Hee; Li, Feng Peng; Tong, Wei; Yu, Jie; Oo, Win Htet; Choi, Buung; Heo, Eun-Beom; Yun, Byoung-Kook; Kwon, Soon-Jae; Kwon, Soon-Wook; Cho, Yoo-Hyun; Lee, Chang-Yong; Park, Beom-Seok; Park, Yong-Jin

2016-05-26

Rice germplasm collections continue to grow in number and size around the world. Since maintaining and screening such massive resources remains challenging, it is important to establish practical methods to manage them. A core collection, by definition, refers to a subset of the entire population that preserves the majority of genetic diversity, enhancing the efficiency of germplasm utilization. Here, we report whole-genome resequencing of the 137 rice mini core collection or Korean rice core set (KRICE_CORE) that represents 25,604 rice germplasms deposited in the Korean genebank of the Rural Development Administration (RDA). We implemented the Illumina HiSeq 2000 and 2500 platform to produce short reads and then assembled those with 9.8 depths using Nipponbare as a reference. Comparisons of the sequences with the reference genome yielded more than 15 million (M) single nucleotide polymorphisms (SNPs) and 1.3 M INDELs. Phylogenetic and population analyses using 2,046,529 high-quality SNPs successfully assigned rice accessions to the relevant rice subgroups, suggesting that these SNPs capture evolutionary signatures that have accumulated in rice subpopulations. Furthermore, genome-wide association studies (GWAS) for four exemplary agronomic traits in the KRIC_CORE manifest the utility of KRICE_CORE; that is, identifying previously defined genes or novel genetic factors that potentially regulate important phenotypes. This study provides strong evidence that the size of KRICE_CORE is small but contains high genetic and functional diversity across the genome. Thus, our resequencing results will be useful for future breeding, as well as functional and evolutionary studies, in the post-genomic era.

Prediction of malting quality traits in barley based on genome-wide marker data to assess the potential of genomic selection.

Science.gov (United States)

Schmidt, Malthe; Kollers, Sonja; Maasberg-Prelle, Anja; Großer, Jörg; Schinkel, Burkhard; Tomerius, Alexandra; Graner, Andreas; Korzun, Viktor

2016-02-01

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.

Prediction of Genes Related to Positive Selection Using Whole-Genome Resequencing in Three Commercial Pig Breeds

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HyoYoung Kim

2015-12-01

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.

Multiple models for Rosaceae genomics.

Science.gov (United States)

Shulaev, Vladimir; Korban, Schuyler S; Sosinski, Bryon; Abbott, Albert G; Aldwinckle, Herb S; Folta, Kevin M; Iezzoni, Amy; Main, Dorrie; Arús, Pere; Dandekar, Abhaya M; Lewers, Kim; Brown, Susan K; Davis, Thomas M; Gardiner, Susan E; Potter, Daniel; Veilleux, Richard E

2008-07-01

The plant family Rosaceae consists of over 100 genera and 3,000 species that include many important fruit, nut, ornamental, and wood crops. Members of this family provide high-value nutritional foods and contribute desirable aesthetic and industrial products. Most rosaceous crops have been enhanced by human intervention through sexual hybridization, asexual propagation, and genetic improvement since ancient times, 4,000 to 5,000 B.C. Modern breeding programs have contributed to the selection and release of numerous cultivars having significant economic impact on the U.S. and world markets. In recent years, the Rosaceae community, both in the United States and internationally, has benefited from newfound organization and collaboration that have hastened progress in developing genetic and genomic resources for representative crops such as apple (Malus spp.), peach (Prunus spp.), and strawberry (Fragaria spp.). These resources, including expressed sequence tags, bacterial artificial chromosome libraries, physical and genetic maps, and molecular markers, combined with genetic transformation protocols and bioinformatics tools, have rendered various rosaceous crops highly amenable to comparative and functional genomics studies. This report serves as a synopsis of the resources and initiatives of the Rosaceae community, recent developments in Rosaceae genomics, and plans to apply newly accumulated knowledge and resources toward breeding and crop improvement.

Breeding performance in the Italian chicken breed Mericanel della Brianza

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Stefano P. Marelli

2010-11-01

Full Text Available In Italy, 90 local avian breeds were described, the majority (61% were classified extinct and only 8.9 % still diffused. Therefore, efforts for conservation of Italian avian breeds are urgently required. The aim of this study was to record the breeding performance of the Italian breed Mericanel della Brianza and multiply a small population, in order to develop a conservation program. Fourteen females and 8 males were available at the beginning of the reproductive season in 2009 and organized in 8 families (1 male/1-2 females kept in floor pens. Birds received a photoperiod of 14L:10D and fed ad libitum. Breeding performance was recorded from March to June. Egg production and egg weight were recorded daily; eggs were set every 2 weeks and fertility, embryo mortality and hatchability were recorded. Mean egg production was 37% and mean egg weight was 34±3.49 g. High fertility values were recorded in the first three settings, from 94 to 87%, and the overall mean fertility value was 81.6%. Overall hatchability was only 49.6% due to a high proportion of dead embryos. Embryo mortality occurred mainly between day 2 and 7 of incubation and during hatch. Highest hatchability values were recorded in setting 1 and 2, 69 and 60% respectively, and a great decrease was found in the following settings. Great variations in egg production, fertility, hatchability and embryo mortality were found among families. The present results are the basic knowledge on reproductive parameters necessary to improve the reproductive efficiency of the breed within a conservation plan.

Genomic selection using indicator traits to reduce the environmental impact of milk production

DEFF Research Database (Denmark)

Hansen Axelsson, H; Fikse, W F; Kargo, Morten

2013-01-01

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...

Mapping of fertility traits in Finnish Ayrshire by genome-wide association analysis

DEFF Research Database (Denmark)

Schulmann, Nina F; Sahana, Goutam; Iso-Touru, T

2011-01-01

A whole-genome scan using single marker association was used to detect chromosome regions associated with seven female fertility traits in Finnish Ayrshire dairy cattle. The phenotypic data consisted of de-regressed estimated breeding values for 340 bulls which were estimated using a single trait...

Astudy on accuracy of predicted breeding value for body weight at eighth week of age in Khorasan native chickens

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faeze ghorbani

2015-12-01

Full Text Available Introduction: Genetic resources in any country are valuable materials which needed to be conserved for a sustainable agriculture. An animal phenotype is generally affected by genetic and environmental factors. To increase mean performance in a population under consideration not only environmental conditions, but also genetic potential of the animals should be improved. Although, environmental improvement could increase the level of animals’ production in a more rapid way, it is not a permanent and non-cumulative progress. In any breeding schemes prediction breeding value of the candidate animals is needed to be obtained with a high precision and accuracy for making a remarkable genetic gain for the traits over the time. The main objective of the present research was to study accuracy of predicted breeding value for body weight at eighth week of age in indigenous chickens of Khorasan Razavi province. Materials and methods: A set of 47,000 body weight (at the age of eight weeks records belonging to 47,000 head of male and female chicks (progeny of 753 sires and 5,154 dams collected during seven generations (2006-2012 was used. The data were obtained in Khorasan Razavi native chicken breeding center. An animal model was applied for analyzing the records. In the model, contemporary group of generation*hatch*sex (GHS as a fixed effect, weight at birth as a covariable, as well as direct and maternal additive genetic random effects were taken into account. In an initial analysis using SAS software, all fixed and covariate factors included in the model were detected to be significant for the trait. All additive genetic relationships among the animals in the pedigree file (47,880 animals were accounted for. Variance and covariance components of direct and maternal additive genetic effects were estimated through restricted maximum likelihood (REML method. Breeding value of the animals was obtained by best linear unbiased prediction (BLUP. Selection

Effects of incorporating environmental cost and risk aversion on economic values of pig breeding goal traits.

Science.gov (United States)

Ali, B M; de Mey, Y; Bastiaansen, J W M; Oude Lansink, A G J M

2018-06-01

Economic values (EVs) of traits, accounting for environmental impacts and risk preferences of farmers, are required to design breeding goals that contribute to both economic and environmental sustainability. The objective of this study was to assess the effects of incorporating environmental costs and the risk preferences of farmers on the EVs of pig breeding goal traits. A breeding goal consisting of both sow efficiency and production traits was defined for a typical Brazilian farrow-to-finish pig farm with 1,500 productive sows. A mean-variance utility function was employed for deriving the EVs at finishing pig level assuming fixed slaughter weight. The inclusion of risk and risk aversion reduces the economic weights of sow efficiency traits (17%) while increasing the importance of production traits (7%). For a risk-neutral producer, inclusion of environmental cost reduces the economic importance of sow efficiency traits (3%) while increasing the importance of production traits (1%). Genetic changes of breeding goal traits by their genetic standard deviations reduce emissions of greenhouse gases, and excretions of nitrogen and phosphorus per finished pig by up to 6% while increasing farm profit. The estimated EVs could be used to improve selection criteria and thereby contribute to the sustainability of pig production systems. © 2018 The Authors. Journal of Animal Breeding and Genetics published by Blackwell Verlag GmbH.

