Landscape genomic prediction for restoration of a Eucalyptus foundation species under climate change.

Science.gov (United States)

Supple, Megan Ann; Bragg, Jason G; Broadhurst, Linda M; Nicotra, Adrienne B; Byrne, Margaret; Andrew, Rose L; Widdup, Abigail; Aitken, Nicola C; Borevitz, Justin O

2018-04-24

As species face rapid environmental change, we can build resilient populations through restoration projects that incorporate predicted future climates into seed sourcing decisions. Eucalyptus melliodora is a foundation species of a critically endangered community in Australia that is a target for restoration. We examined genomic and phenotypic variation to make empirical based recommendations for seed sourcing. We examined isolation by distance and isolation by environment, determining high levels of gene flow extending for 500 km and correlations with climate and soil variables. Growth experiments revealed extensive phenotypic variation both within and among sampling sites, but no site-specific differentiation in phenotypic plasticity. Model predictions suggest that seed can be sourced broadly across the landscape, providing ample diversity for adaptation to environmental change. Application of our landscape genomic model to E. melliodora restoration projects can identify genomic variation suitable for predicted future climates, thereby increasing the long term probability of successful restoration. © 2018, Supple et al.

Genomics and the human genome project: implications for psychiatry

OpenAIRE

Kelsoe, J R

2004-01-01

In the past decade the Human Genome Project has made extraordinary strides in understanding of fundamental human genetics. The complete human genetic sequence has been determined, and the chromosomal location of almost all human genes identified. Presently, a large international consortium, the HapMap Project, is working to identify a large portion of genetic variation in different human populations and the structure and relationship of these variants to each other. The Human Genome Project h...

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...

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.

The Pediatric Cancer Genome Project

Science.gov (United States)

Downing, James R; Wilson, Richard K; Zhang, Jinghui; Mardis, Elaine R; Pui, Ching-Hon; Ding, Li; Ley, Timothy J; Evans, William E

2013-01-01

The St. Jude Children’s Research Hospital–Washington University Pediatric Cancer Genome Project (PCGP) is participating in the international effort to identify somatic mutations that drive cancer. These cancer genome sequencing efforts will not only yield an unparalleled view of the altered signaling pathways in cancer but should also identify new targets against which novel therapeutics can be developed. Although these projects are still deep in the phase of generating primary DNA sequence data, important results are emerging and valuable community resources are being generated that should catalyze future cancer research. We describe here the rationale for conducting the PCGP, present some of the early results of this project and discuss the major lessons learned and how these will affect the application of genomic sequencing in the clinic. PMID:22641210

Genomic Signal Processing: Predicting Basic Molecular Biological Principles

Science.gov (United States)

Alter, Orly

2005-03-01

Advances in high-throughput technologies enable acquisition of different types of molecular biological data, monitoring the flow of biological information as DNA is transcribed to RNA, and RNA is translated to proteins, on a genomic scale. Future discovery in biology and medicine will come from the mathematical modeling of these data, which hold the key to fundamental understanding of life on the molecular level, as well as answers to questions regarding diagnosis, treatment and drug development. Recently we described data-driven models for genome-scale molecular biological data, which use singular value decomposition (SVD) and the comparative generalized SVD (GSVD). Now we describe an integrative data-driven model, which uses pseudoinverse projection (1). We also demonstrate the predictive power of these matrix algebra models (2). The integrative pseudoinverse projection model formulates any number of genome-scale molecular biological data sets in terms of one chosen set of data samples, or of profiles extracted mathematically from data samples, designated the ``basis'' set. The mathematical variables of this integrative model, the pseudoinverse correlation patterns that are uncovered in the data, represent independent processes and corresponding cellular states (such as observed genome-wide effects of known regulators or transcription factors, the biological components of the cellular machinery that generate the genomic signals, and measured samples in which these regulators or transcription factors are over- or underactive). Reconstruction of the data in the basis simulates experimental observation of only the cellular states manifest in the data that correspond to those of the basis. Classification of the data samples according to their reconstruction in the basis, rather than their overall measured profiles, maps the cellular states of the data onto those of the basis, and gives a global picture of the correlations and possibly also causal coordination of

A probabilistic model to predict clinical phenotypic traits from genome sequencing.

Science.gov (United States)

Chen, Yun-Ching; Douville, Christopher; Wang, Cheng; Niknafs, Noushin; Yeo, Grace; Beleva-Guthrie, Violeta; Carter, Hannah; Stenson, Peter D; Cooper, David N; Li, Biao; Mooney, Sean; Karchin, Rachel

2014-09-01

Genetic screening is becoming possible on an unprecedented scale. However, its utility remains controversial. Although most variant genotypes cannot be easily interpreted, many individuals nevertheless attempt to interpret their genetic information. Initiatives such as the Personal Genome Project (PGP) and Illumina's Understand Your Genome are sequencing thousands of adults, collecting phenotypic information and developing computational pipelines to identify the most important variant genotypes harbored by each individual. These pipelines consider database and allele frequency annotations and bioinformatics classifications. We propose that the next step will be to integrate these different sources of information to estimate the probability that a given individual has specific phenotypes of clinical interest. To this end, we have designed a Bayesian probabilistic model to predict the probability of dichotomous phenotypes. When applied to a cohort from PGP, predictions of Gilbert syndrome, Graves' disease, non-Hodgkin lymphoma, and various blood groups were accurate, as individuals manifesting the phenotype in question exhibited the highest, or among the highest, predicted probabilities. Thirty-eight PGP phenotypes (26%) were predicted with area-under-the-ROC curve (AUC)>0.7, and 23 (15.8%) of these were statistically significant, based on permutation tests. Moreover, in a Critical Assessment of Genome Interpretation (CAGI) blinded prediction experiment, the models were used to match 77 PGP genomes to phenotypic profiles, generating the most accurate prediction of 16 submissions, according to an independent assessor. Although the models are currently insufficiently accurate for diagnostic utility, we expect their performance to improve with growth of publicly available genomics data and model refinement by domain experts.

The RadGenomics project. Prediction for radio-susceptibility of individuals with genetic predisposition

International Nuclear Information System (INIS)

Imai, Takashi

2003-01-01

The ultimate goal of our project, named RadGenomics, is to elucidate the heterogeneity of the response to ionizing radiation arising from genetic variation among individuals, for the purpose of developing personalized radiation therapy regimens for cancer patients. Cancer patients exhibit patient-to-patient variability in normal tissue reactions after radiotherapy. Several observations support the hypothesis that the radiosensitivity of normal tissue is influenced by genetic factors. The rapid progression of human genome sequencing and the recent development of new technologies in molecular biology are providing new opportunities for elucidating the genetic basis of individual differences in susceptibility to radiation exposure. The development of a sufficiently robust, predictive assay enabling individual dose adjustment would improve the outcome of radiation therapy in patients. Our strategy for identification of DNA polymorphisms that contribute to the individual radiosensitivity is as follows. First, we have been categorizing DNA samples obtained from cancer patients, who have been kindly introduced to us through many collaborators, according to their clinical characteristics including the method and effect of treatment and side effects as scored by toxicity criteria, and also the result of an in vitro radiosensitivity assay, e.g., the micronuclei assay of their lymphocytes. Second, we have identified candidate genes for genotyping mainly by using our custom-designed oligonucleotide array with RNA samples, in which the probes were obtained from more than 40 cancer and 3 fibroblast cell lines whose radiosensitivity level was quite heterogeneous. We have also been studying the modification of proteins after irradiation of cells which may be caused by mainly phosphorylation or dephosphorylation, using mass spectrometry. Genes encoding the modified proteins and/or other proteins with which they interact such as specific protein kinases and phosphatases are also

A decade of human genome project conclusion: Scientific diffusion about our genome knowledge.

Science.gov (United States)

Moraes, Fernanda; Góes, Andréa

2016-05-06

The Human Genome Project (HGP) was initiated in 1990 and completed in 2003. It aimed to sequence the whole human genome. Although it represented an advance in understanding the human genome and its complexity, many questions remained unanswered. Other projects were launched in order to unravel the mysteries of our genome, including the ENCyclopedia of DNA Elements (ENCODE). This review aims to analyze the evolution of scientific knowledge related to both the HGP and ENCODE projects. Data were retrieved from scientific articles published in 1990-2014, a period comprising the development and the 10 years following the HGP completion. The fact that only 20,000 genes are protein and RNA-coding is one of the most striking HGP results. A new concept about the organization of genome arose. The ENCODE project was initiated in 2003 and targeted to map the functional elements of the human genome. This project revealed that the human genome is pervasively transcribed. Therefore, it was determined that a large part of the non-protein coding regions are functional. Finally, a more sophisticated view of chromatin structure emerged. The mechanistic functioning of the genome has been redrafted, revealing a much more complex picture. Besides, a gene-centric conception of the organism has to be reviewed. A number of criticisms have emerged against the ENCODE project approaches, raising the question of whether non-conserved but biochemically active regions are truly functional. Thus, HGP and ENCODE projects accomplished a great map of the human genome, but the data generated still requires further in depth analysis. © 2016 by The International Union of Biochemistry and Molecular Biology, 44:215-223, 2016. © 2016 The International Union of Biochemistry and Molecular Biology.

The 1000 Genomes Project: new opportunities for research and social challenges

Science.gov (United States)

2010-01-01

The 1000 Genomes Project, an international collaboration, is sequencing the whole genome of approximately 2,000 individuals from different worldwide populations. The central goal of this project is to describe most of the genetic variation that occurs at a population frequency greater than 1%. The results of this project will allow scientists to identify genetic variation at an unprecedented degree of resolution and will also help improve the imputation methods for determining unobserved genetic variants that are not represented on current genotyping arrays. By identifying novel or rare functional genetic variants, researchers will be able to pinpoint disease-causing genes in genomic regions initially identified by association studies. This level of detailed sequence information will also improve our knowledge of the evolutionary processes and the genomic patterns that have shaped the human species as we know it today. The new data will also lay the foundation for future clinical applications, such as prediction of disease susceptibility and drug response. However, the forthcoming availability of whole genome sequences at affordable prices will raise ethical concerns and pose potential threats to individual privacy. Nevertheless, we believe that these potential risks are outweighed by the benefits in terms of diagnosis and research, so long as rigorous safeguards are kept in place through legislation that prevents discrimination on the basis of the results of genetic testing. PMID:20193048

GAPIT: genome association and prediction integrated tool.

Science.gov (United States)

Lipka, Alexander E; Tian, Feng; Wang, Qishan; Peiffer, Jason; Li, Meng; Bradbury, Peter J; Gore, Michael A; Buckler, Edward S; Zhang, Zhiwu

2012-09-15

Software programs that conduct genome-wide association studies and genomic prediction and selection need to use methodologies that maximize statistical power, provide high prediction accuracy and run in a computationally efficient manner. We developed an R package called Genome Association and Prediction Integrated Tool (GAPIT) that implements advanced statistical methods including the compressed mixed linear model (CMLM) and CMLM-based genomic prediction and selection. The GAPIT package can handle large datasets in excess of 10 000 individuals and 1 million single-nucleotide polymorphisms with minimal computational time, while providing user-friendly access and concise tables and graphs to interpret results. http://www.maizegenetics.net/GAPIT. zhiwu.zhang@cornell.edu Supplementary data are available at Bioinformatics online.

Predictive genomics: A cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data

OpenAIRE

Wang, Edwin; Zaman, Naif; Mcgee, Shauna; Milanese, Jean-Sébastien; Masoudi-Nejad, Ali; O'Connor, Maureen

2014-01-01

We discuss a cancer hallmark network framework for modelling genome-sequencing data to predict cancer clonal evolution and associated clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for a cancer patient, as well as cancer risks for a healthy individual are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial i...

Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.

Science.gov (United States)

Li, Yifeng; Shi, Wenqiang; Wasserman, Wyeth W

2018-05-31

In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. The developments of high-throughput sequencing and machine learning technologies make it possible to predict cis-regulatory regions genome wide. Based on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we introduce DECRES based on supervised deep learning approaches for the identification of enhancer and promoter regions in the human genome. Due to their ability to discover patterns in large and complex data, the introduction of deep learning methods enables a significant advance in our knowledge of the genomic locations of cis-regulatory regions. Using models for well-characterized cell lines, we identify key experimental features that contribute to the predictive performance. Applying DECRES, we delineate locations of 300,000 candidate enhancers genome wide (6.8% of the genome, of which 40,000 are supported by bidirectional transcription data), and 26,000 candidate promoters (0.6% of the genome). The predicted annotations of cis-regulatory regions will provide broad utility for genome interpretation from functional genomics to clinical applications. The DECRES model demonstrates potentials of deep learning technologies when combined with high-throughput sequencing data, and inspires the development of other advanced neural network models for further improvement of genome annotations.

Skate Genome Project: Cyber-Enabled Bioinformatics Collaboration

Science.gov (United States)

Vincent, J.

2011-01-01

The Skate Genome Project, a pilot project of the North East Cyber infrastructure Consortium, aims to produce a draft genome sequence of Leucoraja erinacea, the Little Skate. The pilot project was designed to also develop expertise in large scale collaborations across the NECC region. An overview of the bioinformatics and infrastructure challenges faced during the first year of the project will be presented. Results to date and lessons learned from the perspective of a bioinformatics core will be highlighted.

Origins of the Human Genome Project

Science.gov (United States)

Cook-Deegan, Robert (Affiliation: Institute of Medicine, National Academy of Sciences)

1993-07-01

The human genome project was borne of technology, grew into a science bureaucracy in the United States and throughout the world, and is now being transformed into a hybrid academic and commercial enterprise. The next phase of the project promises to veer more sharply toward commercial application, harnessing both the technical prowess of molecular biology and the rapidly growing body of knowledge about DNA structure to the pursuit of practical benefits. Faith that the systematic analysis of DNA structure will prove to be a powerful research tool underlies the rationale behind the genome project. The notion that most genetic information is embedded in the sequence of CNA base pairs comprising chromosomes is a central tenet. A rough analogy is to liken an organism's genetic code to computer code. The coal of the genome project, in this parlance, is to identify and catalog 75,000 or more files (genes) in the software that directs construction of a self-modifying and self-replicating system -- a living organism.

Origins of the Human Genome Project

Energy Technology Data Exchange (ETDEWEB)

Cook-Deegan, Robert

1993-07-01

The human genome project was borne of technology, grew into a science bureaucracy in the US and throughout the world, and is now being transformed into a hybrid academic and commercial enterprise. The next phase of the project promises to veer more sharply toward commercial application, harnessing both the technical prowess of molecular biology and the rapidly growing body of knowledge about DNA structure to the pursuit of practical benefits. Faith that the systematic analysis of DNA structure will prove to be a powerful research tool underlies the rationale behind the genome project. The notion that most genetic information is embedded in the sequence of CNA base pairs comprising chromosomes is a central tenet. A rough analogy is to liken an organism's genetic code to computer code. The coal of the genome project, in this parlance, is to identify and catalog 75,000 or more files (genes) in the software that directs construction of a self-modifying and self-replicating system -- a living organism.

MAKER2: an annotation pipeline and genome-database management tool for second-generation genome projects.

Science.gov (United States)

Holt, Carson; Yandell, Mark

2011-12-22

Second-generation sequencing technologies are precipitating major shifts with regards to what kinds of genomes are being sequenced and how they are annotated. While the first generation of genome projects focused on well-studied model organisms, many of today's projects involve exotic organisms whose genomes are largely terra incognita. This complicates their annotation, because unlike first-generation projects, there are no pre-existing 'gold-standard' gene-models with which to train gene-finders. Improvements in genome assembly and the wide availability of mRNA-seq data are also creating opportunities to update and re-annotate previously published genome annotations. Today's genome projects are thus in need of new genome annotation tools that can meet the challenges and opportunities presented by second-generation sequencing technologies. We present MAKER2, a genome annotation and data management tool designed for second-generation genome projects. MAKER2 is a multi-threaded, parallelized application that can process second-generation datasets of virtually any size. We show that MAKER2 can produce accurate annotations for novel genomes where training-data are limited, of low quality or even non-existent. MAKER2 also provides an easy means to use mRNA-seq data to improve annotation quality; and it can use these data to update legacy annotations, significantly improving their quality. We also show that MAKER2 can evaluate the quality of genome annotations, and identify and prioritize problematic annotations for manual review. MAKER2 is the first annotation engine specifically designed for second-generation genome projects. MAKER2 scales to datasets of any size, requires little in the way of training data, and can use mRNA-seq data to improve annotation quality. It can also update and manage legacy genome annotation datasets.

From Mendel to the Human Genome Project: The Implications for Nurse Education.

Science.gov (United States)

Burton, Hilary; Stewart, Alison

2003-01-01

The Human Genome Project is brining new opportunities to predict and prevent diseases. Although pediatric nurses are the closest to these developments, most nurses will encounter genetic aspects of practice and must understand the basic science and its ethical, legal, and social dimensions. (Includes commentary by Peter Birchenall.) (SK)

Genomic Prediction of Barley Hybrid Performance

Directory of Open Access Journals (Sweden)

Norman Philipp

2016-07-01

Full Text Available Hybrid breeding in barley ( L. offers great opportunities to accelerate the rate of genetic improvement and to boost yield stability. A crucial requirement consists of the efficient selection of superior hybrid combinations. We used comprehensive phenotypic and genomic data from a commercial breeding program with the goal of examining the potential to predict the hybrid performances. The phenotypic data were comprised of replicated grain yield trials for 385 two-way and 408 three-way hybrids evaluated in up to 47 environments. The parental lines were genotyped using a 3k single nucleotide polymorphism (SNP array based on an Illumina Infinium assay. We implemented ridge regression best linear unbiased prediction modeling for additive and dominance effects and evaluated the prediction ability using five-fold cross validations. The prediction ability of hybrid performances based on general combining ability (GCA effects was moderate, amounting to 0.56 and 0.48 for two- and three-way hybrids, respectively. The potential of GCA-based hybrid prediction requires that both parental components have been evaluated in a hybrid background. This is not necessary for genomic prediction for which we also observed moderate cross-validated prediction abilities of 0.51 and 0.58 for two- and three-way hybrids, respectively. This exemplifies the potential of genomic prediction in hybrid barley. Interestingly, prediction ability using the two-way hybrids as training population and the three-way hybrids as test population or vice versa was low, presumably, because of the different genetic makeup of the parental source populations. Consequently, further research is needed to optimize genomic prediction approaches combining different source populations in barley.

Importing statistical measures into Artemis enhances gene identification in the Leishmania genome project

Directory of Open Access Journals (Sweden)

McDonagh Paul D

2003-06-01

Full Text Available Abstract Background Seattle Biomedical Research Institute (SBRI as part of the Leishmania Genome Network (LGN is sequencing chromosomes of the trypanosomatid protozoan species Leishmania major. At SBRI, chromosomal sequence is annotated using a combination of trained and untrained non-consensus gene-prediction algorithms with ARTEMIS, an annotation platform with rich and user-friendly interfaces. Results Here we describe a methodology used to import results from three different protein-coding gene-prediction algorithms (GLIMMER, TESTCODE and GENESCAN into the ARTEMIS sequence viewer and annotation tool. Comparison of these methods, along with the CODONUSAGE algorithm built into ARTEMIS, shows the importance of combining methods to more accurately annotate the L. major genomic sequence. Conclusion An improvised and powerful tool for gene prediction has been developed by importing data from widely-used algorithms into an existing annotation platform. This approach is especially fruitful in the Leishmania genome project where there is large proportion of novel genes requiring manual annotation.

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...

Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data.

Science.gov (United States)

Wang, Edwin; Zaman, Naif; Mcgee, Shauna; Milanese, Jean-Sébastien; Masoudi-Nejad, Ali; O'Connor-McCourt, Maureen

2015-02-01

Tumor genome sequencing leads to documenting thousands of DNA mutations and other genomic alterations. At present, these data cannot be analyzed adequately to aid in the understanding of tumorigenesis and its evolution. Moreover, we have little insight into how to use these data to predict clinical phenotypes and tumor progression to better design patient treatment. To meet these challenges, we discuss a cancer hallmark network framework for modeling genome sequencing data to predict cancer clonal evolution and associated clinical phenotypes. The framework includes: (1) cancer hallmarks that can be represented by a few molecular/signaling networks. 'Network operational signatures' which represent gene regulatory logics/strengths enable to quantify state transitions and measures of hallmark traits. Thus, sets of genomic alterations which are associated with network operational signatures could be linked to the state/measure of hallmark traits. The network operational signature transforms genotypic data (i.e., genomic alterations) to regulatory phenotypic profiles (i.e., regulatory logics/strengths), to cellular phenotypic profiles (i.e., hallmark traits) which lead to clinical phenotypic profiles (i.e., a collection of hallmark traits). Furthermore, the framework considers regulatory logics of the hallmark networks under tumor evolutionary dynamics and therefore also includes: (2) a self-promoting positive feedback loop that is dominated by a genomic instability network and a cell survival/proliferation network is the main driver of tumor clonal evolution. Surrounding tumor stroma and its host immune systems shape the evolutionary paths; (3) cell motility initiating metastasis is a byproduct of the above self-promoting loop activity during tumorigenesis; (4) an emerging hallmark network which triggers genome duplication dominates a feed-forward loop which in turn could act as a rate-limiting step for tumor formation; (5) mutations and other genomic alterations have

Genomic prediction when some animals are not genotyped

Directory of Open Access Journals (Sweden)

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.

Citrus sinensis annotation project (CAP): a comprehensive database for sweet orange genome.

Science.gov (United States)

Wang, Jia; Chen, Dijun; Lei, Yang; Chang, Ji-Wei; Hao, Bao-Hai; Xing, Feng; Li, Sen; Xu, Qiang; Deng, Xiu-Xin; Chen, Ling-Ling

2014-01-01

Citrus is one of the most important and widely grown fruit crop with global production ranking firstly among all the fruit crops in the world. Sweet orange accounts for more than half of the Citrus production both in fresh fruit and processed juice. We have sequenced the draft genome of a double-haploid sweet orange (C. sinensis cv. Valencia), and constructed the Citrus sinensis annotation project (CAP) to store and visualize the sequenced genomic and transcriptome data. CAP provides GBrowse-based organization of sweet orange genomic data, which integrates ab initio gene prediction, EST, RNA-seq and RNA-paired end tag (RNA-PET) evidence-based gene annotation. Furthermore, we provide a user-friendly web interface to show the predicted protein-protein interactions (PPIs) and metabolic pathways in sweet orange. CAP provides comprehensive information beneficial to the researchers of sweet orange and other woody plants, which is freely available at http://citrus.hzau.edu.cn/.

The Genome 10K Project: a way forward.

Science.gov (United States)

Koepfli, Klaus-Peter; Paten, Benedict; O'Brien, Stephen J

2015-01-01

The Genome 10K Project was established in 2009 by a consortium of biologists and genome scientists determined to facilitate the sequencing and analysis of the complete genomes of 10,000 vertebrate species. Since then the number of selected and initiated species has risen from ∼26 to 277 sequenced or ongoing with funding, an approximately tenfold increase in five years. Here we summarize the advances and commitments that have occurred by mid-2014 and outline the achievements and present challenges of reaching the 10,000-species goal. We summarize the status of known vertebrate genome projects, recommend standards for pronouncing a genome as sequenced or completed, and provide our present and future vision of the landscape of Genome 10K. The endeavor is ambitious, bold, expensive, and uncertain, but together the Genome 10K Consortium of Scientists and the worldwide genomics community are moving toward their goal of delivering to the coming generation the gift of genome empowerment for many vertebrate species.

Genome projects and the functional-genomic era.

Science.gov (United States)

Sauer, Sascha; Konthur, Zoltán; Lehrach, Hans

2005-12-01

The problems we face today in public health as a result of the -- fortunately -- increasing age of people and the requirements of developing countries create an urgent need for new and innovative approaches in medicine and in agronomics. Genomic and functional genomic approaches have a great potential to at least partially solve these problems in the future. Important progress has been made by procedures to decode genomic information of humans, but also of other key organisms. The basic comprehension of genomic information (and its transfer) should now give us the possibility to pursue the next important step in life science eventually leading to a basic understanding of biological information flow; the elucidation of the function of all genes and correlative products encoded in the genome, as well as the discovery of their interactions in a molecular context and the response to environmental factors. As a result of the sequencing projects, we are now able to ask important questions about sequence variation and can start to comprehensively study the function of expressed genes on different levels such as RNA, protein or the cell in a systematic context including underlying networks. In this article we review and comment on current trends in large-scale systematic biological research. A particular emphasis is put on technology developments that can provide means to accomplish the tasks of future lines of functional genomics.

Human genome project: revolutionizing biology through leveraging technology

Science.gov (United States)

Dahl, Carol A.; Strausberg, Robert L.

1996-04-01

The Human Genome Project (HGP) is an international project to develop genetic, physical, and sequence-based maps of the human genome. Since the inception of the HGP it has been clear that substantially improved technology would be required to meet the scientific goals, particularly in order to acquire the complete sequence of the human genome, and that these technologies coupled with the information forthcoming from the project would have a dramatic effect on the way biomedical research is performed in the future. In this paper, we discuss the state-of-the-art for genomic DNA sequencing, technological challenges that remain, and the potential technological paths that could yield substantially improved genomic sequencing technology. The impact of the technology developed from the HGP is broad-reaching and a discussion of other research and medical applications that are leveraging HGP-derived DNA analysis technologies is included. The multidisciplinary approach to the development of new technologies that has been successful for the HGP provides a paradigm for facilitating new genomic approaches toward understanding the biological role of functional elements and systems within the cell, including those encoded within genomic DNA and their molecular products.

Genomic Prediction of Manganese Efficiency in Winter Barley

Directory of Open Access Journals (Sweden)

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.

Risk Prediction Using Genome-Wide Association Studies on Type 2 Diabetes

Directory of Open Access Journals (Sweden)

Sungkyoung Choi

2016-12-01

Full Text Available The success of genome-wide association studies (GWASs has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the “large p and small n” problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR, least absolute shrinkage and selection operator (LASSO, and Elastic-Net (EN. We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

The Human Genome Project: big science transforms biology and medicine.

Science.gov (United States)

Hood, Leroy; Rowen, Lee

2013-01-01

The Human Genome Project has transformed biology through its integrated big science approach to deciphering a reference human genome sequence along with the complete sequences of key model organisms. The project exemplifies the power, necessity and success of large, integrated, cross-disciplinary efforts - so-called 'big science' - directed towards complex major objectives. In this article, we discuss the ways in which this ambitious endeavor led to the development of novel technologies and analytical tools, and how it brought the expertise of engineers, computer scientists and mathematicians together with biologists. It established an open approach to data sharing and open-source software, thereby making the data resulting from the project accessible to all. The genome sequences of microbes, plants and animals have revolutionized many fields of science, including microbiology, virology, infectious disease and plant biology. Moreover, deeper knowledge of human sequence variation has begun to alter the practice of medicine. The Human Genome Project has inspired subsequent large-scale data acquisition initiatives such as the International HapMap Project, 1000 Genomes, and The Cancer Genome Atlas, as well as the recently announced Human Brain Project and the emerging Human Proteome Project.

All about the Human Genome Project (HGP)

Science.gov (United States)

... Care Genomic Medicine Working Group New Horizons and Research Patient Management Policy and Ethics Issues Quick Links for Patient Care Education All About the Human Genome Project Fact Sheets Genetic Education Resources for ...

Assessing Predictive Properties of Genome-Wide Selection in Soybeans

Directory of Open Access Journals (Sweden)

Alencar Xavier

2016-08-01

Full Text Available Many economically important traits in plant breeding have low heritability or are difficult to measure. For these traits, genomic selection has attractive features and may boost genetic gains. Our goal was to evaluate alternative scenarios to implement genomic selection for yield components in soybean (Glycine max L. merr. We used a nested association panel with cross validation to evaluate the impacts of training population size, genotyping density, and prediction model on the accuracy of genomic prediction. Our results indicate that training population size was the factor most relevant to improvement in genome-wide prediction, with greatest improvement observed in training sets up to 2000 individuals. We discuss assumptions that influence the choice of the prediction model. Although alternative models had minor impacts on prediction accuracy, the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB. Higher genotyping density marginally improved accuracy. Our study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set.

Assessing Predictive Properties of Genome-Wide Selection in Soybeans.

Science.gov (United States)

Xavier, Alencar; Muir, William M; Rainey, Katy Martin

2016-08-09

Many economically important traits in plant breeding have low heritability or are difficult to measure. For these traits, genomic selection has attractive features and may boost genetic gains. Our goal was to evaluate alternative scenarios to implement genomic selection for yield components in soybean (Glycine max L. merr). We used a nested association panel with cross validation to evaluate the impacts of training population size, genotyping density, and prediction model on the accuracy of genomic prediction. Our results indicate that training population size was the factor most relevant to improvement in genome-wide prediction, with greatest improvement observed in training sets up to 2000 individuals. We discuss assumptions that influence the choice of the prediction model. Although alternative models had minor impacts on prediction accuracy, the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB. Higher genotyping density marginally improved accuracy. Our study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set. Copyright © 2016 Xavie et al.

Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models

Science.gov (United States)

Spiliopoulou, Athina; Nagy, Reka; Bermingham, Mairead L.; Huffman, Jennifer E.; Hayward, Caroline; Vitart, Veronique; Rudan, Igor; Campbell, Harry; Wright, Alan F.; Wilson, James F.; Pong-Wong, Ricardo; Agakov, Felix; Navarro, Pau; Haley, Chris S.

2015-01-01

We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge. PMID:25918167

The Human Genome Project: how do we protect Australians?

Science.gov (United States)

Stott Despoja, N

It is the moon landing of the nineties: the ambitious Human Genome Project--identifying the up to 100,000 genes that make up human DNA and the sequences of the three billion base-pairs that comprise the human genome. However, unlike the moon landing, the effects of the genome project will have a fundamental impact on the way we see ourselves and each other.

A 1000 Arab genome project to study the Emirati population.

Science.gov (United States)

Al-Ali, Mariam; Osman, Wael; Tay, Guan K; AlSafar, Habiba S

2018-04-01

Discoveries from the human genome, HapMap, and 1000 genome projects have collectively contributed toward the creation of a catalog of human genetic variations that has improved our understanding of human diversity. Despite the collegial nature of many of these genome study consortiums, which has led to the cataloging of genetic variations of different ethnic groups from around the world, genome data on the Arab population remains overwhelmingly underrepresented. The National Arab Genome project in the United Arab Emirates (UAE) aims to address this deficiency by using Next Generation Sequencing (NGS) technology to provide data to improve our understanding of the Arab genome and catalog variants that are unique to the Arab population of the UAE. The project was conceived to shed light on the similarities and differences between the Arab genome and those of the other ethnic groups.

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...

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

A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data.

Science.gov (United States)

Lu, Qiongshi; Hu, Yiming; Sun, Jiehuan; Cheng, Yuwei; Cheung, Kei-Hoi; Zhao, Hongyu

2015-05-27

Identifying functional regions in the human genome is a major goal in human genetics. Great efforts have been made to functionally annotate the human genome either through computational predictions, such as genomic conservation, or high-throughput experiments, such as the ENCODE project. These efforts have resulted in a rich collection of functional annotation data of diverse types that need to be jointly analyzed for integrated interpretation and annotation. Here we present GenoCanyon, a whole-genome annotation method that performs unsupervised statistical learning using 22 computational and experimental annotations thereby inferring the functional potential of each position in the human genome. With GenoCanyon, we are able to predict many of the known functional regions. The ability of predicting functional regions as well as its generalizable statistical framework makes GenoCanyon a unique and powerful tool for whole-genome annotation. The GenoCanyon web server is available at http://genocanyon.med.yale.edu.

Genomic Prediction of Sunflower Hybrids Oil Content

Directory of Open Access Journals (Sweden)

Brigitte Mangin

2017-09-01

Full Text Available Prediction of hybrid performance using incomplete factorial mating designs is widely used in breeding programs including different heterotic groups. Based on the general combining ability (GCA of the parents, predictions are accurate only if the genetic variance resulting from the specific combining ability is small and both parents have phenotyped descendants. Genomic selection (GS can predict performance using a model trained on both phenotyped and genotyped hybrids that do not necessarily include all hybrid parents. Therefore, GS could overcome the issue of unknown parent GCA. Here, we compared the accuracy of classical GCA-based and genomic predictions for oil content of sunflower seeds using several GS models. Our study involved 452 sunflower hybrids from an incomplete factorial design of 36 female and 36 male lines. Re-sequencing of parental lines allowed to identify 468,194 non-redundant SNPs and to infer the hybrid genotypes. Oil content was observed in a multi-environment trial (MET over 3 years, leading to nine different environments. We compared GCA-based model to different GS models including female and male genomic kinships with the addition of the female-by-male interaction genomic kinship, the use of functional knowledge as SNPs in genes of oil metabolic pathways, and with epistasis modeling. When both parents have descendants in the training set, the predictive ability was high even for GCA-based prediction, with an average MET value of 0.782. GS performed slightly better (+0.2%. Neither the inclusion of the female-by-male interaction, nor functional knowledge of oil metabolism, nor epistasis modeling improved the GS accuracy. GS greatly improved predictive ability when one or both parents were untested in the training set, increasing GCA-based predictive ability by 10.4% from 0.575 to 0.635 in the MET. In this scenario, performing GS only considering SNPs in oil metabolic pathways did not improve whole genome GS prediction but

Attitudes towards the Human Genome Project.

Science.gov (United States)

Shahroudi, Julie; Shaw, Geraldine

Attitudes concerning the Human Genome Project were reported by faculty (N=40) and students (N=66) from a liberal arts college. Positive attitudes toward the project involved privacy, insurance and health, economic purposes, reproductive purposes, genetic counseling, religion and overall opinions. Negative attitudes were expressed regarding…

Comparative genomic data of the Avian Phylogenomics Project.

Science.gov (United States)

Zhang, Guojie; Li, Bo; Li, Cai; Gilbert, M Thomas P; Jarvis, Erich D; Wang, Jun

2014-01-01

genomic data that has been generated and used in our Avian Phylogenomics Project. To the best of our knowledge, the Avian Phylogenomics Project is the biggest vertebrate comparative genomics project to date. The genomic data presented here is expected to accelerate further analyses in many fields, including phylogenetics, comparative genomics, evolution, neurobiology, development biology, and other related areas.

Freedom and Responsibility in Synthetic Genomics: The Synthetic Yeast Project

OpenAIRE

Sliva, Anna; Yang, Huanming; Boeke, Jef D.; Mathews, Debra J. H.

2015-01-01

First introduced in 2011, the Synthetic Yeast Genome (Sc2.0) Project is a large international synthetic genomics project that will culminate in the first eukaryotic cell (Saccharomyces cerevisiae) with a fully synthetic genome. With collaborators from across the globe and from a range of institutions spanning from do-it-yourself biology (DIYbio) to commercial enterprises, it is important that all scientists working on this project are cognizant of the ethical and policy issues associated with...

Justice and the Human Genome Project

Energy Technology Data Exchange (ETDEWEB)

Murphy, T.F.; Lappe, M. (eds.)