Extent of linkage disequilibrium and effective population size in four South African Sanga cattle breeds

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Sithembile Olga Makina

2015-12-01

Full Text Available Knowledge on the extent of linkage disequilibrium (LD in livestock populations is essential to determine the minimum distance between markers required for effective coverage when conducting genome-wide association studies. This study evaluated the extent of LD, persistence of allelic phase and effective population size (Ne for four Sanga cattle breeds in South Africa including the Afrikaner (n=44, Nguni (n=54, Drakensberger (n=47 and Bonsmara breeds (n=46, using Angus (n=31 and Holstein (n=29 as reference populations. We found that moderate LD extends up to inter-marker distances of 40-60 kb in Angus (0.21 and Holstein (0.21 and up to 100 kb in Afrikaner (0.20. This suggests that genomic selection and association studies performed within these breeds using an average inter-marker r2 ≥ 0.20 would require about 30,000 -50,000 SNPs. However, r2 ≥ 0.20 extended only up to 10-20 kb in the Nguni and Drakensberger and 20-40 kb in the Bonsmara indicating that 75,000 to 150,000 SNPs would be necessary for genome-wide association studies in these breeds. Correlation between alleles at contiguous loci indicated that phase was not strongly preserved between breeds. This suggests the need for breed-specific reference populations in which a much greater density of markers should be scored to identify breed specific haplotypes which may then be imputed into multi-breed commercial populations. Analysis of effective population size based on the extent of LD, revealed Ne=95 (Nguni, Ne=87 (Drakensberger, Ne=77 (Bonsmara and Ne=41 (Afrikaner. Results of this study form the basis for implementation of genomic selection programs in the Sanga breeds of South Africa.

Genomic selection prediction accuracy in a perennial crop: case study of oil palm (Elaeis guineensis Jacq.).

Science.gov (United States)

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

2015-03-01

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.

Plant breeding and genetics newsletter. No. 11

International Nuclear Information System (INIS)

2003-07-01

Implementation of a new CRP on Physical mapping technologies for the identification and characterization of mutated genes contributing to crop quality, organization of mutant germplasm database and repository, implementation of new TC projects and activation of work on molecular characterization of Musa putative germplasm as well as sequencing of BAC clones were the major activities of our sub-Programme on Plant Breeding and Genetics during the last six months. A lot of work has been concentrated on organizing a mutant germplasm repository. The first collections of rice and linseed mutants have already arrived and their descriptions have been introduced into the mutated germplasm database. We found this activity especially important to stimulate exchange of crop germplasm among plant breeders. Similarly there is an urgent need to collect mutants of various crops as necessary material for functional genomics and germplasm enhancement. Nevertheless, many crop research institutes are initiating large-scale mutation programmes with the use of their own plant material. To help them in selecting the mutagen, doses and mutation treatment procedure, we published the third issue of Mutation Breeding Newsletter Index of No. 21-44. The Index is also available through our website http://www.iaea.org/programmes/nafa/d2/index.html. The numerous requests for issues of the Mutation Breeding Newsletter already received from various countries indicate the value of this 80-page index for plant breeders and research institutes. We were invited to present the activities, achievements and trends of our sub-Programme at two very important, international meetings: The International Conference on the Status of Plant and Animal Genome Research, known as the Plant and Animal Genome (PAG XI), and The International Congress on 'In the Wake of the Double Helix - From the Green Revolution to the Gene Revolution'. At this last meeting, an initiative was taken to organize the Crop Root Research

Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP

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Jeffrey B. Endelman

2011-11-01

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.

Genome-wide identification of breed-informative single-nucleotide ...

African Journals Online (AJOL)

Avhashoni AA. Zwane

2016-09-20

Sep 20, 2016 ... ... high temperatures and low-quality grass and for their resistance to ... growth rate, early marketability, grazing performance and good ... development of the Bonsmara breed (Scholtz, 2010). ..... transferability to water buffalo.

Genomic prediction accounting for genotype by environment interaction offers an effective framework for breeding simultaneously for adaptation to an abiotic stress and performance under normal cropping conditions in rice

OpenAIRE

Ahmadi, Nourollah; Cao, Tuong-Vi; Valé, Giampiero; Bartholomé, Jérôme; Hassen, Manel

2018-01-01

Developing rice varieties adapted to alternate wetting and drying water management is crucial for the sustainability of irrigated rice cropping systems. Here we report the first study exploring the feasibility of breeding rice for adaptation to alternate wetting and drying using genomic prediction methods that account for genotype by environment interactions. Two breeding populations (a reference panel of 284 accessions and a progeny population of 97 advanced lines) were evaluated under alter...

The influence of cross-breeding Zlotnicka Spotted native breed sows ...

African Journals Online (AJOL)

To sum up, it is possible to say that the raw meat of Zlotnicka Spotted pigs and their cross-breeds with Duroc and Polish Large White breeds is characterised by good quality and because of its considerable intramuscular fat content, it has a high culinary and processing value, especially for ripening products. Key words: Pigs ...

Progress in the molecular and genetic modification breeding of beef cattle in China.

Science.gov (United States)

Tong, Bin; Zhang, Li; Li, Guang-Peng

2017-11-20

The studies of beef cattle breeding in China have been greatly improved with the rapid development of the international beef cattle industrialization. The beef cattle breeding technologies have rapidly transformed from traditional breeding to molecular marker-assisted breeding, genomic selection and genetic modification breeding. Hundreds of candidate genes and molecular markers associated with growth, meat quality, reproduction performance and diseases resistance have been identified, and some of them have already been used in cattle breeding. Genes and molecular markers associated with growth and development are focused on the growth hormone, muscle regulatory factors, myostatin and insulin-like growth factors. Meat quality is mediated by fatty acid transport and deposition related signals, calpains and calpain system, muscle regulatory factors and muscle growth regulation pathways. Reproduction performance is regulated by GnRH-FSH-LH, growth differentiation factor 9, prolactin receptor and forkhead box protein O1. Disease resistance is modulated by the major histocompatibility complex gene family, toll-like receptors, mannose-binding lectin and interferon gene signals. In this review, we summarize the most recent progress in beef cattle breeding in marker-assisted selection, genome-wide selection and genetic modification breeding, aiming to provide a reference for further genetic breeding research of beef cattle in China.

Potential of Genomic Selection in Mass Selection Breeding of an Allogamous Crop: An Empirical Study to Increase Yield of Common Buckwheat.

Science.gov (United States)

Yabe, Shiori; Hara, Takashi; Ueno, Mariko; Enoki, Hiroyuki; Kimura, Tatsuro; Nishimura, Satoru; Yasui, Yasuo; Ohsawa, Ryo; Iwata, Hiroyoshi

2018-01-01

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.

Strategies for defining traits when calculating economic values for livestock breeding: a review.

Science.gov (United States)

Wolfová, M; Wolf, J

2013-09-01

The objective of the present review was (i) to survey different approaches for choosing the complex of traits for which economic values (EVs) are calculated, (ii) to call attention to the proper definition of traits and (iii) to discuss the manner and extent to which relationships among traits have been considered in the calculation of EVs. For this purpose, papers dealing with the estimation of EVs of traits in livestock were reviewed. The most important reasons for incompatibility of EVs for similar traits estimated in different countries and by different authors were found to be inconsistencies in trait definitions and in assumptions being made about relationships among traits. An important problem identified was how to choose the most appropriate criterion to characterise production or functional ability for a particular class of animals. Accordingly, the review covered the following three topics: (i) which trait(s) would best characterise the growth ability of an animal; (ii) how to define traits expressed repeatedly in subsequent reproductive cycles of breeding females and (iii) how to deal with traits that differ in average value between sexes or among animal groups. Various approaches that have been used to solve these problems were discussed. Furthermore, the manner in which diverse authors chose one or more traits from a group of alternatives for describing a specific biological potential were reviewed and commented on. The consequences of including or excluding relationships among economically important traits when estimating the EV for a specific trait were also examined. An important conclusion of the review is that, for a better comparability and interpretability of estimated EVs in the literature, it is desirable that clear and unique definitions of the traits, complete information on assumptions used in analytical models and details on inter-relationships between traits are documented. Furthermore, the method and the model used for the genetic

Short communication: Genomic selection in a crossbred cattle population using data from the Dairy Genetics East Africa Project.

Science.gov (United States)

Brown, A; Ojango, J; Gibson, J; Coffey, M; Okeyo, M; Mrode, R

2016-09-01

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.

Use of biological priors enhances understanding of genetic architecture and genomic prediction of complex traits within and between dairy cattle breeds.

Science.gov (United States)

Fang, Lingzhao; Sahana, Goutam; Ma, Peipei; Su, Guosheng; Yu, Ying; Zhang, Shengli; Lund, Mogens Sandø; Sørensen, Peter

2017-08-10

A better understanding of the genetic architecture underlying complex traits (e.g., the distribution of causal variants and their effects) may aid in the genomic prediction. Here, we hypothesized that the genomic variants of complex traits might be enriched in a subset of genomic regions defined by genes grouped on the basis of "Gene Ontology" (GO), and that incorporating this independent biological information into genomic prediction models might improve their predictive ability. Four complex traits (i.e., milk, fat and protein yields, and mastitis) together with imputed sequence variants in Holstein (HOL) and Jersey (JER) cattle were analysed. We first carried out a post-GWAS analysis in a HOL training population to assess the degree of enrichment of the association signals in the gene regions defined by each GO term. We then extended the genomic best linear unbiased prediction model (GBLUP) to a genomic feature BLUP (GFBLUP) model, including an additional genomic effect quantifying the joint effect of a group of variants located in a genomic feature. The GBLUP model using a single random effect assumes that all genomic variants contribute to the genomic relationship equally, whereas GFBLUP attributes different weights to the individual genomic relationships in the prediction equation based on the estimated genomic parameters. Our results demonstrate that the immune-relevant GO terms were more associated with mastitis than milk production, and several biologically meaningful GO terms improved the prediction accuracy with GFBLUP for the four traits, as compared with GBLUP. The improvement of the genomic prediction between breeds (the average increase across the four traits was 0.161) was more apparent than that it was within the HOL (the average increase across the four traits was 0.020). Our genomic feature modelling approaches provide a framework to simultaneously explore the genetic architecture and genomic prediction of complex traits by taking advantage of

Visualization of Genome Diversity in German Shepherd Dogs

OpenAIRE

Sally-Anne Mortlock; Rachel Booth; Hamutal Mazrier; Mehar S. Khatkar; Peter Williamson

2016-01-01

A loss of genetic diversity may lead to increased disease risks in subpopulations of dogs. The canine breed structure has contributed to relatively small effective population size in many breeds and can limit the options for selective breeding strategies to maintain diversity. With the completion of the canine genome sequencing project, and the subsequent reduction in the cost of genotyping on a genomic scale, evaluating diversity in dogs has become much more accurate and accessible. This pro...

Multiple Models for Rosaceae Genomics[OA

Science.gov (United States)

Shulaev, Vladimir; Korban, Schuyler S.; Sosinski, Bryon; Abbott, Albert G.; Aldwinckle, Herb S.; Folta, Kevin M.; Iezzoni, Amy; Main, Dorrie; Arús, Pere; Dandekar, Abhaya M.; Lewers, Kim; Brown, Susan K.; Davis, Thomas M.; Gardiner, Susan E.; Potter, Daniel; Veilleux, Richard E.

2008-01-01

The plant family Rosaceae consists of over 100 genera and 3,000 species that include many important fruit, nut, ornamental, and wood crops. Members of this family provide high-value nutritional foods and contribute desirable aesthetic and industrial products. Most rosaceous crops have been enhanced by human intervention through sexual hybridization, asexual propagation, and genetic improvement since ancient times, 4,000 to 5,000 B.C. Modern breeding programs have contributed to the selection and release of numerous cultivars having significant economic impact on the U.S. and world markets. In recent years, the Rosaceae community, both in the United States and internationally, has benefited from newfound organization and collaboration that have hastened progress in developing genetic and genomic resources for representative crops such as apple (Malus spp.), peach (Prunus spp.), and strawberry (Fragaria spp.). These resources, including expressed sequence tags, bacterial artificial chromosome libraries, physical and genetic maps, and molecular markers, combined with genetic transformation protocols and bioinformatics tools, have rendered various rosaceous crops highly amenable to comparative and functional genomics studies. This report serves as a synopsis of the resources and initiatives of the Rosaceae community, recent developments in Rosaceae genomics, and plans to apply newly accumulated knowledge and resources toward breeding and crop improvement. PMID:18487361

Genomic predictions for dry matter intake using the international reference population of gDMI

NARCIS (Netherlands)

Haas, de Y.; Pryce, J.E.; Calus, M.P.L.; Hulsegge, B.; Spurlock, D.M.; Berry, D.P.; Wall, E.; Lovendahl, P.; Weigel, K.; MacDonald, K.; Miglior, F.; Krattenmacher, N.; Veerkamp, R.F.

2014-01-01

In this study, we have demonstrated that using dry matter intake (DMI) phenotypes from multiplecountries increases the accuracy of genomic breeding values for this important trait, provided a multi-trait approach is used. Data from Australia, Canada, Denmark, Germany, Ireland, the Netherlands,New

Implications of the difference between true and predicted breeding values for the study of natural selection and micro-evolution

NARCIS (Netherlands)

Postma, E.

2006-01-01

The ability to predict individual breeding values in natural populations with known pedigrees has provided a powerful tool to separate phenotypic values into their genetic and environmental components in a nonexperimental setting. This has allowed sophisticated analyses of selection, as well as

Animal breeding strategies can improve meat quality attributes within entire populations.

Science.gov (United States)

Berry, D P; Conroy, S; Pabiou, T; Cromie, A R

2017-10-01

The contribution of animal breeding to changes in animal performance is well documented across a range of species. Once genetic variation in a trait exists, then breeding to improve the characteristics of that trait is possible, if so desired. Considerable genetic variation exists in a range of meat quality attributes across a range of species. The genetic variation that exists for meat quality is as large as observed for most performance traits; thus, within a well-structured breeding program, rapid genetic gain for meat quality could be possible. The rate of genetic gain can be augmented through the integration of DNA-based technologies into the breeding program; such DNA-based technologies should, however, be based on thousands of DNA markers dispersed across the entire genome. Genetic and genomic technologies can also have beneficial impact outside the farm gate as a tool to segregate carcasses or meat cuts based on expected meat quality features. Copyright © 2017 Elsevier Ltd. All rights reserved.

The genome draft of coconut (Cocos nucifera).

Science.gov (United States)

Xiao, Yong; Xu, Pengwei; Fan, Haikuo; Baudouin, Luc; Xia, Wei; Bocs, Stéphanie; Xu, Junyang; Li, Qiong; Guo, Anping; Zhou, Lixia; Li, Jing; Wu, Yi; Ma, Zilong; Armero, Alix; Issali, Auguste Emmanuel; Liu, Na; Peng, Ming; Yang, Yaodong

2017-11-01

Coconut palm (Cocos nucifera,2n = 32), a member of genus Cocos and family Arecaceae (Palmaceae), is an important tropical fruit and oil crop. Currently, coconut palm is cultivated in 93 countries, including Central and South America, East and West Africa, Southeast Asia and the Pacific Islands, with a total growth area of more than 12 million hectares [1]. Coconut palm is generally classified into 2 main categories: "Tall" (flowering 8-10 years after planting) and "Dwarf" (flowering 4-6 years after planting), based on morphological characteristics and breeding habits. This Palmae species has a long growth period before reproductive years, which hinders conventional breeding progress. In spite of initial successes, improvements made by conventional breeding have been very slow. In the present study, we obtained de novo sequences of the Cocos nucifera genome: a major genomic resource that could be used to facilitate molecular breeding in Cocos nucifera and accelerate the breeding process in this important crop. A total of 419.67 gigabases (Gb) of raw reads were generated by the Illumina HiSeq 2000 platform using a series of paired-end and mate-pair libraries, covering the predicted Cocos nucifera genome length (2.42 Gb, variety "Hainan Tall") to an estimated ×173.32 read depth. A total scaffold length of 2.20 Gb was generated (N50 = 418 Kb), representing 90.91% of the genome. The coconut genome was predicted to harbor 28 039 protein-coding genes, which is less than in Phoenix dactylifera (PDK30: 28 889), Phoenix dactylifera (DPV01: 41 660), and Elaeis guineensis (EG5: 34 802). BUSCO evaluation demonstrated that the obtained scaffold sequences covered 90.8% of the coconut genome and that the genome annotation was 74.1% complete. Genome annotation results revealed that 72.75% of the coconut genome consisted of transposable elements, of which long-terminal repeat retrotransposons elements (LTRs) accounted for the largest proportion (92.23%). Comparative analysis of the

Ornamental Plant Breeding

Directory of Open Access Journals (Sweden)

Flávia Barbosa Silva Botelho

2015-04-01

Full Text Available World’s ornamental plant market, including domestic market of several countries and its exports, is currently evaluated in 107 billion dollars yearly. Such estimate highlights the importance of the sector in the economy of the countries, as well as its important social role, as it represents one of the main activities, which contributes to income and employment. Therefore a well-structured plant breeding program, which is connected with consumers’ demands, is required in order to fulfill these market needs globally. Activities related to pre-breeding, conventional breeding, and breeding by biotechnological techniques constitute the basis for the successful development of new ornamental plant cultivars. Techniques that involve tissue culture, protoplast fusion and genetic engineering greatly aid conventional breeding (germplasm introduction, plant selection and hybridization, aiming the obtention of superior genotypes. Therefore it makes evident, in the literature, the successful employment of genetic breeding, since it aims to develop plants with commercial value that are also competitive with the ones available in the market.