1992-01-01

Most of the essays gathered in this volume were first presented at a conference, Justice and the Human Genome, in Chicago in early November, 1991. The goal of the, conference was to consider questions of justice as they are and will be raised by the Human Genome Project. To achieve its goal of identifying and elucidating the challenges of justice inherent in genomic research and its social applications the conference drew together in one forum members from academia, medicine, and industry with interests divergent as rate-setting for insurance, the care of newborns, and the history of ethics. The essays in this volume address a number of theoretical and practical concerns relative to the meaning of genomic research.

Justice and the Human Genome Project

Energy Technology Data Exchange (ETDEWEB)

Murphy, T.F.; Lappe, M. [eds.

1992-12-31

Most of the essays gathered in this volume were first presented at a conference, Justice and the Human Genome, in Chicago in early November, 1991. The goal of the, conference was to consider questions of justice as they are and will be raised by the Human Genome Project. To achieve its goal of identifying and elucidating the challenges of justice inherent in genomic research and its social applications the conference drew together in one forum members from academia, medicine, and industry with interests divergent as rate-setting for insurance, the care of newborns, and the history of ethics. The essays in this volume address a number of theoretical and practical concerns relative to the meaning of genomic research.

Correction for Measurement Error from Genotyping-by-Sequencing in Genomic Variance and Genomic Prediction Models

DEFF Research Database (Denmark)

Ashraf, Bilal; Janss, Luc; Jensen, Just

sample). The GBSeq data can be used directly in genomic models in the form of individual SNP allele-frequency estimates (e.g., reference reads/total reads per polymorphic site per individual), but is subject to measurement error due to the low sequencing depth per individual. Due to technical reasons....... In the current work we show how the correction for measurement error in GBSeq can also be applied in whole genome genomic variance and genomic prediction models. Bayesian whole-genome random regression models are proposed to allow implementation of large-scale SNP-based models with a per-SNP correction...... for measurement error. We show correct retrieval of genomic explained variance, and improved genomic prediction when accounting for the measurement error in GBSeq data...

Genomic value prediction for quantitative traits under the epistatic model

Directory of Open Access Journals (Sweden)

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.

SNP-associations and phenotype predictions from hundreds of microbial genomes without genome alignments.

Science.gov (United States)

Hall, Barry G

2014-01-01

SNP-association studies are a starting point for identifying genes that may be responsible for specific phenotypes, such as disease traits. The vast bulk of tools for SNP-association studies are directed toward SNPs in the human genome, and I am unaware of any tools designed specifically for such studies in bacterial or viral genomes. The PPFS (Predict Phenotypes From SNPs) package described here is an add-on to kSNP , a program that can identify SNPs in a data set of hundreds of microbial genomes. PPFS identifies those SNPs that are non-randomly associated with a phenotype based on the χ² probability, then uses those diagnostic SNPs for two distinct, but related, purposes: (1) to predict the phenotypes of strains whose phenotypes are unknown, and (2) to identify those diagnostic SNPs that are most likely to be causally related to the phenotype. In the example illustrated here, from a set of 68 E. coli genomes, for 67 of which the pathogenicity phenotype was known, there were 418,500 SNPs. Using the phenotypes of 36 of those strains, PPFS identified 207 diagnostic SNPs. The diagnostic SNPs predicted the phenotypes of all of the genomes with 97% accuracy. It then identified 97 SNPs whose probability of being causally related to the pathogenic phenotype was >0.999. In a second example, from a set of 116 E. coli genome sequences, using the phenotypes of 65 strains PPFS identified 101 SNPs that predicted the source host (human or non-human) with 90% accuracy.

The Qatar genome project: translation of whole-genome sequencing into clinical practice.

Science.gov (United States)

Zayed, Hatem

2016-10-01

Qatar Genome Project was launched in 2013 with the intent to sequence the genome of each Qatari citizen in an effort to protect Qataris from the high rate of indigenous genetic diseases by allowing the mapping of disease-causing variants/rare variants and establishing a Qatari reference genome. Indeed, this project is expected to have numerous global benefits because the elevated homogeneity of the Qatari population, that will make Qatar an excellent genetic laboratory that will generate a wealth of data that will allow us to make sense of the genotype-phenotype correlations of many diseases, especially the complex multifactorial diseases, and will pave the way for changing the traditional medical practice of looking first at the phenotype rather than the genotype. © 2016 John Wiley & Sons Ltd.

Empirical and deterministic accuracies of across-population genomic prediction

NARCIS (Netherlands)

Wientjes, Y.C.J.; Veerkamp, R.F.; Bijma, P.; Bovenhuis, H.; Schrooten, C.; Calus, M.P.L.

2015-01-01

Background: Differences in linkage disequilibrium and in allele substitution effects of QTL (quantitative trait loci) may hinder genomic prediction across populations. Our objective was to develop a deterministic formula to estimate the accuracy of across-population genomic prediction, for which

Genomic Prediction in Barley

DEFF Research Database (Denmark)

Edriss, Vahid; Cericola, Fabio; Jensen, Jens D

2015-01-01

to next generation. The main goal of this study was to see the potential of using genomic prediction in a commercial Barley breeding program. The data used in this study was from Nordic Seed company which is located in Denmark. Around 350 advanced lines were genotyped with 9K Barely chip from Illumina....... Traits used in this study were grain yield, plant height and heading date. Heading date is number days it takes after 1st June for plant to head. Heritabilities were 0.33, 0.44 and 0.48 for yield, height and heading, respectively for the average of nine plots. The GBLUP model was used for genomic...

Harvard Personal Genome Project: lessons from participatory public research

Science.gov (United States)

2014-01-01

Background Since its initiation in 2005, the Harvard Personal Genome Project has enrolled thousands of volunteers interested in publicly sharing their genome, health and trait data. Because these data are highly identifiable, we use an ‘open consent’ framework that purposefully excludes promises about privacy and requires participants to demonstrate comprehension prior to enrollment. Discussion Our model of non-anonymous, public genomes has led us to a highly participatory model of researcher-participant communication and interaction. The participants, who are highly committed volunteers, self-pursue and donate research-relevant datasets, and are actively engaged in conversations with both our staff and other Personal Genome Project participants. We have quantitatively assessed these communications and donations, and report our experiences with returning research-grade whole genome data to participants. We also observe some of the community growth and discussion that has occurred related to our project. Summary We find that public non-anonymous data is valuable and leads to a participatory research model, which we encourage others to consider. The implementation of this model is greatly facilitated by web-based tools and methods and participant education. Project results are long-term proactive participant involvement and the growth of a community that benefits both researchers and participants. PMID:24713084

Harvard Personal Genome Project: lessons from participatory public research.

Science.gov (United States)

Ball, Madeleine P; Bobe, Jason R; Chou, Michael F; Clegg, Tom; Estep, Preston W; Lunshof, Jeantine E; Vandewege, Ward; Zaranek, Alexander; Church, George M

2014-02-28

Since its initiation in 2005, the Harvard Personal Genome Project has enrolled thousands of volunteers interested in publicly sharing their genome, health and trait data. Because these data are highly identifiable, we use an 'open consent' framework that purposefully excludes promises about privacy and requires participants to demonstrate comprehension prior to enrollment. Our model of non-anonymous, public genomes has led us to a highly participatory model of researcher-participant communication and interaction. The participants, who are highly committed volunteers, self-pursue and donate research-relevant datasets, and are actively engaged in conversations with both our staff and other Personal Genome Project participants. We have quantitatively assessed these communications and donations, and report our experiences with returning research-grade whole genome data to participants. We also observe some of the community growth and discussion that has occurred related to our project. We find that public non-anonymous data is valuable and leads to a participatory research model, which we encourage others to consider. The implementation of this model is greatly facilitated by web-based tools and methods and participant education. Project results are long-term proactive participant involvement and the growth of a community that benefits both researchers and participants.

Implications of the Human Genome Project

Energy Technology Data Exchange (ETDEWEB)

Kitcher, P.

1998-11-01

The Human Genome Project (HGP), launched in 1991, aims to map and sequence the human genome by 2006. During the fifteen-year life of the project, it is projected that $3 billion in federal funds will be allocated to it. The ultimate aims of spending this money are to analyze the structure of human DNA, to identify all human genes, to recognize the functions of those genes, and to prepare for the biology and medicine of the twenty-first century. The following summary examines some of the implications of the program, concentrating on its scientific import and on the ethical and social problems that it raises. Its aim is to expose principles that might be used in applying the information which the HGP will generate. There is no attempt here to translate the principles into detailed proposals for legislation. Arguments and discussion can be found in the full report, but, like this summary, that report does not contain any legislative proposals.

Ethical considerations of research policy for personal genome analysis: the approach of the Genome Science Project in Japan.

Science.gov (United States)

Minari, Jusaku; Shirai, Tetsuya; Kato, Kazuto

2014-12-01

As evidenced by high-throughput sequencers, genomic technologies have recently undergone radical advances. These technologies enable comprehensive sequencing of personal genomes considerably more efficiently and less expensively than heretofore. These developments present a challenge to the conventional framework of biomedical ethics; under these changing circumstances, each research project has to develop a pragmatic research policy. Based on the experience with a new large-scale project-the Genome Science Project-this article presents a novel approach to conducting a specific policy for personal genome research in the Japanese context. In creating an original informed-consent form template for the project, we present a two-tiered process: making the draft of the template following an analysis of national and international policies; refining the draft template in conjunction with genome project researchers for practical application. Through practical use of the template, we have gained valuable experience in addressing challenges in the ethical review process, such as the importance of sharing details of the latest developments in genomics with members of research ethics committees. We discuss certain limitations of the conventional concept of informed consent and its governance system and suggest the potential of an alternative process using information technology.

Genomic Prediction of Testcross Performance in Canola (Brassica napus)

Science.gov (United States)

Jan, Habib U.; Abbadi, Amine; Lücke, Sophie; Nichols, Richard A.; Snowdon, Rod J.

2016-01-01

Genomic selection (GS) is a modern breeding approach where genome-wide single-nucleotide polymorphism (SNP) marker profiles are simultaneously used to estimate performance of untested genotypes. In this study, the potential of genomic selection methods to predict testcross performance for hybrid canola breeding was applied for various agronomic traits based on genome-wide marker profiles. A total of 475 genetically diverse spring-type canola pollinator lines were genotyped at 24,403 single-copy, genome-wide SNP loci. In parallel, the 950 F1 testcross combinations between the pollinators and two representative testers were evaluated for a number of important agronomic traits including seedling emergence, days to flowering, lodging, oil yield and seed yield along with essential seed quality characters including seed oil content and seed glucosinolate content. A ridge-regression best linear unbiased prediction (RR-BLUP) model was applied in combination with 500 cross-validations for each trait to predict testcross performance, both across the whole population as well as within individual subpopulations or clusters, based solely on SNP profiles. Subpopulations were determined using multidimensional scaling and K-means clustering. Genomic prediction accuracy across the whole population was highest for seed oil content (0.81) followed by oil yield (0.75) and lowest for seedling emergence (0.29). For seed yieId, seed glucosinolate, lodging resistance and days to onset of flowering (DTF), prediction accuracies were 0.45, 0.61, 0.39 and 0.56, respectively. Prediction accuracies could be increased for some traits by treating subpopulations separately; a strategy which only led to moderate improvements for some traits with low heritability, like seedling emergence. No useful or consistent increase in accuracy was obtained by inclusion of a population substructure covariate in the model. Testcross performance prediction using genome-wide SNP markers shows considerable

Los Alamos Science: The Human Genome Project. Number 20, 1992

Science.gov (United States)

Cooper, N. G.; Shea, N. eds.

1992-01-01

This document provides a broad overview of the Human Genome Project, with particular emphasis on work being done at Los Alamos. It tries to emphasize the scientific aspects of the project, compared to the more speculative information presented in the popular press. There is a brief introduction to modern genetics, including a review of classic work. There is a broad overview of the Genome Project, describing what the project is, what are some of its major five-year goals, what are major technological challenges ahead of the project, and what can the field of biology, as well as society expect to see as benefits from this project. Specific results on the efforts directed at mapping chromosomes 16 and 5 are discussed. A brief introduction to DNA libraries is presented, bearing in mind that Los Alamos has housed such libraries for many years prior to the Genome Project. Information on efforts to do applied computational work related to the project are discussed, as well as experimental efforts to do rapid DNA sequencing by means of single-molecule detection using applied spectroscopic methods. The article introduces the Los Alamos staff which are working on the Genome Project, and concludes with brief discussions on ethical, legal, and social implications of this work; a brief glimpse of genetics as it may be practiced in the next century; and a glossary of relevant terms.

Los Alamos Science: The Human Genome Project. Number 20, 1992

Energy Technology Data Exchange (ETDEWEB)

Cooper, N G; Shea, N [eds.

1992-01-01

This article provides a broad overview of the Human Genome Project, with particular emphasis on work being done at Los Alamos. It tries to emphasize the scientific aspects of the project, compared to the more speculative information presented in the popular press. There is a brief introduction to modern genetics, including a review of classic work. There is a broad overview of the Genome Project, describing what the project is, what are some of its major five-year goals, what are major technological challenges ahead of the project, and what can the field of biology, as well as society expect to see as benefits from this project. Specific results on the efforts directed at mapping chromosomes 16 and 5 are discussed. A brief introduction to DNA libraries is presented, bearing in mind that Los Alamos has housed such libraries for many years prior to the Genome Project. Information on efforts to do applied computational work related to the project are discussed, as well as experimental efforts to do rapid DNA sequencing by means of single-molecule detection using applied spectroscopic methods. The article introduces the Los Alamos staff which are working on the Genome Project, and concludes with brief discussions on ethical, legal, and social implications of this work; a brief glimpse of genetics as it may be practiced in the next century; and a glossary of relevant terms.

Accounting for discovery bias in genomic prediction

Science.gov (United States)

Our objective was to evaluate an approach to mitigating discovery bias in genomic prediction. Accuracy may be improved by placing greater emphasis on regions of the genome expected to be more influential on a trait. Methods emphasizing regions result in a phenomenon known as “discovery bias” if info...

Genomic breeding value prediction:methods and procedures

NARCIS (Netherlands)

Calus, M.P.L.

2010-01-01

Animal breeding faces one of the most significant changes of the past decades – the implementation of genomic selection. Genomic selection uses dense marker maps to predict the breeding value of animals with reported accuracies that are up to 0.31 higher than those of pedigree indexes, without the

The effect of using genealogy-based haplotypes for genomic prediction.

Science.gov (United States)

Edriss, Vahid; Fernando, Rohan L; Su, Guosheng; Lund, Mogens S; Guldbrandtsen, Bernt

2013-03-06

Genomic prediction uses two sources of information: linkage disequilibrium between markers and quantitative trait loci, and additive genetic relationships between individuals. One way to increase the accuracy of genomic prediction is to capture more linkage disequilibrium by regression on haplotypes instead of regression on individual markers. The aim of this study was to investigate the accuracy of genomic prediction using haplotypes based on local genealogy information. A total of 4429 Danish Holstein bulls were genotyped with the 50K SNP chip. Haplotypes were constructed using local genealogical trees. Effects of haplotype covariates were estimated with two types of prediction models: (1) assuming that effects had the same distribution for all haplotype covariates, i.e. the GBLUP method and (2) assuming that a large proportion (π) of the haplotype covariates had zero effect, i.e. a Bayesian mixture method. About 7.5 times more covariate effects were estimated when fitting haplotypes based on local genealogical trees compared to fitting individuals markers. Genealogy-based haplotype clustering slightly increased the accuracy of genomic prediction and, in some cases, decreased the bias of prediction. With the Bayesian method, accuracy of prediction was less sensitive to parameter π when fitting haplotypes compared to fitting markers. Use of haplotypes based on genealogy can slightly increase the accuracy of genomic prediction. Improved methods to cluster the haplotypes constructed from local genealogy could lead to additional gains in accuracy.

The 1000 bull genome project

Science.gov (United States)

To meet growing global demands for high value protein from milk and meat, rates of genetic gain in domestic cattle must be accelerated. At the same time, animal health and welfare must be considered. The 1000 bull genomes project supports these goals by providing annotated sequence variants and ge...

Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models

Directory of Open Access Journals (Sweden)

Surovcik Katharina

2006-03-01

Full Text Available Abstract Background Horizontal gene transfer (HGT is considered a strong evolutionary force shaping the content of microbial genomes in a substantial manner. It is the difference in speed enabling the rapid adaptation to changing environmental demands that distinguishes HGT from gene genesis, duplications or mutations. For a precise characterization, algorithms are needed that identify transfer events with high reliability. Frequently, the transferred pieces of DNA have a considerable length, comprise several genes and are called genomic islands (GIs or more specifically pathogenicity or symbiotic islands. Results We have implemented the program SIGI-HMM that predicts GIs and the putative donor of each individual alien gene. It is based on the analysis of codon usage (CU of each individual gene of a genome under study. CU of each gene is compared against a carefully selected set of CU tables representing microbial donors or highly expressed genes. Multiple tests are used to identify putatively alien genes, to predict putative donors and to mask putatively highly expressed genes. Thus, we determine the states and emission probabilities of an inhomogeneous hidden Markov model working on gene level. For the transition probabilities, we draw upon classical test theory with the intention of integrating a sensitivity controller in a consistent manner. SIGI-HMM was written in JAVA and is publicly available. It accepts as input any file created according to the EMBL-format. It generates output in the common GFF format readable for genome browsers. Benchmark tests showed that the output of SIGI-HMM is in agreement with known findings. Its predictions were both consistent with annotated GIs and with predictions generated by different methods. Conclusion SIGI-HMM is a sensitive tool for the identification of GIs in microbial genomes. It allows to interactively analyze genomes in detail and to generate or to test hypotheses about the origin of acquired

Helminth genome projects: all or nothing

Czech Academy of Sciences Publication Activity Database

Lukeš, Julius; Horák, Aleš; Scholz, Tomáš

2005-01-01

Roč. 21, č. 6 (2005), s. 265-266 ISSN 1471-4922 Institutional research plan: CEZ:AV0Z60220518 Keywords : genome project * helminth * Dracunculus Subject RIV: EG - Zoology Impact factor: 4.526, year: 2005

Using Markov chains of nucleotide sequences as a possible precursor to predict functional roles of human genome: a case study on inactive chromatin regions.

Science.gov (United States)

Lee, K-E; Lee, E-J; Park, H-S

2016-08-30

Recent advances in computational epigenetics have provided new opportunities to evaluate n-gram probabilistic language models. In this paper, we describe a systematic genome-wide approach for predicting functional roles in inactive chromatin regions by using a sequence-based Markovian chromatin map of the human genome. We demonstrate that Markov chains of sequences can be used as a precursor to predict functional roles in heterochromatin regions and provide an example comparing two publicly available chromatin annotations of large-scale epigenomics projects: ENCODE project consortium and Roadmap Epigenomics consortium.

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.

Genomic prediction for tuberculosis resistance in dairy cattle.

Directory of Open Access Journals (Sweden)

Smaragda Tsairidou

Full Text Available The increasing prevalence of bovine tuberculosis (bTB in the UK and the limitations of the currently available diagnostic and control methods require the development of complementary approaches to assist in the sustainable control of the disease. One potential approach is the identification of animals that are genetically more resistant to bTB, to enable breeding of animals with enhanced resistance. This paper focuses on prediction of resistance to bTB. We explore estimation of direct genomic estimated breeding values (DGVs for bTB resistance in UK dairy cattle, using dense SNP chip data, and test these genomic predictions for situations when disease phenotypes are not available on selection candidates.We estimated DGVs using genomic best linear unbiased prediction methodology, and assessed their predictive accuracies with a cross validation procedure and receiver operator characteristic (ROC curves. Furthermore, these results were compared with theoretical expectations for prediction accuracy and area-under-the-ROC-curve (AUC. The dataset comprised 1151 Holstein-Friesian cows (bTB cases or controls. All individuals (592 cases and 559 controls were genotyped for 727,252 loci (Illumina Bead Chip. The estimated observed heritability of bTB resistance was 0.23±0.06 (0.34 on the liability scale and five-fold cross validation, replicated six times, provided a prediction accuracy of 0.33 (95% C.I.: 0.26, 0.40. ROC curves, and the resulting AUC, gave a probability of 0.58, averaged across six replicates, of correctly classifying cows as diseased or as healthy based on SNP chip genotype alone using these data.These results provide a first step in the investigation of the potential feasibility of genomic selection for bTB resistance using SNP data. Specifically, they demonstrate that genomic selection is possible, even in populations with no pedigree data and on animals lacking bTB phenotypes. However, a larger training population will be required to

The Human Genome Diversity Project

Energy Technology Data Exchange (ETDEWEB)

Cavalli-Sforza, L. [Stanford Univ., CA (United States)

1994-12-31

The Human Genome Diversity Project (HGD Project) is an international anthropology project that seeks to study the genetic richness of the entire human species. This kind of genetic information can add a unique thread to the tapestry knowledge of humanity. Culture, environment, history, and other factors are often more important, but humanity`s genetic heritage, when analyzed with recent technology, brings another type of evidence for understanding species` past and present. The Project will deepen the understanding of this genetic richness and show both humanity`s diversity and its deep and underlying unity. The HGD Project is still largely in its planning stages, seeking the best ways to reach its goals. The continuing discussions of the Project, throughout the world, should improve the plans for the Project and their implementation. The Project is as global as humanity itself; its implementation will require the kinds of partnerships among different nations and cultures that make the involvement of UNESCO and other international organizations particularly appropriate. The author will briefly discuss the Project`s history, describe the Project, set out the core principles of the Project, and demonstrate how the Project will help combat the scourge of racism.

Kernel-based whole-genome prediction of complex traits: a review.

Science.gov (United States)

Morota, Gota; Gianola, Daniel

2014-01-01

Prediction of genetic values has been a focus of applied quantitative genetics since the beginning of the 20th century, with renewed interest following the advent of the era of whole genome-enabled prediction. Opportunities offered by the emergence of high-dimensional genomic data fueled by post-Sanger sequencing technologies, especially molecular markers, have driven researchers to extend Ronald Fisher and Sewall Wright's models to confront new challenges. In particular, kernel methods are gaining consideration as a regression method of choice for genome-enabled prediction. Complex traits are presumably influenced by many genomic regions working in concert with others (clearly so when considering pathways), thus generating interactions. Motivated by this view, a growing number of statistical approaches based on kernels attempt to capture non-additive effects, either parametrically or non-parametrically. This review centers on whole-genome regression using kernel methods applied to a wide range of quantitative traits of agricultural importance in animals and plants. We discuss various kernel-based approaches tailored to capturing total genetic variation, with the aim of arriving at an enhanced predictive performance in the light of available genome annotation information. Connections between prediction machines born in animal breeding, statistics, and machine learning are revisited, and their empirical prediction performance is discussed. Overall, while some encouraging results have been obtained with non-parametric kernels, recovering non-additive genetic variation in a validation dataset remains a challenge in quantitative genetics.

Kernel-based whole-genome prediction of complex traits: a review

Directory of Open Access Journals (Sweden)

Gota eMorota

2014-10-01

Full Text Available Prediction of genetic values has been a focus of applied quantitative genetics since the beginning of the 20th century, with renewed interest following the advent of the era of whole genome-enabled prediction. Opportunities offered by the emergence of high-dimensional genomic data fueled by post-Sanger sequencing technologies, especially molecular markers, have driven researchers to extend Ronald Fisher and Sewall Wright's models to confront new challenges. In particular, kernel methods are gaining consideration as a regression method of choice for genome-enabled prediction. Complex traits are presumably influenced by many genomic regions working in concert with others (clearly so when considering pathways, thus generating interactions. Motivated by this view, a growing number of statistical approaches based on kernels attempt to capture non-additive effects, either parametrically or non-parametrically. This review centers on whole-genome regression using kernel methods applied to a wide range of quantitative traits of agricultural importance in animals and plants. We discuss various kernel-based approaches tailored to capturing total genetic variation, with the aim of arriving at an enhanced predictive performance in the light of available genome annotation information. Connections between prediction machines born in animal breeding, statistics, and machine learning are revisited, and their empirical prediction performance is discussed. Overall, while some encouraging results have been obtained with non-parametric kernels, recovering non-additive genetic variation in a validation dataset remains a challenge in quantitative genetics.

The Human Genome Project (HGP): dividends and challenges: a ...

African Journals Online (AJOL)

The Human Genome Project (HGP): dividends and challenges: a review. ... Genomic studies have given profound insights into the genetic organization of ... with it will be an essential part of modern medicine and biology for years to come.

Genomic prediction based on data from three layer lines: a comparison between linear methods

NARCIS (Netherlands)

Calus, M.P.L.; Huang, H.; Vereijken, J.; Visscher, J.; Napel, ten J.; Windig, J.J.

2014-01-01

Background The prediction accuracy of several linear genomic prediction models, which have previously been used for within-line genomic prediction, was evaluated for multi-line genomic prediction. Methods Compared to a conventional BLUP (best linear unbiased prediction) model using pedigree data, we

Predicting Tissue-Specific Enhancers in the Human Genome

Energy Technology Data Exchange (ETDEWEB)

Pennacchio, Len A.; Loots, Gabriela G.; Nobrega, Marcelo A.; Ovcharenko, Ivan

2006-07-01

Determining how transcriptional regulatory signals areencoded in vertebrate genomes is essential for understanding the originsof multi-cellular complexity; yet the genetic code of vertebrate generegulation remains poorly understood. In an attempt to elucidate thiscode, we synergistically combined genome-wide gene expression profiling,vertebrate genome comparisons, and transcription factor binding siteanalysis to define sequence signatures characteristic of candidatetissue-specific enhancers in the human genome. We applied this strategyto microarray-based gene expression profiles from 79 human tissues andidentified 7,187 candidate enhancers that defined their flanking geneexpression, the majority of which were located outside of knownpromoters. We cross-validated this method for its ability to de novopredict tissue-specific gene expression and confirmed its reliability in57 of the 79 available human tissues, with an average precision inenhancer recognition ranging from 32 percent to 63 percent, and asensitivity of 47 percent. We used the sequence signatures identified bythis approach to assign tissue-specific predictions to ~;328,000human-mouse conserved noncoding elements in the human genome. Byoverlapping these genome-wide predictions with a large in vivo dataset ofenhancers validated in transgenic mice, we confirmed our results with a28 percent sensitivity and 50 percent precision. These results indicatethe power of combining complementary genomic datasets as an initialcomputational foray into the global view of tissue-specific generegulation in vertebrates.

Freedom and Responsibility in Synthetic Genomics: The Synthetic Yeast Project.

Science.gov (United States)

Sliva, Anna; Yang, Huanming; Boeke, Jef D; Mathews, Debra J H

2015-08-01

First introduced in 2011, the Synthetic Yeast Genome (Sc2.0) PROJECT is a large international synthetic genomics project that will culminate in the first eukaryotic cell (Saccharomyces cerevisiae) with a fully synthetic genome. With collaborators from across the globe and from a range of institutions spanning from do-it-yourself biology (DIYbio) to commercial enterprises, it is important that all scientists working on this project are cognizant of the ethical and policy issues associated with this field of research and operate under a common set of principles. In this commentary, we survey the current ethics and regulatory landscape of synthetic biology and present the Sc2.0 Statement of Ethics and Governance to which all members of the project adhere. This statement focuses on four aspects of the Sc2.0 PROJECT: societal benefit, intellectual property, safety, and self-governance. We propose that such project-level agreements are an important, valuable, and flexible model of self-regulation for similar global, large-scale synthetic biology projects in order to maximize the benefits and minimize potential harms. Copyright © 2015 by the Genetics Society of America.

Using whole-genome sequence data to predict quantitative trait phenotypes in Drosophila melanogaster.

Directory of Open Access Journals (Sweden)

Ulrike Ober

Full Text Available Predicting organismal phenotypes from genotype data is important for plant and animal breeding, medicine, and evolutionary biology. Genomic-based phenotype prediction has been applied for single-nucleotide polymorphism (SNP genotyping platforms, but not using complete genome sequences. Here, we report genomic prediction for starvation stress resistance and startle response in Drosophila melanogaster, using ∼2.5 million SNPs determined by sequencing the Drosophila Genetic Reference Panel population of inbred lines. We constructed a genomic relationship matrix from the SNP data and used it in a genomic best linear unbiased prediction (GBLUP model. We assessed predictive ability as the correlation between predicted genetic values and observed phenotypes by cross-validation, and found a predictive ability of 0.239±0.008 (0.230±0.012 for starvation resistance (startle response. The predictive ability of BayesB, a Bayesian method with internal SNP selection, was not greater than GBLUP. Selection of the 5% SNPs with either the highest absolute effect or variance explained did not improve predictive ability. Predictive ability decreased only when fewer than 150,000 SNPs were used to construct the genomic relationship matrix. We hypothesize that predictive power in this population stems from the SNP-based modeling of the subtle relationship structure caused by long-range linkage disequilibrium and not from population structure or SNPs in linkage disequilibrium with causal variants. We discuss the implications of these results for genomic prediction in other organisms.

Using Genome-scale Models to Predict Biological Capabilities

DEFF Research Database (Denmark)

O’Brien, Edward J.; Monk, Jonathan M.; Palsson, Bernhard O.

2015-01-01

Constraint-based reconstruction and analysis (COBRA) methods at the genome scale have been under development since the first whole-genome sequences appeared in the mid-1990s. A few years ago, this approach began to demonstrate the ability to predict a range of cellular functions, including cellul...

Genomic Prediction in Animals and Plants: Simulation of Data, Validation, Reporting, and Benchmarking

Science.gov (United States)

Daetwyler, Hans D.; Calus, Mario P. L.; Pong-Wong, Ricardo; de los Campos, Gustavo; Hickey, John 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 results are reported. In addition, some new methods have been compared only in limited genetic architectures, leading to potentially misleading conclusions. In this article we review simulation procedures, discuss validation and reporting of results, and apply benchmark procedures for a variety of genomic prediction methods in simulated and real example data. Plant and animal breeding programs are being transformed by the use of genomic data, which are becoming widely available and cost-effective to predict genetic merit. A large number of genomic prediction studies have been published using both simulated and real data. The relative novelty of this area of research has made the development of scientific conventions difficult with regard to description of the real data, simulation of genomes, validation and reporting of results, and forward in time methods. In this review article we discuss the generation of simulated genotype and phenotype data, using approaches such as the coalescent and forward in time simulation. We outline ways to validate simulated data and genomic prediction results, including cross-validation. The accuracy and bias of genomic prediction are highlighted as performance indicators that should be reported. We suggest that a measure of relatedness between the reference and validation individuals be reported, as its impact on the accuracy of genomic prediction is substantial. A large number of methods were compared in example simulated and real (pine and wheat) data sets, all of which are publicly available. In our limited simulations, most methods performed similarly in traits with a large number of quantitative trait loci (QTL), whereas in traits

An overview of the human genome project

Energy Technology Data Exchange (ETDEWEB)

Batzer, M.A.

1994-01-01

The human genome project is one of the most ambitious scientific projects to date, with the ultimate goal being a nucleotide sequence for all four billion bases of human DNA. In the process of determining the nucleotide sequence for each base, the location, function, and regulatory regions from the estimated 100,000 human genes will be identified. The genome project itself relies upon maps of the human genetic code derived from several different levels of resolution. Genetic linkage analysis provides a low resolution genome map. The information for genetic linkage maps is derived from the analysis of chromosome specific markers such as Sequence Tagged Sites (STSs), Variable Number of Tandem Repeats (VNTRs) or other polymorphic (highly informative) loci in a number of different-families. Using this information the location of an unknown disease gene can be limited to a region comprised of one million base pairs of DNA or less. After this point, one must construct or have access to a physical map of the region of interest. Physical mapping involves the construction of an ordered overlapping (contiguous) set of recombinant DNA clones. These clones may be derived from a number of different vectors including cosmids, Bacterial Artificial Chromosomes (BACs), P1 derived Artificial Chromosomes (PACs), somatic cell hybrids, or Yeast Artificial Chromosomes (YACs). The ultimate goal for physical mapping is to establish a completely overlapping (contiguous) set of clones for the entire genome. After a gene or region of interest has been localized using physical mapping the nucleotide sequence is determined. The overlap between genetic mapping, physical mapping and DNA sequencing has proven to be a powerful tool for the isolation of disease genes through positional cloning.

Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models

Science.gov (United States)

Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A.; Burgueño, Juan; Pérez-Rodríguez, Paulino; de los Campos, Gustavo

2016-01-01

The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects (u) that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model (u) plus an extra component, f, that captures random effects between environments that were not captured by the random effects u. We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u. PMID:27793970

Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models

Directory of Open Access Journals (Sweden)

Jaime Cuevas

2017-01-01

Full Text Available The phenomenon of genotype × environment (G × E interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects ( u that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP and Gaussian (Gaussian kernel, GK. The other model has the same genetic component as the first model ( u plus an extra component, f, that captures random effects between environments that were not captured by the random effects u . We used five CIMMYT data sets (one maize and four wheat that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u   and   f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u .

The international Genome sample resource (IGSR): A worldwide collection of genome variation incorporating the 1000 Genomes Project data.

Science.gov (United States)

Clarke, Laura; Fairley, Susan; Zheng-Bradley, Xiangqun; Streeter, Ian; Perry, Emily; Lowy, Ernesto; Tassé, Anne-Marie; Flicek, Paul

2017-01-04

The International Genome Sample Resource (IGSR; http://www.internationalgenome.org) expands in data type and population diversity the resources from the 1000 Genomes Project. IGSR represents the largest open collection of human variation data and provides easy access to these resources. IGSR was established in 2015 to maintain and extend the 1000 Genomes Project data, which has been widely used as a reference set of human variation and by researchers developing analysis methods. IGSR has mapped all of the 1000 Genomes sequence to the newest human reference (GRCh38), and will release updated variant calls to ensure maximal usefulness of the existing data. IGSR is collecting new structural variation data on the 1000 Genomes samples from long read sequencing and other technologies, and will collect relevant functional data into a single comprehensive resource. IGSR is extending coverage with new populations sequenced by collaborating groups. Here, we present the new data and analysis that IGSR has made available. We have also introduced a new data portal that increases discoverability of our data-previously only browseable through our FTP site-by focusing on particular samples, populations or data sets of interest. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Alignment of 1000 Genomes Project reads to reference assembly GRCh38.

Science.gov (United States)

Zheng-Bradley, Xiangqun; Streeter, Ian; Fairley, Susan; Richardson, David; Clarke, Laura; Flicek, Paul

2017-07-01

The 1000 Genomes Project produced more than 100 trillion basepairs of short read sequence from more than 2600 samples in 26 populations over a period of five years. In its final phase, the project released over 85 million genotyped and phased variants on human reference genome assembly GRCh37. An updated reference assembly, GRCh38, was released in late 2013, but there was insufficient time for the final phase of the project analysis to change to the new assembly. Although it is possible to lift the coordinates of the 1000 Genomes Project variants to the new assembly, this is a potentially error-prone process as coordinate remapping is most appropriate only for non-repetitive regions of the genome and those that did not see significant change between the two assemblies. It will also miss variants in any region that was newly added to GRCh38. Thus, to produce the highest quality variants and genotypes on GRCh38, the best strategy is to realign the reads and recall the variants based on the new alignment. As the first step of variant calling for the 1000 Genomes Project data, we have finished remapping all of the 1000 Genomes sequence reads to GRCh38 with alternative scaffold-aware BWA-MEM. The resulting alignments are available as CRAM, a reference-based sequence compression format. The data have been released on our FTP site and are also available from European Nucleotide Archive to facilitate researchers discovering variants on the primary sequences and alternative contigs of GRCh38. © The Authors 2017. Published by Oxford University Press.