Advances in genetics and molecular breeding of three legume crops ...

Indian Academy of Sciences (India)

Molecular markers are the most powerful genomic tools to increase the efficiency and precision of breeding practices for crop improvement. Progress in the development of genomic resources in the leading legume crops of the semi-arid tropics (SAT), namely, chickpea (Cicer arietinum), pigeonpea (Cajanus cajan) and ...

Long-term response to genomic selection: effects of estimation method and reference population structure for different genetic architectures.

Science.gov (United States)

Bastiaansen, John W M; Coster, Albart; Calus, Mario P L; van Arendonk, Johan A M; Bovenhuis, Henk

2012-01-24

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

Structuring an Efficient Organic Wheat Breeding Program

Directory of Open Access Journals (Sweden)

P. Stephen Baenziger

2011-08-01

Full Text Available Our long-term goal is to develop wheat cultivars that will improve the profitability and competitiveness of organic producers in Nebraska and the Northern Great Plains. Our approach is to select in early generations for highly heritable traits that are needed for both organic and conventional production (another breeding goal, followed by a targeted organic breeding effort with testing at two organic locations (each in a different ecological region beginning with the F6 generation. Yield analyses from replicated trials at two organic breeding sites and 7 conventional breeding sites from F6 through F12 nurseries revealed, using analyses of variance, biplots, and comparisons of selected lines that it is inappropriate to use data from conventional testing for making germplasm selections for organic production. Selecting and testing lines under organic production practices in different ecological regions was also needed and cultivar selections for organic production were different than those for conventional production. Modifications to this breeding protocol may include growing early generation bulks in an organic cropping system. In the future, our selection efforts should also focus on using state-of-the-art, non-transgenic breeding technologies (genomic selection, marker-assisted breeding, and high throughput phenotyping to synergistically improve organic and conventional wheat breeding.

Manual on mutation breeding. 2. ed.

International Nuclear Information System (INIS)

1977-01-01

The manual is a compilation of work done on the use of induced mutations in plant breeding, and presents general methods and techniques in this field. The use of chemical mutagens and ionizing radiations (X-rays, gamma rays, α- and β-particles, protons, neutrons) are described as well as the effects of these mutagens. The different types of mutations achieved can be divided into genome mutations, chromosome mutations and extra nuclear mutations. Separate chapters deal with mutation techniques in breeding seed-propagated species and asexually propagated plants (examples of development of cultivars given). Plant characters which can be improved by mutation breeding include yield, ripening time, growth habit, disease resistance and tolerance to environmental factors (temperature, salinity etc.). The use of mutagens for some specific plant breeding problems is discussed and attention is also paid to somatic cell genetics in connection with induced mutations. The manual contains a comprehensive bibliography (60 p. references) and a subject index

Genomic Prediction Accounting for Genotype by Environment Interaction Offers an Effective Framework for Breeding Simultaneously for Adaptation to an Abiotic Stress and Performance Under Normal Cropping Conditions in Rice.

Science.gov (United States)

Ben Hassen, Manel; Bartholomé, Jérôme; Valè, Giampiero; Cao, Tuong-Vi; Ahmadi, Nourollah

2018-05-09

Developing rice varieties adapted to alternate wetting and drying water management is crucial for the sustainability of irrigated rice cropping systems. Here we report the first study exploring the feasibility of breeding rice for adaptation to alternate wetting and drying using genomic prediction methods that account for genotype by environment interactions. Two breeding populations (a reference panel of 284 accessions and a progeny population of 97 advanced lines) were evaluated under alternate wetting and drying and continuous flooding management systems. The predictive ability of genomic prediction for response variables (index of relative performance and the slope of the joint regression) and for multi-environment genomic prediction models were compared. For the three traits considered (days to flowering, panicle weight and nitrogen-balance index), significant genotype by environment interactions were observed in both populations. In cross validation, predictive ability for the index was on average lower (0.31) than that of the slope of the joint regression (0.64) whatever the trait considered. Similar results were found for progeny validation. Both cross-validation and progeny validation experiments showed that the performance of multi-environment models predicting unobserved phenotypes of untested entrees was similar to the performance of single environment models with differences in predictive ability ranging from -6% to 4% depending on the trait and on the statistical model concerned. The predictive ability of multi-environment models predicting unobserved phenotypes of entrees evaluated under both water management systems outperformed single environment models by an average of 30%. Practical implications for breeding rice for adaptation to alternate wetting and drying system are discussed. Copyright © 2018, G3: Genes, Genomes, Genetics.

New genomic resources for switchgrass: a BAC library and comparative analysis of homoeologous genomic regions harboring bioenergy traits

Directory of Open Access Journals (Sweden)

Feltus Frank A

2011-07-01

Full Text Available Abstract Background Switchgrass, a C4 species and a warm-season grass native to the prairies of North America, has been targeted for development into an herbaceous biomass fuel crop. Genetic improvement of switchgrass feedstock traits through marker-assisted breeding and biotechnology approaches calls for genomic tools development. Establishment of integrated physical and genetic maps for switchgrass will accelerate mapping of value added traits useful to breeding programs and to isolate important target genes using map based cloning. The reported polyploidy series in switchgrass ranges from diploid (2X = 18 to duodecaploid (12X = 108. Like in other large, repeat-rich plant genomes, this genomic complexity will hinder whole genome sequencing efforts. An extensive physical map providing enough information to resolve the homoeologous genomes would provide the necessary framework for accurate assembly of the switchgrass genome. Results A switchgrass BAC library constructed by partial digestion of nuclear DNA with EcoRI contains 147,456 clones covering the effective genome approximately 10 times based on a genome size of 3.2 Gigabases (~1.6 Gb effective. Restriction digestion and PFGE analysis of 234 randomly chosen BACs indicated that 95% of the clones contained inserts, ranging from 60 to 180 kb with an average of 120 kb. Comparative sequence analysis of two homoeologous genomic regions harboring orthologs of the rice OsBRI1 locus, a low-copy gene encoding a putative protein kinase and associated with biomass, revealed that orthologous clones from homoeologous chromosomes can be unambiguously distinguished from each other and correctly assembled to respective fingerprint contigs. Thus, the data obtained not only provide genomic resources for further analysis of switchgrass genome, but also improve efforts for an accurate genome sequencing strategy. Conclusions The construction of the first switchgrass BAC library and comparative analysis of

Considering genetic characteristics in German Holstein breeding programs.

Science.gov (United States)

Segelke, D; Täubert, H; Reinhardt, F; Thaller, G

2016-01-01

Recently, several research groups have demonstrated that several haplotypes may cause embryonic loss in the homozygous state. Up to now, carriers of genetic disorders were often excluded from mating, resulting in a decrease of genetic gain and a reduced number of sires available for the breeding program. Ongoing research is very likely to identify additional genetic defects causing embryonic loss and calf mortality by genotyping a large proportion of the female cattle population and sequencing key ancestors. Hence, a clear demand is present to develop a method combining selection against recessive defects (e.g., Holstein haplotypes HH1-HH5) with selection for economically beneficial traits (e.g., polled) for mating decisions. Our proposed method is a genetic index that accounts for the allele frequencies in the population and the economic value of the genetic characteristic without excluding carriers from breeding schemes. Fertility phenotypes from routine genetic evaluations were used to determine the economic value per embryo lost. Previous research has shown that embryo loss caused by HH1 and HH2 occurs later than the loss for HH3, HH4, and HH5. Therefore, an economic value of € 97 was used against HH1 and HH2 and € 70 against HH3, HH4, and HH5. For polled, € 7 per polled calf was considered. Minor allele frequencies of the defects ranged between 0.8 and 3.3%. The polled allele has a frequency of 4.1% in the German Holstein population. A genomic breeding program was simulated to study the effect of changing the selection criteria from assortative mating based on breeding values to selecting the females using the genetic index. Selection for a genetic index on the female path is a useful method to control the allele frequencies by reducing undesirable alleles and simultaneously increasing economical beneficial characteristics maintaining most of the genetic gain in production and functional traits. Additionally, we applied the genetic index to real data and

Genomic tools and and prospects for new breeding techniques in flower bulb crops

Science.gov (United States)

For many of the new breeding techniques, sequence information is of the utmost importance. In addition to current breeding techniques, such as marker-assisted selection (MAS) and genetic modification (GM), new breeding techniques such as zinc finger nucleases, oligonucleotide-mediated mutagenesis, R...

Potential of Genomic Selection in Mass Selection Breeding of an Allogamous Crop: An Empirical Study to Increase Yield of Common Buckwheat

Directory of Open Access Journals (Sweden)

Shiori Yabe

2018-03-01

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.

Genotyping by sequencing (GBS, an ultimate marker-assisted selection (MAS tool to accelerate plant breeding

Directory of Open Access Journals (Sweden)

Jiangfeng eHe

2014-09-01

Full Text Available Marker-assisted selection (MAS refers to the use of molecular markers to assist phenotypic selections in crop improvement. Several types of molecular markers, such as single nucleotide polymorphism (SNP, have been identified and effectively used in plant breeding. The application of next-generation sequencing (NGS technologies has led to remarkable advances in whole genome sequencing, which provides ultra-throughput sequences to revolutionize plant genotyping and breeding. To further broaden NGS usages to large crop genomes such as maize and wheat, genotyping by sequencing (GBS has been developed and applied in sequencing multiplexed samples that combine molecular marker discovery and genotyping. GBS is a novel application of NGS protocols for discovering and genotyping SNPs in crop genomes and populations. The GBS approach includes the digestion of genomic DNA with restriction enzymes followed by the ligation of barcode adapter, PCR amplification and sequencing of the amplified DNA pool on a single lane of flow cells. Bioinformatic pipelines are needed to analyze and interpret GBS datasets. As an ultimate MAS tool and a cost-effective technique, GBS has been successfully used in implementing genome-wide association study (GWAS, genomic diversity study, genetic linkage analysis, molecular marker discovery and genomic selection (GS under a large scale of plant breeding programs.

Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding.

Science.gov (United States)

He, Jiangfeng; Zhao, Xiaoqing; Laroche, André; Lu, Zhen-Xiang; Liu, HongKui; Li, Ziqin

2014-01-01

Marker-assisted selection (MAS) refers to the use of molecular markers to assist phenotypic selections in crop improvement. Several types of molecular markers, such as single nucleotide polymorphism (SNP), have been identified and effectively used in plant breeding. The application of next-generation sequencing (NGS) technologies has led to remarkable advances in whole genome sequencing, which provides ultra-throughput sequences to revolutionize plant genotyping and breeding. To further broaden NGS usages to large crop genomes such as maize and wheat, genotyping-by-sequencing (GBS) has been developed and applied in sequencing multiplexed samples that combine molecular marker discovery and genotyping. GBS is a novel application of NGS protocols for discovering and genotyping SNPs in crop genomes and populations. The GBS approach includes the digestion of genomic DNA with restriction enzymes followed by the ligation of barcode adapter, PCR amplification and sequencing of the amplified DNA pool on a single lane of flow cells. Bioinformatic pipelines are needed to analyze and interpret GBS datasets. As an ultimate MAS tool and a cost-effective technique, GBS has been successfully used in implementing genome-wide association study (GWAS), genomic diversity study, genetic linkage analysis, molecular marker discovery and genomic selection under a large scale of plant breeding programs.

Genomics technologies to study structural variations in the grapevine genome

Directory of Open Access Journals (Sweden)

Cardone Maria Francesca

2016-01-01

Full Text Available Grapevine is one of the most important crop plants in the world. Recently there was great expansion of genomics resources about grapevine genome, thus providing increasing efforts for molecular breeding. Current cultivars display a great level of inter-specific differentiation that needs to be investigated to reach a comprehensive understanding of the genetic basis of phenotypic differences, and to find responsible genes selected by cross breeding programs. While there have been significant advances in resolving the pattern and nature of single nucleotide polymorphisms (SNPs on plant genomes, few data are available on copy number variation (CNV. Furthermore association between structural variations and phenotypes has been described in only a few cases. We combined high throughput biotechnologies and bioinformatics tools, to reveal the first inter-varietal atlas of structural variation (SV for the grapevine genome. We sequenced and compared four table grape cultivars with the Pinot noir inbred line PN40024 genome as the reference. We detected roughly 8% of the grapevine genome affected by genomic variations. Taken into account phenotypic differences existing among the studied varieties we performed comparison of SVs among them and the reference and next we performed an in-depth analysis of gene content of polymorphic regions. This allowed us to identify genes showing differences in copy number as putative functional candidates for important traits in grapevine cultivation.

Effect on milk production of F1 crossbreds resulted from Alpine breed (♂ x Albanian local goat breed (♀

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Kristaq Kume

2012-07-01

Full Text Available About 950,000 goats, farmed mostly in hilly and mountainous areas of Albania, contribute about 8% of the country’s total milk production. In order to increase milk production, farmers are currently using crosses of the local goat breed with exotic breeds, mainly the Alpine breed from France. This study examines milk production data of first lactation from 45 goats of the local breed, 82 goats of the Alpine breed and 58 F1 crosses (♂Alpine breed x ♀local breed. The goats were kept on small-scale farms according to the traditional Albanian system. Milking was carried out in the morning and evening. Kids were weaned at 65 days of age after which milking started. Milk yield was recorded twice with a 15-day interval between the two readings. Total milk yield was calculated using the Fleischmann method. The F1 goats produced 37.8 kg more milk than local breed goats although the lactation length (P<0.05 of F1 goats was six days shorter compared to that of local breed goats (P<0.05. Analysis of variance showed a highly significant effect (P<0.01 of the genotype factor on milk production. The average Cappio-Borlino curves of three genotypes indicated that the lactation curves of local breed and F1 crosses were similar. Although the F1 cross goats had 50% of their genomes from a genetically improved breed they were still able to deal with the difficult conditions that characterize the traditional extensive farming systems in Albania. Breeding pure Alpine breed or its crosses with the local goat breed improved milk production in an extensive traditional system.

Improving Genetic Gain with Genomic Selection in Autotetraploid Potato

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Anthony T. Slater

2016-11-01

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.

New biotechnology enhances the application of cisgenesis in plant breeding

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Hongwei eHou

2014-08-01

Full Text Available Cisgenesis is genetic modification to transfer beneficial alleles from crossable species into a recipient plant. The donor genes transferred by cisgenesis are the same as those used in traditional breeding. It can avoid linkage drag, enhance the use of existing gene alleles. This approach combines traditional breeding techniques with modern biotechnology and dramatically speeds up the breeding process. This allows plant genomes to be modified while remaining plants within the gene pool. Therefore, cisgenic plants should not be assessed as transgenics for environmental impacts.

Novel Graphical Analyses of Runs of Homozygosity among Species and Livestock Breeds

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Laura Iacolina

2016-01-01

Full Text Available Runs of homozygosity (ROH, uninterrupted stretches of homozygous genotypes resulting from parents transmitting identical haplotypes to their offspring, have emerged as informative genome-wide estimates of autozygosity (inbreeding. We used genomic profiles based on 698 K single nucleotide polymorphisms (SNPs from nine breeds of domestic cattle (Bos taurus and the European bison (Bison bonasus to investigate how ROH distributions can be compared within and among species. We focused on two length classes: 0.5–15 Mb to investigate ancient events and >15 Mb to address recent events (approximately three generations. For each length class, we chose a few chromosomes with a high number of ROH, calculated the percentage of times a SNP appeared in a ROH, and plotted the results. We selected areas with distinct patterns including regions where (1 all groups revealed an increase or decrease of ROH, (2 bison differed from cattle, (3 one cattle breed or groups of breeds differed (e.g., dairy versus meat cattle. Examination of these regions in the cattle genome showed genes potentially important for natural and human-induced selection, concerning, for example, meat and milk quality, metabolism, growth, and immune function. The comparative methodology presented here permits visual identification of regions of interest for selection, breeding programs, and conservation.

Ricebase: a breeding and genetics platform for rice, integrating individual molecular markers, pedigrees and whole-genome-based data.

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Edwards, J D; Baldo, A M; Mueller, L A

2016-01-01

Ricebase (http://ricebase.org) is an integrative genomic database for rice (Oryza sativa) with an emphasis on combining datasets in a way that maintains the key links between past and current genetic studies. Ricebase includes DNA sequence data, gene annotations, nucleotide variation data and molecular marker fragment size data. Rice research has benefited from early adoption and extensive use of simple sequence repeat (SSR) markers; however, the majority of rice SSR markers were developed prior to the latest rice pseudomolecule assembly. Interpretation of new research using SNPs in the context of literature citing SSRs requires a common coordinate system. A new pipeline, using a stepwise relaxation of stringency, was used to map SSR primers onto the latest rice pseudomolecule assembly. The SSR markers and experimentally assayed amplicon sizes are presented in a relational database with a web-based front end, and are available as a track loaded in a genome browser with links connecting the browser and database. The combined capabilities of Ricebase link genetic markers, genome context, allele states across rice germplasm and potentially user curated phenotypic interpretations as a community resource for genetic discovery and breeding in rice. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the United States.