The Human Genome Diversity (HGD) Project. Summary document

Energy Technology Data Exchange (ETDEWEB)

NONE

1993-12-31

In 1991 a group of human geneticists and molecular biologists proposed to the scientific community that a world wide survey be undertaken of variation in the human genome. To aid their considerations, the committee therefore decided to hold a small series of international workshops to explore the major scientific issues involved. The intention was to define a framework for the project which could provide a basis for much wider and more detailed discussion and planning--it was recognized that the successful implementation of the proposed project, which has come to be known as the Human Genome Diversity (HGD) Project, would not only involve scientists but also various national and international non-scientific groups all of which should contribute to the project`s development. The international HGD workshop held in Sardinia in September 1993 was the last in the initial series of planning workshops. As such it not only explored new ground but also pulled together into a more coherent form much of the formal and informal discussion that had taken place in the preceding two years. This report presents the deliberations of the Sardinia workshop within a consideration of the overall development of the HGD Project to date.

National human genome projects: an update and an agenda.

Science.gov (United States)

An, Joon Yong

2017-01-01

Population genetic and human genetic studies are being accelerated with genome technology and data sharing. Accordingly, in the past 10 years, several countries have initiated genetic research using genome technology and identified the genetic architecture of the ethnic groups living in the corresponding country or suggested the genetic foundation of a social phenomenon. Genetic research has been conducted from epidemiological studies that previously described the health or disease conditions in defined population. This perspective summarizes national genome projects conducted in the past 10 years and introduces case studies to utilize genomic data in genetic research.

Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.

Science.gov (United States)

Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A; Burgueño, Juan; Pérez-Rodríguez, Paulino; de Los Campos, Gustavo

2017-01-05

The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects [Formula: see text] that can be assessed by the Kronecker product of variance-covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model [Formula: see text] plus an extra component, F: , that captures random effects between environments that were not captured by the random effects [Formula: see text] We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with [Formula: see text] over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect [Formula: see text]. Copyright © 2017 Cuevas et al.

Mapping our genes: The genome projects: How big, how fast

Energy Technology Data Exchange (ETDEWEB)

none,

1988-04-01

For the past 2 years, scientific and technical journals in biology and medicine have extensively covered a debate about whether and how to determine the function and order of human genes on human chromosomes and when to determine the sequence of molecular building blocks that comprise DNA in those chromosomes. In 1987, these issues rose to become part of the public agenda. The debate involves science, technology, and politics. Congress is responsible for /open quotes/writing the rules/close quotes/ of what various federal agencies do and for funding their work. This report surveys the points made so far in the debate, focusing on those that most directly influence the policy options facing the US Congress. Congressional interest focused on how to assess the rationales for conducting human genome projects, how to fund human genome projects (at what level and through which mechanisms), how to coordinate the scientific and technical programs of the several federal agencies and private interests already supporting various genome projects, and how to strike a balance regarding the impact of genome projects on international scientific cooperation and international economic competition in biotechnology. OTA prepared this report with the assistance of several hundred experts throughout the world. 342 refs., 26 figs., 11 tabs.

Mapping Our Genes: The Genome Projects: How Big, How Fast

Science.gov (United States)

1988-04-01

For the past 2 years, scientific and technical journals in biology and medicine have extensively covered a debate about whether and how to determine the function and order of human genes on human chromosomes and when to determine the sequence of molecular building blocks that comprise DNA in those chromosomes. In 1987, these issues rose to become part of the public agenda. The debate involves science, technology, and politics. Congress is responsible for ?writing the rules? of what various federal agencies do and for funding their work. This report surveys the points made so far in the debate, focusing on those that most directly influence the policy options facing the US Congress. Congressional interest focused on how to assess the rationales for conducting human genome projects, how to fund human genome projects (at what level and through which mechanisms), how to coordinate the scientific and technical programs of the several federal agencies and private interests already supporting various genome projects, and how to strike a balance regarding the impact of genome projects on international scientific cooperation and international economic competition in biotechnology. The Office of Technology Assessment (OTA) prepared this report with the assistance of several hundred experts throughout the world.

Genome-Wide Prediction and Analysis of 3D-Domain Swapped Proteins in the Human Genome from Sequence Information.

Science.gov (United States)

Upadhyay, Atul Kumar; Sowdhamini, Ramanathan

2016-01-01

3D-domain swapping is one of the mechanisms of protein oligomerization and the proteins exhibiting this phenomenon have many biological functions. These proteins, which undergo domain swapping, have acquired much attention owing to their involvement in human diseases, such as conformational diseases, amyloidosis, serpinopathies, proteionopathies etc. Early realisation of proteins in the whole human genome that retain tendency to domain swap will enable many aspects of disease control management. Predictive models were developed by using machine learning approaches with an average accuracy of 78% (85.6% of sensitivity, 87.5% of specificity and an MCC value of 0.72) to predict putative domain swapping in protein sequences. These models were applied to many complete genomes with special emphasis on the human genome. Nearly 44% of the protein sequences in the human genome were predicted positive for domain swapping. Enrichment analysis was performed on the positively predicted sequences from human genome for their domain distribution, disease association and functional importance based on Gene Ontology (GO). Enrichment analysis was also performed to infer a better understanding of the functional importance of these sequences. Finally, we developed hinge region prediction, in the given putative domain swapped sequence, by using important physicochemical properties of amino acids.

GI-SVM: A sensitive method for predicting genomic islands based on unannotated sequence of a single genome.

Science.gov (United States)

Lu, Bingxin; Leong, Hon Wai

2016-02-01

Genomic islands (GIs) are clusters of functionally related genes acquired by lateral genetic transfer (LGT), and they are present in many bacterial genomes. GIs are extremely important for bacterial research, because they not only promote genome evolution but also contain genes that enhance adaption and enable antibiotic resistance. Many methods have been proposed to predict GI. But most of them rely on either annotations or comparisons with other closely related genomes. Hence these methods cannot be easily applied to new genomes. As the number of newly sequenced bacterial genomes rapidly increases, there is a need for methods to detect GI based solely on sequences of a single genome. In this paper, we propose a novel method, GI-SVM, to predict GIs given only the unannotated genome sequence. GI-SVM is based on one-class support vector machine (SVM), utilizing composition bias in terms of k-mer content. From our evaluations on three real genomes, GI-SVM can achieve higher recall compared with current methods, without much loss of precision. Besides, GI-SVM allows flexible parameter tuning to get optimal results for each genome. In short, GI-SVM provides a more sensitive method for researchers interested in a first-pass detection of GI in newly sequenced genomes.

The Human Genome Project: big science transforms biology and medicine

OpenAIRE

Hood, Leroy; Rowen, Lee

2013-01-01

The Human Genome Project has transformed biology through its integrated big science approach to deciphering a reference human genome sequence along with the complete sequences of key model organisms. The project exemplifies the power, necessity and success of large, integrated, cross-disciplinary efforts - so-called ‘big science’ - directed towards complex major objectives. In this article, we discuss the ways in which this ambitious endeavor led to the development of novel technologies and a...

The Genomes On Line Database (GOLD) in 2009: status of genomic and metagenomic projects and their associated metadata

Science.gov (United States)

Liolios, Konstantinos; Chen, I-Min A.; Mavromatis, Konstantinos; Tavernarakis, Nektarios; Hugenholtz, Philip; Markowitz, Victor M.; Kyrpides, Nikos C.

2010-01-01

The Genomes On Line Database (GOLD) is a comprehensive resource for centralized monitoring of genome and metagenome projects worldwide. Both complete and ongoing projects, along with their associated metadata, can be accessed in GOLD through precomputed tables and a search page. As of September 2009, GOLD contains information for more than 5800 sequencing projects, of which 1100 have been completed and their sequence data deposited in a public repository. GOLD continues to expand, moving toward the goal of providing the most comprehensive repository of metadata information related to the projects and their organisms/environments in accordance with the Minimum Information about a (Meta)Genome Sequence (MIGS/MIMS) specification. GOLD is available at: http://www.genomesonline.org and has a mirror site at the Institute of Molecular Biology and Biotechnology, Crete, Greece, at: http://gold.imbb.forth.gr/ PMID:19914934

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

Genomic Prediction Accuracy for Resistance Against Piscirickettsia salmonis in Farmed Rainbow Trout

Directory of Open Access Journals (Sweden)

Grazyella M. Yoshida

2018-02-01

Full Text Available Salmonid rickettsial syndrome (SRS, caused by the intracellular bacterium Piscirickettsia salmonis, is one of the main diseases affecting rainbow trout (Oncorhynchus mykiss farming. To accelerate genetic progress, genomic selection methods can be used as an effective approach to control the disease. The aims of this study were: (i to compare the accuracy of estimated breeding values using pedigree-based best linear unbiased prediction (PBLUP with genomic BLUP (GBLUP, single-step GBLUP (ssGBLUP, Bayes C, and Bayesian Lasso (LASSO; and (ii to test the accuracy of genomic prediction and PBLUP using different marker densities (0.5, 3, 10, 20, and 27 K for resistance against P. salmonis in rainbow trout. Phenotypes were recorded as number of days to death (DD and binary survival (BS from 2416 fish challenged with P. salmonis. A total of 1934 fish were genotyped using a 57 K single-nucleotide polymorphism (SNP array. All genomic prediction methods achieved higher accuracies than PBLUP. The relative increase in accuracy for different genomic models ranged from 28 to 41% for both DD and BS at 27 K SNP. Between different genomic models, the highest relative increase in accuracy was obtained with Bayes C (∼40%, where 3 K SNP was enough to achieve a similar accuracy to that of the 27 K SNP for both traits. For resistance against P. salmonis in rainbow trout, we showed that genomic predictions using GBLUP, ssGBLUP, Bayes C, and LASSO can increase accuracy compared with PBLUP. Moreover, it is possible to use relatively low-density SNP panels for genomic prediction without compromising accuracy predictions for resistance against P. salmonis in rainbow trout.

Using Genetic Distance to Infer the Accuracy of Genomic Prediction.

Directory of Open Access Journals (Sweden)

Marco Scutari

2016-09-01

Full Text Available The prediction of phenotypic traits using high-density genomic data has many applications such as the selection of plants and animals of commercial interest; and it is expected to play an increasing role in medical diagnostics. Statistical models used for this task are usually tested using cross-validation, which implicitly assumes that new individuals (whose phenotypes we would like to predict originate from the same population the genomic prediction model is trained on. In this paper we propose an approach based on clustering and resampling to investigate the effect of increasing genetic distance between training and target populations when predicting quantitative traits. This is important for plant and animal genetics, where genomic selection programs rely on the precision of predictions in future rounds of breeding. Therefore, estimating how quickly predictive accuracy decays is important in deciding which training population to use and how often the model has to be recalibrated. We find that the correlation between true and predicted values decays approximately linearly with respect to either FST or mean kinship between the training and the target populations. We illustrate this relationship using simulations and a collection of data sets from mice, wheat and human genetics.

Prediction in medicine – genome contra envirome

Czech Academy of Sciences Publication Activity Database

Brdička, Radim

2012-01-01

Roč. 151, č. 1 (2012), s. 22-25 ISSN 0008-7335 R&D Projects: GA MZd NS9804 Institutional research plan: CEZ:AV0Z50390703 Keywords : genome * genotype * phenotype Subject RIV: FP - Other Medical Disciplines

The Human Genome Project: Information access, management, and regulation. Final report

Energy Technology Data Exchange (ETDEWEB)

McInerney, J.D.; Micikas, L.B.

1996-08-31

The Human Genome Project is a large, internationally coordinated effort in biological research directed at creating a detailed map of human DNA. This report describes the access of information, management, and regulation of the project. The project led to the development of an instructional module titled The Human Genome Project: Biology, Computers, and Privacy, designed for use in high school biology classes. The module consists of print materials and both Macintosh and Windows versions of related computer software-Appendix A contains a copy of the print materials and discs containing the two versions of the software.

The African Genome Variation Project shapes medical genetics in Africa

Science.gov (United States)

Gurdasani, Deepti; Carstensen, Tommy; Tekola-Ayele, Fasil; Pagani, Luca; Tachmazidou, Ioanna; Hatzikotoulas, Konstantinos; Karthikeyan, Savita; Iles, Louise; Pollard, Martin O.; Choudhury, Ananyo; Ritchie, Graham R. S.; Xue, Yali; Asimit, Jennifer; Nsubuga, Rebecca N.; Young, Elizabeth H.; Pomilla, Cristina; Kivinen, Katja; Rockett, Kirk; Kamali, Anatoli; Doumatey, Ayo P.; Asiki, Gershim; Seeley, Janet; Sisay-Joof, Fatoumatta; Jallow, Muminatou; Tollman, Stephen; Mekonnen, Ephrem; Ekong, Rosemary; Oljira, Tamiru; Bradman, Neil; Bojang, Kalifa; Ramsay, Michele; Adeyemo, Adebowale; Bekele, Endashaw; Motala, Ayesha; Norris, Shane A.; Pirie, Fraser; Kaleebu, Pontiano; Kwiatkowski, Dominic; Tyler-Smith, Chris; Rotimi, Charles; Zeggini, Eleftheria; Sandhu, Manjinder S.

2015-01-01

Given the importance of Africa to studies of human origins and disease susceptibility, detailed characterization of African genetic diversity is needed. The African Genome Variation Project provides a resource with which to design, implement and interpret genomic studies in sub-Saharan Africa and worldwide. The African Genome Variation Project represents dense genotypes from 1,481 individuals and whole-genome sequences from 320 individuals across sub-Saharan Africa. Using this resource, we find novel evidence of complex, regionally distinct hunter-gatherer and Eurasian admixture across sub-Saharan Africa. We identify new loci under selection, including loci related to malaria susceptibility and hypertension. We show that modern imputation panels (sets of reference genotypes from which unobserved or missing genotypes in study sets can be inferred) can identify association signals at highly differentiated loci across populations in sub-Saharan Africa. Using whole-genome sequencing, we demonstrate further improvements in imputation accuracy, strengthening the case for large-scale sequencing efforts of diverse African haplotypes. Finally, we present an efficient genotype array design capturing common genetic variation in Africa.

The African Genome Variation Project shapes medical genetics in Africa.

Science.gov (United States)

Gurdasani, Deepti; Carstensen, Tommy; Tekola-Ayele, Fasil; Pagani, Luca; Tachmazidou, Ioanna; Hatzikotoulas, Konstantinos; Karthikeyan, Savita; Iles, Louise; Pollard, Martin O; Choudhury, Ananyo; Ritchie, Graham R S; Xue, Yali; Asimit, Jennifer; Nsubuga, Rebecca N; Young, Elizabeth H; Pomilla, Cristina; Kivinen, Katja; Rockett, Kirk; Kamali, Anatoli; Doumatey, Ayo P; Asiki, Gershim; Seeley, Janet; Sisay-Joof, Fatoumatta; Jallow, Muminatou; Tollman, Stephen; Mekonnen, Ephrem; Ekong, Rosemary; Oljira, Tamiru; Bradman, Neil; Bojang, Kalifa; Ramsay, Michele; Adeyemo, Adebowale; Bekele, Endashaw; Motala, Ayesha; Norris, Shane A; Pirie, Fraser; Kaleebu, Pontiano; Kwiatkowski, Dominic; Tyler-Smith, Chris; Rotimi, Charles; Zeggini, Eleftheria; Sandhu, Manjinder S

2015-01-15

Given the importance of Africa to studies of human origins and disease susceptibility, detailed characterization of African genetic diversity is needed. The African Genome Variation Project provides a resource with which to design, implement and interpret genomic studies in sub-Saharan Africa and worldwide. The African Genome Variation Project represents dense genotypes from 1,481 individuals and whole-genome sequences from 320 individuals across sub-Saharan Africa. Using this resource, we find novel evidence of complex, regionally distinct hunter-gatherer and Eurasian admixture across sub-Saharan Africa. We identify new loci under selection, including loci related to malaria susceptibility and hypertension. We show that modern imputation panels (sets of reference genotypes from which unobserved or missing genotypes in study sets can be inferred) can identify association signals at highly differentiated loci across populations in sub-Saharan Africa. Using whole-genome sequencing, we demonstrate further improvements in imputation accuracy, strengthening the case for large-scale sequencing efforts of diverse African haplotypes. Finally, we present an efficient genotype array design capturing common genetic variation in Africa.

Estimation of genomic prediction accuracy from reference populations with varying degrees of relationship.

Directory of Open Access Journals (Sweden)

S Hong Lee

Full Text Available Genomic prediction is emerging in a wide range of fields including animal and plant breeding, risk prediction in human precision medicine and forensic. It is desirable to establish a theoretical framework for genomic prediction accuracy when the reference data consists of information sources with varying degrees of relationship to the target individuals. A reference set can contain both close and distant relatives as well as 'unrelated' individuals from the wider population in the genomic prediction. The various sources of information were modeled as different populations with different effective population sizes (Ne. Both the effective number of chromosome segments (Me and Ne are considered to be a function of the data used for prediction. We validate our theory with analyses of simulated as well as real data, and illustrate that the variation in genomic relationships with the target is a predictor of the information content of the reference set. With a similar amount of data available for each source, we show that close relatives can have a substantially larger effect on genomic prediction accuracy than lesser related individuals. We also illustrate that when prediction relies on closer relatives, there is less improvement in prediction accuracy with an increase in training data or marker panel density. We release software that can estimate the expected prediction accuracy and power when combining different reference sources with various degrees of relationship to the target, which is useful when planning genomic prediction (before or after collecting data in animal, plant and human genetics.

The Genomes OnLine Database (GOLD) v.5: a metadata management system based on a four level (meta)genome project classification

Science.gov (United States)

Reddy, T.B.K.; Thomas, Alex D.; Stamatis, Dimitri; Bertsch, Jon; Isbandi, Michelle; Jansson, Jakob; Mallajosyula, Jyothi; Pagani, Ioanna; Lobos, Elizabeth A.; Kyrpides, Nikos C.

2015-01-01

The Genomes OnLine Database (GOLD; http://www.genomesonline.org) is a comprehensive online resource to catalog and monitor genetic studies worldwide. GOLD provides up-to-date status on complete and ongoing sequencing projects along with a broad array of curated metadata. Here we report version 5 (v.5) of the database. The newly designed database schema and web user interface supports several new features including the implementation of a four level (meta)genome project classification system and a simplified intuitive web interface to access reports and launch search tools. The database currently hosts information for about 19 200 studies, 56 000 Biosamples, 56 000 sequencing projects and 39 400 analysis projects. More than just a catalog of worldwide genome projects, GOLD is a manually curated, quality-controlled metadata warehouse. The problems encountered in integrating disparate and varying quality data into GOLD are briefly highlighted. GOLD fully supports and follows the Genomic Standards Consortium (GSC) Minimum Information standards. PMID:25348402

The Genomes OnLine Database (GOLD) v.5: a metadata management system based on a four level (meta)genome project classification

Energy Technology Data Exchange (ETDEWEB)

Reddy, Tatiparthi B. K. [USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Thomas, Alex D. [USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Stamatis, Dimitri [USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Bertsch, Jon [USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Isbandi, Michelle [USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Jansson, Jakob [USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Mallajosyula, Jyothi [USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Pagani, Ioanna [USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Lobos, Elizabeth A. [USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Kyrpides, Nikos C. [USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); King Abdulaziz Univ., Jeddah (Saudi Arabia)

2014-10-27

The Genomes OnLine Database (GOLD; http://www.genomesonline.org) is a comprehensive online resource to catalog and monitor genetic studies worldwide. GOLD provides up-to-date status on complete and ongoing sequencing projects along with a broad array of curated metadata. Within this paper, we report version 5 (v.5) of the database. The newly designed database schema and web user interface supports several new features including the implementation of a four level (meta)genome project classification system and a simplified intuitive web interface to access reports and launch search tools. The database currently hosts information for about 19 200 studies, 56 000 Biosamples, 56 000 sequencing projects and 39 400 analysis projects. More than just a catalog of worldwide genome projects, GOLD is a manually curated, quality-controlled metadata warehouse. The problems encountered in integrating disparate and varying quality data into GOLD are briefly highlighted. Lastly, GOLD fully supports and follows the Genomic Standards Consortium (GSC) Minimum Information standards.

A human genome-wide library of local phylogeny predictions for whole-genome inference problems

Directory of Open Access Journals (Sweden)

Schwartz Russell

2008-08-01

Full Text Available Abstract Background Many common inference problems in computational genetics depend on inferring aspects of the evolutionary history of a data set given a set of observed modern sequences. Detailed predictions of the full phylogenies are therefore of value in improving our ability to make further inferences about population history and sources of genetic variation. Making phylogenetic predictions on the scale needed for whole-genome analysis is, however, extremely computationally demanding. Results In order to facilitate phylogeny-based predictions on a genomic scale, we develop a library of maximum parsimony phylogenies within local regions spanning all autosomal human chromosomes based on Haplotype Map variation data. We demonstrate the utility of this library for population genetic inferences by examining a tree statistic we call 'imperfection,' which measures the reuse of variant sites within a phylogeny. This statistic is significantly predictive of recombination rate, shows additional regional and population-specific conservation, and allows us to identify outlier genes likely to have experienced unusual amounts of variation in recent human history. Conclusion Recent theoretical advances in algorithms for phylogenetic tree reconstruction have made it possible to perform large-scale inferences of local maximum parsimony phylogenies from single nucleotide polymorphism (SNP data. As results from the imperfection statistic demonstrate, phylogeny predictions encode substantial information useful for detecting genomic features and population history. This data set should serve as a platform for many kinds of inferences one may wish to make about human population history and genetic variation.

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

Gene disruptions using P transposable elements: an integral component of the Drosophila genome project.

OpenAIRE

Spradling, A C; Stern, D M; Kiss, I; Roote, J; Laverty, T; Rubin, G M

1995-01-01

Biologists require genetic as well as molecular tools to decipher genomic information and ultimately to understand gene function. The Berkeley Drosophila Genome Project is addressing these needs with a massive gene disruption project that uses individual, genetically engineered P transposable elements to target open reading frames throughout the Drosophila genome. DNA flanking the insertions is sequenced, thereby placing an extensive series of genetic markers on the physical genomic map and a...

The human Genome project and the future of oncology

International Nuclear Information System (INIS)

Collins, Francis S.

1996-01-01

The Human Genome Project is an ambitious 15-year effort to devise maps and sequence of the 3-billion base pair human genome, including all 100,000 genes. The project is running ahead of schedule and under budget. Already the effects on progress in disease gene discovery have been dramatic, especially for cancer. The most appropriate uses of susceptibility testing for breast, ovarian, and colon cancer are being investigated in research protocols, and the need to prevent genetic discrimination in employment and health insurance is becoming more urgent. In the longer term, these gene discoveries are likely to usher in a new era of therapeutic molecular medicine

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

The advent of high-throughput genotyping technologies coupled to genomic prediction methods established a new paradigm to integrate genomics and breeding. We carried out whole-genome prediction and contrasted it to a genome-wide association study (GWAS) for growth traits in breeding populations of Eucalyptus benthamii (n =505) and Eucalyptus pellita (n =732). Both species are of increasing commercial interest for the development of germplasm adapted to environmental stresses. Predictive ability reached 0.16 in E. benthamii and 0.44 in E. pellita for diameter growth. Predictive abilities using either Genomic BLUP or different Bayesian methods were similar, suggesting that growth adequately fits the infinitesimal model. Genomic prediction models using ~5000-10,000 SNPs provided predictive abilities equivalent to using all 13,787 and 19,506 SNPs genotyped in the E. benthamii and E. pellita populations, respectively. No difference was detected in predictive ability when different sets of SNPs were utilized, based on position (equidistantly genome-wide, inside genes, linkage disequilibrium pruned or on single chromosomes), as long as the total number of SNPs used was above ~5000. Predictive abilities obtained by removing relatedness between training and validation sets fell near zero for E. benthamii and were halved for E. pellita. These results corroborate the current view that relatedness is the main driver of genomic prediction, although some short-range historical linkage disequilibrium (LD) was likely captured for E. pellita. A GWAS identified only one significant association for volume growth in E. pellita, illustrating the fact that while genome-wide regression is able to account for large proportions of the heritability, very little or none of it is captured into significant associations using GWAS in breeding populations of the size evaluated in this study. This study provides further experimental data supporting positive prospects of using genome-wide data to

Genomic prediction within and across biparental families

NARCIS (Netherlands)

Schopp, Pascal; Müller, Dominik; Wientjes, Yvonne C.J.; Melchinger, Albrecht E.

2017-01-01

A major application of genomic prediction (GP) in plant breeding is the identification of superior inbred lines within families derived from biparental crosses. When models for various traits were trained within related or unrelated biparental families (BPFs), experimental studies found substantial

The Human Genome Project and Biology Education.

Science.gov (United States)

McInerney, Joseph D.

1996-01-01

Highlights the importance of the Human Genome Project in educating the public about genetics. Discusses four challenges that science educators must address: teaching for conceptual understanding, the nature of science, the personal and social impact of science and technology, and the principles of technology. Contains 45 references. (JRH)

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.

SPOCS: Software for Predicting and Visualizing Orthology/Paralogy Relationships Among Genomes

Energy Technology Data Exchange (ETDEWEB)

Curtis, Darren S.; Phillips, Aaron R.; Callister, Stephen J.; Conlan, Sean; McCue, Lee Ann

2013-10-15

At the rate that prokaryotic genomes can now be generated, comparative genomics studies require a flexible method for quickly and accurately predicting orthologs among the rapidly changing set of genomes available. SPOCS implements a graph-based ortholog prediction method to generate a simple tab-delimited table of orthologs and in addition, html files that provide a visualization of the predicted ortholog/paralog relationships to which gene/protein expression metadata may be overlaid. AVAILABILITY AND IMPLEMENTATION: A SPOCS web application is freely available at http://cbb.pnnl.gov/portal/tools/spocs.html. Source code for Linux systems is also freely available under an open source license at http://cbb.pnnl.gov/portal/software/spocs.html; the Boost C++ libraries and BLAST are required.

iPat: intelligent prediction and association tool for genomic research.

Science.gov (United States)

Chen, Chunpeng James; Zhang, Zhiwu

2018-06-01

The ultimate goal of genomic research is to effectively predict phenotypes from genotypes so that medical management can improve human health and molecular breeding can increase agricultural production. Genomic prediction or selection (GS) plays a complementary role to genome-wide association studies (GWAS), which is the primary method to identify genes underlying phenotypes. Unfortunately, most computing tools cannot perform data analyses for both GWAS and GS. Furthermore, the majority of these tools are executed through a command-line interface (CLI), which requires programming skills. Non-programmers struggle to use them efficiently because of the steep learning curves and zero tolerance for data formats and mistakes when inputting keywords and parameters. To address these problems, this study developed a software package, named the Intelligent Prediction and Association Tool (iPat), with a user-friendly graphical user interface. With iPat, GWAS or GS can be performed using a pointing device to simply drag and/or click on graphical elements to specify input data files, choose input parameters and select analytical models. Models available to users include those implemented in third party CLI packages such as GAPIT, PLINK, FarmCPU, BLINK, rrBLUP and BGLR. Users can choose any data format and conduct analyses with any of these packages. File conversions are automatically conducted for specified input data and selected packages. A GWAS-assisted genomic prediction method was implemented to perform genomic prediction using any GWAS method such as FarmCPU. iPat was written in Java for adaptation to multiple operating systems including Windows, Mac and Linux. The iPat executable file, user manual, tutorials and example datasets are freely available at http://zzlab.net/iPat. zhiwu.zhang@wsu.edu.

Genomic prediction of traits related to canine hip dysplasia

Directory of Open Access Journals (Sweden)

Enrique eSanchez-Molano

2015-03-01

Full Text Available Increased concern for the welfare of pedigree dogs has led to development of selection programs against inherited diseases. An example is canine hip dysplasia (CHD, which has a moderate heritability and a high prevalence in some large-sized breeds. To date, selection using phenotypes has led to only modest improvement, and alternative strategies such as genomic selection may prove more effective. The primary aims of this study were to compare the performance of pedigree- and genomic-based breeding against CHD in the UK Labrador retriever population and to evaluate the performance of different genomic selection methods. A sample of 1179 Labrador Retrievers evaluated for CHD according to the UK scoring method (hip score, HS was genotyped with the Illumina CanineHD BeadChip. Twelve functions of HS and its component traits were analyzed using different statistical methods (GBLUP, Bayes C and Single-Step methods, and results were compared with a pedigree-based approach (BLUP using cross-validation. Genomic methods resulted in similar or higher accuracies than pedigree-based methods with training sets of 944 individuals for all but the untransformed HS, suggesting that genomic selection is an effective strategy. GBLUP and Bayes C gave similar prediction accuracies for HS and related traits, indicating a polygenic architecture. This conclusion was also supported by the low accuracies obtained in additional GBLUP analyses performed using only the SNPs with highest test statistics, also indicating that marker-assisted selection would not be as effective as genomic selection. A Single-Step method that combines genomic and pedigree information also showed higher accuracy than GBLUP and Bayes C for the log-transformed HS, which is currently used for pedigree based evaluations in UK. In conclusion, genomic selection is a promising alternative to pedigree-based selection against CHD, requiring more phenotypes with genomic data to improve further the accuracy

Accounting for genetic architecture improves sequence based genomic prediction for a Drosophila fitness trait.

Science.gov (United States)

Ober, Ulrike; Huang, Wen; Magwire, Michael; Schlather, Martin; Simianer, Henner; Mackay, Trudy F C

2015-01-01

The ability to predict quantitative trait phenotypes from molecular polymorphism data will revolutionize evolutionary biology, medicine and human biology, and animal and plant breeding. Efforts to map quantitative trait loci have yielded novel insights into the biology of quantitative traits, but the combination of individually significant quantitative trait loci typically has low predictive ability. Utilizing all segregating variants can give good predictive ability in plant and animal breeding populations, but gives little insight into trait biology. Here, we used the Drosophila Genetic Reference Panel to perform both a genome wide association analysis and genomic prediction for the fitness-related trait chill coma recovery time. We found substantial total genetic variation for chill coma recovery time, with a genetic architecture that differs between males and females, a small number of molecular variants with large main effects, and evidence for epistasis. Although the top additive variants explained 36% (17%) of the genetic variance among lines in females (males), the predictive ability using genomic best linear unbiased prediction and a relationship matrix using all common segregating variants was very low for females and zero for males. We hypothesized that the low predictive ability was due to the mismatch between the infinitesimal genetic architecture assumed by the genomic best linear unbiased prediction model and the true genetic architecture of chill coma recovery time. Indeed, we found that the predictive ability of the genomic best linear unbiased prediction model is markedly improved when we combine quantitative trait locus mapping with genomic prediction by only including the top variants associated with main and epistatic effects in the relationship matrix. This trait-associated prediction approach has the advantage that it yields biologically interpretable prediction models.

Data Mining Approaches for Genomic Biomarker Development: Applications Using Drug Screening Data from the Cancer Genome Project and the Cancer Cell Line Encyclopedia.

Directory of Open Access Journals (Sweden)

David G Covell

Full Text Available Developing reliable biomarkers of tumor cell drug sensitivity and resistance can guide hypothesis-driven basic science research and influence pre-therapy clinical decisions. A popular strategy for developing biomarkers uses characterizations of human tumor samples against a range of cancer drug responses that correlate with genomic change; developed largely from the efforts of the Cancer Cell Line Encyclopedia (CCLE and Sanger Cancer Genome Project (CGP. The purpose of this study is to provide an independent analysis of this data that aims to vet existing and add novel perspectives to biomarker discoveries and applications. Existing and alternative data mining and statistical methods will be used to a evaluate drug responses of compounds with similar mechanism of action (MOA, b examine measures of gene expression (GE, copy number (CN and mutation status (MUT biomarkers, combined with gene set enrichment analysis (GSEA, for hypothesizing biological processes important for drug response, c conduct global comparisons of GE, CN and MUT as biomarkers across all drugs screened in the CGP dataset, and d assess the positive predictive power of CGP-derived GE biomarkers as predictors of drug response in CCLE tumor cells. The perspectives derived from individual and global examinations of GEs, MUTs and CNs confirm existing and reveal unique and shared roles for these biomarkers in tumor cell drug sensitivity and resistance. Applications of CGP-derived genomic biomarkers to predict the drug response of CCLE tumor cells finds a highly significant ROC, with a positive predictive power of 0.78. The results of this study expand the available data mining and analysis methods for genomic biomarker development and provide additional support for using biomarkers to guide hypothesis-driven basic science research and pre-therapy clinical decisions.

Genome-enabled predictions for binomial traits in sugar beet populations.

Science.gov (United States)

Biscarini, Filippo; Stevanato, Piergiorgio; Broccanello, Chiara; Stella, Alessandra; Saccomani, Massimo

2014-07-22

Genomic information can be used to predict not only continuous but also categorical (e.g. binomial) traits. Several traits of interest in human medicine and agriculture present a discrete distribution of phenotypes (e.g. disease status). Root vigor in sugar beet (B. vulgaris) is an example of binomial trait of agronomic importance. In this paper, a panel of 192 SNPs (single nucleotide polymorphisms) was used to genotype 124 sugar beet individual plants from 18 lines, and to classify them as showing "high" or "low" root vigor. A threshold model was used to fit the relationship between binomial root vigor and SNP genotypes, through the matrix of genomic relationships between individuals in a genomic BLUP (G-BLUP) approach. From a 5-fold cross-validation scheme, 500 testing subsets were generated. The estimated average cross-validation error rate was 0.000731 (0.073%). Only 9 out of 12326 test observations (500 replicates for an average test set size of 24.65) were misclassified. The estimated prediction accuracy was quite high. Such accurate predictions may be related to the high estimated heritability for root vigor (0.783) and to the few genes with large effect underlying the trait. Despite the sparse SNP panel, there was sufficient within-scaffold LD where SNPs with large effect on root vigor were located to allow for genome-enabled predictions to work.

Genome-enabled prediction models for yield related traits in chickpea

Science.gov (United States)

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...

Genomes to life project quarterly report June 2004.

Energy Technology Data Exchange (ETDEWEB)

Heffelfinger, Grant S.

2005-01-01

This SAND report provides the technical progress through June 2004 of the Sandia-led project, ''Carbon Sequestration in Synechococcus Sp.: From Molecular Machines to Hierarchical Modeling'', funded by the DOE Office of Science Genomes to Life Program. Understanding, predicting, and perhaps manipulating carbon fixation in the oceans has long been a major focus of biological oceanography and has more recently been of interest to a broader audience of scientists and policy makers. It is clear that the oceanic sinks and sources of CO{sub 2} are important terms in the global environmental response to anthropogenic atmospheric inputs of CO{sub 2} and that oceanic microorganisms play a key role in this response. However, the relationship between this global phenomenon and the biochemical mechanisms of carbon fixation in these microorganisms is poorly understood. In this project, we will investigate the carbon sequestration behavior of Synechococcus Sp., an abundant marine cyanobacteria known to be important to environmental responses to carbon dioxide levels, through experimental and computational methods. This project is a combined experimental and computational effort with emphasis on developing and applying new computational tools and methods. Our experimental effort will provide the biology and data to drive the computational efforts and include significant investment in developing new experimental methods for uncovering protein partners, characterizing protein complexes, identifying new binding domains. We will also develop and apply new data measurement and statistical methods for analyzing microarray experiments. Computational tools will be essential to our efforts to discover and characterize the function of the molecular machines of Synechococcus. To this end, molecular simulation methods will be coupled with knowledge discovery from diverse biological data sets for high-throughput discovery and characterization of protein-protein complexes

RegPredict: an integrated system for regulon inference in prokaryotes by comparative genomics approach

Energy Technology Data Exchange (ETDEWEB)

Novichkov, Pavel S.; Rodionov, Dmitry A.; Stavrovskaya, Elena D.; Novichkova, Elena S.; Kazakov, Alexey E.; Gelfand, Mikhail S.; Arkin, Adam P.; Mironov, Andrey A.; Dubchak, Inna

2010-05-26

RegPredict web server is designed to provide comparative genomics tools for reconstruction and analysis of microbial regulons using comparative genomics approach. The server allows the user to rapidly generate reference sets of regulons and regulatory motif profiles in a group of prokaryotic genomes. The new concept of a cluster of co-regulated orthologous operons allows the user to distribute the analysis of large regulons and to perform the comparative analysis of multiple clusters independently. Two major workflows currently implemented in RegPredict are: (i) regulon reconstruction for a known regulatory motif and (ii) ab initio inference of a novel regulon using several scenarios for the generation of starting gene sets. RegPredict provides a comprehensive collection of manually curated positional weight matrices of regulatory motifs. It is based on genomic sequences, ortholog and operon predictions from the MicrobesOnline. An interactive web interface of RegPredict integrates and presents diverse genomic and functional information about the candidate regulon members from several web resources. RegPredict is freely accessible at http://regpredict.lbl.gov.

Predicting genome-wide redundancy using machine learning

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Shasha Dennis E

2010-11-01

Full Text Available Abstract Background Gene duplication can lead to genetic redundancy, which masks the function of mutated genes in genetic analyses. Methods to increase sensitivity in identifying genetic redundancy can improve the efficiency of reverse genetics and lend insights into the evolutionary outcomes of gene duplication. Machine learning techniques are well suited to classifying gene family members into redundant and non-redundant gene pairs in model species where sufficient genetic and genomic data is available, such as Arabidopsis thaliana, the test case used here. Results Machine learning techniques that combine multiple attributes led to a dramatic improvement in predicting genetic redundancy over single trait classifiers alone, such as BLAST E-values or expression correlation. In withholding analysis, one of the methods used here, Support Vector Machines, was two-fold more precise than single attribute classifiers, reaching a level where the majority of redundant calls were correctly labeled. Using this higher confidence in identifying redundancy, machine learning predicts that about half of all genes in Arabidopsis showed the signature of predicted redundancy with at least one but typically less than three other family members. Interestingly, a large proportion of predicted redundant gene pairs were relatively old duplications (e.g., Ks > 1, suggesting that redundancy is stable over long evolutionary periods. Conclusions Machine learning predicts that most genes will have a functionally redundant paralog but will exhibit redundancy with relatively few genes within a family. The predictions and gene pair attributes for Arabidopsis provide a new resource for research in genetics and genome evolution. These techniques can now be applied to other organisms.

Improving genomic prediction for Danish Jersey using a joint Danish-US reference population

DEFF Research Database (Denmark)

Su, Guosheng; Nielsen, Ulrik Sander; Wiggans, G

Accuracy of genomic prediction depends on the information in the reference population. Achieving an adequate sized reference population is a challenge for genomic prediction in small cattle populations. One way to increase the size of reference population is to combine reference data from different...... populations. The objective of this study was to assess the gain of genomic prediction accuracy when including US Jersey bulls in the Danish Jersey reference population. The data included 1,262 Danish progeny-tested bulls and 1,157 US progeny-tested bulls. Genomic breeding values (GEBV) were predicted using...... a GBLUP model from the Danish reference population and the joint Danish-US reference population. The traits in the analysis were milk yield, fat yield, protein yield, fertility, mastitis, longevity, body conformation, feet & legs, and longevity. Eight of the nine traits benefitted from the inclusion of US...

Coordinated international action to accelerate genome-to-phenome with FAANG, the Functional Annotation of Animal Genomes project : open letter

NARCIS (Netherlands)

Archibald, A.L.; Bottema, C.D.; Brauning, R.; Burgess, S.C.; Burt, D.W.; Casas, E.; Cheng, H.H.; Clarke, L.; Couldrey, C.; Dalrymple, B.P.; Elsik, C.G.; Foissac, S.; Giuffra, E.; Groenen, M.A.M.; Hayes, B.J.; Huang, L.S.; Khatib, H.; Kijas, J.W.; Kim, H.; Lunney, J.K.; McCarthy, F.M.; McEwan, J.; Moore, S.; Nanduri, B.; Notredame, C.; Palti, Y.; Plastow, G.S.; Reecy, J.M.; Rohrer, G.; Sarropoulou, E.; Schmidt, C.J.; Silverstein, J.; Tellam, R.L.; Tixier-Boichard, M.; Tosser-klopp, G.; Tuggle, C.K.; Vilkki, J.; White, S.N.; Zhao, S.; Zhou, H.

2015-01-01

We describe the organization of a nascent international effort, the Functional Annotation of Animal Genomes (FAANG) project, whose aim is to produce comprehensive maps of functional elements in the genomes of domesticated animal species.

MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes

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Yang Yi-Fan

2007-03-01

Full Text Available Abstract Background Despite a remarkable success in the computational prediction of genes in Bacteria and Archaea, a lack of comprehensive understanding of prokaryotic gene structures prevents from further elucidation of differences among genomes. It continues to be interesting to develop new ab initio algorithms which not only accurately predict genes, but also facilitate comparative studies of prokaryotic genomes. Results This paper describes a new prokaryotic genefinding algorithm based on a comprehensive statistical model of protein coding Open Reading Frames (ORFs and Translation Initiation Sites (TISs. The former is based on a linguistic "Entropy Density Profile" (EDP model of coding DNA sequence and the latter comprises several relevant features related to the translation initiation. They are combined to form a so-called Multivariate Entropy Distance (MED algorithm, MED 2.0, that incorporates several strategies in the iterative program. The iterations enable us to develop a non-supervised learning process and to obtain a set of genome-specific parameters for the gene structure, before making the prediction of genes. Conclusion Results of extensive tests show that MED 2.0 achieves a competitive high performance in the gene prediction for both 5' and 3' end matches, compared to the current best prokaryotic gene finders. The advantage of the MED 2.0 is particularly evident for GC-rich genomes and archaeal genomes. Furthermore, the genome-specific parameters given by MED 2.0 match with the current understanding of prokaryotic genomes and may serve as tools for comparative genomic studies. In particular, MED 2.0 is shown to reveal divergent translation initiation mechanisms in archaeal genomes while making a more accurate prediction of TISs compared to the existing gene finders and the current GenBank annotation.

Accounting for genetic architecture improves sequence based genomic prediction for a Drosophila fitness trait.

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Ulrike Ober

Full Text Available The ability to predict quantitative trait phenotypes from molecular polymorphism data will revolutionize evolutionary biology, medicine and human biology, and animal and plant breeding. Efforts to map quantitative trait loci have yielded novel insights into the biology of quantitative traits, but the combination of individually significant quantitative trait loci typically has low predictive ability. Utilizing all segregating variants can give good predictive ability in plant and animal breeding populations, but gives little insight into trait biology. Here, we used the Drosophila Genetic Reference Panel to perform both a genome wide association analysis and genomic prediction for the fitness-related trait chill coma recovery time. We found substantial total genetic variation for chill coma recovery time, with a genetic architecture that differs between males and females, a small number of molecular variants with large main effects, and evidence for epistasis. Although the top additive variants explained 36% (17% of the genetic variance among lines in females (males, the predictive ability using genomic best linear unbiased prediction and a relationship matrix using all common segregating variants was very low for females and zero for males. We hypothesized that the low predictive ability was due to the mismatch between the infinitesimal genetic architecture assumed by the genomic best linear unbiased prediction model and the true genetic architecture of chill coma recovery time. Indeed, we found that the predictive ability of the genomic best linear unbiased prediction model is markedly improved when we combine quantitative trait locus mapping with genomic prediction by only including the top variants associated with main and epistatic effects in the relationship matrix. This trait-associated prediction approach has the advantage that it yields biologically interpretable prediction models.

A Bayesian antedependence model for whole genome prediction.

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Yang, Wenzhao; Tempelman, Robert J

2012-04-01

Hierarchical mixed effects models have been demonstrated to be powerful for predicting genomic merit of livestock and plants, on the basis of high-density single-nucleotide polymorphism (SNP) marker panels, and their use is being increasingly advocated for genomic predictions in human health. Two particularly popular approaches, labeled BayesA and BayesB, are based on specifying all SNP-associated effects to be independent of each other. BayesB extends BayesA by allowing a large proportion of SNP markers to be associated with null effects. We further extend these two models to specify SNP effects as being spatially correlated due to the chromosomally proximal effects of causal variants. These two models, that we respectively dub as ante-BayesA and ante-BayesB, are based on a first-order nonstationary antedependence specification between SNP effects. In a simulation study involving 20 replicate data sets, each analyzed at six different SNP marker densities with average LD levels ranging from r(2) = 0.15 to 0.31, the antedependence methods had significantly (P 0. 24) with differences exceeding 3%. A cross-validation study was also conducted on the heterogeneous stock mice data resource (http://mus.well.ox.ac.uk/mouse/HS/) using 6-week body weights as the phenotype. The antedependence methods increased cross-validation prediction accuracies by up to 3.6% compared to their classical counterparts (P benchmark data sets and demonstrated that the antedependence methods were more accurate than their classical counterparts for genomic predictions, even for individuals several generations beyond the training data.

Genomes to Proteomes

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Panisko, Ellen A. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Grigoriev, Igor [USDOE Joint Genome Inst., Walnut Creek, CA (United States); Daly, Don S. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Webb-Robertson, Bobbie-Jo [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Baker, Scott E. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

2009-03-01

Biologists are awash with genomic sequence data. In large part, this is due to the rapid acceleration in the generation of DNA sequence that occurred as public and private research institutes raced to sequence the human genome. In parallel with the large human genome effort, mostly smaller genomes of other important model organisms were sequenced. Projects following on these initial efforts have made use of technological advances and the DNA sequencing infrastructure that was built for the human and other organism genome projects. As a result, the genome sequences of many organisms are available in high quality draft form. While in many ways this is good news, there are limitations to the biological insights that can be gleaned from DNA sequences alone; genome sequences offer only a bird's eye view of the biological processes endemic to an organism or community. Fortunately, the genome sequences now being produced at such a high rate can serve as the foundation for other global experimental platforms such as proteomics. Proteomic methods offer a snapshot of the proteins present at a point in time for a given biological sample. Current global proteomics methods combine enzymatic digestion, separations, mass spectrometry and database searching for peptide identification. One key aspect of proteomics is the prediction of peptide sequences from mass spectrometry data. Global proteomic analysis uses computational matching of experimental mass spectra with predicted spectra based on databases of gene models that are often generated computationally. Thus, the quality of gene models predicted from a genome sequence is crucial in the generation of high quality peptide identifications. Once peptides are identified they can be assigned to their parent protein. Proteins identified as expressed in a given experiment are most useful when compared to other expressed proteins in a larger biological context or biochemical pathway. In this chapter we will discuss the automatic

Multiple-Trait Genomic Selection Methods Increase Genetic Value Prediction Accuracy

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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

Genomic Prediction of Gene Bank Wheat Landraces

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José Crossa

2016-07-01

Full Text Available This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H for the highly heritable traits, days to heading (DTH, and days to maturity (DTM. Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E. Two alternative prediction strategies were studied: (1 random cross-validation of the data in 20% training (TRN and 80% testing (TST (TRN20-TST80 sets, and (2 two types of core sets, “diversity” and “prediction”, including 10% and 20%, respectively, of the total collections. Accounting for population structure decreased prediction accuracy by 15–20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces, and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces, and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections, but slightly lower for Iranian collections. Prediction accuracy when incorporating G × E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G × E term. For Iranian landraces, accuracies were 0.55 for the G × E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm

Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower.

Science.gov (United States)

Thorwarth, Patrick; Yousef, Eltohamy A A; Schmid, Karl J

2018-02-02

Genetic resources are an important source of genetic variation for plant breeding. Genome-wide association studies (GWAS) and genomic prediction greatly facilitate the analysis and utilization of useful genetic diversity for improving complex phenotypic traits in crop plants. We explored the potential of GWAS and genomic prediction for improving curd-related traits in cauliflower ( Brassica oleracea var. botrytis ) by combining 174 randomly selected cauliflower gene bank accessions from two different gene banks. The collection was genotyped with genotyping-by-sequencing (GBS) and phenotyped for six curd-related traits at two locations and three growing seasons. A GWAS analysis based on 120,693 single-nucleotide polymorphisms identified a total of 24 significant associations for curd-related traits. The potential for genomic prediction was assessed with a genomic best linear unbiased prediction model and BayesB. Prediction abilities ranged from 0.10 to 0.66 for different traits and did not differ between prediction methods. Imputation of missing genotypes only slightly improved prediction ability. Our results demonstrate that GWAS and genomic prediction in combination with GBS and phenotyping of highly heritable traits can be used to identify useful quantitative trait loci and genotypes among genetically diverse gene bank material for subsequent utilization as genetic resources in cauliflower breeding. Copyright © 2018 Thorwarth et al.

Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower

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Patrick Thorwarth

2018-02-01

Full Text Available Genetic resources are an important source of genetic variation for plant breeding. Genome-wide association studies (GWAS and genomic prediction greatly facilitate the analysis and utilization of useful genetic diversity for improving complex phenotypic traits in crop plants. We explored the potential of GWAS and genomic prediction for improving curd-related traits in cauliflower (Brassica oleracea var. botrytis by combining 174 randomly selected cauliflower gene bank accessions from two different gene banks. The collection was genotyped with genotyping-by-sequencing (GBS and phenotyped for six curd-related traits at two locations and three growing seasons. A GWAS analysis based on 120,693 single-nucleotide polymorphisms identified a total of 24 significant associations for curd-related traits. The potential for genomic prediction was assessed with a genomic best linear unbiased prediction model and BayesB. Prediction abilities ranged from 0.10 to 0.66 for different traits and did not differ between prediction methods. Imputation of missing genotypes only slightly improved prediction ability. Our results demonstrate that GWAS and genomic prediction in combination with GBS and phenotyping of highly heritable traits can be used to identify useful quantitative trait loci and genotypes among genetically diverse gene bank material for subsequent utilization as genetic resources in cauliflower breeding.

Sequencing of a new target genome: the Pediculus humanus humanus (Phthiraptera: Pediculidae) genome project.

Science.gov (United States)

Pittendrigh, B R; Clark, J M; Johnston, J S; Lee, S H; Romero-Severson, J; Dasch, G A

2006-11-01

The human body louse, Pediculus humanus humanus (L.), and the human head louse, Pediculus humanus capitis, belong to the hemimetabolous order Phthiraptera. The body louse is the primary vector that transmits the bacterial agents of louse-borne relapsing fever, trench fever, and epidemic typhus. The genomes of the bacterial causative agents of several of these aforementioned diseases have been sequenced. Thus, determining the body louse genome will enhance studies of host-vector-pathogen interactions. Although not important as a major disease vector, head lice are of major social concern. Resistance to traditional pesticides used to control head and body lice have developed. It is imperative that new molecular targets be discovered for the development of novel compounds to control these insects. No complete genome sequence exists for a hemimetabolous insect species primarily because hemimetabolous insects often have large (2000 Mb) to very large (up to 16,300 Mb) genomes. Fortuitously, we determined that the human body louse has one of the smallest genome sizes known in insects, suggesting it may be a suitable choice as a minimal hemimetabolous genome in which many genes have been eliminated during its adaptation to human parasitism. Because many louse species infest birds and mammals, the body louse genome-sequencing project will facilitate studies of their comparative genomics. A 6-8X coverage of the body louse genome, plus sequenced expressed sequence tags, should provide the entomological, evolutionary biology, medical, and public health communities with useful genetic information.

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines

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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.

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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.

Experimental-confirmation and functional-annotation of predicted proteins in the chicken genome

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McCarthy Fiona M

2007-11-01

Full Text Available Abstract Background The chicken genome was sequenced because of its phylogenetic position as a non-mammalian vertebrate, its use as a biomedical model especially to study embryology and development, its role as a source of human disease organisms and its importance as the major source of animal derived food protein. However, genomic sequence data is, in itself, of limited value; generally it is not equivalent to understanding biological function. The benefit of having a genome sequence is that it provides a basis for functional genomics. However, the sequence data currently available is poorly structurally and functionally annotated and many genes do not have standard nomenclature assigned. Results We analysed eight chicken tissues and improved the chicken genome structural annotation by providing experimental support for the in vivo expression of 7,809 computationally predicted proteins, including 30 chicken proteins that were only electronically predicted or hypothetical translations in human. To improve functional annotation (based on Gene Ontology, we mapped these identified proteins to their human and mouse orthologs and used this orthology to transfer Gene Ontology (GO functional annotations to the chicken proteins. The 8,213 orthology-based GO annotations that we produced represent an 8% increase in currently available chicken GO annotations. Orthologous chicken products were also assigned standardized nomenclature based on current chicken nomenclature guidelines. Conclusion We demonstrate the utility of high-throughput expression proteomics for rapid experimental structural annotation of a newly sequenced eukaryote genome. These experimentally-supported predicted proteins were further annotated by assigning the proteins with standardized nomenclature and functional annotation. This method is widely applicable to a diverse range of species. Moreover, information from one genome can be used to improve the annotation of other genomes and

The effect of genealogy-based haplotypes on genomic prediction

DEFF Research Database (Denmark)

Edriss, Vahid; Fernando, Rohan L.; Su, Guosheng

2013-01-01

on haplotypes instead of regression on individual markers. The aim of this study was to investigate the accuracy of genomic prediction using haplotypes based on local genealogy information. Methods A total of 4429 Danish Holstein bulls were genotyped with the 50K SNP chip. Haplotypes were constructed using...... local genealogical trees. Effects of haplotype covariates were estimated with two types of prediction models: (1) assuming that effects had the same distribution for all haplotype covariates, i.e. the GBLUP method and (2) assuming that a large proportion (pi) of the haplotype covariates had zero effect......, i.e. a Bayesian mixture method. Results About 7.5 times more covariate effects were estimated when fitting haplotypes based on local genealogical trees compared to fitting individuals markers. Genealogy-based haplotype clustering slightly increased the accuracy of genomic prediction and, in some...

Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato

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Benjamin Stich

2018-03-01

Full Text Available Genomic prediction is a routine tool in breeding programs of most major animal and plant species. However, its usefulness for potato breeding has not yet been evaluated in detail. The objectives of this study were to (i examine the prospects of genomic prediction of key performance traits in a diversity panel of tetraploid potato modeling additive, dominance, and epistatic effects, (ii investigate the effects of size and make up of training set, number of test environments and molecular markers on prediction accuracy, and (iii assess the effect of including markers from candidate genes on the prediction accuracy. With genomic best linear unbiased prediction (GBLUP, BayesA, BayesCπ, and Bayesian LASSO, four different prediction methods were used for genomic prediction of relative area under disease progress curve after a Phytophthora infestans infection, plant maturity, maturity corrected resistance, tuber starch content, tuber starch yield (TSY, and tuber yield (TY of 184 tetraploid potato clones or subsets thereof genotyped with the SolCAP 8.3k SNP array. The cross-validated prediction accuracies with GBLUP and the three Bayesian approaches for the six evaluated traits ranged from about 0.5 to about 0.8. For traits with a high expected genetic complexity, such as TSY and TY, we observed an 8% higher prediction accuracy using a model with additive and dominance effects compared with a model with additive effects only. Our results suggest that for oligogenic traits in general and when diagnostic markers are available in particular, the use of Bayesian methods for genomic prediction is highly recommended and that the diagnostic markers should be modeled as fixed effects. The evaluation of the relative performance of genomic prediction vs. phenotypic selection indicated that the former is superior, assuming cycle lengths and selection intensities that are possible to realize in commercial potato breeding programs.

Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor

DEFF Research Database (Denmark)

de los Campos, Gustavo; Vazquez, Ana I; Fernando, Rohan

2013-01-01

Despite important advances from Genome Wide Association Studies (GWAS), for most complex human traits and diseases, a sizable proportion of genetic variance remains unexplained and prediction accuracy (PA) is usually low. Evidence suggests that PA can be improved using Whole-Genome Regression (WGR......) models where phenotypes are regressed on hundreds of thousands of variants simultaneously. The Genomic Best Linear Unbiased Prediction G-BLUP, a ridge-regression type method) is a commonly used WGR method and has shown good predictive performance when applied to plant and animal breeding populations....... However, breeding and human populations differ greatly in a number of factors that can affect the predictive performance of G-BLUP. Using theory, simulations, and real data analysis, we study the erformance of G-BLUP when applied to data from related and unrelated human subjects. Under perfect linkage...

Documenting genomics: Applying archival theory to preserving the records of the Human Genome Project.

Science.gov (United States)

Shaw, Jennifer

2016-02-01

The Human Genome Archive Project (HGAP) aimed to preserve the documentary heritage of the UK's contribution to the Human Genome Project (HGP) by using archival theory to develop a suitable methodology for capturing the results of modern, collaborative science. After assessing past projects and different archival theories, the HGAP used an approach based on the theory of documentation strategy to try to capture the records of a scientific project that had an influence beyond the purely scientific sphere. The HGAP was an archival survey that ran for two years. It led to ninety scientists being contacted and has, so far, led to six collections being deposited in the Wellcome Library, with additional collections being deposited in other UK repositories. In applying documentation strategy the HGAP was attempting to move away from traditional archival approaches to science, which have generally focused on retired Nobel Prize winners. It has been partially successful in this aim, having managed to secure collections from people who are not 'big names', but who made an important contribution to the HGP. However, the attempt to redress the gender imbalance in scientific collections and to improve record-keeping in scientific organisations has continued to be difficult to achieve. Copyright © 2015 The Author. Published by Elsevier Ltd.. All rights reserved.

An assessment on epitope prediction methods for protozoa genomes

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Resende Daniela M

2012-11-01

Full Text Available Abstract Background Epitope prediction using computational methods represents one of the most promising approaches to vaccine development. Reduction of time, cost, and the availability of completely sequenced genomes are key points and highly motivating regarding the use of reverse vaccinology. Parasites of genus Leishmania are widely spread and they are the etiologic agents of leishmaniasis. Currently, there is no efficient vaccine against this pathogen and the drug treatment is highly toxic. The lack of sufficiently large datasets of experimentally validated parasites epitopes represents a serious limitation, especially for trypanomatids genomes. In this work we highlight the predictive performances of several algorithms that were evaluated through the development of a MySQL database built with the purpose of: a evaluating individual algorithms prediction performances and their combination for CD8+ T cell epitopes, B-cell epitopes and subcellular localization by means of AUC (Area Under Curve performance and a threshold dependent method that employs a confusion matrix; b integrating data from experimentally validated and in silico predicted epitopes; and c integrating the subcellular localization predictions and experimental data. NetCTL, NetMHC, BepiPred, BCPred12, and AAP12 algorithms were used for in silico epitope prediction and WoLF PSORT, Sigcleave and TargetP for in silico subcellular localization prediction against trypanosomatid genomes. Results A database-driven epitope prediction method was developed with built-in functions that were capable of: a removing experimental data redundancy; b parsing algorithms predictions and storage experimental validated and predict data; and c evaluating algorithm performances. Results show that a better performance is achieved when the combined prediction is considered. This is particularly true for B cell epitope predictors, where the combined prediction of AAP12 and BCPred12 reached an AUC value

The Genomes OnLine Database (GOLD) v.4: status of genomic and metagenomic projects and their associated metadata

Science.gov (United States)

Pagani, Ioanna; Liolios, Konstantinos; Jansson, Jakob; Chen, I-Min A.; Smirnova, Tatyana; Nosrat, Bahador; Markowitz, Victor M.; Kyrpides, Nikos C.

2012-01-01

The Genomes OnLine Database (GOLD, http://www.genomesonline.org/) is a comprehensive resource for centralized monitoring of genome and metagenome projects worldwide. Both complete and ongoing projects, along with their associated metadata, can be accessed in GOLD through precomputed tables and a search page. As of September 2011, GOLD, now on version 4.0, contains information for 11 472 sequencing projects, of which 2907 have been completed and their sequence data has been deposited in a public repository. Out of these complete projects, 1918 are finished and 989 are permanent drafts. Moreover, GOLD contains information for 340 metagenome studies associated with 1927 metagenome samples. GOLD continues to expand, moving toward the goal of providing the most comprehensive repository of metadata information related to the projects and their organisms/environments in accordance with the Minimum Information about any (x) Sequence specification and beyond. PMID:22135293

Genomic prediction in a nuclear population of layers using single-step models.

Science.gov (United States)

Yan, Yiyuan; Wu, Guiqin; Liu, Aiqiao; Sun, Congjiao; Han, Wenpeng; Li, Guangqi; Yang, Ning

2018-02-01

Single-step genomic prediction method has been proposed to improve the accuracy of genomic prediction by incorporating information of both genotyped and ungenotyped animals. The objective of this study is to compare the prediction performance of single-step model with a 2-step models and the pedigree-based models in a nuclear population of layers. A total of 1,344 chickens across 4 generations were genotyped by a 600 K SNP chip. Four traits were analyzed, i.e., body weight at 28 wk (BW28), egg weight at 28 wk (EW28), laying rate at 38 wk (LR38), and Haugh unit at 36 wk (HU36). In predicting offsprings, individuals from generation 1 to 3 were used as training data and females from generation 4 were used as validation set. The accuracies of predicted breeding values by pedigree BLUP (PBLUP), genomic BLUP (GBLUP), SSGBLUP and single-step blending (SSBlending) were compared for both genotyped and ungenotyped individuals. For genotyped females, GBLUP performed no better than PBLUP because of the small size of training data, while the 2 single-step models predicted more accurately than the PBLUP model. The average predictive ability of SSGBLUP and SSBlending were 16.0% and 10.8% higher than the PBLUP model across traits, respectively. Furthermore, the predictive abilities for ungenotyped individuals were also enhanced. The average improvements of prediction abilities were 5.9% and 1.5% for SSGBLUP and SSBlending model, respectively. It was concluded that single-step models, especially the SSGBLUP model, can yield more accurate prediction of genetic merits and are preferable for practical implementation of genomic selection in layers. © 2017 Poultry Science Association Inc.

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

The human genome project and the future of medical practice ...

African Journals Online (AJOL)

Contrary to the scepticism that characterised the planning stages of the human genome project, the technology and sequence data resulting from the project are set to revolutionise medical practice for good. The expected benefits include: enhanced discovery of disease genes, which will lead to improved knowledge on the ...

The Human Genome Project: An Imperative for International Collaboration.

Science.gov (United States)

Allende, J. E.

1989-01-01

Discussed is the Human Genome Project which aims to decipher the totality of the human genetic information. The historical background, the objectives, international cooperation, ethical discussion, and the role of UNESCO are included. (KR)

Multi-population genomic prediction using a multi-task Bayesian learning model.

Science.gov (United States)

Chen, Liuhong; Li, Changxi; Miller, Stephen; Schenkel, Flavio

2014-05-03

Genomic prediction in multiple populations can be viewed as a multi-task learning problem where tasks are to derive prediction equations for each population and multi-task learning property can be improved by sharing information across populations. The goal of this study was to develop a multi-task Bayesian learning model for multi-population genomic prediction with a strategy to effectively share information across populations. Simulation studies and real data from Holstein and Ayrshire dairy breeds with phenotypes on five milk production traits were used to evaluate the proposed multi-task Bayesian learning model and compare with a single-task model and a simple data pooling method. A multi-task Bayesian learning model was proposed for multi-population genomic prediction. Information was shared across populations through a common set of latent indicator variables while SNP effects were allowed to vary in different populations. Both simulation studies and real data analysis showed the effectiveness of the multi-task model in improving genomic prediction accuracy for the smaller Ayshire breed. Simulation studies suggested that the multi-task model was most effective when the number of QTL was small (n = 20), with an increase of accuracy by up to 0.09 when QTL effects were lowly correlated between two populations (ρ = 0.2), and up to 0.16 when QTL effects were highly correlated (ρ = 0.8). When QTL genotypes were included for training and validation, the improvements were 0.16 and 0.22, respectively, for scenarios of the low and high correlation of QTL effects between two populations. When the number of QTL was large (n = 200), improvement was small with a maximum of 0.02 when QTL genotypes were not included for genomic prediction. Reduction in accuracy was observed for the simple pooling method when the number of QTL was small and correlation of QTL effects between the two populations was low. For the real data, the multi-task model achieved an

Reconsidering democracy - History of the human genome project

NARCIS (Netherlands)

Huijer, M

What options are open for people-citizens, politicians, and other nonscientists-to become actively involved in and anticipate new directions in the life sciences? In addressing this question, this article focuses on the start of the Human Genome Project (1985-1990). By contrasting various models of

Enabling a Community to Dissect an Organism: Overview of the Neurospora Functional Genomics Project

OpenAIRE

Dunlap, Jay C.; Borkovich, Katherine A.; Henn, Matthew R.; Turner, Gloria E.; Sachs, Matthew S.; Glass, N. Louise; McCluskey, Kevin; Plamann, Michael; Galagan, James E.; Birren, Bruce W.; Weiss, Richard L.; Townsend, Jeffrey P.; Loros, Jennifer J.; Nelson, Mary Anne; Lambreghts, Randy

2007-01-01

A consortium of investigators is engaged in a functional genomics project centered on the filamentous fungus Neurospora, with an eye to opening up the functional genomic analysis of all the filamentous fungi. The overall goal of the four interdependent projects in this effort is to acccomplish functional genomics, annotation, and expression analyses of Neurospora crassa, a filamentous fungus that is an established model for the assemblage of over 250,000 species of nonyeast fungi. Building fr...

From structure prediction to genomic screens for novel non-coding RNAs

DEFF Research Database (Denmark)

Gorodkin, Jan; Hofacker, Ivo L.

2011-01-01

Abstract: Non-coding RNAs (ncRNAs) are receiving more and more attention not only as an abundant class of genes, but also as regulatory structural elements (some located in mRNAs). A key feature of RNA function is its structure. Computational methods were developed early for folding and prediction....... This and the increased amount of available genomes have made it possible to employ structure-based methods for genomic screens. The field has moved from folding prediction of single sequences to computational screens for ncRNAs in genomic sequence using the RNA structure as the main characteristic feature. Whereas early...... upon some of the concepts in current methods that have been applied in genomic screens for de novo RNA structures in searches for novel ncRNA genes and regulatory RNA structure on mRNAs. We discuss the strengths and weaknesses of the different strategies and how they can complement each other....

Reconsidering democracy. History of the Human Genome Project.

NARCIS (Netherlands)

Marli Huijer

2003-01-01

What options are open for people—citizens, politicians, and other nonscientists—to become actively involved in and anticipate new directions in the life sciences? In addressing this question, this article focuses on the start of the Human Genome Project (1985-1990). By contrasting various models of

Improving accuracy of genomic prediction in Brangus cattle by adding animals with imputed low-density SNP genotypes.

Science.gov (United States)

Lopes, F B; Wu, X-L; Li, H; Xu, J; Perkins, T; Genho, J; Ferretti, R; Tait, R G; Bauck, S; Rosa, G J M

2018-02-01

Reliable genomic prediction of breeding values for quantitative traits requires the availability of sufficient number of animals with genotypes and phenotypes in the training set. As of 31 October 2016, there were 3,797 Brangus animals with genotypes and phenotypes. These Brangus animals were genotyped using different commercial SNP chips. Of them, the largest group consisted of 1,535 animals genotyped by the GGP-LDV4 SNP chip. The remaining 2,262 genotypes were imputed to the SNP content of the GGP-LDV4 chip, so that the number of animals available for training the genomic prediction models was more than doubled. The present study showed that the pooling of animals with both original or imputed 40K SNP genotypes substantially increased genomic prediction accuracies on the ten traits. By supplementing imputed genotypes, the relative gains in genomic prediction accuracies on estimated breeding values (EBV) were from 12.60% to 31.27%, and the relative gain in genomic prediction accuracies on de-regressed EBV was slightly small (i.e. 0.87%-18.75%). The present study also compared the performance of five genomic prediction models and two cross-validation methods. The five genomic models predicted EBV and de-regressed EBV of the ten traits similarly well. Of the two cross-validation methods, leave-one-out cross-validation maximized the number of animals at the stage of training for genomic prediction. Genomic prediction accuracy (GPA) on the ten quantitative traits was validated in 1,106 newly genotyped Brangus animals based on the SNP effects estimated in the previous set of 3,797 Brangus animals, and they were slightly lower than GPA in the original data. The present study was the first to leverage currently available genotype and phenotype resources in order to harness genomic prediction in Brangus beef cattle. © 2018 Blackwell Verlag GmbH.

Reduce manual curation by combining gene predictions from multiple annotation engines, a case study of start codon prediction.

Directory of Open Access Journals (Sweden)

Thomas H A Ederveen

Full Text Available Nowadays, prokaryotic genomes are sequenced faster than the capacity to manually curate gene annotations. Automated genome annotation engines provide users a straight-forward and complete solution for predicting ORF coordinates and function. For many labs, the use of AGEs is therefore essential to decrease the time necessary for annotating a given prokaryotic genome. However, it is not uncommon for AGEs to provide different and sometimes conflicting predictions. Combining multiple AGEs might allow for more accurate predictions. Here we analyzed the ab initio open reading frame (ORF calling performance of different AGEs based on curated genome annotations of eight strains from different bacterial species with GC% ranging from 35-52%. We present a case study which demonstrates a novel way of comparative genome annotation, using combinations of AGEs in a pre-defined order (or path to predict ORF start codons. The order of AGE combinations is from high to low specificity, where the specificity is based on the eight genome annotations. For each AGE combination we are able to derive a so-called projected confidence value, which is the average specificity of ORF start codon prediction based on the eight genomes. The projected confidence enables estimating likeliness of a correct prediction for a particular ORF start codon by a particular AGE combination, pinpointing ORFs notoriously difficult to predict start codons. We correctly predict start codons for 90.5±4.8% of the genes in a genome (based on the eight genomes with an accuracy of 81.1±7.6%. Our consensus-path methodology allows a marked improvement over majority voting (9.7±4.4% and with an optimal path ORF start prediction sensitivity is gained while maintaining a high specificity.