Assessment of adaptability of zebu cattle (Bos indicus) breeds in two different climatic conditions: using cytogenetic techniques on genome integrity.

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Kumar, Anil; Waiz, Syma Ashraf; Sridhar Goud, T; Tonk, R K; Grewal, Anita; Singh, S V; Yadav, B R; Upadhyay, R C

2016-06-01

The aim of this study was to evaluate the genome integrity so as to assess the adaptability of three breeds of indigenous cattle reared under arid and semi-arid regions of Rajasthan (Bikaner) and Haryana (Karnal) India. The cattle were of homogenous group (same age and sex) of indigenous breeds viz. Sahiwal, Tharparkar and Kankrej. A total of 100 animals were selected for this study from both climatic conditions. The sister chromatid exchanges (SCE's), chromosomal gaps and chromatid breaks were observed in metaphase plates of chromosome preparations obtained from in vitro culture of peripheral blood lymphocytes. The mean number of breaks and gaps in Sahiwal and Tharparkar of semi-arid zone were 8.56 ± 3.16, 6.4 ± 3.39 and 8.72 ± 2.04, 3.52 ± 6.29, respectively. Similarly, the mean number of breaks and gaps in Tharparkar and Kankrej cattle of arid zone were 5.26 ± 1.76, 2.74 ± 1.76 and 5.24 ± 1.84, 2.5 ± 1.26, respectively. The frequency of SCEs in chromosomes was found significantly higher (P  0.05) was observed in the same zone. The analysis of frequency of CAs and SCEs revealed significant effects of environmental conditions on the genome integrity of animals, thereby indicating an association with their adaptability.

Defining the breeding goal for a sheep breed including production and functional traits using market data.

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Theodoridis, A; Ragkos, A; Rose, G; Roustemis, D; Arsenos, G

2017-11-16

In this study, the economic values for production and functional traits of dairy sheep are estimated through the application of a profit function model using farm-level technical and economic data. The traits incorporated in the model were milk production, prolificacy, fertility, milking speed, longevity and mastitis occurrence. The economic values for these traits were derived as the approximate partial derivative of the specified profit function. A sensitivity analysis was also conducted in order to examine how potential changes in input and output prices would affect the breeding goal. The estimated economic values of the traits revealed their economic impact on the definition of the breeding goal for the specified production system. Milk production and fertility had the highest economic values (€40.30 and €20.28 per standard genetic deviation (SDa)), while, mastitis only had a low negative value of -0.57 €/SDa. Therefore, breeding for clinical mastitis will have a minor impact on farm profitability because it affects a small proportion of the flock and has low additive variance. The production traits, which include milk production, prolificacy and milking speed, contributed most to the breeding goal (70.0%), but functional traits still had a considerable share (30.0%). The results of this study highlight the importance of the knowledge of economic values of traits in the design of a breeding program. It is also suggested that the production and functional traits under consideration can be categorized as those which can be efficiently treated through genetic improvement (e.g. milk production and fertility) while others would be better dealt with through managerial interventions (e.g. mastitis occurrence). Also, sub-clinical mastitis that affects a higher proportion of flocks could have a higher contribution to breeding goals.

A note on mate allocation for dominance handling in genomic selection

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Toro Miguel A

2010-08-01

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.

Genomic prediction by single-step genomic BLUP using cow reference population in Holstein crossbred cattle in India

DEFF Research Database (Denmark)

Nayee, Nilesh Kumar; Su, Guosheng; Gajjar, Swapnil

2018-01-01

Advantages of genomic selection in breeds with limited numbers of progeny tested bulls have been demonstrated by adding genotypes of females to the reference population (Thomasen et al., 2014). The current study was conducted to explore the feasibility of implementing genomic selection in a Holst......Advantages of genomic selection in breeds with limited numbers of progeny tested bulls have been demonstrated by adding genotypes of females to the reference population (Thomasen et al., 2014). The current study was conducted to explore the feasibility of implementing genomic selection...... in a Holstein Friesian crossbred population with cows kept under small holder conditions using test day records and single step genomic BLUP (ssGBLUP). Milk yield records from 10,797 daughters sired by 258 bulls were used Of these 2194 daughters and 109 sires were genotyped with customized genotyping chip...

Application of genomics to forage crop breeding for quality traits

DEFF Research Database (Denmark)

Lübberstedt, Thomas

2007-01-01

Forage quality depends on the digestibility of fodder, and can be directly measured by the intake and metabolic conversion in animal trials. However, animal trials are time-consuming, laborious, and thus expensive. It is not possible to study thousands of plant genotypes, as required in breeding...... studied in detail and sequence motifs with likely effect on forage quality have been identified by association studies. Moreover, transgenic approaches substantiated the effect of several of these genes on forage quality. Perspectives and limitations of these findings for forage crop breeding...

Genome-wide population structure and admixture analysis reveals weak differentiation among Ugandan goat breeds.

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Onzima, R B; Upadhyay, M R; Mukiibi, R; Kanis, E; Groenen, M A M; Crooijmans, R P M A

2018-02-01

Uganda has a large population of goats, predominantly from indigenous breeds reared in diverse production systems, whose existence is threatened by crossbreeding with exotic Boer goats. Knowledge about the genetic characteristics and relationships among these Ugandan goat breeds and the potential admixture with Boer goats is still limited. Using a medium-density single nucleotide polymorphism (SNP) panel, we assessed the genetic diversity, population structure and admixture in six goat breeds in Uganda: Boer, Karamojong, Kigezi, Mubende, Small East African and Sebei. All the animals had genotypes for about 46 105 SNPs after quality control. We found high proportions of polymorphic SNPs ranging from 0.885 (Kigezi) to 0.928 (Sebei). The overall mean observed (H O ) and expected (H E ) heterozygosity across breeds was 0.355 ± 0.147 and 0.384 ± 0.143 respectively. Principal components, genetic distances and admixture analyses revealed weak population sub-structuring among the breeds. Principal components separated Kigezi and weakly Small East African from other indigenous goats. Sebei and Karamojong were tightly entangled together, whereas Mubende occupied a more central position with high admixture from all other local breeds. The Boer breed showed a unique cluster from the Ugandan indigenous goat breeds. The results reflect common ancestry but also some level of geographical differentiation. admixture and f 4 statistics revealed gene flow from Boer and varying levels of genetic admixture among the breeds. Generally, moderate to high levels of genetic variability were observed. Our findings provide useful insights into maintaining genetic diversity and designing appropriate breeding programs to exploit within-breed diversity and heterozygote advantage in crossbreeding schemes. © 2018 The Authors. Animal Genetics published by John Wiley & Sons Ltd on behalf of Stichting International Foundation for Animal Genetics.

RESEARCH ON A SIMPLIFIED MIXED MODEL VERSUS CONTEMPORARY COMPARISON USED IN BREEDING VALUE ESTIMATION AND BULLS CLASSIFICATION FOR MILK PRODUCTION CHARACTERS

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Agatha POPESCU

2014-10-01

Full Text Available The paper goal was to set up a simplified BLUP model in order to estimate the bulls' breeding value for milk production characters and establish their hierarchy, Also, it aimed to compare the bulls' hierarchy set up by means of the simplified BLUP model with their hierarchy established by using the traditional contemporary comparison method. In this purpose, a number of 51 Romanian Friesian bulls were used for evaluating their breeding value for milk production characters: milk yield, fat percentage and fat yield during the 305 days of the 1st lactation of a number of 1,989 daughters in various dairy herds. The simplified BLUP model set up in this research work has demonstrated its high precision of breeding value, which varied between 55 and 92, and more than this it proved that in some cases, the position occupied by bulls could be similar with the one registered by using the contemporary comparison. The higher precision assured by the simplified BLUP model is the guarantee that the bulls' hierarchy in catalogues is a correct one. In this way, farmers could chose the best bulls for improving milk yield in their dairy herds.

A genome-wide association study to detect QTL for commercially important traits in Swiss Large White boars.