The Genome of the Netherlands: design, and project goals

Science.gov (United States)

Boomsma, Dorret I; Wijmenga, Cisca; Slagboom, Eline P; Swertz, Morris A; Karssen, Lennart C; Abdellaoui, Abdel; Ye, Kai; Guryev, Victor; Vermaat, Martijn; van Dijk, Freerk; Francioli, Laurent C; Hottenga, Jouke Jan; Laros, Jeroen F J; Li, Qibin; Li, Yingrui; Cao, Hongzhi; Chen, Ruoyan; Du, Yuanping; Li, Ning; Cao, Sujie; van Setten, Jessica; Menelaou, Androniki; Pulit, Sara L; Hehir-Kwa, Jayne Y; Beekman, Marian; Elbers, Clara C; Byelas, Heorhiy; de Craen, Anton J M; Deelen, Patrick; Dijkstra, Martijn; den Dunnen, Johan T; de Knijff, Peter; Houwing-Duistermaat, Jeanine; Koval, Vyacheslav; Estrada, Karol; Hofman, Albert; Kanterakis, Alexandros; Enckevort, David van; Mai, Hailiang; Kattenberg, Mathijs; van Leeuwen, Elisabeth M; Neerincx, Pieter B T; Oostra, Ben; Rivadeneira, Fernanodo; Suchiman, Eka H D; Uitterlinden, Andre G; Willemsen, Gonneke; Wolffenbuttel, Bruce H; Wang, Jun; de Bakker, Paul I W; van Ommen, Gert-Jan; van Duijn, Cornelia M

2014-01-01

Within the Netherlands a national network of biobanks has been established (Biobanking and Biomolecular Research Infrastructure-Netherlands (BBMRI-NL)) as a national node of the European BBMRI. One of the aims of BBMRI-NL is to enrich biobanks with different types of molecular and phenotype data. Here, we describe the Genome of the Netherlands (GoNL), one of the projects within BBMRI-NL. GoNL is a whole-genome-sequencing project in a representative sample consisting of 250 trio-families from all provinces in the Netherlands, which aims to characterize DNA sequence variation in the Dutch population. The parent–offspring trios include adult individuals ranging in age from 19 to 87 years (mean=53 years; SD=16 years) from birth cohorts 1910–1994. Sequencing was done on blood-derived DNA from uncultured cells and accomplished coverage was 14–15x. The family-based design represents a unique resource to assess the frequency of regional variants, accurately reconstruct haplotypes by family-based phasing, characterize short indels and complex structural variants, and establish the rate of de novo mutational events. GoNL will also serve as a reference panel for imputation in the available genome-wide association studies in Dutch and other cohorts to refine association signals and uncover population-specific variants. GoNL will create a catalog of human genetic variation in this sample that is uniquely characterized with respect to micro-geographic location and a wide range of phenotypes. The resource will be made available to the research and medical community to guide the interpretation of sequencing projects. The present paper summarizes the global characteristics of the project. PMID:23714750

Impact of relationships between test and training animals and among training animals on reliability of genomic prediction.

Science.gov (United States)

Wu, X; Lund, M S; Sun, D; Zhang, Q; Su, G

2015-10-01

One of the factors affecting the reliability of genomic prediction is the relationship among the animals of interest. This study investigated the reliability of genomic prediction in various scenarios with regard to the relationship between test and training animals, and among animals within the training data set. Different training data sets were generated from EuroGenomics data and a group of Nordic Holstein bulls (born in 2005 and afterwards) as a common test data set. Genomic breeding values were predicted using a genomic best linear unbiased prediction model and a Bayesian mixture model. The results showed that a closer relationship between test and training animals led to a higher reliability of genomic predictions for the test animals, while a closer relationship among training animals resulted in a lower reliability. In addition, the Bayesian mixture model in general led to a slightly higher reliability of genomic prediction, especially for the scenario of distant relationships between training and test animals. Therefore, to prevent a decrease in reliability, constant updates of the training population with animals from more recent generations are required. Moreover, a training population consisting of less-related animals is favourable for reliability of genomic prediction. © 2015 Blackwell Verlag GmbH.

Genotype Imputation for Latinos Using the HapMap and 1000 Genomes Project Reference Panels

Directory of Open Access Journals (Sweden)

Xiaoyi eGao

2012-06-01

Full Text Available Genotype imputation is a vital tool in genome-wide association studies (GWAS and meta-analyses of multiple GWAS results. Imputation enables researchers to increase genomic coverage and to pool data generated using different genotyping platforms. HapMap samples are often employed as the reference panel. More recently, the 1000 Genomes Project resource is becoming the primary source for reference panels. Multiple GWAS and meta-analyses are targeting Latinos, the most populous and fastest growing minority group in the US. However, genotype imputation resources for Latinos are rather limited compared to individuals of European ancestry at present, largely because of the lack of good reference data. One choice of reference panel for Latinos is one derived from the population of Mexican individuals in Los Angeles contained in the HapMap Phase 3 project and the 1000 Genomes Project. However, a detailed evaluation of the quality of the imputed genotypes derived from the public reference panels has not yet been reported. Using simulation studies, the Illumina OmniExpress GWAS data from the Los Angles Latino Eye Study and the MACH software package, we evaluated the accuracy of genotype imputation in Latinos. Our results show that the 1000 Genomes Project AMR+CEU+YRI reference panel provides the highest imputation accuracy for Latinos, and that also including Asian samples in the panel can reduce imputation accuracy. We also provide the imputation accuracy for each autosomal chromosome using the 1000 Genomes Project panel for Latinos. Our results serve as a guide to future imputation-based analysis in Latinos.

The Human Genome Project: applications in the diagnosis and treatment of neurologic disease.

Science.gov (United States)

Evans, G A

1998-10-01

The Human Genome Project (HGP), an international program to decode the entire DNA sequence of the human genome in 15 years, represents the largest biological experiment ever conducted. This set of information will contain the blueprint for the construction and operation of a human being. While the primary driving force behind the genome project is the potential to vastly expand the amount of genetic information available for biomedical research, the ramifications for other fields of study in biological research, the biotechnology and pharmaceutical industry, our understanding of evolution, effects on agriculture, and implications for bioethics are likely to be profound.

A common reference population from four European Holstein populations increases reliability of genomic predictions

DEFF Research Database (Denmark)

Lund, Mogens Sandø; de Ross, Sander PW; de Vries, Alfred G

2011-01-01

Background Size of the reference population and reliability of phenotypes are crucial factors influencing the reliability of genomic predictions. It is therefore useful to combine closely related populations. Increased accuracies of genomic predictions depend on the number of individuals added to...

Earth BioGenome Project: Sequencing life for the future of life.

Science.gov (United States)

Lewin, Harris A; Robinson, Gene E; Kress, W John; Baker, William J; Coddington, Jonathan; Crandall, Keith A; Durbin, Richard; Edwards, Scott V; Forest, Félix; Gilbert, M Thomas P; Goldstein, Melissa M; Grigoriev, Igor V; Hackett, Kevin J; Haussler, David; Jarvis, Erich D; Johnson, Warren E; Patrinos, Aristides; Richards, Stephen; Castilla-Rubio, Juan Carlos; van Sluys, Marie-Anne; Soltis, Pamela S; Xu, Xun; Yang, Huanming; Zhang, Guojie

2018-04-24

Increasing our understanding of Earth's biodiversity and responsibly stewarding its resources are among the most crucial scientific and social challenges of the new millennium. These challenges require fundamental new knowledge of the organization, evolution, functions, and interactions among millions of the planet's organisms. Herein, we present a perspective on the Earth BioGenome Project (EBP), a moonshot for biology that aims to sequence, catalog, and characterize the genomes of all of Earth's eukaryotic biodiversity over a period of 10 years. The outcomes of the EBP will inform a broad range of major issues facing humanity, such as the impact of climate change on biodiversity, the conservation of endangered species and ecosystems, and the preservation and enhancement of ecosystem services. We describe hurdles that the project faces, including data-sharing policies that ensure a permanent, freely available resource for future scientific discovery while respecting access and benefit sharing guidelines of the Nagoya Protocol. We also describe scientific and organizational challenges in executing such an ambitious project, and the structure proposed to achieve the project's goals. The far-reaching potential benefits of creating an open digital repository of genomic information for life on Earth can be realized only by a coordinated international effort.

Prediction of expected years of life using whole-genome markers.

Directory of Open Access Journals (Sweden)

Gustavo de los Campos

Full Text Available Genetic factors are believed to account for 25% of the interindividual differences in Years of Life (YL among humans. However, the genetic loci that have thus far been found to be associated with YL explain a very small proportion of the expected genetic variation in this trait, perhaps reflecting the complexity of the trait and the limitations of traditional association studies when applied to traits affected by a large number of small-effect genes. Using data from the Framingham Heart Study and statistical methods borrowed largely from the field of animal genetics (whole-genome prediction, WGP, we developed a WGP model for the study of YL and evaluated the extent to which thousands of genetic variants across the genome examined simultaneously can be used to predict interindividual differences in YL. We find that a sizable proportion of differences in YL--which were unexplained by age at entry, sex, smoking and BMI--can be accounted for and predicted using WGP methods. The contribution of genomic information to prediction accuracy was even higher than that of smoking and body mass index (BMI combined; two predictors that are considered among the most important life-shortening factors. We evaluated the impacts of familial relationships and population structure (as described by the first two marker-derived principal components and concluded that in our dataset population structure explained partially, but not fully the gains in prediction accuracy obtained with WGP. Further inspection of prediction accuracies by age at death indicated that most of the gains in predictive ability achieved with WGP were due to the increased accuracy of prediction of early mortality, perhaps reflecting the ability of WGP to capture differences in genetic risk to deadly diseases such as cancer, which are most often responsible for early mortality in our sample.

A manually annotated Actinidia chinensis var. chinensis (kiwifruit) genome highlights the challenges associated with draft genomes and gene prediction in plants.

Science.gov (United States)

Pilkington, Sarah M; Crowhurst, Ross; Hilario, Elena; Nardozza, Simona; Fraser, Lena; Peng, Yongyan; Gunaseelan, Kularajathevan; Simpson, Robert; Tahir, Jibran; Deroles, Simon C; Templeton, Kerry; Luo, Zhiwei; Davy, Marcus; Cheng, Canhong; McNeilage, Mark; Scaglione, Davide; Liu, Yifei; Zhang, Qiong; Datson, Paul; De Silva, Nihal; Gardiner, Susan E; Bassett, Heather; Chagné, David; McCallum, John; Dzierzon, Helge; Deng, Cecilia; Wang, Yen-Yi; Barron, Lorna; Manako, Kelvina; Bowen, Judith; Foster, Toshi M; Erridge, Zoe A; Tiffin, Heather; Waite, Chethi N; Davies, Kevin M; Grierson, Ella P; Laing, William A; Kirk, Rebecca; Chen, Xiuyin; Wood, Marion; Montefiori, Mirco; Brummell, David A; Schwinn, Kathy E; Catanach, Andrew; Fullerton, Christina; Li, Dawei; Meiyalaghan, Sathiyamoorthy; Nieuwenhuizen, Niels; Read, Nicola; Prakash, Roneel; Hunter, Don; Zhang, Huaibi; McKenzie, Marian; Knäbel, Mareike; Harris, Alastair; Allan, Andrew C; Gleave, Andrew; Chen, Angela; Janssen, Bart J; Plunkett, Blue; Ampomah-Dwamena, Charles; Voogd, Charlotte; Leif, Davin; Lafferty, Declan; Souleyre, Edwige J F; Varkonyi-Gasic, Erika; Gambi, Francesco; Hanley, Jenny; Yao, Jia-Long; Cheung, Joey; David, Karine M; Warren, Ben; Marsh, Ken; Snowden, Kimberley C; Lin-Wang, Kui; Brian, Lara; Martinez-Sanchez, Marcela; Wang, Mindy; Ileperuma, Nadeesha; Macnee, Nikolai; Campin, Robert; McAtee, Peter; Drummond, Revel S M; Espley, Richard V; Ireland, Hilary S; Wu, Rongmei; Atkinson, Ross G; Karunairetnam, Sakuntala; Bulley, Sean; Chunkath, Shayhan; Hanley, Zac; Storey, Roy; Thrimawithana, Amali H; Thomson, Susan; David, Charles; Testolin, Raffaele; Huang, Hongwen; Hellens, Roger P; Schaffer, Robert J

2018-04-16

Most published genome sequences are drafts, and most are dominated by computational gene prediction. Draft genomes typically incorporate considerable sequence data that are not assigned to chromosomes, and predicted genes without quality confidence measures. The current Actinidia chinensis (kiwifruit) 'Hongyang' draft genome has 164 Mb of sequences unassigned to pseudo-chromosomes, and omissions have been identified in the gene models. A second genome of an A. chinensis (genotype Red5) was fully sequenced. This new sequence resulted in a 554.0 Mb assembly with all but 6 Mb assigned to pseudo-chromosomes. Pseudo-chromosomal comparisons showed a considerable number of translocation events have occurred following a whole genome duplication (WGD) event some consistent with centromeric Robertsonian-like translocations. RNA sequencing data from 12 tissues and ab initio analysis informed a genome-wide manual annotation, using the WebApollo tool. In total, 33,044 gene loci represented by 33,123 isoforms were identified, named and tagged for quality of evidential support. Of these 3114 (9.4%) were identical to a protein within 'Hongyang' The Kiwifruit Information Resource (KIR v2). Some proportion of the differences will be varietal polymorphisms. However, as most computationally predicted Red5 models required manual re-annotation this proportion is expected to be small. The quality of the new gene models was tested by fully sequencing 550 cloned 'Hort16A' cDNAs and comparing with the predicted protein models for Red5 and both the original 'Hongyang' assembly and the revised annotation from KIR v2. Only 48.9% and 63.5% of the cDNAs had a match with 90% identity or better to the original and revised 'Hongyang' annotation, respectively, compared with 90.9% to the Red5 models. Our study highlights the need to take a cautious approach to draft genomes and computationally predicted genes. Our use of the manual annotation tool WebApollo facilitated manual checking and

TcruziDB, an Integrated Database, and the WWW Information Server for the Trypanosoma cruzi Genome Project

Directory of Open Access Journals (Sweden)

Degrave Wim

1997-01-01

Full Text Available Data analysis, presentation and distribution is of utmost importance to a genome project. A public domain software, ACeDB, has been chosen as the common basis for parasite genome databases, and a first release of TcruziDB, the Trypanosoma cruzi genome database, is available by ftp from ftp://iris.dbbm.fiocruz.br/pub/genomedb/TcruziDB as well as versions of the software for different operating systems (ftp://iris.dbbm.fiocruz.br/pub/unixsoft/. Moreover, data originated from the project are available from the WWW server at http://www.dbbm.fiocruz.br. It contains biological and parasitological data on CL Brener, its karyotype, all available T. cruzi sequences from Genbank, data on the EST-sequencing project and on available libraries, a T. cruzi codon table and a listing of activities and participating groups in the genome project, as well as meeting reports. T. cruzi discussion lists (tcruzi-l@iris.dbbm.fiocruz.br and tcgenics@iris.dbbm.fiocruz.br are being maintained for communication and to promote collaboration in the genome project

Genome-based prediction of common diseases: Methodological considerations for future research

NARCIS (Netherlands)

A.C.J.W. Janssens (Cécile); P. Tikka-Kleemola (Päivi)

2009-01-01

textabstractThe translation of emerging genomic knowledge into public health and clinical care is one of the major challenges for the coming decades. At the moment, genome-based prediction of common diseases, such as type 2 diabetes, coronary heart disease and cancer, is still not informative. Our

CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

Data.gov (United States)

U.S. Environmental Protection Agency — Data from a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) demonstrating using predictive computational...

Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates.

Science.gov (United States)

Onogi, Akio; Watanabe, Maya; Mochizuki, Toshihiro; Hayashi, Takeshi; Nakagawa, Hiroshi; Hasegawa, Toshihiro; Iwata, Hiroyoshi

2016-04-01

It is suggested that accuracy in predicting plant phenotypes can be improved by integrating genomic prediction with crop modelling in a single hierarchical model. Accurate prediction of phenotypes is important for plant breeding and management. Although genomic prediction/selection aims to predict phenotypes on the basis of whole-genome marker information, it is often difficult to predict phenotypes of complex traits in diverse environments, because plant phenotypes are often influenced by genotype-environment interaction. A possible remedy is to integrate genomic prediction with crop/ecophysiological modelling, which enables us to predict plant phenotypes using environmental and management information. To this end, in the present study, we developed a novel method for integrating genomic prediction with phenological modelling of Asian rice (Oryza sativa, L.), allowing the heading date of untested genotypes in untested environments to be predicted. The method simultaneously infers the phenological model parameters and whole-genome marker effects on the parameters in a Bayesian framework. By cultivating backcross inbred lines of Koshihikari × Kasalath in nine environments, we evaluated the potential of the proposed method in comparison with conventional genomic prediction, phenological modelling, and two-step methods that applied genomic prediction to phenological model parameters inferred from Nelder-Mead or Markov chain Monte Carlo algorithms. In predicting heading dates of untested lines in untested environments, the proposed and two-step methods tended to provide more accurate predictions than the conventional genomic prediction methods, particularly in environments where phenotypes from environments similar to the target environment were unavailable for training genomic prediction. The proposed method showed greater accuracy in prediction than the two-step methods in all cross-validation schemes tested, suggesting the potential of the integrated approach in

Enhancing Biology Instruction with the Human Genome Project

Science.gov (United States)

Buxeda, Rosa J.; Moore-Russo, Deborah A.

2003-01-01

The Human Genome Project (HGP) is a recent scientific milestone that has received notable attention. This article shows how a biology course is using the HGP to enhance students' experiences by providing awareness of cutting edge research, with information on new emerging career options, and with opportunities to consider ethical questions raised…

Predicting human height by Victorian and genomic methods.

Science.gov (United States)

Aulchenko, Yurii S; Struchalin, Maksim V; Belonogova, Nadezhda M; Axenovich, Tatiana I; Weedon, Michael N; Hofman, Albert; Uitterlinden, Andre G; Kayser, Manfred; Oostra, Ben A; van Duijn, Cornelia M; Janssens, A Cecile J W; Borodin, Pavel M

2009-08-01

In the Victorian era, Sir Francis Galton showed that 'when dealing with the transmission of stature from parents to children, the average height of the two parents, ... is all we need care to know about them' (1886). One hundred and twenty-two years after Galton's work was published, 54 loci showing strong statistical evidence for association to human height were described, providing us with potential genomic means of human height prediction. In a population-based study of 5748 people, we find that a 54-loci genomic profile explained 4-6% of the sex- and age-adjusted height variance, and had limited ability to discriminate tall/short people, as characterized by the area under the receiver-operating characteristic curve (AUC). In a family-based study of 550 people, with both parents having height measurements, we find that the Galtonian mid-parental prediction method explained 40% of the sex- and age-adjusted height variance, and showed high discriminative accuracy. We have also explored how much variance a genomic profile should explain to reach certain AUC values. For highly heritable traits such as height, we conclude that in applications in which parental phenotypic information is available (eg, medicine), the Victorian Galton's method will long stay unsurpassed, in terms of both discriminative accuracy and costs. For less heritable traits, and in situations in which parental information is not available (eg, forensics), genomic methods may provide an alternative, given that the variants determining an essential proportion of the trait's variation can be identified.

Predicting genotypes environmental range from genome-environment associations.

Science.gov (United States)

Manel, Stéphanie; Andrello, Marco; Henry, Karine; Verdelet, Daphné; Darracq, Aude; Guerin, Pierre-Edouard; Desprez, Bruno; Devaux, Pierre

2018-05-17

Genome-environment association methods aim to detect genetic markers associated with environmental variables. The detected associations are usually analysed separately to identify the genomic regions involved in local adaptation. However, a recent study suggests that single-locus associations can be combined and used in a predictive way to estimate environmental variables for new individuals on the basis of their genotypes. Here, we introduce an original approach to predict the environmental range (values and upper and lower limits) of species genotypes from the genetic markers significantly associated with those environmental variables in an independent set of individuals. We illustrate this approach to predict aridity in a database constituted of 950 individuals of wild beets and 299 individuals of cultivated beets genotyped at 14,409 random Single Nucleotide Polymorphisms (SNPs). We detected 66 alleles associated with aridity and used them to calculate the fraction (I) of aridity-associated alleles in each individual. The fraction I correctly predicted the values of aridity in an independent validation set of wild individuals and was then used to predict aridity in the 299 cultivated individuals. Wild individuals had higher median values and a wider range of values of aridity than the cultivated individuals, suggesting that wild individuals have higher ability to resist to stress-aridity conditions and could be used to improve the resistance of cultivated varieties to aridity. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

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

Directory of Open Access Journals (Sweden)

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.

Genomes to Life Project Quartely Report October 2004.

Energy Technology Data Exchange (ETDEWEB)

Heffelfinger, Grant S.; Martino, Anthony; Rintoul, Mark Daniel; Geist, Al; Gorin, Andrey; Xu, Ying; Palenik, Brian

2005-02-01

This SAND report provides the technical progress through October 2004 of the Sandia-led project, %22Carbon Sequestration in Synechococcus Sp.: From Molecular Machines to Hierarchical Modeling,%22 funded by the DOE Office of Science Genomes to Life Program. Understanding, predicting, and perhaps manipulating carbon fixation in the oceans has long been a major focus of biological oceanography and has more recently been of interest to a broader audience of scientists and policy makers. It is clear that the oceanic sinks and sources of CO2 are important terms in the global environmental response to anthropogenic atmospheric inputs of CO2 and that oceanic microorganisms play a key role in this response. However, the relationship between this global phenomenon and the biochemical mechanisms of carbon fixation in these microorganisms is poorly understood. In this project, we will investigate the carbon sequestration behavior of Synechococcus Sp., an abundant marine cyanobacteria known to be important to environmental responses to carbon dioxide levels, through experimental and computational methods. This project is a combined experimental and computational effort with emphasis on developing and applying new computational tools and methods. Our experimental effort will provide the biology and data to drive the computational efforts and include significant investment in developing new experimental methods for uncovering protein partners, characterizing protein complexes, identifying new binding domains. We will also develop and apply new data measurement and statistical methods for analyzing microarray experiments. Computational tools will be essential to our efforts to discover and characterize the function of the molecular machines of Synechococcus. To this end, molecular simulation methods will be coupled with knowledge discovery from diverse biological data sets for high-throughput discovery and characterization of protein-protein complexes. In addition, we will develop

Systematic bias of correlation coefficient may explain negative accuracy of genomic prediction.

Science.gov (United States)

Zhou, Yao; Vales, M Isabel; Wang, Aoxue; Zhang, Zhiwu

2017-09-01

Accuracy of genomic prediction is commonly calculated as the Pearson correlation coefficient between the predicted and observed phenotypes in the inference population by using cross-validation analysis. More frequently than expected, significant negative accuracies of genomic prediction have been reported in genomic selection studies. These negative values are surprising, given that the minimum value for prediction accuracy should hover around zero when randomly permuted data sets are analyzed. We reviewed the two common approaches for calculating the Pearson correlation and hypothesized that these negative accuracy values reflect potential bias owing to artifacts caused by the mathematical formulas used to calculate prediction accuracy. The first approach, Instant accuracy, calculates correlations for each fold and reports prediction accuracy as the mean of correlations across fold. The other approach, Hold accuracy, predicts all phenotypes in all fold and calculates correlation between the observed and predicted phenotypes at the end of the cross-validation process. Using simulated and real data, we demonstrated that our hypothesis is true. Both approaches are biased downward under certain conditions. The biases become larger when more fold are employed and when the expected accuracy is low. The bias of Instant accuracy can be corrected using a modified formula. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A haplotype regression approach for genetic evaluation using sequences from the 1000 bull genomes Project

International Nuclear Information System (INIS)

Lakhssassi, K.; González-Recio, O.

2017-01-01

Haplotypes from sequencing data may improve the prediction accuracy in genomic evaluations as haplotypes are in stronger linkage disequilibrium with quantitative trait loci than markers from SNP chips. This study focuses first, on the creation of haplotypes in a population sample of 450 Holstein animals, with full-sequence data from the 1000 bull genomes project; and second, on incorporating them into the whole genome prediction model. In total, 38,319,258 SNPs (and indels) from Next Generation Sequencing were included in the analysis. After filtering variants with minor allele frequency (MAF< 0.025) 13,912,326 SNPs were available for the haplotypes extraction with findhap.f90. The number of SNPs in the haploblocks was on average 924 SNP (166,552 bp). Unique haplotypes were around 97% in all chromosomes and were ignored leaving 153,428 haplotypes. Estimated haplotypes had a large contribution to the total variance of genomic estimated breeding values for kilogram of protein, Global Type Index, Somatic Cell Score and Days Open (between 32 and 99.9%). Haploblocks containing haplotypes with large effects were selected by filtering for each trait, haplotypes whose effect was larger/lower than the mean plus/minus 3 times the standard deviation (SD) and 1 SD above the mean of the haplotypes effect distribution. Results showed that filtering by 3 SD would not be enough to capture a large proportion of genetic variance, whereas filtering by 1 SD could be useful but model convergence should be considered. Additionally, sequence haplotypes were able to capture additional genetic variance to the polygenic effect for traits undergoing lower selection intensity like fertility and health traits.

A haplotype regression approach for genetic evaluation using sequences from the 1000 bull genomes Project

Energy Technology Data Exchange (ETDEWEB)

Lakhssassi, K.; González-Recio, O.

2017-07-01

Haplotypes from sequencing data may improve the prediction accuracy in genomic evaluations as haplotypes are in stronger linkage disequilibrium with quantitative trait loci than markers from SNP chips. This study focuses first, on the creation of haplotypes in a population sample of 450 Holstein animals, with full-sequence data from the 1000 bull genomes project; and second, on incorporating them into the whole genome prediction model. In total, 38,319,258 SNPs (and indels) from Next Generation Sequencing were included in the analysis. After filtering variants with minor allele frequency (MAF< 0.025) 13,912,326 SNPs were available for the haplotypes extraction with findhap.f90. The number of SNPs in the haploblocks was on average 924 SNP (166,552 bp). Unique haplotypes were around 97% in all chromosomes and were ignored leaving 153,428 haplotypes. Estimated haplotypes had a large contribution to the total variance of genomic estimated breeding values for kilogram of protein, Global Type Index, Somatic Cell Score and Days Open (between 32 and 99.9%). Haploblocks containing haplotypes with large effects were selected by filtering for each trait, haplotypes whose effect was larger/lower than the mean plus/minus 3 times the standard deviation (SD) and 1 SD above the mean of the haplotypes effect distribution. Results showed that filtering by 3 SD would not be enough to capture a large proportion of genetic variance, whereas filtering by 1 SD could be useful but model convergence should be considered. Additionally, sequence haplotypes were able to capture additional genetic variance to the polygenic effect for traits undergoing lower selection intensity like fertility and health traits.

Methods to improve genomic prediction and GWAS using combined Holstein populations

DEFF Research Database (Denmark)

Li, Xiujin

The thesis focuses on methods to improve GWAS and genomic prediction using combined Holstein populations and investigations G by E interaction. The conclusions are: 1) Prediction reliabilities for Brazilian Holsteins can be increased by adding Nordic and Frensh genotyped bulls and a large G by E...... interaction exists between populations. 2) Combining data from Chinese and Danish Holstein populations increases the power of GWAS and detects new QTL regions for milk fatty acid traits. 3) The novel multi-trait Bayesian model efficiently estimates region-specific genomic variances, covariances...

The Human Genome Project: Biology, Computers, and Privacy.

Science.gov (United States)

Cutter, Mary Ann G.; Drexler, Edward; Gottesman, Kay S.; Goulding, Philip G.; McCullough, Laurence B.; McInerney, Joseph D.; Micikas, Lynda B.; Mural, Richard J.; Murray, Jeffrey C.; Zola, John

This module, for high school teachers, is the second of two modules about the Human Genome Project (HGP) produced by the Biological Sciences Curriculum Study (BSCS). The first section of this module provides background information for teachers about the structure and objectives of the HGP, aspects of the science and technology that underlie the…

Detailed analysis of inversions predicted between two human genomes: errors, real polymorphisms, and their origin and population distribution.

Science.gov (United States)

Vicente-Salvador, David; Puig, Marta; Gayà-Vidal, Magdalena; Pacheco, Sarai; Giner-Delgado, Carla; Noguera, Isaac; Izquierdo, David; Martínez-Fundichely, Alexander; Ruiz-Herrera, Aurora; Estivill, Xavier; Aguado, Cristina; Lucas-Lledó, José Ignacio; Cáceres, Mario

2017-02-01

The growing catalogue of structural variants in humans often overlooks inversions as one of the most difficult types of variation to study, even though they affect phenotypic traits in diverse organisms. Here, we have analysed in detail 90 inversions predicted from the comparison of two independently assembled human genomes: the reference genome (NCBI36/HG18) and HuRef. Surprisingly, we found that two thirds of these predictions (62) represent errors either in assembly comparison or in one of the assemblies, including 27 misassembled regions in HG18. Next, we validated 22 of the remaining 28 potential polymorphic inversions using different PCR techniques and characterized their breakpoints and ancestral state. In addition, we determined experimentally the derived allele frequency in Europeans for 17 inversions (DAF = 0.01-0.80), as well as the distribution in 14 worldwide populations for 12 of them based on the 1000 Genomes Project data. Among the validated inversions, nine have inverted repeats (IRs) at their breakpoints, and two show nucleotide variation patterns consistent with a recurrent origin. Conversely, inversions without IRs have a unique origin and almost all of them show deletions or insertions at the breakpoints in the derived allele mediated by microhomology sequences, which highlights the importance of mechanisms like FoSTeS/MMBIR in the generation of complex rearrangements in the human genome. Finally, we found several inversions located within genes and at least one candidate to be positively selected in Africa. Thus, our study emphasizes the importance of careful analysis and validation of large-scale genomic predictions to extract reliable biological conclusions. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Genomic Predictability of Interconnected Biparental Maize Populations

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Riedelsheimer, Christian; Endelman, Jeffrey B.; Stange, Michael; Sorrells, Mark E.; Jannink, Jean-Luc; Melchinger, Albrecht E.

2013-01-01

Intense structuring of plant breeding populations challenges the design of the training set (TS) in genomic selection (GS). An important open question is how the TS should be constructed from multiple related or unrelated small biparental families to predict progeny from individual crosses. Here, we used a set of five interconnected maize (Zea mays L.) populations of doubled-haploid (DH) lines derived from four parents to systematically investigate how the composition of the TS affects the prediction accuracy for lines from individual crosses. A total of 635 DH lines genotyped with 16,741 polymorphic SNPs were evaluated for five traits including Gibberella ear rot severity and three kernel yield component traits. The populations showed a genomic similarity pattern, which reflects the crossing scheme with a clear separation of full sibs, half sibs, and unrelated groups. Prediction accuracies within full-sib families of DH lines followed closely theoretical expectations, accounting for the influence of sample size and heritability of the trait. Prediction accuracies declined by 42% if full-sib DH lines were replaced by half-sib DH lines, but statistically significantly better results could be achieved if half-sib DH lines were available from both instead of only one parent of the validation population. Once both parents of the validation population were represented in the TS, including more crosses with a constant TS size did not increase accuracies. Unrelated crosses showing opposite linkage phases with the validation population resulted in negative or reduced prediction accuracies, if used alone or in combination with related families, respectively. We suggest identifying and excluding such crosses from the TS. Moreover, the observed variability among populations and traits suggests that these uncertainties must be taken into account in models optimizing the allocation of resources in GS. PMID:23535384

ProteinSplit: splitting of multi-domain proteins using prediction of ordered and disordered regions in protein sequences for virtual structural genomics

International Nuclear Information System (INIS)

Wyrwicz, Lucjan S; Koczyk, Grzegorz; Rychlewski, Leszek; Plewczynski, Dariusz

2007-01-01

The annotation of protein folds within newly sequenced genomes is the main target for semi-automated protein structure prediction (virtual structural genomics). A large number of automated methods have been developed recently with very good results in the case of single-domain proteins. Unfortunately, most of these automated methods often fail to properly predict the distant homology between a given multi-domain protein query and structural templates. Therefore a multi-domain protein should be split into domains in order to overcome this limitation. ProteinSplit is designed to identify protein domain boundaries using a novel algorithm that predicts disordered regions in protein sequences. The software utilizes various sequence characteristics to assess the local propensity of a protein to be disordered or ordered in terms of local structure stability. These disordered parts of a protein are likely to create interdomain spacers. Because of its speed and portability, the method was successfully applied to several genome-wide fold annotation experiments. The user can run an automated analysis of sets of proteins or perform semi-automated multiple user projects (saving the results on the server). Additionally the sequences of predicted domains can be sent to the Bioinfo.PL Protein Structure Prediction Meta-Server for further protein three-dimensional structure and function prediction. The program is freely accessible as a web service at http://lucjan.bioinfo.pl/proteinsplit together with detailed benchmark results on the critical assessment of a fully automated structure prediction (CAFASP) set of sequences. The source code of the local version of protein domain boundary prediction is available upon request from the authors

A Novel Method to Predict Genomic Islands Based on Mean Shift Clustering Algorithm.

Directory of Open Access Journals (Sweden)

Daniel M de Brito

Full Text Available Genomic Islands (GIs are regions of bacterial genomes that are acquired from other organisms by the phenomenon of horizontal transfer. These regions are often responsible for many important acquired adaptations of the bacteria, with great impact on their evolution and behavior. Nevertheless, these adaptations are usually associated with pathogenicity, antibiotic resistance, degradation and metabolism. Identification of such regions is of medical and industrial interest. For this reason, different approaches for genomic islands prediction have been proposed. However, none of them are capable of predicting precisely the complete repertory of GIs in a genome. The difficulties arise due to the changes in performance of different algorithms in the face of the variety of nucleotide distribution in different species. In this paper, we present a novel method to predict GIs that is built upon mean shift clustering algorithm. It does not require any information regarding the number of clusters, and the bandwidth parameter is automatically calculated based on a heuristic approach. The method was implemented in a new user-friendly tool named MSGIP--Mean Shift Genomic Island Predictor. Genomes of bacteria with GIs discussed in other papers were used to evaluate the proposed method. The application of this tool revealed the same GIs predicted by other methods and also different novel unpredicted islands. A detailed investigation of the different features related to typical GI elements inserted in these new regions confirmed its effectiveness. Stand-alone and user-friendly versions for this new methodology are available at http://msgip.integrativebioinformatics.me.

PReMod: a database of genome-wide mammalian cis-regulatory module predictions.