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Doreen Becker

Full Text Available The improvement of meat quality and production traits has high priority in the pork industry. Many of these traits show a low to moderate heritability and are difficult and expensive to measure. Their improvement by targeted breeding programs is challenging and requires knowledge of the genetic and molecular background. For this study we genotyped 192 artificial insemination boars of a commercial line derived from the Swiss Large White breed using the PorcineSNP60 BeadChip with 62,163 evenly spaced SNPs across the pig genome. We obtained 26 estimated breeding values (EBVs for various traits including exterior, meat quality, reproduction, and production. The subsequent genome-wide association analysis allowed us to identify four QTL with suggestive significance for three of these traits (p-values ranging from 4.99×10⁻⁶ to 2.73×10⁻⁵. Single QTL for the EBVs pH one hour post mortem (pH1 and carcass length were on pig chromosome (SSC 14 and SSC 2, respectively. Two QTL for the EBV rear view hind legs were on SSC 10 and SSC 16.

The effects of recent changes in breeding preferences on maintaining traditional Dutch chicken genomic diversity

NARCIS (Netherlands)

Bortoluzzi, Chiara; Crooijmans, Richard P.M.A.; Bosse, Mirte; Hiemstra, Sipke Joost; Groenen, Martien A.M.; Megens, Hendrik Jan

2018-01-01

Traditional Dutch chicken breeds are marginalised breeds of ornamental and cultural-historical importance. In the last decades, miniaturising of existing breeds (so called neo-bantam) has become popular and resulted in alternatives to original large breeds. However, while backcrossing is increasing

Impact of the allium genomes on plant breeding

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An understanding of the structures and characteristics of the chloroplast, mitochondrial, and nuclear genomes have played significant roles in the genetic improvement of Allium crops. In this chapter I reflect upon the practical use of this genomic information for genetic improvement of the Alliums....

Bee Queen Breeding Methods - Review

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Silvia Patruica

2016-05-01

Full Text Available The biological potential of a bee family is mainly generated by the biological value of the queen. Whether we grow queens widely or just for our own apiaries, we must consider the acquisition of high-quality biological material, and also the creation of optimal feeding and caring conditions, in order to obtain high genetic value queens. Queen breeding technology starts with the setting of hoeing families, nurse families, drone-breeding families – necessary for the pairing of young queens, and also of the families which will provide the bees used to populate the nuclei where the next queens will hatch. The complex of requirements for the breeding of good, high-production queens is sometimes hard to met, under the application of artificial methods. The selection of breeding method must rely on all these requirements and on the beekeeper’s level of training.

Genomic selection: genome-wide prediction in plant improvement.

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Desta, Zeratsion Abera; Ortiz, Rodomiro

2014-09-01

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.

From plant genomes to phenotypes

OpenAIRE

Bolger, Marie; Gundlach, Heidrun; Scholz, Uwe; Mayer, Klaus; Usadel, Björn; Schwacke, Rainer; Schmutzer, Thomas; Chen, Jinbo; Arend, Daniel; Oppermann, Markus; Weise, Stephan; Lange, Matthias; Fiorani, Fabio; Spannagl, Manuel

2017-01-01

Recent advances in sequencing technologies have greatly accelerated the rate of plant genome and applied breeding research. Despite this advancing trend, plant genomes continue to present numerous difficulties to the standard tools and pipelines not only for genome assembly but also gene annotation and downstream analysis.Here we give a perspective on tools, resources and services necessary to assemble and analyze plant genomes and link them to plant phenotypes.

Genomic prediction using different estimation methodology, blending and cross-validation techniques for growth traits and visual scores in Hereford and Braford cattle.

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Campos, G S; Reimann, F A; Cardoso, L L; Ferreira, C E R; Junqueira, V S; Schmidt, P I; Braccini Neto, J; Yokoo, M J I; Sollero, B P; Boligon, A A; Cardoso, F F

2018-05-07

The objective of the present study was to evaluate the accuracy and bias of direct and blended genomic predictions using different methods and cross-validation techniques for growth traits (weight and weight gains) and visual scores (conformation, precocity, muscling and size) obtained at weaning and at yearling in Hereford and Braford breeds. Phenotypic data contained 126,290 animals belonging to the Delta G Connection genetic improvement program, and a set of 3,545 animals genotyped with the 50K chip and 131 sires with the 777K. After quality control, 41,045 markers remained for all animals. An animal model was used to estimate (co)variances components and to predict breeding values, which were later used to calculate the deregressed estimated breeding values (DEBV). Animals with genotype and phenotype for the traits studied were divided into four or five groups by random and k-means clustering cross-validation strategies. The values of accuracy of the direct genomic values (DGV) were moderate to high magnitude for at weaning and at yearling traits, ranging from 0.19 to 0.45 for the k-means and 0.23 to 0.78 for random clustering among all traits. The greatest gain in relation to the pedigree BLUP (PBLUP) was 9.5% with the BayesB method with both the k-means and the random clustering. Blended genomic value accuracies ranged from 0.19 to 0.56 for k-means and from 0.21 to 0.82 for random clustering. The analyzes using the historical pedigree and phenotypes contributed additional information to calculate the GEBV and in general, the largest gains were for the single-step (ssGBLUP) method in bivariate analyses with a mean increase of 43.00% among all traits measured at weaning and of 46.27% for those evaluated at yearling. The accuracy values for the marker effects estimation methods were lower for k-means clustering, indicating that the training set relationship to the selection candidates is a major factor affecting accuracy of genomic predictions. The gains in

Chromosome-scale scaffolding of the black raspberry (Rubus occidentalis L.) genome based on chromatin interaction data

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Black raspberry (Rubus occidentalis L.) is a niche fruit crop valued for its flavour and potential health benefits. The improvement of fruit and cane characteristics via molecular breeding technologies has been hindered by the lack of a high-quality reference genome. The recently released draft geno...

Quantitative trait loci markers derived from whole genome sequence data increases the reliability of genomic prediction

DEFF Research Database (Denmark)

Brøndum, Rasmus Froberg; Su, Guosheng; Janss, Luc

2015-01-01

This study investigated the effect on the reliability of genomic prediction when a small number of significant variants from single marker analysis based on whole genome sequence data were added to the regular 54k single nucleotide polymorphism (SNP) array data. The extra markers were selected...... with the aim of augmenting the custom low-density Illumina BovineLD SNP chip (San Diego, CA) used in the Nordic countries. The single-marker analysis was done breed-wise on all 16 index traits included in the breeding goals for Nordic Holstein, Danish Jersey, and Nordic Red cattle plus the total merit index...... itself. Depending on the trait’s economic weight, 15, 10, or 5 quantitative trait loci (QTL) were selected per trait per breed and 3 to 5 markers were selected to tag each QTL. After removing duplicate markers (same marker selected for more than one trait or breed) and filtering for high pairwise linkage...

Root phenotyping: from component trait in the lab to breeding.

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Kuijken, René C P; van Eeuwijk, Fred A; Marcelis, Leo F M; Bouwmeester, Harro J

2015-09-01

In the last decade cheaper and faster sequencing methods have resulted in an enormous increase in genomic data. High throughput genotyping, genotyping by sequencing and genomic breeding are becoming a standard in plant breeding. As a result, the collection of phenotypic data is increasingly becoming a limiting factor in plant breeding. Genetic studies on root traits are being hampered by the complexity of these traits and the inaccessibility of the rhizosphere. With an increasing interest in phenotyping, breeders and scientists try to overcome these limitations, resulting in impressive developments in automated phenotyping platforms. Recently, many such platforms have been thoroughly described, yet their efficiency to increase genetic gain often remains undiscussed. This efficiency depends on the heritability of the phenotyped traits as well as the correlation of these traits with agronomically relevant breeding targets. This review provides an overview of the latest developments in root phenotyping and describes the environmental and genetic factors influencing root phenotype and heritability. It also intends to give direction to future phenotyping and breeding strategies for optimizing root system functioning. A quantitative framework to determine the efficiency of phenotyping platforms for genetic gain is described. By increasing heritability, managing effects caused by interactions between genotype and environment and by quantifying the genetic relation between traits phenotyped in platforms and ultimate breeding targets, phenotyping platforms can be utilized to their maximum potential. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Development of genomic prediction in sorghum

NARCIS (Netherlands)

Hunt, Colleen H.; Eeuwijk, van Fred A.; Mace, Emma S.; Hayes, Ben J.; Jordan, David R.

2018-01-01

Genomic selection can increase the rate of genetic gain in plant breeding programs by shortening the breeding cycle. Gain can also be increased through higher selection intensities, as the size of the population available for selection can be increased by predicting performance of nonphenotyped, but

The evolutionary value of recombination is constrained by genome modularity.

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Darren P Martin

2005-10-01

Full Text Available Genetic recombination is a fundamental evolutionary mechanism promoting biological adaptation. Using engineered recombinants of the small single-stranded DNA plant virus, Maize streak virus (MSV, we experimentally demonstrate that fragments of genetic material only function optimally if they reside within genomes similar to those in which they evolved. The degree of similarity necessary for optimal functionality is correlated with the complexity of intragenomic interaction networks within which genome fragments must function. There is a striking correlation between our experimental results and the types of MSV recombinants that are detectable in nature, indicating that obligatory maintenance of intragenome interaction networks strongly constrains the evolutionary value of recombination for this virus and probably for genomes in general.

Genetic parameter and breeding value estimation of donkeys' problem-focused coping styles.

Science.gov (United States)

Navas González, Francisco Javier; Jordana Vidal, Jordi; León Jurado, José Manuel; Arando Arbulu, Ander; McLean, Amy Katherine; Delgado Bermejo, Juan Vicente

2018-05-12

Donkeys are recognized therapy or leisure-riding animals. Anecdotal evidence has suggested that more reactive donkeys or those more easily engaging flight mechanisms tend to be easier to train compared to those displaying the natural donkey behaviour of fight. This context brings together the need to quantify such traits and to genetically select donkeys displaying a neutral reaction during training, because of its implication with handler/rider safety and trainability. We analysed the scores for coping style traits from 300 Andalusian donkeys from 2013 to 2015. Three scales were applied to describe donkeys' response to 12 stimuli. Genetic parameters were estimated using multivariate models with year, sex, husbandry system and stimulus as fixed effects and age as a linear and quadratic covariable. Heritabilities were moderate, 0.18 ± 0.020 to 0.21 ± 0.021. Phenotypic correlations between intensity and mood/emotion or response type were negative and moderate (-0.21 and -0.25, respectively). Genetic correlations between the same variables were negative and moderately high (-0.46 and -0.53, respectively). Phenotypic and genetic correlations between mood/emotion and response type were positive and high (0.92 and 0.95, respectively). Breeding values enable selection methods that could lead to endangered breed preservation and genetically selecting donkeys for the uses that they may be most suitable. Copyright © 2018 Elsevier B.V. All rights reserved.

Genomic prediction in families of perennial ryegrass based on genotyping-by-sequencing

DEFF Research Database (Denmark)

Ashraf, Bilal

In this thesis we investigate the potential for genomic prediction in perennial ryegrass using genotyping-by-sequencing (GBS) data. Association method based on family-based breeding systems was developed, genomic heritabilities, genomic prediction accurancies and effects of some key factors wer...... explored. Results show that low sequencing depth caused underestimation of allele substitution effects in GWAS and overestimation of genomic heritability in prediction studies. Other factors susch as SNP marker density, population structure and size of training population influenced accuracy of genomic...... prediction. Overall, GBS allows for genomic prediction in breeding families of perennial ryegrass and holds good potential to expedite genetic gain and encourage the application of genomic prediction...

The bald and the beautiful: hairlessness in domestic dog breeds

Science.gov (United States)

Harris, Alexander; Dreger, Dayna L.; Davis, Brian W.; Ostrander, Elaine A.

2017-01-01

An extraordinary amount of genomic variation is contained within the chromosomes of domestic dogs, manifesting as dramatic differences in morphology, behaviour and disease susceptibility. Morphology, in particular, has been a topic of enormous interest as biologists struggle to understand the small window of dog domestication from wolves, and the division of dogs into pure breeding, closed populations termed breeds. Many traits related to morphology, including body size, leg length and skull shape, have been under selection as part of the standard descriptions for the nearly 400 breeds recognized worldwide. Just as important, however, are the minor traits that have undergone selection by fanciers and breeders to define dogs of a particular appearance, such as tail length, ear position, back arch and variation in fur (pelage) growth patterns. In this paper, we both review and present new data for traits associated with pelage including fur length, curl, growth, shedding and even the presence or absence of fur. Finally, we report the discovery of a new gene associated with the absence of coat in the American Hairless Terrier breed. This article is part of the themed issue ‘Evo-devo in the genomics era, and the origins of morphological diversity’. PMID:27994129

Biotechnology in maize breeding

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Mladenović-Drinić Snežana

2004-01-01

Full Text Available Maize is one of the most important economic crops and the best studied and most tractable genetic system among monocots. The development of biotechnology has led to a great increase in our knowledge of maize genetics and understanding of the structure and behaviour of maize genomes. Conventional breeding practices can now be complemented by a number of new and powerful techniques. Some of these often referred to as molecular methods, enable scientists to see the layout of the entire genome of any organism and to select plants with preferred characteristics by "reading" at the molecular level, saving precious time and resources. DNA markers have provided valuable tools in various analyses ranging from phylogenetic analysis to the positional cloning of genes. Application of molecular markers for genetic studies of maize include: assessment of genetic variability and characterization of germ plasm, identification and fingerprinting of genotypes, estimation of genetic distance, detection of monogamic and quantitative trait loci, marker assisted selection, identification of sequence of useful candidate genes, etc. The development of high-density molecular maps which has been facilitated by PCR-based markers, have made the mapping and tagging of almost any trait possible and serve as bases for marker assisted selection. Sequencing of maize genomes would help to elucidate gene function, gene regulation and their expression. Modern biotechnology also includes an array of tools for introducing or deieting a particular gene or genes to produce plants with novel traits. Development of informatics and biotechnology are resulted in bioinformatic as well as in expansion of microarrey technique. Modern biotechnologies could complement and improve the efficiency of traditional selection and breeding techniques to enhance agricultural productivity.

Genomic prediction of reproduction traits for Merino sheep.

Science.gov (United States)

Bolormaa, S; Brown, D J; Swan, A A; van der Werf, J H J; Hayes, B J; Daetwyler, H D

2017-06-01

Economically important reproduction traits in sheep, such as number of lambs weaned and litter size, are expressed only in females and later in life after most selection decisions are made, which makes them ideal candidates for genomic selection. Accurate genomic predictions would lead to greater genetic gain for these traits by enabling accurate selection of young rams with high genetic merit. The aim of this study was to design and evaluate the accuracy of a genomic prediction method for female reproduction in sheep using daughter trait deviations (DTD) for sires and ewe phenotypes (when individual ewes were genotyped) for three reproduction traits: number of lambs born (NLB), litter size (LSIZE) and number of lambs weaned. Genomic best linear unbiased prediction (GBLUP), BayesR and pedigree BLUP analyses of the three reproduction traits measured on 5340 sheep (4503 ewes and 837 sires) with real and imputed genotypes for 510 174 SNPs were performed. The prediction of breeding values using both sire and ewe trait records was validated in Merino sheep. Prediction accuracy was evaluated by across sire family and random cross-validations. Accuracies of genomic estimated breeding values (GEBVs) were assessed as the mean Pearson correlation adjusted by the accuracy of the input phenotypes. The addition of sire DTD into the prediction analysis resulted in higher accuracies compared with using only ewe records in genomic predictions or pedigree BLUP. Using GBLUP, the average accuracy based on the combined records (ewes and sire DTD) was 0.43 across traits, but the accuracies varied by trait and type of cross-validations. The accuracies of GEBVs from random cross-validations (range 0.17-0.61) were higher than were those from sire family cross-validations (range 0.00-0.51). The GEBV accuracies of 0.41-0.54 for NLB and LSIZE based on the combined records were amongst the highest in the study. Although BayesR was not significantly different from GBLUP in prediction accuracy

MHC variability in heritage breeds of chickens.

Science.gov (United States)

Fulton, J E; Lund, A R; McCarron, A M; Pinegar, K N; Korver, D R; Classen, H L; Aggrey, S; Utterbach, C; Anthony, N B; Berres, M E

2016-02-01

The chicken Major Histocompatibility Complex (MHC) is very strongly associated with disease resistance and thus is a very important region of the chicken genome. Historically, MHC (B locus) has been identified by the use of serology with haplotype specific alloantisera. These antisera can be difficult to produce and frequently cross-react with multiple haplotypes and hence their application is generally limited to inbred and MHC-defined lines. As a consequence, very little information about MHC variability in heritage chicken breeds is available. DNA-based methods are now available for examining MHC variability in these previously uncharacterized populations. A high density SNP panel consisting of 101 SNP that span a 230,000 bp region of the chicken MHC was used to examine MHC variability in 17 heritage populations of chickens from five universities from Canada and the United States. The breeds included 6 heritage broiler lines, 3 Barred Plymouth Rock, 2 New Hampshire and one each of Rhode Island Red, Light Sussex, White Leghorn, Dark Brown Leghorn, and 2 synthetic lines. These heritage breeds contained from one to 11 haplotypes per line. A total of 52 unique MHC haplotypes were found with only 10 of them identical to serologically defined haplotypes. Furthermore, nine MHC recombinants with their respective parental haplotypes were identified. This survey confirms the value of these non-commercially utilized lines in maintaining genetic diversity. The identification of multiple MHC haplotypes and novel MHC recombinants indicates that diversity is being generated and maintained within these heritage populations. © 2016 Poultry Science Association Inc.