Science.gov (United States)

Ferretti, Vincent; Poitras, Christian; Bergeron, Dominique; Coulombe, Benoit; Robert, François; Blanchette, Mathieu

2007-01-01

We describe PReMod, a new database of genome-wide cis-regulatory module (CRM) predictions for both the human and the mouse genomes. The prediction algorithm, described previously in Blanchette et al. (2006) Genome Res., 16, 656-668, exploits the fact that many known CRMs are made of clusters of phylogenetically conserved and repeated transcription factors (TF) binding sites. Contrary to other existing databases, PReMod is not restricted to modules located proximal to genes, but in fact mostly contains distal predicted CRMs (pCRMs). Through its web interface, PReMod allows users to (i) identify pCRMs around a gene of interest; (ii) identify pCRMs that have binding sites for a given TF (or a set of TFs) or (iii) download the entire dataset for local analyses. Queries can also be refined by filtering for specific chromosomal regions, for specific regions relative to genes or for the presence of CpG islands. The output includes information about the binding sites predicted within the selected pCRMs, and a graphical display of their distribution within the pCRMs. It also provides a visual depiction of the chromosomal context of the selected pCRMs in terms of neighboring pCRMs and genes, all of which are linked to the UCSC Genome Browser and the NCBI. PReMod: http://genomequebec.mcgill.ca/PReMod.

Genome-scale characterization of RNA tertiary structures and their functional impact by RNA solvent accessibility prediction.

Science.gov (United States)

Yang, Yuedong; Li, Xiaomei; Zhao, Huiying; Zhan, Jian; Wang, Jihua; Zhou, Yaoqi

2017-01-01

As most RNA structures are elusive to structure determination, obtaining solvent accessible surface areas (ASAs) of nucleotides in an RNA structure is an important first step to characterize potential functional sites and core structural regions. Here, we developed RNAsnap, the first machine-learning method trained on protein-bound RNA structures for solvent accessibility prediction. Built on sequence profiles from multiple sequence alignment (RNAsnap-prof), the method provided robust prediction in fivefold cross-validation and an independent test (Pearson correlation coefficients, r, between predicted and actual ASA values are 0.66 and 0.63, respectively). Application of the method to 6178 mRNAs revealed its positive correlation to mRNA accessibility by dimethyl sulphate (DMS) experimentally measured in vivo (r = 0.37) but not in vitro (r = 0.07), despite the lack of training on mRNAs and the fact that DMS accessibility is only an approximation to solvent accessibility. We further found strong association across coding and noncoding regions between predicted solvent accessibility of the mutation site of a single nucleotide variant (SNV) and the frequency of that variant in the population for 2.2 million SNVs obtained in the 1000 Genomes Project. Moreover, mapping solvent accessibility of RNAs to the human genome indicated that introns, 5' cap of 5' and 3' cap of 3' untranslated regions, are more solvent accessible, consistent with their respective functional roles. These results support conformational selections as the mechanism for the formation of RNA-protein complexes and highlight the utility of genome-scale characterization of RNA tertiary structures by RNAsnap. The server and its stand-alone downloadable version are available at http://sparks-lab.org. © 2016 Yang et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Genomics England's implementation of its public engagement strategy: Blurred boundaries between engagement for the United Kingdom's 100,000 Genomes project and the need for public support.

Science.gov (United States)

Samuel, Gabrielle Natalie; Farsides, Bobbie

2018-04-01

The United Kingdom's 100,000 Genomes Project has the aim of sequencing 100,000 genomes from National Health Service patients such that whole genome sequencing becomes routine clinical practice. It also has a research-focused goal to provide data for scientific discovery. Genomics England is the limited company established by the Department of Health to deliver the project. As an innovative scientific/clinical venture, it is interesting to consider how Genomics England positions itself in relation to public engagement activities. We set out to explore how individuals working at, or associated with, Genomics England enacted public engagement in practice. Our findings show that individuals offered a narrative in which public engagement performed more than one function. On one side, public engagement was seen as 'good practice'. On the other, public engagement was presented as core to the project's success - needed to encourage involvement and ultimately recruitment. We discuss the implications of this in this article.

Comparing genetic variants detected in the 1000 genomes project ...

Indian Academy of Sciences (India)

Single-nucleotide polymorphisms (SNPs) determined based on SNP arrays from the international HapMap consortium (HapMap) and the genetic variants detected in the 1000 genomes project (1KGP) can serve as two references for genomewide association studies (GWAS). We conducted comparative analyses to provide ...

Enabling a community to dissect an organism: overview of the Neurospora functional genomics project.

Science.gov (United States)

Dunlap, Jay C; Borkovich, Katherine A; Henn, Matthew R; Turner, Gloria E; Sachs, Matthew S; Glass, N Louise; McCluskey, Kevin; Plamann, Michael; Galagan, James E; Birren, Bruce W; Weiss, Richard L; Townsend, Jeffrey P; Loros, Jennifer J; Nelson, Mary Anne; Lambreghts, Randy; Colot, Hildur V; Park, Gyungsoon; Collopy, Patrick; Ringelberg, Carol; Crew, Christopher; Litvinkova, Liubov; DeCaprio, Dave; Hood, Heather M; Curilla, Susan; Shi, Mi; Crawford, Matthew; Koerhsen, Michael; Montgomery, Phil; Larson, Lisa; Pearson, Matthew; Kasuga, Takao; Tian, Chaoguang; Baştürkmen, Meray; Altamirano, Lorena; Xu, Junhuan

2007-01-01

A consortium of investigators is engaged in a functional genomics project centered on the filamentous fungus Neurospora, with an eye to opening up the functional genomic analysis of all the filamentous fungi. The overall goal of the four interdependent projects in this effort is to accomplish functional genomics, annotation, and expression analyses of Neurospora crassa, a filamentous fungus that is an established model for the assemblage of over 250,000 species of non yeast fungi. Building from the completely sequenced 43-Mb Neurospora genome, Project 1 is pursuing the systematic disruption of genes through targeted gene replacements, phenotypic analysis of mutant strains, and their distribution to the scientific community at large. Project 2, through a primary focus in Annotation and Bioinformatics, has developed a platform for electronically capturing community feedback and data about the existing annotation, while building and maintaining a database to capture and display information about phenotypes. Oligonucleotide-based microarrays created in Project 3 are being used to collect baseline expression data for the nearly 11,000 distinguishable transcripts in Neurospora under various conditions of growth and development, and eventually to begin to analyze the global effects of loss of novel genes in strains created by Project 1. cDNA libraries generated in Project 4 document the overall complexity of expressed sequences in Neurospora, including alternative splicing alternative promoters and antisense transcripts. In addition, these studies have driven the assembly of an SNP map presently populated by nearly 300 markers that will greatly accelerate the positional cloning of genes.

DEFINING THE CHEMICAL SPACE OF PUBLIC GENOMIC ...

Science.gov (United States)

The current project aims to chemically index the genomics content of public genomic databases to make these data accessible in relation to other publicly available, chemically-indexed toxicological information. By defining the chemical space of public genomic data, it is possible to identify classes of chemicals on which to develop methodologies for the integration of chemogenomic data into predictive toxicology. The chemical space of public genomic data will be presented as well as the methodologies and tools developed to identify this chemical space.

Human Genome Teacher Networking Project, Final Report, April 1, 1992 - March 31, 1998

Energy Technology Data Exchange (ETDEWEB)

Collins, Debra

1999-10-01

Project to provide education regarding ethical legal and social implications of Human Genome Project to high school science teachers through two consecutive summer workshops, in class activities, and peer teaching workshops.

Genome-derived vaccines.

Science.gov (United States)

De Groot, Anne S; Rappuoli, Rino

2004-02-01

Vaccine research entered a new era when the complete genome of a pathogenic bacterium was published in 1995. Since then, more than 97 bacterial pathogens have been sequenced and at least 110 additional projects are now in progress. Genome sequencing has also dramatically accelerated: high-throughput facilities can draft the sequence of an entire microbe (two to four megabases) in 1 to 2 days. Vaccine developers are using microarrays, immunoinformatics, proteomics and high-throughput immunology assays to reduce the truly unmanageable volume of information available in genome databases to a manageable size. Vaccines composed by novel antigens discovered from genome mining are already in clinical trials. Within 5 years we can expect to see a novel class of vaccines composed by genome-predicted, assembled and engineered T- and Bcell epitopes. This article addresses the convergence of three forces--microbial genome sequencing, computational immunology and new vaccine technologies--that are shifting genome mining for vaccines onto the forefront of immunology research.

Psoriasis prediction from genome-wide SNP profiles

Directory of Open Access Journals (Sweden)

Fang Xiangzhong

2011-01-01

Full Text Available Abstract Background With the availability of large-scale genome-wide association study (GWAS data, choosing an optimal set of SNPs for disease susceptibility prediction is a challenging task. This study aimed to use single nucleotide polymorphisms (SNPs to predict psoriasis from searching GWAS data. Methods Totally we had 2,798 samples and 451,724 SNPs. Process for searching a set of SNPs to predict susceptibility for psoriasis consisted of two steps. The first one was to search top 1,000 SNPs with high accuracy for prediction of psoriasis from GWAS dataset. The second one was to search for an optimal SNP subset for predicting psoriasis. The sequential information bottleneck (sIB method was compared with classical linear discriminant analysis(LDA for classification performance. Results The best test harmonic mean of sensitivity and specificity for predicting psoriasis by sIB was 0.674(95% CI: 0.650-0.698, while only 0.520(95% CI: 0.472-0.524 was reported for predicting disease by LDA. Our results indicate that the new classifier sIB performs better than LDA in the study. Conclusions The fact that a small set of SNPs can predict disease status with average accuracy of 68% makes it possible to use SNP data for psoriasis prediction.

Impact of Relationships between Test and Reference Animals and between Reference Animals on Reliability of Genomic Prediction

DEFF Research Database (Denmark)

Wu, Xiaoping; Lund, Mogens Sandø; Sun, Dongxiao

This study investigated reliability of genomic prediction in various scenarios with regard to relationship between test and reference animals and between animals within the reference population. Different reference populations were generated from EuroGenomics data and 1288 Nordic Holstein bulls...... as a common test population. A GBLUP model and a Bayesian mixture model were applied to predict Genomic breeding values for bulls in the test data. Result showed that a closer relationship between test and reference animals led to a higher reliability, while a closer relationship between reference animal...... resulted in a lower reliability. Therefore, the design of reference population is important for improving the reliability of genomic prediction. With regard to model, the Bayesian mixture model in general led to slightly a higher reliability of genomic prediction than the GBLUP model...

Genome-Wide Prediction of the Performance of Three-Way Hybrids in Barley

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Zuo Li

2017-03-01

Full Text Available Predicting the grain yield performance of three-way hybrids is challenging. Three-way crosses are relevant for hybrid breeding in barley ( L. and maize ( L. adapted to East Africa. The main goal of our study was to implement and evaluate genome-wide prediction approaches of the performance of three-way hybrids using data of single-cross hybrids for a scenario in which parental lines of the three-way hybrids originate from three genetically distinct subpopulations. We extended the ridge regression best linear unbiased prediction (RRBLUP and devised a genomic selection model allowing for subpopulation-specific marker effects (GSA-RRBLUP: general and subpopulation-specific additive RRBLUP. Using an empirical barley data set, we showed that applying GSA-RRBLUP tripled the prediction ability of three-way hybrids from 0.095 to 0.308 compared with RRBLUP, modeling one additive effect for all three subpopulations. The experimental findings were further substantiated with computer simulations. Our results emphasize the potential of GSA-RRBLUP to improve genome-wide hybrid prediction of three-way hybrids for scenarios of genetically diverse parental populations. Because of the advantages of the GSA-RRBLUP model in dealing with hybrids from different parental populations, it may also be a promising approach to boost the prediction ability for hybrid breeding programs based on genetically diverse heterotic groups.

Computational prediction and molecular confirmation of Helitron transposons in the maize genome

Directory of Open Access Journals (Sweden)

He Limei

2008-01-01

Full Text Available Abstract Background Helitrons represent a new class of transposable elements recently uncovered in plants and animals. One remarkable feature of Helitrons is their ability to capture gene sequences, which makes them of considerable potential evolutionary importance. However, because Helitrons lack the typical structural features of other DNA transposable elements, identifying them is a challenge. Currently, most researchers identify Helitrons manually by comparing sequences. With the maize whole genome sequencing project underway, an automated computational Helitron searching tool is needed. The characterization of Helitron activities in maize needs to be addressed in order to better understand the impact of Helitrons on the organization of the genome. Results We developed and implemented a heuristic searching algorithm in PERL for identifying Helitrons. Our HelitronFinder program will (i take FASTA-formatted DNA sequences as input and identify the hairpin looping patterns, and (ii exploit the consensus 5' and 3' end sequences of known Helitrons to identify putative ends. We randomly selected five predicted Helitrons from the program's high quality output for molecular verification. Four out of the five predicted Helitrons were confirmed by PCR assays and DNA sequencing in different maize inbred lines. The HelitronFinder program identified two head-to-head dissimilar Helitrons in a maize BAC sequence. Conclusion We have identified 140 new Helitron candidates in maize with our computational tool HelitronFinder by searching maize DNA sequences currently available in GenBank. Four out of five candidates were confirmed to be real by empirical methods, thus validating the predictions of HelitronFinder. Additional points to emerge from our study are that Helitrons do not always insert at an AT dinucleotide in the host sequences, that they can insert immediately adjacent to an existing Helitron, and that their movement may cause changes in the flanking

Comparative Genomics and Disorder Prediction Identify Biologically Relevant SH3 Protein Interactions.

Directory of Open Access Journals (Sweden)

2005-08-01

Full Text Available Protein interaction networks are an important part of the post-genomic effort to integrate a part-list view of the cell into system-level understanding. Using a set of 11 yeast genomes we show that combining comparative genomics and secondary structure information greatly increases consensus-based prediction of SH3 targets. Benchmarking of our method against positive and negative standards gave 83% accuracy with 26% coverage. The concept of an optimal divergence time for effective comparative genomics studies was analyzed, demonstrating that genomes of species that diverged very recently from Saccharomyces cerevisiae(S. mikatae, S. bayanus, and S. paradoxus, or a long time ago (Neurospora crassa and Schizosaccharomyces pombe, contain less information for accurate prediction of SH3 targets than species within the optimal divergence time proposed. We also show here that intrinsically disordered SH3 domain targets are more probable sites of interaction than equivalent sites within ordered regions. Our findings highlight several novel S. cerevisiae SH3 protein interactions, the value of selection of optimal divergence times in comparative genomics studies, and the importance of intrinsic disorder for protein interactions. Based on our results we propose novel roles for the S. cerevisiae proteins Abp1p in endocytosis and Hse1p in endosome protein sorting.

Comparative genomics and disorder prediction identify biologically relevant SH3 protein interactions.

Directory of Open Access Journals (Sweden)

Pedro Beltrao

2005-08-01

Full Text Available Protein interaction networks are an important part of the post-genomic effort to integrate a part-list view of the cell into system-level understanding. Using a set of 11 yeast genomes we show that combining comparative genomics and secondary structure information greatly increases consensus-based prediction of SH3 targets. Benchmarking of our method against positive and negative standards gave 83% accuracy with 26% coverage. The concept of an optimal divergence time for effective comparative genomics studies was analyzed, demonstrating that genomes of species that diverged very recently from Saccharomyces cerevisiae(S. mikatae, S. bayanus, and S. paradoxus, or a long time ago (Neurospora crassa and Schizosaccharomyces pombe, contain less information for accurate prediction of SH3 targets than species within the optimal divergence time proposed. We also show here that intrinsically disordered SH3 domain targets are more probable sites of interaction than equivalent sites within ordered regions. Our findings highlight several novel S. cerevisiae SH3 protein interactions, the value of selection of optimal divergence times in comparative genomics studies, and the importance of intrinsic disorder for protein interactions. Based on our results we propose novel roles for the S. cerevisiae proteins Abp1p in endocytosis and Hse1p in endosome protein sorting.

Modeling heterogeneous (co)variances from adjacent-SNP groups improves genomic prediction for milk protein composition traits

DEFF Research Database (Denmark)

Gebreyesus, Grum; Lund, Mogens Sandø; Buitenhuis, Albert Johannes

2017-01-01

Accurate genomic prediction requires a large reference population, which is problematic for traits that are expensive to measure. Traits related to milk protein composition are not routinely recorded due to costly procedures and are considered to be controlled by a few quantitative trait loci...... of large effect. The amount of variation explained may vary between regions leading to heterogeneous (co)variance patterns across the genome. Genomic prediction models that can efficiently take such heterogeneity of (co)variances into account can result in improved prediction reliability. In this study, we...... developed and implemented novel univariate and bivariate Bayesian prediction models, based on estimates of heterogeneous (co)variances for genome segments (BayesAS). Available data consisted of milk protein composition traits measured on cows and de-regressed proofs of total protein yield derived for bulls...

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

The Human Genome Project and the social contract: a law policy approach.

Science.gov (United States)

Byk, C

1992-08-01

For the first time in history, genetics will enable science to completely identify each human as genetically unique. Will this knowledge reinforce the trend for more individual liberties or will it create a 'brave new world'? A law policy approach to the problems raised by the human genome project shows how far our democratic institutions are from being the proper forum to discuss such issues. Because of the fears and anxiety raised in the population, and also because of its wide implications on the everyday life, the human genome analysis more than any other project needs to succeed in setting up such a social assessment.

Human genetics: international projects and personalized medicine.

Science.gov (United States)

Apellaniz-Ruiz, Maria; Gallego, Cristina; Ruiz-Pinto, Sara; Carracedo, Angel; Rodríguez-Antona, Cristina

2016-03-01

In this article, we present the progress driven by the recent technological advances and new revolutionary massive sequencing technologies in the field of human genetics. We discuss this knowledge in relation with drug response prediction, from the germline genetic variation compiled in the 1000 Genomes Project or in the Genotype-Tissue Expression project, to the phenome-genome archives, the international cancer projects, such as The Cancer Genome Atlas or the International Cancer Genome Consortium, and the epigenetic variation and its influence in gene expression, including the regulation of drug metabolism. This review is based on the lectures presented by the speakers of the Symposium "Human Genetics: International Projects & New Technologies" from the VII Conference of the Spanish Pharmacogenetics and Pharmacogenomics Society, held on the 20th and 21st of April 2015.

Identification of a Genomic Signature Predicting for Recurrence in Early Stage Ovarian Cancer

Science.gov (United States)

2015-12-01

do it. Thus, instead of simply sequencing all the FFPE samples, we used 10 tumor samples (5 recurrent and 5 non recurrent ) to test sequencing and...Award Number: W81XWH-12-1-0521 TITLE: Identification of a Genomic Signature Predicting for Recurrence in Early-Stage Ovarian Cancer PRINCIPAL...4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-12-1-0521 Identification of a Genomic Signature Predicting for Recurrence in

Genome-wide analysis of wild-type Epstein-Barr virus genomes derived from healthy individuals of the 1,000 Genomes Project.

Science.gov (United States)

Santpere, Gabriel; Darre, Fleur; Blanco, Soledad; Alcami, Antonio; Villoslada, Pablo; Mar Albà, M; Navarro, Arcadi

2014-04-01

Most people in the world (∼90%) are infected by the Epstein-Barr virus (EBV), which establishes itself permanently in B cells. Infection by EBV is related to a number of diseases including infectious mononucleosis, multiple sclerosis, and different types of cancer. So far, only seven complete EBV strains have been described, all of them coming from donors presenting EBV-related diseases. To perform a detailed comparative genomic analysis of EBV including, for the first time, EBV strains derived from healthy individuals, we reconstructed EBV sequences infecting lymphoblastoid cell lines (LCLs) from the 1000 Genomes Project. As strain B95-8 was used to transform B cells to obtain LCLs, it is always present, but a specific deletion in its genome sets it apart from natural EBV strains. After studying hundreds of individuals, we determined the presence of natural EBV in at least 10 of them and obtained a set of variants specific to wild-type EBV. By mapping the natural EBV reads into the EBV reference genome (NC007605), we constructed nearly complete wild-type viral genomes from three individuals. Adding them to the five disease-derived EBV genomic sequences available in the literature, we performed an in-depth comparative genomic analysis. We found that latency genes harbor more nucleotide diversity than lytic genes and that six out of nine latency-related genes, as well as other genes involved in viral attachment and entry into host cells, packaging, and the capsid, present the molecular signature of accelerated protein evolution rates, suggesting rapid host-parasite coevolution.

Genomic and pedigree-based prediction for leaf, stem, and stripe rust resistance in wheat.

Science.gov (United States)

Juliana, Philomin; Singh, Ravi P; Singh, Pawan K; Crossa, Jose; Huerta-Espino, Julio; Lan, Caixia; Bhavani, Sridhar; Rutkoski, Jessica E; Poland, Jesse A; Bergstrom, Gary C; Sorrells, Mark E

2017-07-01

Genomic prediction for seedling and adult plant resistance to wheat rusts was compared to prediction using few markers as fixed effects in a least-squares approach and pedigree-based prediction. The unceasing plant-pathogen arms race and ephemeral nature of some rust resistance genes have been challenging for wheat (Triticum aestivum L.) breeding programs and farmers. Hence, it is important to devise strategies for effective evaluation and exploitation of quantitative rust resistance. One promising approach that could accelerate gain from selection for rust resistance is 'genomic selection' which utilizes dense genome-wide markers to estimate the breeding values (BVs) for quantitative traits. Our objective was to compare three genomic prediction models including genomic best linear unbiased prediction (GBLUP), GBLUP A that was GBLUP with selected loci as fixed effects and reproducing kernel Hilbert spaces-markers (RKHS-M) with least-squares (LS) approach, RKHS-pedigree (RKHS-P), and RKHS markers and pedigree (RKHS-MP) to determine the BVs for seedling and/or adult plant resistance (APR) to leaf rust (LR), stem rust (SR), and stripe rust (YR). The 333 lines in the 45th IBWSN and the 313 lines in the 46th IBWSN were genotyped using genotyping-by-sequencing and phenotyped in replicated trials. The mean prediction accuracies ranged from 0.31-0.74 for LR seedling, 0.12-0.56 for LR APR, 0.31-0.65 for SR APR, 0.70-0.78 for YR seedling, and 0.34-0.71 for YR APR. For most datasets, the RKHS-MP model gave the highest accuracies, while LS gave the lowest. GBLUP, GBLUP A, RKHS-M, and RKHS-P models gave similar accuracies. Using genome-wide marker-based models resulted in an average of 42% increase in accuracy over LS. We conclude that GS is a promising approach for improvement of quantitative rust resistance and can be implemented in the breeding pipeline.

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

Directory of Open Access Journals (Sweden)

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.

A Genomics-Based Model for Prediction of Severe Bioprosthetic Mitral Valve Calcification.

Science.gov (United States)

Ponasenko, Anastasia V; Khutornaya, Maria V; Kutikhin, Anton G; Rutkovskaya, Natalia V; Tsepokina, Anna V; Kondyukova, Natalia V; Yuzhalin, Arseniy E; Barbarash, Leonid S

2016-08-31

Severe bioprosthetic mitral valve calcification is a significant problem in cardiovascular surgery. Unfortunately, clinical markers did not demonstrate efficacy in prediction of severe bioprosthetic mitral valve calcification. Here, we examined whether a genomics-based approach is efficient in predicting the risk of severe bioprosthetic mitral valve calcification. A total of 124 consecutive Russian patients who underwent mitral valve replacement surgery were recruited. We investigated the associations of the inherited variation in innate immunity, lipid metabolism and calcium metabolism genes with severe bioprosthetic mitral valve calcification. Genotyping was conducted utilizing the TaqMan assay. Eight gene polymorphisms were significantly associated with severe bioprosthetic mitral valve calcification and were therefore included into stepwise logistic regression which identified male gender, the T/T genotype of the rs3775073 polymorphism within the TLR6 gene, the C/T genotype of the rs2229238 polymorphism within the IL6R gene, and the A/A genotype of the rs10455872 polymorphism within the LPA gene as independent predictors of severe bioprosthetic mitral valve calcification. The developed genomics-based model had fair predictive value with area under the receiver operating characteristic (ROC) curve of 0.73. In conclusion, our genomics-based approach is efficient for the prediction of severe bioprosthetic mitral valve calcification.

Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation.

Directory of Open Access Journals (Sweden)

Frank Technow

Full Text Available Genomic selection, enabled by whole genome prediction (WGP methods, is revolutionizing plant breeding. Existing WGP methods have been shown to deliver accurate predictions in the most common settings, such as prediction of across environment performance for traits with additive gene effects. However, prediction of traits with non-additive gene effects and prediction of genotype by environment interaction (G×E, continues to be challenging. Previous attempts to increase prediction accuracy for these particularly difficult tasks employed prediction methods that are purely statistical in nature. Augmenting the statistical methods with biological knowledge has been largely overlooked thus far. Crop growth models (CGMs attempt to represent the impact of functional relationships between plant physiology and the environment in the formation of yield and similar output traits of interest. Thus, they can explain the impact of G×E and certain types of non-additive gene effects on the expressed phenotype. Approximate Bayesian computation (ABC, a novel and powerful computational procedure, allows the incorporation of CGMs directly into the estimation of whole genome marker effects in WGP. Here we provide a proof of concept study for this novel approach and demonstrate its use with synthetic data sets. We show that this novel approach can be considerably more accurate than the benchmark WGP method GBLUP in predicting performance in environments represented in the estimation set as well as in previously unobserved environments for traits determined by non-additive gene effects. We conclude that this proof of concept demonstrates that using ABC for incorporating biological knowledge in the form of CGMs into WGP is a very promising and novel approach to improving prediction accuracy for some of the most challenging scenarios in plant breeding and applied genetics.

Genome-wide association study, genomic prediction and marker-assisted selection for seed weight in soybean (Glycine max).

Science.gov (United States)

Zhang, Jiaoping; Song, Qijian; Cregan, Perry B; Jiang, Guo-Liang

2016-01-01

Twenty-two loci for soybean SW and candidate genes conditioning seed development were identified; and prediction accuracies of GS and MAS were estimated through cross-validation and validation with unrelated populations. Soybean (Glycine max) is a major crop for plant protein and oil production, and seed weight (SW) is important for yield and quality in food/vegetable uses of soybean. However, our knowledge of genes controlling SW remains limited. To better understand the molecular mechanism underlying the trait and explore marker-based breeding approaches, we conducted a genome-wide association study in a population of 309 soybean germplasm accessions using 31,045 single nucleotide polymorphisms (SNPs), and estimated the prediction accuracy of genomic selection (GS) and marker-assisted selection (MAS) for SW. Twenty-two loci of minor effect associated with SW were identified, including hotspots on Gm04 and Gm19. The mixed model containing these loci explained 83.4% of phenotypic variation. Candidate genes with Arabidopsis orthologs conditioning SW were also proposed. The prediction accuracies of GS and MAS by cross-validation were 0.75-0.87 and 0.62-0.75, respectively, depending on the number of SNPs used and the size of training population. GS also outperformed MAS when the validation was performed using unrelated panels across a wide range of maturities, with an average prediction accuracy of 0.74 versus 0.53. This study convincingly demonstrated that soybean SW is controlled by numerous minor-effect loci. It greatly enhances our understanding of the genetic basis of SW in soybean and facilitates the identification of genes controlling the trait. It also suggests that GS holds promise for accelerating soybean breeding progress. The results are helpful for genetic improvement and genomic prediction of yield in soybean.

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

Accuracy of genomic breeding value prediction for intramuscular fat using different genomic relationship matrices in Hanwoo (Korean cattle).

Science.gov (United States)

Choi, Taejeong; Lim, Dajeong; Park, Byoungho; Sharma, Aditi; Kim, Jong-Joo; Kim, Sidong; Lee, Seung Hwan

2017-07-01

Intramuscular fat is one of the meat quality traits that is considered in the selection strategies for Hanwoo (Korean cattle). Different methods are used to estimate the breeding value of selection candidates. In the present work we focused on accuracy of different genotype relationship matrices as described by forni and pedigree based relationship matrix. The data set included a total of 778 animals that were genotyped for BovineSNP50 BeadChip. Among these 778 animals, 72 animals were sires for 706 reference animals and were used as a validation dataset. Single trait animal model (best linear unbiased prediction and genomic best linear unbiased prediction) was used to estimate the breeding values from genomic and pedigree information. The diagonal elements for the pedigree based coefficients were slightly higher for the genomic relationship matrices (GRM) based coefficients while off diagonal elements were considerably low for GRM based coefficients. The accuracy of breeding value for the pedigree based relationship matrix (A) was 13% while for GRM (GOF, G05, and Yang) it was 0.37, 0.45, and 0.38, respectively. Accuracy of GRM was 1.5 times higher than A in this study. Therefore, genomic information will be more beneficial than pedigree information in the Hanwoo breeding program.

CERAPP: Collaborative estrogen receptor activity prediction project

DEFF Research Database (Denmark)

Mansouri, Kamel; Abdelaziz, Ahmed; Rybacka, Aleksandra

2016-01-01

). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. oBjectives: We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project...... States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical......: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing. conclusion: This project demonstrated...

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.

From structure prediction to genomic screens for novel non-coding RNAs.

Science.gov (United States)

Gorodkin, Jan; Hofacker, Ivo L

2011-08-01

Non-coding RNAs (ncRNAs) are receiving more and more attention not only as an abundant class of genes, but also as regulatory structural elements (some located in mRNAs). A key feature of RNA function is its structure. Computational methods were developed early for folding and prediction of RNA structure with the aim of assisting in functional analysis. With the discovery of more and more ncRNAs, it has become clear that a large fraction of these are highly structured. Interestingly, a large part of the structure is comprised of regular Watson-Crick and GU wobble base pairs. This and the increased amount of available genomes have made it possible to employ structure-based methods for genomic screens. The field has moved from folding prediction of single sequences to computational screens for ncRNAs in genomic sequence using the RNA structure as the main characteristic feature. Whereas early methods focused on energy-directed folding of single sequences, comparative analysis based on structure preserving changes of base pairs has been efficient in improving accuracy, and today this constitutes a key component in genomic screens. Here, we cover the basic principles of RNA folding and touch upon some of the concepts in current methods that have been applied in genomic screens for de novo RNA structures in searches for novel ncRNA genes and regulatory RNA structure on mRNAs. We discuss the strengths and weaknesses of the different strategies and how they can complement each other.

Getting the Word Out on the Human Genome Project: A Course for Physicians

Energy Technology Data Exchange (ETDEWEB)

Sara L. Tobin

2004-09-29

Our project, ''Getting the Word Out on the Human Genome Project: A Course for Physicians,'' presented educational goals to convey the power and promise of the Human Genome Program to a variety of professional, educational, and public audiences. Our initial goal was to provide practicing physicians with a comprehensive multimedia tool to update their skills in the genomic era. We therefore created the multimedia courseware, ''The New Genetics: Courseware for Physicians. Molecular Concepts, Applications, and Ramifications.'' However, as the project moved forward, several unanticipated audiences found the courseware to be useful for instruction and for self-education, so an additional edition of the courseware ''The New Genetics: Medicine and the Human Genome. Molecular Concepts, Applications, and Ramifications'' was published simultaneously with the physician version. At the time that both versions of the courseware were being completed, Stanford's Office of Technology Licensing opted not to commercialize the courseware and offered a license-back agreement if the authors founded a commercial business. The authors thus became closely involved in marketing and sales, and several thousand copies of the courseware have been sold. Surprisingly, the non-physician version has turned out to be more in demand, and this has led us in several new directions, most of which involve undergraduate education. These are discussed in detail in the Report.

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

Science.gov (United States)

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...

The PredictAD project

DEFF Research Database (Denmark)

Antila, Kari; Lötjönen, Jyrki; Thurfjell, Lennart

2013-01-01

Alzheimer's disease (AD) is the most common cause of dementia affecting 36 million people worldwide. As the demographic transition in the developed countries progresses towards older population, the worsening ratio of workers per retirees and the growing number of patients with age-related illnes...... candidates and implement the framework in software. The results are currently used in several research projects, licensed to commercial use and being tested for clinical use in several trials....... objective of the PredictAD project was to find and integrate efficient biomarkers from heterogeneous patient data to make early diagnosis and to monitor the progress of AD in a more efficient, reliable and objective manner. The project focused on discovering biomarkers from biomolecular data...

Accuracy of Genomic Prediction in Switchgrass (Panicum virgatum L. Improved by Accounting for Linkage Disequilibrium

Directory of Open Access Journals (Sweden)

Guillaume P. Ramstein

2016-04-01

Full Text Available Switchgrass is a relatively high-yielding and environmentally sustainable biomass crop, but further genetic gains in biomass yield must be achieved to make it an economically viable bioenergy feedstock. Genomic selection (GS is an attractive technology to generate rapid genetic gains in switchgrass, and meet the goals of a substantial displacement of petroleum use with biofuels in the near future. In this study, we empirically assessed prediction procedures for genomic selection in two different populations, consisting of 137 and 110 half-sib families of switchgrass, tested in two locations in the United States for three agronomic traits: dry matter yield, plant height, and heading date. Marker data were produced for the families’ parents by exome capture sequencing, generating up to 141,030 polymorphic markers with available genomic-location and annotation information. We evaluated prediction procedures that varied not only by learning schemes and prediction models, but also by the way the data were preprocessed to account for redundancy in marker information. More complex genomic prediction procedures were generally not significantly more accurate than the simplest procedure, likely due to limited population sizes. Nevertheless, a highly significant gain in prediction accuracy was achieved by transforming the marker data through a marker correlation matrix. Our results suggest that marker-data transformations and, more generally, the account of linkage disequilibrium among markers, offer valuable opportunities for improving prediction procedures in GS. Some of the achieved prediction accuracies should motivate implementation of GS in switchgrass breeding programs.

Comparing strategies for selection of low-density SNPs for imputation-mediated genomic prediction in U. S. Holsteins.

Science.gov (United States)

He, Jun; Xu, Jiaqi; Wu, Xiao-Lin; Bauck, Stewart; Lee, Jungjae; Morota, Gota; Kachman, Stephen D; Spangler, Matthew L

2018-04-01

SNP chips are commonly used for genotyping animals in genomic selection but strategies for selecting low-density (LD) SNPs for imputation-mediated genomic selection have not been addressed adequately. The main purpose of the present study was to compare the performance of eight LD (6K) SNP panels, each selected by a different strategy exploiting a combination of three major factors: evenly-spaced SNPs, increased minor allele frequencies, and SNP-trait associations either for single traits independently or for all the three traits jointly. The imputation accuracies from 6K to 80K SNP genotypes were between 96.2 and 98.2%. Genomic prediction accuracies obtained using imputed 80K genotypes were between 0.817 and 0.821 for daughter pregnancy rate, between 0.838 and 0.844 for fat yield, and between 0.850 and 0.863 for milk yield. The two SNP panels optimized on the three major factors had the highest genomic prediction accuracy (0.821-0.863), and these accuracies were very close to those obtained using observed 80K genotypes (0.825-0.868). Further exploration of the underlying relationships showed that genomic prediction accuracies did not respond linearly to imputation accuracies, but were significantly affected by genotype (imputation) errors of SNPs in association with the traits to be predicted. SNPs optimal for map coverage and MAF were favorable for obtaining accurate imputation of genotypes whereas trait-associated SNPs improved genomic prediction accuracies. Thus, optimal LD SNP panels were the ones that combined both strengths. The present results have practical implications on the design of LD SNP chips for imputation-enabled genomic prediction.

Genome-wide prediction of discrete traits using bayesian regressions and machine learning

Directory of Open Access Journals (Sweden)

Forni Selma

2011-02-01

Full Text Available Abstract Background Genomic selection has gained much attention and the main goal is to increase the predictive accuracy and the genetic gain in livestock using dense marker information. Most methods dealing with the large p (number of covariates small n (number of observations problem have dealt only with continuous traits, but there are many important traits in livestock that are recorded in a discrete fashion (e.g. pregnancy outcome, disease resistance. It is necessary to evaluate alternatives to analyze discrete traits in a genome-wide prediction context. Methods This study shows two threshold versions of Bayesian regressions (Bayes A and Bayesian LASSO and two machine learning algorithms (boosting and random forest to analyze discrete traits in a genome-wide prediction context. These methods were evaluated using simulated and field data to predict yet-to-be observed records. Performances were compared based on the models' predictive ability. Results The simulation showed that machine learning had some advantages over Bayesian regressions when a small number of QTL regulated the trait under pure additivity. However, differences were small and disappeared with a large number of QTL. Bayesian threshold LASSO and boosting achieved the highest accuracies, whereas Random Forest presented the highest classification performance. Random Forest was the most consistent method in detecting resistant and susceptible animals, phi correlation was up to 81% greater than Bayesian regressions. Random Forest outperformed other methods in correctly classifying resistant and susceptible animals in the two pure swine lines evaluated. Boosting and Bayes A were more accurate with crossbred data. Conclusions The results of this study suggest that the best method for genome-wide prediction may depend on the genetic basis of the population analyzed. All methods were less accurate at correctly classifying intermediate animals than extreme animals. Among the different

Using physicochemical and compositional characteristics of DNA sequence for prediction of genomic signals

KAUST Repository

Mulamba, Pierre Abraham

2014-12-01

The challenge in finding genes in eukaryotic organisms using computational methods is an ongoing problem in the biology. Based on various genomic signals found in eukaryotic genomes, this problem can be divided into many different sub­‐problems such as identification of transcription start sites, translation initiation sites, splice sites, poly (A) signals, etc. Each sub-­problem deals with a particular type of genomic signals and various computational methods are used to solve each sub-­problem. Aggregating information from all these individual sub-­problems can lead to a complete annotation of a gene and its component signals. The fundamental principle of most of these computational methods is the mapping principle – building an input-­output model for the prediction of a particular genomic signal based on a set of known input signals and their corresponding output signal. The type of input signals used to build the model is an essential element in most of these computational methods. The common factor of most of these methods is that they are mainly based on the statistical analysis of the basic nucleotide sequence string composition. 4 Our study is based on a novel approach to predict genomic signals in which uniquely generated structural profiles that combine compressed physicochemical properties with topological and compositional properties of DNA sequences are used to develop machine learning predictive models. The compression of the physicochemical properties is made using principal component analysis transformation. Our ideas are evaluated through prediction models of canonical splice sites using support vector machine models. We demonstrate across several species that the proposed methodology has resulted in the most accurate splice site predictors that are publicly available or described. We believe that the approach in this study is quite general and has various applications in other biological modeling problems.

The modest beginnings of one genome project.

Science.gov (United States)

Kaback, David B

2013-06-01

One of the top things on a geneticist's wish list has to be a set of mutants for every gene in their particular organism. Such a set was produced for the yeast, Saccharomyces cerevisiae near the end of the 20th century by a consortium of yeast geneticists. However, the functional genomic analysis of one chromosome, its smallest, had already begun more than 25 years earlier as a project that was designed to define most or all of that chromosome's essential genes by temperature-sensitive lethal mutations. When far fewer than expected genes were uncovered, the relatively new field of molecular cloning enabled us and indeed, the entire community of yeast researchers to approach this problem more definitively. These studies ultimately led to cloning, genomic sequencing, and the production and phenotypic analysis of the entire set of knockout mutations for this model organism as well as a better concept of what defines an essential function, a wish fulfilled that enables this model eukaryote to continue at the forefront of research in modern biology.

The Personal Genome Project Canada: findings from whole genome sequences of the inaugural 56 participants.

Science.gov (United States)

Reuter, Miriam S; Walker, Susan; Thiruvahindrapuram, Bhooma; Whitney, Joe; Cohn, Iris; Sondheimer, Neal; Yuen, Ryan K C; Trost, Brett; Paton, Tara A; Pereira, Sergio L; Herbrick, Jo-Anne; Wintle, Richard F; Merico, Daniele; Howe, Jennifer; MacDonald, Jeffrey R; Lu, Chao; Nalpathamkalam, Thomas; Sung, Wilson W L; Wang, Zhuozhi; Patel, Rohan V; Pellecchia, Giovanna; Wei, John; Strug, Lisa J; Bell, Sherilyn; Kellam, Barbara; Mahtani, Melanie M; Bassett, Anne S; Bombard, Yvonne; Weksberg, Rosanna; Shuman, Cheryl; Cohn, Ronald D; Stavropoulos, Dimitri J; Bowdin, Sarah; Hildebrandt, Matthew R; Wei, Wei; Romm, Asli; Pasceri, Peter; Ellis, James; Ray, Peter; Meyn, M Stephen; Monfared, Nasim; Hosseini, S Mohsen; Joseph-George, Ann M; Keeley, Fred W; Cook, Ryan A; Fiume, Marc; Lee, Hin C; Marshall, Christian R; Davies, Jill; Hazell, Allison; Buchanan, Janet A; Szego, Michael J; Scherer, Stephen W

2018-02-05

The Personal Genome Project Canada is a comprehensive public data resource that integrates whole genome sequencing data and health information. We describe genomic variation identified in the initial recruitment cohort of 56 volunteers. Volunteers were screened for eligibility and provided informed consent for open data sharing. Using blood DNA, we performed whole genome sequencing and identified all possible classes of DNA variants. A genetic counsellor explained the implication of the results to each participant. Whole genome sequencing of the first 56 participants identified 207 662 805 sequence variants and 27 494 copy number variations. We analyzed a prioritized disease-associated data set ( n = 1606 variants) according to standardized guidelines, and interpreted 19 variants in 14 participants (25%) as having obvious health implications. Six of these variants (e.g., in BRCA1 or mosaic loss of an X chromosome) were pathogenic or likely pathogenic. Seven were risk factors for cancer, cardiovascular or neurobehavioural conditions. Four other variants - associated with cancer, cardiac or neurodegenerative phenotypes - remained of uncertain significance because of discrepancies among databases. We also identified a large structural chromosome aberration and a likely pathogenic mitochondrial variant. There were 172 recessive disease alleles (e.g., 5 individuals carried mutations for cystic fibrosis). Pharmacogenomics analyses revealed another 3.9 potentially relevant genotypes per individual. Our analyses identified a spectrum of genetic variants with potential health impact in 25% of participants. When also considering recessive alleles and variants with potential pharmacologic relevance, all 56 participants had medically relevant findings. Although access is mostly limited to research, whole genome sequencing can provide specific and novel information with the potential of major impact for health care. © 2018 Joule Inc. or its licensors.

From structure prediction to genomic screens for novel non-coding RNAs.

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Jan Gorodkin

2011-08-01

Full Text Available Non-coding RNAs (ncRNAs are receiving more and more attention not only as an abundant class of genes, but also as regulatory structural elements (some located in mRNAs. A key feature of RNA function is its structure. Computational methods were developed early for folding and prediction of RNA structure with the aim of assisting in functional analysis. With the discovery of more and more ncRNAs, it has become clear that a large fraction of these are highly structured. Interestingly, a large part of the structure is comprised of regular Watson-Crick and GU wobble base pairs. This and the increased amount of available genomes have made it possible to employ structure-based methods for genomic screens. The field has moved from folding prediction of single sequences to computational screens for ncRNAs in genomic sequence using the RNA structure as the main characteristic feature. Whereas early methods focused on energy-directed folding of single sequences, comparative analysis based on structure preserving changes of base pairs has been efficient in improving accuracy, and today this constitutes a key component in genomic screens. Here, we cover the basic principles of RNA folding and touch upon some of the concepts in current methods that have been applied in genomic screens for de novo RNA structures in searches for novel ncRNA genes and regulatory RNA structure on mRNAs. We discuss the strengths and weaknesses of the different strategies and how they can complement each other.

Identification of balanced chromosomal rearrangements previously unknown among participants in the 1000 Genomes Project: implications for interpretation of structural variation in genomes and the future of clinical cytogenetics.

Science.gov (United States)

Dong, Zirui; Wang, Huilin; Chen, Haixiao; Jiang, Hui; Yuan, Jianying; Yang, Zhenjun; Wang, Wen-Jing; Xu, Fengping; Guo, Xiaosen; Cao, Ye; Zhu, Zhenzhen; Geng, Chunyu; Cheung, Wan Chee; Kwok, Yvonne K; Yang, Huanming; Leung, Tak Yeung; Morton, Cynthia C; Cheung, Sau Wai; Choy, Kwong Wai

2017-11-02

PurposeRecent studies demonstrate that whole-genome sequencing enables detection of cryptic rearrangements in apparently balanced chromosomal rearrangements (also known as balanced chromosomal abnormalities, BCAs) previously identified by conventional cytogenetic methods. We aimed to assess our analytical tool for detecting BCAs in the 1000 Genomes Project without knowing which bands were affected.MethodsThe 1000 Genomes Project provides an unprecedented integrated map of structural variants in phenotypically normal subjects, but there is no information on potential inclusion of subjects with apparent BCAs akin to those traditionally detected in diagnostic cytogenetics laboratories. We applied our analytical tool to 1,166 genomes from the 1000 Genomes Project with sufficient physical coverage (8.25-fold).ResultsWith this approach, we detected four reciprocal balanced translocations and four inversions, ranging in size from 57.9 kb to 13.3 Mb, all of which were confirmed by cytogenetic methods and polymerase chain reaction studies. One of these DNAs has a subtle translocation that is not readily identified by chromosome analysis because of the similarity of the banding patterns and size of exchanged segments, and another results in disruption of all transcripts of an OMIM gene.ConclusionOur study demonstrates the extension of utilizing low-pass whole-genome sequencing for unbiased detection of BCAs including translocations and inversions previously unknown in the 1000 Genomes Project.GENETICS in MEDICINE advance online publication, 2 November 2017; doi:10.1038/gim.2017.170.

kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets

Science.gov (United States)

Fletez-Brant, Christopher; Lee, Dongwon; McCallion, Andrew S.; Beer, Michael A.

2013-01-01

Massively parallel sequencing technologies have made the generation of genomic data sets a routine component of many biological investigations. For example, Chromatin immunoprecipitation followed by sequence assays detect genomic regions bound (directly or indirectly) by specific factors, and DNase-seq identifies regions of open chromatin. A major bottleneck in the interpretation of these data is the identification of the underlying DNA sequence code that defines, and ultimately facilitates prediction of, these transcription factor (TF) bound or open chromatin regions. We have recently developed a novel computational methodology, which uses a support vector machine (SVM) with kmer sequence features (kmer-SVM) to identify predictive combinations of short transcription factor-binding sites, which determine the tissue specificity of these genomic assays (Lee, Karchin and Beer, Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res. 2011; 21:2167–80). This regulatory information can (i) give confidence in genomic experiments by recovering previously known binding sites, and (ii) reveal novel sequence features for subsequent experimental testing of cooperative mechanisms. Here, we describe the development and implementation of a web server to allow the broader research community to independently apply our kmer-SVM to analyze and interpret their genomic datasets. We analyze five recently published data sets and demonstrate how this tool identifies accessory factors and repressive sequence elements. kmer-SVM is available at http://kmersvm.beerlab.org. PMID:23771147

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.

Competence development organizations in project management on the basis of genomic model methodologies

OpenAIRE

Бушуев, Сергей Дмитриевич; Рогозина, Виктория Борисовна; Ярошенко, Юрий Федерович

2013-01-01

The matrix technology for identification of organisational competencies in project management is presented in the article. Matrix elements are the components of organizational competence in the field of project management and project management methodology represented in the structure of the genome. The matrix model of competence in the framework of the adopted methodologies and scanning method for identifying organizational competences formalised. Proposed methods for building effective proj...

Predicting statistical properties of open reading frames in bacterial genomes.

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Katharina Mir

Full Text Available An analytical model based on the statistical properties of Open Reading Frames (ORFs of eubacterial genomes such as codon composition and sequence length of all reading frames was developed. This new model predicts the average length, maximum length as well as the length distribution of the ORFs of 70 species with GC contents varying between 21% and 74%. Furthermore, the number of annotated genes is predicted with high accordance. However, the ORF length distribution in the five alternative reading frames shows interesting deviations from the predicted distribution. In particular, long ORFs appear more often than expected statistically. The unexpected depletion of stop codons in these alternative open reading frames cannot completely be explained by a biased codon usage in the +1 frame. While it is unknown if the stop codon depletion has a biological function, it could be due to a protein coding capacity of alternative ORFs exerting a selection pressure which prevents the fixation of stop codon mutations. The comparison of the analytical model with bacterial genomes, therefore, leads to a hypothesis suggesting novel gene candidates which can now be investigated in subsequent wet lab experiments.

Genomic prediction of starch content and chipping quality in tetraploid potato using genotyping-by-sequencing

DEFF Research Database (Denmark)

Sverrisdóttir, Elsa; Byrne, Stephen; Nielsen, Ea Høegh Riis

2017-01-01

continue to fall. In this study, we have generated genomic prediction models for starch content and chipping quality in tetraploid potato to facilitate varietal development. Chipping quality was evaluated as the colour of a potato chip after frying following cold induced sweetening. We used genotyping...... genomic estimated breeding values. Cross-validated prediction correlations of 0.56 and 0.73 were obtained within the training population for starch content and chipping quality, respectively, while correlations were lower when predicting performance in the test panel, at 0.30–0.31 and 0...

Sharing reference data and including cows in the reference population improve genomic predictions in Danish Jersey.

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Su, G; Ma, P; Nielsen, U S; Aamand, G P; Wiggans, G; Guldbrandtsen, B; Lund, M S

2016-06-01

Small reference populations limit the accuracy of genomic prediction in numerically small breeds, such like Danish Jersey. The objective of this study was to investigate two approaches to improve genomic prediction by increasing size of reference population in Danish Jersey. The first approach was to include North American Jersey bulls in Danish Jersey reference population. The second was to genotype cows and use them as reference animals. The validation of genomic prediction was carried out on bulls and cows, respectively. In validation on bulls, about 300 Danish bulls (depending on traits) born in 2005 and later were used as validation data, and the reference populations were: (1) about 1050 Danish bulls, (2) about 1050 Danish bulls and about 1150 US bulls. In validation on cows, about 3000 Danish cows from 87 young half-sib families were used as validation data, and the reference populations were: (1) about 1250 Danish bulls, (2) about 1250 Danish bulls and about 1150 US bulls, (3) about 1250 Danish bulls and about 4800 cows, (4) about 1250 Danish bulls, 1150 US bulls and 4800 Danish cows. Genomic best linear unbiased prediction model was used to predict breeding values. De-regressed proofs were used as response variables. In the validation on bulls for eight traits, the joint DK-US bull reference population led to higher reliability of genomic prediction than the DK bull reference population for six traits, but not for fertility and longevity. Averaged over the eight traits, the gain was 3 percentage points. In the validation on cows for six traits (fertility and longevity were not available), the gain from inclusion of US bull in reference population was 6.6 percentage points in average over the six traits, and the gain from inclusion of cows was 8.2 percentage points. However, the gains from cows and US bulls were not accumulative. The total gain of including both US bulls and Danish cows was 10.5 percentage points. The results indicate that sharing reference

RNA 3D modules in genome-wide predictions of RNA 2D structure

DEFF Research Database (Denmark)

Theis, Corinna; Zirbel, Craig L; Zu Siederdissen, Christian Höner

2015-01-01

. These modules can, for example, occur inside structural elements which in RNA 2D predictions appear as internal loops. Hence one question is if the use of such RNA 3D information can improve the prediction accuracy of RNA secondary structure at a genome-wide level. Here, we use RNAz in combination with 3D......Recent experimental and computational progress has revealed a large potential for RNA structure in the genome. This has been driven by computational strategies that exploit multiple genomes of related organisms to identify common sequences and secondary structures. However, these computational...... approaches have two main challenges: they are computationally expensive and they have a relatively high false discovery rate (FDR). Simultaneously, RNA 3D structure analysis has revealed modules composed of non-canonical base pairs which occur in non-homologous positions, apparently by independent evolution...

Understanding our genetic inheritance: The US Human Genome Project, The first five years FY 1991--1995

Energy Technology Data Exchange (ETDEWEB)

None

1990-04-01

The Human Genome Initiative is a worldwide research effort with the goal of analyzing the structure of human DNA and determining the location of the estimated 100,000 human genes. In parallel with this effort, the DNA of a set of model organisms will be studied to provide the comparative information necessary for understanding the functioning of the human genome. The information generated by the human genome project is expected to be the source book for biomedical science in the 21st century and will by of immense benefit to the field of medicine. It will help us to understand and eventually treat many of the more than 4000 genetic diseases that affect mankind, as well as the many multifactorial diseases in which genetic predisposition plays an important role. A centrally coordinated project focused on specific objectives is believed to be the most efficient and least expensive way of obtaining this information. The basic data produced will be collected in electronic databases that will make the information readily accessible on convenient form to all who need it. This report describes the plans for the U.S. human genome project and updates those originally prepared by the Office of Technology Assessment (OTA) and the National Research Council (NRC) in 1988. In the intervening two years, improvements in technology for almost every aspect of genomics research have taken place. As a result, more specific goals can now be set for the project.

Genomic prediction based on data from three layer lines using non-linear regression models.

Science.gov (United States)

Huang, Heyun; Windig, Jack J; Vereijken, Addie; Calus, Mario P L

2014-11-06

Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods. In an attempt to alleviate potential discrepancies between assumptions of linear models and multi-population data, two types of alternative models were used: (1) a multi-trait genomic best linear unbiased prediction (GBLUP) model that modelled trait by line combinations as separate but correlated traits and (2) non-linear models based on kernel learning. These models were compared to conventional linear models for genomic prediction for two lines of brown layer hens (B1 and B2) and one line of white hens (W1). The three lines each had 1004 to 1023 training and 238 to 240 validation animals. Prediction accuracy was evaluated by estimating the correlation between observed phenotypes and predicted breeding values. When the training dataset included only data from the evaluated line, non-linear models yielded at best a similar accuracy as linear models. In some cases, when adding a distantly related line, the linear models showed a slight decrease in performance, while non-linear models generally showed no change in accuracy. When only information from a closely related line was used for training, linear models and non-linear radial basis function (RBF) kernel models performed similarly. The multi-trait GBLUP model took advantage of the estimated genetic correlations between the lines. Combining linear and non-linear models improved the accuracy of multi-line genomic prediction. Linear models and non-linear RBF models performed very similarly for genomic prediction, despite the expectation that non-linear models could deal better with the heterogeneous multi-population data. This heterogeneity of the data can be overcome by modelling trait by line combinations as separate but correlated traits, which avoids the occasional

The Predictive Validity of Projective Measures.

Science.gov (United States)

Suinn, Richard M.; Oskamp, Stuart

Written for use by clinical practitioners as well as psychological researchers, this book surveys recent literature (1950-1965) on projective test validity by reviewing and critically evaluating studies which shed light on what may reliably be predicted from projective test results. Two major instruments are covered: the Rorschach and the Thematic…

The African Genome Variation Project shapes medical genetics in Africa

Science.gov (United States)

Gurdasani, Deepti; Carstensen, Tommy; Tekola-Ayele, Fasil; Pagani, Luca; Tachmazidou, Ioanna; Hatzikotoulas, Konstantinos; Karthikeyan, Savita; Iles, Louise; Pollard, Martin O.; Choudhury, Ananyo; Ritchie, Graham R. S.; Xue, Yali; Asimit, Jennifer; Nsubuga, Rebecca N.; Young, Elizabeth H.; Pomilla, Cristina; Kivinen, Katja; Rockett, Kirk; Kamali, Anatoli; Doumatey, Ayo P.; Asiki, Gershim; Seeley, Janet; Sisay-Joof, Fatoumatta; Jallow, Muminatou; Tollman, Stephen; Mekonnen, Ephrem; Ekong, Rosemary; Oljira, Tamiru; Bradman, Neil; Bojang, Kalifa; Ramsay, Michele; Adeyemo, Adebowale; Bekele, Endashaw; Motala, Ayesha; Norris, Shane A.; Pirie, Fraser; Kaleebu, Pontiano; Kwiatkowski, Dominic; Tyler-Smith, Chris; Rotimi, Charles; Zeggini, Eleftheria; Sandhu, Manjinder S.

2014-01-01

Given the importance of Africa to studies of human origins and disease susceptibility, detailed characterisation of African genetic diversity is needed. The African Genome Variation Project (AGVP) provides a resource to help design, implement and interpret genomic studies in sub-Saharan Africa (SSA) and worldwide. The AGVP represents dense genotypes from 1,481 and whole genome sequences (WGS) from 320 individuals across SSA. Using this resource, we find novel evidence of complex, regionally distinct hunter-gatherer and Eurasian admixture across SSA. We identify new loci under selection, including for malaria and hypertension. We show that modern imputation panels can identify association signals at highly differentiated loci across populations in SSA. Using WGS, we show further improvement in imputation accuracy supporting efforts for large-scale sequencing of diverse African haplotypes. Finally, we present an efficient genotype array design capturing common genetic variation in Africa, showing for the first time that such designs are feasible. PMID:25470054

Perspectives from the Avian Phylogenomics Project: Questions that Can Be Answered with Sequencing All Genomes of a Vertebrate Class.

Science.gov (United States)

Jarvis, Erich D

2016-01-01

The rapid pace of advances in genome technology, with concomitant reductions in cost, makes it feasible that one day in our lifetime we will have available extant genomes of entire classes of species, including vertebrates. I recently helped cocoordinate the large-scale Avian Phylogenomics Project, which collected and sequenced genomes of 48 bird species representing most currently classified orders to address a range of questions in phylogenomics and comparative genomics. The consortium was able to answer questions not previously possible with just a few genomes. This success spurred on the creation of a project to sequence the genomes of at least one individual of all extant ∼10,500 bird species. The initiation of this project has led us to consider what questions now impossible to answer could be answered with all genomes, and could drive new questions now unimaginable. These include the generation of a highly resolved family tree of extant species, genome-wide association studies across species to identify genetic substrates of many complex traits, redefinition of species and the species concept, reconstruction of the genomes of common ancestors, and generation of new computational tools to address these questions. Here I present visions for the future by posing and answering questions regarding what scientists could potentially do with available genomes of an entire vertebrate class.

Drug target prediction and prioritization: using orthology to predict essentiality in parasite genomes

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Hall Ross S

2010-04-01

Full Text Available Abstract Background New drug targets are urgently needed for parasites of socio-economic importance. Genes that are essential for parasite survival are highly desirable targets, but information on these genes is lacking, as gene knockouts or knockdowns are difficult to perform in many species of parasites. We examined the applicability of large-scale essentiality information from four model eukaryotes, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus and Saccharomyces cerevisiae, to discover essential genes in each of their genomes. Parasite genes that lack orthologues in their host are desirable as selective targets, so we also examined prediction of essential genes within this subset. Results Cross-species analyses showed that the evolutionary conservation of genes and the presence of essential orthologues are each strong predictors of essentiality in eukaryotes. Absence of paralogues was also found to be a general predictor of increased relative essentiality. By combining several orthology and essentiality criteria one can select gene sets with up to a five-fold enrichment in essential genes compared with a random selection. We show how quantitative application of such criteria can be used to predict a ranked list of potential drug targets from Ancylostoma caninum and Haemonchus contortus - two blood-feeding strongylid nematodes, for which there are presently limited sequence data but no functional genomic tools. Conclusions The present study demonstrates the utility of using orthology information from multiple, diverse eukaryotes to predict essential genes. The data also emphasize the challenge of identifying essential genes among those in a parasite that are absent from its host.

Analysis and prediction of gene splice sites in four Aspergillus genomes

DEFF Research Database (Denmark)

Wang, Kai; Ussery, David; Brunak, Søren

2009-01-01

Several Aspergillus fungal genomic sequences have been published, with many more in progress. Obviously, it is essential to have high-quality, consistently annotated sets of proteins from each of the genomes, in order to make meaningful comparisons. We have developed a dedicated, publicly available......, splice site prediction program called NetAspGene, for the genus Aspergillus. Gene sequences from Aspergillus fumigatus, the most common mould pathogen, were used to build and test our model. Compared to many animals and plants, Aspergillus contains smaller introns; thus we have applied a larger window...... better splice site prediction than other available tools. NetAspGene will be very helpful for the study in Aspergillus splice sites and especially in alternative splicing. A webpage for NetAspGene is publicly available at http://www.cbs.dtu.dk/services/NetAspGene....

Deep whole-genome sequencing of 90 Han Chinese genomes.

Science.gov (United States)

Lan, Tianming; Lin, Haoxiang; Zhu, Wenjuan; Laurent, Tellier Christian Asker Melchior; Yang, Mengcheng; Liu, Xin; Wang, Jun; Wang, Jian; Yang, Huanming; Xu, Xun; Guo, Xiaosen

2017-09-01

Next-generation sequencing provides a high-resolution insight into human genetic information. However, the focus of previous studies has primarily been on low-coverage data due to the high cost of sequencing. Although the 1000 Genomes Project and the Haplotype Reference Consortium have both provided powerful reference panels for imputation, low-frequency and novel variants remain difficult to discover and call with accuracy on the basis of low-coverage data. Deep sequencing provides an optimal solution for the problem of these low-frequency and novel variants. Although whole-exome sequencing is also a viable choice for exome regions, it cannot account for noncoding regions, sometimes resulting in the absence of important, causal variants. For Han Chinese populations, the majority of variants have been discovered based upon low-coverage data from the 1000 Genomes Project. However, high-coverage, whole-genome sequencing data are limited for any population, and a large amount of low-frequency, population-specific variants remain uncharacterized. We have performed whole-genome sequencing at a high depth (∼×80) of 90 unrelated individuals of Chinese ancestry, collected from the 1000 Genomes Project samples, including 45 Northern Han Chinese and 45 Southern Han Chinese samples. Eighty-three of these 90 have been sequenced by the 1000 Genomes Project. We have identified 12 568 804 single nucleotide polymorphisms, 2 074 210 short InDels, and 26 142 structural variations from these 90 samples. Compared to the Han Chinese data from the 1000 Genomes Project, we have found 7 000 629 novel variants with low frequency (defined as minor allele frequency genome. Compared to the 1000 Genomes Project, these Han Chinese deep sequencing data enhance the characterization of a large number of low-frequency, novel variants. This will be a valuable resource for promoting Chinese genetics research and medical development. Additionally, it will provide a valuable supplement to the 1000

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.

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

Directory of Open Access Journals (Sweden)

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.

Genomic-Enabled Prediction Kernel Models with Random Intercepts for Multi-environment Trials

Science.gov (United States)

Cuevas, Jaime; Granato, Italo; Fritsche-Neto, Roberto; Montesinos-Lopez, Osval A.; Burgueño, Juan; Bandeira e Sousa, Massaine; Crossa, José

2018-01-01

In this study, we compared the prediction accuracy of the main genotypic effect model (MM) without G×E interactions, the multi-environment single variance G×E deviation model (MDs), and the multi-environment environment-specific variance G×E deviation model (MDe) where the random genetic effects of the lines are modeled with the markers (or pedigree). With the objective of further modeling the genetic residual of the lines, we incorporated the random intercepts of the lines (l) and generated another three models. Each of these 6 models were fitted with a linear kernel method (Genomic Best Linear Unbiased Predictor, GB) and a Gaussian Kernel (GK) method. We compared these 12 model-method combinations with another two multi-environment G×E interactions models with unstructured variance-covariances (MUC) using GB and GK kernels (4 model-method). Thus, we compared the genomic-enabled prediction accuracy of a total of 16 model-method combinations on two maize data sets with positive phenotypic correlations among environments, and on two wheat data sets with complex G×E that includes some negative and close to zero phenotypic correlations among environments. The two models (MDs and MDE with the random intercept of the lines and the GK method) were computationally efficient and gave high prediction accuracy in the two maize data sets. Regarding the more complex G×E wheat data sets, the prediction accuracy of the model-method combination with G×E, MDs and MDe, including the random intercepts of the lines with GK method had important savings in computing time as compared with the G×E interaction multi-environment models with unstructured variance-covariances but with lower genomic prediction accuracy. PMID:29476023

Bottlenecks in Software Defect Prediction Implementation in Industrial Projects

OpenAIRE

Hryszko Jarosław; Madeyski Lech

2015-01-01

Case studies focused on software defect prediction in real, industrial software development projects are extremely rare. We report on dedicated R&D project established in cooperation between Wroclaw University of Technology and one of the leading automotive software development companies to research possibilities of introduction of software defect prediction using an open source, extensible software measurement and defect prediction framework called DePress (Defect Prediction in Software Syst...

Optimal Design of Low-Density SNP Arrays for Genomic Prediction: Algorithm and Applications.

Directory of Open Access Journals (Sweden)

Xiao-Lin Wu

Full Text Available Low-density (LD single nucleotide polymorphism (SNP arrays provide a cost-effective solution for genomic prediction and selection, but algorithms and computational tools are needed for the optimal design of LD SNP chips. A multiple-objective, local optimization (MOLO algorithm was developed for design of optimal LD SNP chips that can be imputed accurately to medium-density (MD or high-density (HD SNP genotypes for genomic prediction. The objective function facilitates maximization of non-gap map length and system information for the SNP chip, and the latter is computed either as locus-averaged (LASE or haplotype-averaged Shannon entropy (HASE and adjusted for uniformity of the SNP distribution. HASE performed better than LASE with ≤1,000 SNPs, but required considerably more computing time. Nevertheless, the differences diminished when >5,000 SNPs were selected. Optimization was accomplished conditionally on the presence of SNPs that were obligated to each chromosome. The frame location of SNPs on a chip can be either uniform (evenly spaced or non-uniform. For the latter design, a tunable empirical Beta distribution was used to guide location distribution of frame SNPs such that both ends of each chromosome were enriched with SNPs. The SNP distribution on each chromosome was finalized through the objective function that was locally and empirically maximized. This MOLO algorithm was capable of selecting a set of approximately evenly-spaced and highly-informative SNPs, which in turn led to increased imputation accuracy compared with selection solely of evenly-spaced SNPs. Imputation accuracy increased with LD chip size, and imputation error rate was extremely low for chips with ≥3,000 SNPs. Assuming that genotyping or imputation error occurs at random, imputation error rate can be viewed as the upper limit for genomic prediction error. Our results show that about 25% of imputation error rate was propagated to genomic prediction in an Angus

Characterization of apparently balanced chromosomal rearrangements from the developmental genome anatomy project.

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Higgins, Anne W; Alkuraya, Fowzan S; Bosco, Amy F; Brown, Kerry K; Bruns, Gail A P; Donovan, Diana J; Eisenman, Robert; Fan, Yanli; Farra, Chantal G; Ferguson, Heather L; Gusella, James F; Harris, David J; Herrick, Steven R; Kelly, Chantal; Kim, Hyung-Goo; Kishikawa, Shotaro; Korf, Bruce R; Kulkarni, Shashikant; Lally, Eric; Leach, Natalia T; Lemyre, Emma; Lewis, Janine; Ligon, Azra H; Lu, Weining; Maas, Richard L; MacDonald, Marcy E; Moore, Steven D P; Peters, Roxanna E; Quade, Bradley J; Quintero-Rivera, Fabiola; Saadi, Irfan; Shen, Yiping; Shendure, Jay; Williamson, Robin E; Morton, Cynthia C

2008-03-01

Apparently balanced chromosomal rearrangements in individuals with major congenital anomalies represent natural experiments of gene disruption and dysregulation. These individuals can be studied to identify novel genes critical in human development and to annotate further the function of known genes. Identification and characterization of these genes is the goal of the Developmental Genome Anatomy Project (DGAP). DGAP is a multidisciplinary effort that leverages the recent advances resulting from the Human Genome Project to increase our understanding of birth defects and the process of human development. Clinically significant phenotypes of individuals enrolled in DGAP are varied and, in most cases, involve multiple organ systems. Study of these individuals' chromosomal rearrangements has resulted in the mapping of 77 breakpoints from 40 chromosomal rearrangements by FISH with BACs and fosmids, array CGH, Southern-blot hybridization, MLPA, RT-PCR, and suppression PCR. Eighteen chromosomal breakpoints have been cloned and sequenced. Unsuspected genomic imbalances and cryptic rearrangements were detected, but less frequently than has been reported previously. Chromosomal rearrangements, both balanced and unbalanced, in individuals with multiple congenital anomalies continue to be a valuable resource for gene discovery and annotation.

The Arab genome: Health and wealth.

Science.gov (United States)

Zayed, Hatem

2016-11-05

The 22 Arab nations have a unique genetic structure, which reflects both conserved and diverse gene pools due to the prevalent endogamous and consanguineous marriage culture and the long history of admixture among different ethnic subcultures descended from the Asian, European, and African continents. Human genome sequencing has enabled large-scale genomic studies of different populations and has become a powerful tool for studying disease predictions and diagnosis. Despite the importance of the Arab genome for better understanding the dynamics of the human genome, discovering rare genetic variations, and studying early human migration out of Africa, it is poorly represented in human genome databases, such as HapMap and the 1000 Genomes Project. In this review, I demonstrate the significance of sequencing the Arab genome and setting an Arab genome reference(s) for better understanding the molecular pathogenesis of genetic diseases, discovering novel/rare variants, and identifying a meaningful genotype-phenotype correlation for complex diseases. Copyright © 2016. Published by Elsevier B.V.

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...

Relationship between Deleterious Variation, Genomic Autozygosity, and Disease Risk: Insights from The 1000 Genomes Project.

Science.gov (United States)

Pemberton, Trevor J; Szpiech, Zachary A

2018-04-05

Genomic regions of autozygosity (ROAs) represent segments of individual genomes that are homozygous for haplotypes inherited identical-by-descent (IBD) from a common ancestor. ROAs are nonuniformly distributed across the genome, and increased ROA levels are a reported risk factor for numerous complex diseases. Previously, we hypothesized that long ROAs are enriched for deleterious homozygotes as a result of young haplotypes with recent deleterious mutations-relatively untouched by purifying selection-being paired IBD as a consequence of recent parental relatedness, a pattern supported by ROA and whole-exome sequence data on 27 individuals. Here, we significantly bolster support for our hypothesis and expand upon our original analyses using ROA and whole-genome sequence data on 2,436 individuals from The 1000 Genomes Project. Considering CADD deleteriousness scores, we reaffirm our previous observation that long ROAs are enriched for damaging homozygotes worldwide. We show that strongly damaging homozygotes experience greater enrichment than weaker damaging homozygotes, while overall enrichment varies appreciably among populations. Mendelian disease genes and those encoding FDA-approved drug targets have significantly increased rates of gain in damaging homozygotes with increasing ROA coverage relative to all other genes. In genes implicated in eight complex phenotypes for which ROA levels have been identified as a risk factor, rates of gain in damaging homozygotes vary across phenotypes and populations but frequently differ significantly from non-disease genes. These findings highlight the potential confounding effects of population background in the assessment of associations between ROA levels and complex disease risk, which might underlie reported inconsistencies in ROA-phenotype associations. Copyright © 2018 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Predictions of Gene Family Distributions in Microbial Genomes: Evolution by Gene Duplication and Modification

International Nuclear Information System (INIS)

Yanai, Itai; Camacho, Carlos J.; DeLisi, Charles

2000-01-01

A universal property of microbial genomes is the considerable fraction of genes that are homologous to other genes within the same genome. The process by which these homologues are generated is not well understood, but sequence analysis of 20 microbial genomes unveils a recurrent distribution of gene family sizes. We show that a simple evolutionary model based on random gene duplication and point mutations fully accounts for these distributions and permits predictions for the number of gene families in genomes not yet complete. Our findings are consistent with the notion that a genome evolves from a set of precursor genes to a mature size by gene duplications and increasing modifications. (c) 2000 The American Physical Society

Predictions of Gene Family Distributions in Microbial Genomes: Evolution by Gene Duplication and Modification

Energy Technology Data Exchange (ETDEWEB)

Yanai, Itai; Camacho, Carlos J.; DeLisi, Charles

2000-09-18

A universal property of microbial genomes is the considerable fraction of genes that are homologous to other genes within the same genome. The process by which these homologues are generated is not well understood, but sequence analysis of 20 microbial genomes unveils a recurrent distribution of gene family sizes. We show that a simple evolutionary model based on random gene duplication and point mutations fully accounts for these distributions and permits predictions for the number of gene families in genomes not yet complete. Our findings are consistent with the notion that a genome evolves from a set of precursor genes to a mature size by gene duplications and increasing modifications. (c) 2000 The American Physical Society.

Cloning, production, and purification of proteins for a medium-scale structural genomics project.

Science.gov (United States)

Quevillon-Cheruel, Sophie; Collinet, Bruno; Trésaugues, Lionel; Minard, Philippe; Henckes, Gilles; Aufrère, Robert; Blondeau, Karine; Zhou, Cong-Zhao; Liger, Dominique; Bettache, Nabila; Poupon, Anne; Aboulfath, Ilham; Leulliot, Nicolas; Janin, Joël; van Tilbeurgh, Herman

2007-01-01

The South-Paris Yeast Structural Genomics Pilot Project (http://www.genomics.eu.org) aims at systematically expressing, purifying, and determining the three-dimensional structures of Saccharomyces cerevisiae proteins. We have already cloned 240 yeast open reading frames in the Escherichia coli pET system. Eighty-two percent of the targets can be expressed in E. coli, and 61% yield soluble protein. We have currently purified 58 proteins. Twelve X-ray structures have been solved, six are in progress, and six other proteins gave crystals. In this chapter, we present the general experimental flowchart applied for this project. One of the main difficulties encountered in this pilot project was the low solubility of a great number of target proteins. We have developed parallel strategies to recover these proteins from inclusion bodies, including refolding, coexpression with chaperones, and an in vitro expression system. A limited proteolysis protocol, developed to localize flexible regions in proteins that could hinder crystallization, is also described.

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

MicroScope: a platform for microbial genome annotation and comparative genomics.

Science.gov (United States)

Vallenet, D; Engelen, S; Mornico, D; Cruveiller, S; Fleury, L; Lajus, A; Rouy, Z; Roche, D; Salvignol, G; Scarpelli, C; Médigue, C

2009-01-01

The initial outcome of genome sequencing is the creation of long text strings written in a four letter alphabet. The role of in silico sequence analysis is to assist biologists in the act of associating biological knowledge with these sequences, allowing investigators to make inferences and predictions that can be tested experimentally. A wide variety of software is available to the scientific community, and can be used to identify genomic objects, before predicting their biological functions. However, only a limited number of biologically interesting features can be revealed from an isolated sequence. Comparative genomics tools, on the other hand, by bringing together the information contained in numerous genomes simultaneously, allow annotators to make inferences based on the idea that evolution and natural selection are central to the definition of all biological processes. We have developed the MicroScope platform in order to offer a web-based framework for the systematic and efficient revision of microbial genome annotation and comparative analysis (http://www.genoscope.cns.fr/agc/microscope). Starting with the description of the flow chart of the annotation processes implemented in the MicroScope pipeline, and the development of traditional and novel microbial annotation and comparative analysis tools, this article emphasizes the essential role of expert annotation as a complement of automatic annotation. Several examples illustrate the use of implemented tools for the review and curation of annotations of both new and publicly available microbial genomes within MicroScope's rich integrated genome framework. The platform is used as a viewer in order to browse updated annotation information of available microbial genomes (more than 440 organisms to date), and in the context of new annotation projects (117 bacterial genomes). The human expertise gathered in the MicroScope database (about 280,000 independent annotations) contributes to improve the quality of

De novo prediction of human chromosome structures: Epigenetic marking patterns encode genome architecture.

Science.gov (United States)

Di Pierro, Michele; Cheng, Ryan R; Lieberman Aiden, Erez; Wolynes, Peter G; Onuchic, José N

2017-11-14

Inside the cell nucleus, genomes fold into organized structures that are characteristic of cell type. Here, we show that this chromatin architecture can be predicted de novo using epigenetic data derived from chromatin immunoprecipitation-sequencing (ChIP-Seq). We exploit the idea that chromosomes encode a 1D sequence of chromatin structural types. Interactions between these chromatin types determine the 3D structural ensemble of chromosomes through a process similar to phase separation. First, a neural network is used to infer the relation between the epigenetic marks present at a locus, as assayed by ChIP-Seq, and the genomic compartment in which those loci reside, as measured by DNA-DNA proximity ligation (Hi-C). Next, types inferred from this neural network are used as an input to an energy landscape model for chromatin organization [Minimal Chromatin Model (MiChroM)] to generate an ensemble of 3D chromosome conformations at a resolution of 50 kilobases (kb). After training the model, dubbed Maximum Entropy Genomic Annotation from Biomarkers Associated to Structural Ensembles (MEGABASE), on odd-numbered chromosomes, we predict the sequences of chromatin types and the subsequent 3D conformational ensembles for the even chromosomes. We validate these structural ensembles by using ChIP-Seq tracks alone to predict Hi-C maps, as well as distances measured using 3D fluorescence in situ hybridization (FISH) experiments. Both sets of experiments support the hypothesis of phase separation being the driving process behind compartmentalization. These findings strongly suggest that epigenetic marking patterns encode sufficient information to determine the global architecture of chromosomes and that de novo structure prediction for whole genomes may be increasingly possible. Copyright © 2017 the Author(s). Published by PNAS.

Performance of genomic prediction within and across generations in maritime pine

NARCIS (Netherlands)

Bartholomé, Jérôme; Heerwaarden, Van Joost; Isik, Fikret; Boury, Christophe; Vidal, Marjorie; Plomion, Christophe; Bouffier, Laurent

2016-01-01

Background: Genomic selection (GS) is a promising approach for decreasing breeding cycle length in forest trees. Assessment of progeny performance and of the prediction accuracy of GS models over generations is therefore a key issue. Results: A reference population of maritime pine (Pinus

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...

The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

DEFF Research Database (Denmark)

Hudson, Lawrence N; Newbold, Tim; Contu, Sara

2017-01-01

The PREDICTS project-Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)-has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity ...

The IGNITE network: a model for genomic medicine implementation and research.

Science.gov (United States)

Weitzel, Kristin Wiisanen; Alexander, Madeline; Bernhardt, Barbara A; Calman, Neil; Carey, David J; Cavallari, Larisa H; Field, Julie R; Hauser, Diane; Junkins, Heather A; Levin, Phillip A; Levy, Kenneth; Madden, Ebony B; Manolio, Teri A; Odgis, Jacqueline; Orlando, Lori A; Pyeritz, Reed; Wu, R Ryanne; Shuldiner, Alan R; Bottinger, Erwin P; Denny, Joshua C; Dexter, Paul R; Flockhart, David A; Horowitz, Carol R; Johnson, Julie A; Kimmel, Stephen E; Levy, Mia A; Pollin, Toni I; Ginsburg, Geoffrey S

2016-01-05

Patients, clinicians, researchers and payers are seeking to understand the value of using genomic information (as reflected by genotyping, sequencing, family history or other data) to inform clinical decision-making. However, challenges exist to widespread clinical implementation of genomic medicine, a prerequisite for developing evidence of its real-world utility. To address these challenges, the National Institutes of Health-funded IGNITE (Implementing GeNomics In pracTicE; www.ignite-genomics.org ) Network, comprised of six projects and a coordinating center, was established in 2013 to support the development, investigation and dissemination of genomic medicine practice models that seamlessly integrate genomic data into the electronic health record and that deploy tools for point of care decision making. IGNITE site projects are aligned in their purpose of testing these models, but individual projects vary in scope and design, including exploring genetic markers for disease risk prediction and prevention, developing tools for using family history data, incorporating pharmacogenomic data into clinical care, refining disease diagnosis using sequence-based mutation discovery, and creating novel educational approaches. This paper describes the IGNITE Network and member projects, including network structure, collaborative initiatives, clinical decision support strategies, methods for return of genomic test results, and educational initiatives for patients and providers. Clinical and outcomes data from individual sites and network-wide projects are anticipated to begin being published over the next few years. The IGNITE Network is an innovative series of projects and pilot demonstrations aiming to enhance translation of validated actionable genomic information into clinical settings and develop and use measures of outcome in response to genome-based clinical interventions using a pragmatic framework to provide early data and proofs of concept on the utility of these

Research on cross - Project software defect prediction based on transfer learning

Science.gov (United States)

Chen, Ya; Ding, Xiaoming

2018-04-01

According to the two challenges in the prediction of cross-project software defects, the distribution differences between the source project and the target project dataset and the class imbalance in the dataset, proposing a cross-project software defect prediction method based on transfer learning, named NTrA. Firstly, solving the source project data's class imbalance based on the Augmented Neighborhood Cleaning Algorithm. Secondly, the data gravity method is used to give different weights on the basis of the attribute similarity of source project and target project data. Finally, a defect prediction model is constructed by using Trad boost algorithm. Experiments were conducted using data, come from NASA and SOFTLAB respectively, from a published PROMISE dataset. The results show that the method has achieved good values of recall and F-measure, and achieved good prediction results.

The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

NARCIS (Netherlands)

Hudson, Lawrence N; Newbold, Tim; Contu, Sara; Hill, Samantha L L; Lysenko, Igor; De Palma, Adriana; Phillips, Helen R P; Alhusseini, Tamera I; Bedford, Felicity E; Bennett, Dominic J; Booth, Hollie; Burton, Victoria J; Chng, Charlotte W T; Choimes, Argyrios; Correia, David L P; Day, Julie; Echeverría-Londoño, Susy; Emerson, Susan R; Gao, Di; Garon, Morgan; Harrison, Michelle L K; Ingram, Daniel J; Jung, Martin; Kemp, Victoria; Kirkpatrick, Lucinda; Martin, Callum D; Pan, Yuan; Pask-Hale, Gwilym D; Pynegar, Edwin L; Robinson, Alexandra N; Sanchez-Ortiz, Katia; Senior, Rebecca A; Simmons, Benno I; White, Hannah J; Zhang, Hanbin; Aben, Job; Abrahamczyk, Stefan; Adum, Gilbert B; Aguilar-Barquero, Virginia; Aizen, Marcelo A; Albertos, Belén; Alcala, E L; Del Mar Alguacil, Maria; Alignier, Audrey; Ancrenaz, Marc; Andersen, Alan N; Arbeláez-Cortés, Enrique; Armbrecht, Inge; Arroyo-Rodríguez, Víctor; Aumann, Tom; Axmacher, Jan C; Azhar, Badrul; Azpiroz, Adrián B; Baeten, Lander; Bakayoko, Adama; Báldi, András; Banks, John E; Baral, Sharad K; Barlow, Jos; Barratt, Barbara I P; Barrico, Lurdes; Bartolommei, Paola; Barton, Diane M; Basset, Yves; Batáry, Péter; Bates, Adam J; Baur, Bruno; Bayne, Erin M; Beja, Pedro; Benedick, Suzan; Berg, Åke; Bernard, Henry; Berry, Nicholas J; Bhatt, Dinesh; Bicknell, Jake E; Bihn, Jochen H; Blake, Robin J; Bobo, Kadiri S; Bóçon, Roberto; Boekhout, Teun; Böhning-Gaese, Katrin; Bonham, Kevin J; Borges, Paulo A V; Borges, Sérgio H; Boutin, Céline; Bouyer, Jérémy; Bragagnolo, Cibele; Brandt, Jodi S; Brearley, Francis Q; Brito, Isabel; Bros, Vicenç; Brunet, Jörg; Buczkowski, Grzegorz; Buddle, Christopher M; Bugter, Rob; Buscardo, Erika; Buse, Jörn; Cabra-García, Jimmy; Cáceres, Nilton C; Cagle, Nicolette L; Calviño-Cancela, María; Cameron, Sydney A; Cancello, Eliana M; Caparrós, Rut; Cardoso, Pedro; Carpenter, Dan; Carrijo, Tiago F; Carvalho, Anelena L; Cassano, Camila R; Castro, Helena; Castro-Luna, Alejandro A; Rolando, Cerda B; Cerezo, Alexis; Chapman, Kim Alan; Chauvat, Matthieu; Christensen, Morten; Clarke, Francis M; Cleary, Daniel F R; Colombo, Giorgio; Connop, Stuart P; Craig, Michael D; Cruz-López, Leopoldo; Cunningham, Saul A; D'Aniello, Biagio; D'Cruze, Neil; da Silva, Pedro Giovâni; Dallimer, Martin; Danquah, Emmanuel; Darvill, Ben; Dauber, Jens; Davis, Adrian L V; Dawson, Jeff; de Sassi, Claudio; de Thoisy, Benoit; Deheuvels, Olivier; Dejean, Alain; Devineau, Jean-Louis; Diekötter, Tim; Dolia, Jignasu V; Domínguez, Erwin; Dominguez-Haydar, Yamileth; Dorn, Silvia; Draper, Isabel; Dreber, Niels; Dumont, Bertrand; Dures, Simon G; Dynesius, Mats; Edenius, Lars; Eggleton, Paul; Eigenbrod, Felix; Elek, Zoltán; Entling, Martin H; Esler, Karen J; de Lima, Ricardo F; Faruk, Aisyah; Farwig, Nina; Fayle, Tom M; Felicioli, Antonio; Felton, Annika M; Fensham, Roderick J; Fernandez, Ignacio C; Ferreira, Catarina C; Ficetola, Gentile F; Fiera, Cristina; Filgueiras, Bruno K C; Fırıncıoğlu, Hüseyin K; Flaspohler, David; Floren, Andreas; Fonte, Steven J; Fournier, Anne; Fowler, Robert E; Franzén, Markus; Fraser, Lauchlan H; Fredriksson, Gabriella M; Freire, Geraldo B; Frizzo, Tiago L M; Fukuda, Daisuke; Furlani, Dario; Gaigher, René; Ganzhorn, Jörg U; García, Karla P; Garcia-R, Juan C; Garden, Jenni G; Garilleti, Ricardo; Ge, Bao-Ming; Gendreau-Berthiaume, Benoit; Gerard, Philippa J; Gheler-Costa, Carla; Gilbert, Benjamin; Giordani, Paolo; Giordano, Simonetta; Golodets, Carly; Gomes, Laurens G L; Gould, Rachelle K; Goulson, Dave; Gove, Aaron D; Granjon, Laurent; Grass, Ingo; Gray, Claudia L; Grogan, James; Gu, Weibin; Guardiola, Moisès; Gunawardene, Nihara R; Gutierrez, Alvaro G; Gutiérrez-Lamus, Doris L; Haarmeyer, Daniela H; Hanley, Mick E; Hanson, Thor; Hashim, Nor R; Hassan, Shombe N; Hatfield, Richard G; Hawes, Joseph E; Hayward, Matt W; Hébert, Christian; Helden, Alvin J; Henden, John-André; Henschel, Philipp; Hernández, Lionel; Herrera, James P; Herrmann, Farina; Herzog, Felix; Higuera-Diaz, Diego; Hilje, Branko; Höfer, Hubert; Hoffmann, Anke; Horgan, Finbarr G; Hornung, Elisabeth; Horváth, Roland; Hylander, Kristoffer; Isaacs-Cubides, Paola; Ishida, Hiroaki; Ishitani, Masahiro; Jacobs, Carmen T; Jaramillo, Víctor J; Jauker, Birgit; Hernández, F Jiménez; Johnson, McKenzie F; Jolli, Virat; Jonsell, Mats; Juliani, S Nur; Jung, Thomas S; Kapoor, Vena; Kappes, Heike; Kati, Vassiliki; Katovai, Eric; Kellner, Klaus; Kessler, Michael; Kirby, Kathryn R; Kittle, Andrew M; Knight, Mairi E; Knop, Eva; Kohler, Florian; Koivula, Matti; Kolb, Annette; Kone, Mouhamadou; Kőrösi, Ádám; Krauss, Jochen; Kumar, Ajith; Kumar, Raman; Kurz, David J; Kutt, Alex S; Lachat, Thibault; Lantschner, Victoria; Lara, Francisco; Lasky, Jesse R; Latta, Steven C; Laurance, William F; Lavelle, Patrick; Le Féon, Violette; LeBuhn, Gretchen; Légaré, Jean-Philippe; Lehouck, Valérie; Lencinas, María V; Lentini, Pia E; Letcher, Susan G; Li, Qi; Litchwark, Simon A; Littlewood, Nick A; Liu, Yunhui; Lo-Man-Hung, Nancy; López-Quintero, Carlos A; Louhaichi, Mounir; Lövei, Gabor L; Lucas-Borja, Manuel Esteban; Luja, Victor H; Luskin, Matthew S; MacSwiney G, M Cristina; Maeto, Kaoru; Magura, Tibor; Mallari, Neil Aldrin; Malone, Louise A; Malonza, Patrick K; Malumbres-Olarte, Jagoba; Mandujano, Salvador; Måren, Inger E; Marin-Spiotta, Erika; Marsh, Charles J; Marshall, E J P; Martínez, Eliana; Martínez Pastur, Guillermo; Moreno Mateos, David; Mayfield, Margaret M; Mazimpaka, Vicente; McCarthy, Jennifer L; McCarthy, Kyle P; McFrederick, Quinn S; McNamara, Sean; Medina, Nagore G; Medina, Rafael; Mena, Jose L; Mico, Estefania; Mikusinski, Grzegorz; Milder, Jeffrey C; Miller, James R; Miranda-Esquivel, Daniel R; Moir, Melinda L; Morales, Carolina L; Muchane, Mary N; Muchane, Muchai; Mudri-Stojnic, Sonja; Munira, A Nur; Muoñz-Alonso, Antonio; Munyekenye, B F; Naidoo, Robin; Naithani, A; Nakagawa, Michiko; Nakamura, Akihiro; Nakashima, Yoshihiro; Naoe, Shoji; Nates-Parra, Guiomar; Navarrete Gutierrez, Dario A; Navarro-Iriarte, Luis; Ndang'ang'a, Paul K; Neuschulz, Eike L; Ngai, Jacqueline T; Nicolas, Violaine; Nilsson, Sven G; Noreika, Norbertas; Norfolk, Olivia; Noriega, Jorge Ari; Norton, David A; Nöske, Nicole M; Nowakowski, A Justin; Numa, Catherine; O'Dea, Niall; O'Farrell, Patrick J; Oduro, William; Oertli, Sabine; Ofori-Boateng, Caleb; Oke, Christopher Omamoke; Oostra, Vicencio; Osgathorpe, Lynne M; Otavo, Samuel Eduardo; Page, Navendu V; Paritsis, Juan; Parra-H, Alejandro; Parry, Luke; Pe'er, Guy; Pearman, Peter B; Pelegrin, Nicolás; Pélissier, Raphaël; Peres, Carlos A; Peri, Pablo L; Persson, Anna S; Petanidou, Theodora; Peters, Marcell K; Pethiyagoda, Rohan S; Phalan, Ben; Philips, T Keith; Pillsbury, Finn C; Pincheira-Ulbrich, Jimmy; Pineda, Eduardo; Pino, Joan; Pizarro-Araya, Jaime; Plumptre, A J; Poggio, Santiago L; Politi, Natalia; Pons, Pere; Poveda, Katja; Power, Eileen F; Presley, Steven J; Proença, Vânia; Quaranta, Marino; Quintero, Carolina; Rader, Romina; Ramesh, B R; Ramirez-Pinilla, Martha P; Ranganathan, Jai; Rasmussen, Claus; Redpath-Downing, Nicola A; Reid, J Leighton; Reis, Yana T; Rey Benayas, José M; Rey-Velasco, Juan Carlos; Reynolds, Chevonne; Ribeiro, Danilo Bandini; Richards, Miriam H; Richardson, Barbara A; Richardson, Michael J; Ríos, Rodrigo Macip; Robinson, Richard; Robles, Carolina A; Römbke, Jörg; Romero-Duque, Luz Piedad; Rös, Matthias; Rosselli, Loreta; Rossiter, Stephen J; Roth, Dana S; Roulston, T'ai H; Rousseau, Laurent; Rubio, André V; Ruel, Jean-Claude; Sadler, Jonathan P; Sáfián, Szabolcs; Saldaña-Vázquez, Romeo A; Sam, Katerina; Samnegård, Ulrika; Santana, Joana; Santos, Xavier; Savage, Jade; Schellhorn, Nancy A; Schilthuizen, Menno; Schmiedel, Ute; Schmitt, Christine B; Schon, Nicole L; Schüepp, Christof; Schumann, Katharina; Schweiger, Oliver; Scott, Dawn M; Scott, Kenneth A; Sedlock, Jodi L; Seefeldt, Steven S; Shahabuddin, Ghazala; Shannon, Graeme; Sheil, Douglas; Sheldon, Frederick H; Shochat, Eyal; Siebert, Stefan J; Silva, Fernando A B; Simonetti, Javier A; Slade, Eleanor M; Smith, Jo; Smith-Pardo, Allan H; Sodhi, Navjot S; Somarriba, Eduardo J; Sosa, Ramón A; Soto Quiroga, Grimaldo; St-Laurent, Martin-Hugues; Starzomski, Brian M; Stefanescu, Constanti; Steffan-Dewenter, Ingolf; Stouffer, Philip C; Stout, Jane C; Strauch, Ayron M; Struebig, Matthew J; Su, Zhimin; Suarez-Rubio, Marcela; Sugiura, Shinji; Summerville, Keith S; Sung, Yik-Hei; Sutrisno, Hari; Svenning, Jens-Christian; Teder, Tiit; Threlfall, Caragh G; Tiitsaar, Anu; Todd, Jacqui H; Tonietto, Rebecca K; Torre, Ignasi; Tóthmérész, Béla; Tscharntke, Teja; Turner, Edgar C; Tylianakis, Jason M; Uehara-Prado, Marcio; Urbina-Cardona, Nicolas; Vallan, Denis; Vanbergen, Adam J; Vasconcelos, Heraldo L; Vassilev, Kiril; Verboven, Hans A F; Verdasca, Maria João; Verdú, José R; Vergara, Carlos H; Vergara, Pablo M; Verhulst, Jort; Virgilio, Massimiliano; Vu, Lien Van; Waite, Edward M; Walker, Tony R; Wang, Hua-Feng; Wang, Yanping; Watling, James I; Weller, Britta; Wells, Konstans; Westphal, Catrin; Wiafe, Edward D; Williams, Christopher D; Willig, Michael R; Woinarski, John C Z; Wolf, Jan H D; Wolters, Volkmar; Woodcock, Ben A; Wu, Jihua; Wunderle, Joseph M; Yamaura, Yuichi; Yoshikura, Satoko; Yu, Douglas W; Zaitsev, Andrey S; Zeidler, Juliane; Zou, Fasheng; Collen, Ben; Ewers, Rob M; Mace, Georgina M; Purves, Drew W; Scharlemann, Jörn P W; Purvis, Andy

The PREDICTS project-Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)-has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of

Genomics of antibiotic-resistance prediction in Pseudomonas aeruginosa.

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Jeukens, Julie; Freschi, Luca; Kukavica-Ibrulj, Irena; Emond-Rheault, Jean-Guillaume; Tucker, Nicholas P; Levesque, Roger C

2017-06-02

Antibiotic resistance is a worldwide health issue spreading quickly among human and animal pathogens, as well as environmental bacteria. Misuse of antibiotics has an impact on the selection of resistant bacteria, thus contributing to an increase in the occurrence of resistant genotypes that emerge via spontaneous mutation or are acquired by horizontal gene transfer. There is a specific and urgent need not only to detect antimicrobial resistance but also to predict antibiotic resistance in silico. We now have the capability to sequence hundreds of bacterial genomes per week, including assembly and annotation. Novel and forthcoming bioinformatics tools can predict the resistome and the mobilome with a level of sophistication not previously possible. Coupled with bacterial strain collections and databases containing strain metadata, prediction of antibiotic resistance and the potential for virulence are moving rapidly toward a novel approach in molecular epidemiology. Here, we present a model system in antibiotic-resistance prediction, along with its promises and limitations. As it is commonly multidrug resistant, Pseudomonas aeruginosa causes infections that are often difficult to eradicate. We review novel approaches for genotype prediction of antibiotic resistance. We discuss the generation of microbial sequence data for real-time patient management and the prediction of antimicrobial resistance. © 2017 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals Inc. on behalf of The New York Academy of Sciences.

Mycoplasma hyopneumoniae Transcription Unit Organization: Genome Survey and Prediction

Science.gov (United States)

Siqueira, Franciele Maboni; Schrank, Augusto; Schrank, Irene Silveira

2011-01-01

Mycoplasma hyopneumoniae is associated with swine respiratory diseases. Although gene organization and regulation are well known in many prokaryotic organisms, knowledge on mycoplasma is limited. This study performed a comparative analysis of three strains of M. hyopneumoniae (7448, J and 232), with a focus on genome organization and gene comparison for open read frame (ORF) cluster (OC) identification. An in silico analysis of gene organization demonstrated 117 OCs and 34 single ORFs in M. hyopneumoniae 7448 and J, while 116 OCs and 36 single ORFs were identified in M. hyopneumoniae 232. Genomic comparison revealed high synteny and conservation of gene order between the OCs defined for 7448 and J strains as well as for 7448 and 232 strains. Twenty-one OCs were chosen and experimentally confirmed by reverse transcription–PCR from M. hyopneumoniae 7448 genome, validating our prediction. A subset of the ORFs within an OC could be independently transcribed due to the presence of internal promoters. Our results suggest that transcription occurs in ‘run-on’ from an upstream promoter in M. hyopneumoniae, thus forming large ORF clusters (from 2 to 29 ORFs in the same orientation) and indicating a complex transcriptional organization. PMID:22086999

Statistical properties of thermodynamically predicted RNA secondary structures in viral genomes

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Spanò, M.; Lillo, F.; Miccichè, S.; Mantegna, R. N.

2008-10-01

By performing a comprehensive study on 1832 segments of 1212 complete genomes of viruses, we show that in viral genomes the hairpin structures of thermodynamically predicted RNA secondary structures are more abundant than expected under a simple random null hypothesis. The detected hairpin structures of RNA secondary structures are present both in coding and in noncoding regions for the four groups of viruses categorized as dsDNA, dsRNA, ssDNA and ssRNA. For all groups, hairpin structures of RNA secondary structures are detected more frequently than expected for a random null hypothesis in noncoding rather than in coding regions. However, potential RNA secondary structures are also present in coding regions of dsDNA group. In fact, we detect evolutionary conserved RNA secondary structures in conserved coding and noncoding regions of a large set of complete genomes of dsDNA herpesviruses.

Prediction of transcriptional regulatory sites in the complete genome sequence of Escherichia coli K-12.

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Thieffry, D; Salgado, H; Huerta, A M; Collado-Vides, J

1998-06-01

As one of the best-characterized free-living organisms, Escherichia coli and its recently completed genomic sequence offer a special opportunity to exploit systematically the variety of regulatory data available in the literature in order to make a comprehensive set of regulatory predictions in the whole genome. The complete genome sequence of E.coli was analyzed for the binding of transcriptional regulators upstream of coding sequences. The biological information contained in RegulonDB (Huerta, A.M. et al., Nucleic Acids Res.,26,55-60, 1998) for 56 different transcriptional proteins was the support to implement a stringent strategy combining string search and weight matrices. We estimate that our search included representatives of 15-25% of the total number of regulatory binding proteins in E.coli. This search was performed on the set of 4288 putative regulatory regions, each 450 bp long. Within the regions with predicted sites, 89% are regulated by one protein and 81% involve only one site. These numbers are reasonably consistent with the distribution of experimental regulatory sites. Regulatory sites are found in 603 regions corresponding to 16% of operon regions and 10% of intra-operonic regions. Additional evidence gives stronger support to some of these predictions, including the position of the site, biological consistency with the function of the downstream gene, as well as genetic evidence for the regulatory interaction. The predictions described here were incorporated into the map presented in the paper describing the complete E.coli genome (Blattner,F.R. et al., Science, 277, 1453-1461, 1997). The complete set of predictions in GenBank format is available at the url: http://www. cifn.unam.mx/Computational_Biology/E.coli-predictions ecoli-reg@cifn.unam.mx, collado@cifn.unam.mx

Hunting for genes for hypertension: the Millennium Genome Project for Hypertension.

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Tabara, Yasuharu; Kohara, Katsuhiko; Miki, Tetsuro

2012-06-01

The Millennium Genome Project for Hypertension was started in 2000 to identify genetic variants conferring susceptibility to hypertension, with the aim of furthering the understanding of the pathogenesis of this condition and realizing genome-based personalized medical care. Two different approaches were launched, genome-wide association analysis using single-nucleotide polymorphisms (SNPs) and microsatellite markers, and systematic candidate gene analysis, under the hypothesis that common variants have an important role in the etiology of common diseases. These multilateral approaches identified ATP2B1 as a gene responsible for hypertension in not only Japanese but also Caucasians. The high blood pressure susceptibility conferred by certain alleles of ATP2B1 has been widely replicated in various populations. Ex vivo mRNA expression analysis in umbilical artery smooth muscle cells indicated that reduced expression of this gene associated with the risk allele may be an underlying mechanism relating the ATP2B1 variant to hypertension. However, the effect size of a SNP was too small to clarify the entire picture of the genetic basis of hypertension. Further, dense genome analysis with accurate phenotype data may be required.

Genome-wide prediction of traits with different genetic architecture through efficient variable selection.

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Wimmer, Valentin; Lehermeier, Christina; Albrecht, Theresa; Auinger, Hans-Jürgen; Wang, Yu; Schön, Chris-Carolin

2013-10-01

In genome-based prediction there is considerable uncertainty about the statistical model and method required to maximize prediction accuracy. For traits influenced by a small number of quantitative trait loci (QTL), predictions are expected to benefit from methods performing variable selection [e.g., BayesB or the least absolute shrinkage and selection operator (LASSO)] compared to methods distributing effects across the genome [ridge regression best linear unbiased prediction (RR-BLUP)]. We investigate the assumptions underlying successful variable selection by combining computer simulations with large-scale experimental data sets from rice (Oryza sativa L.), wheat (Triticum aestivum L.), and Arabidopsis thaliana (L.). We demonstrate that variable selection can be successful when the number of phenotyped individuals is much larger than the number of causal mutations contributing to the trait. We show that the sample size required for efficient variable selection increases dramatically with decreasing trait heritabilities and increasing extent of linkage disequilibrium (LD). We contrast and discuss contradictory results from simulation and experimental studies with respect to superiority of variable selection methods over RR-BLUP. Our results demonstrate that due to long-range LD, medium heritabilities, and small sample sizes, superiority of variable selection methods cannot be expected in plant breeding populations even for traits like FRIGIDA gene expression in Arabidopsis and flowering time in rice, assumed to be influenced by a few major QTL. We extend our conclusions to the analysis of whole-genome sequence data and infer upper bounds for the number of causal mutations which can be identified by LASSO. Our results have major impact on the choice of statistical method needed to make credible inferences about genetic architecture and prediction accuracy of complex traits.

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

Directory of Open Access Journals (Sweden)

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.

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.

Evaluation of genome-enabled selection for bacterial cold water disease resistance using progeny performance data in Rainbow Trout: Insights on genotyping methods and genomic prediction models

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Bacterial cold water disease (BCWD) causes significant economic losses in salmonid aquaculture, and traditional family-based breeding programs aimed at improving BCWD resistance have been limited to exploiting only between-family variation. We used genomic selection (GS) models to predict genomic br...

Genomic and metagenomic challenges and opportunities for bioleaching: a mini-review.

Science.gov (United States)

Cárdenas, Juan Pablo; Quatrini, Raquel; Holmes, David S

2016-09-01

High-throughput genomic technologies are accelerating progress in understanding the diversity of microbial life in many environments. Here we highlight advances in genomics and metagenomics of microorganisms from bioleaching heaps and related acidic mining environments. Bioleaching heaps used for copper recovery provide significant opportunities to study the processes and mechanisms underlying microbial successions and the influence of community composition on ecosystem functioning. Obtaining quantitative and process-level knowledge of these dynamics is pivotal for understanding how microorganisms contribute to the solubilization of copper for industrial recovery. Advances in DNA sequencing technology provide unprecedented opportunities to obtain information about the genomes of bioleaching microorganisms, allowing predictive models of metabolic potential and ecosystem-level interactions to be constructed. These approaches are enabling predictive phenotyping of organisms many of which are recalcitrant to genetic approaches or are unculturable. This mini-review describes current bioleaching genomic and metagenomic projects and addresses the use of genome information to: (i) build metabolic models; (ii) predict microbial interactions; (iii) estimate genetic diversity; and (iv) study microbial evolution. Key challenges and perspectives of bioleaching genomics/metagenomics are addressed. Copyright © 2016 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.

Genome-Wide Locations of Potential Epimutations Associated with Environmentally Induced Epigenetic Transgenerational Inheritance of Disease Using a Sequential Machine Learning Prediction Approach.

Science.gov (United States)

Haque, M Muksitul; Holder, Lawrence B; Skinner, Michael K

2015-01-01

Environmentally induced epigenetic transgenerational inheritance of disease and phenotypic variation involves germline transmitted epimutations. The primary epimutations identified involve altered differential DNA methylation regions (DMRs). Different environmental toxicants have been shown to promote exposure (i.e., toxicant) specific signatures of germline epimutations. Analysis of genomic features associated with these epimutations identified low-density CpG regions (machine learning computational approach to predict all potential epimutations in the genome. A number of previously identified sperm epimutations were used as training sets. A novel machine learning approach using a sequential combination of Active Learning and Imbalance Class Learner analysis was developed. The transgenerational sperm epimutation analysis identified approximately 50K individual sites with a 1 kb mean size and 3,233 regions that had a minimum of three adjacent sites with a mean size of 3.5 kb. A select number of the most relevant genomic features were identified with the low density CpG deserts being a critical genomic feature of the features selected. A similar independent analysis with transgenerational somatic cell epimutation training sets identified a smaller number of 1,503 regions of genome-wide predicted sites and differences in genomic feature contributions. The predicted genome-wide germline (sperm) epimutations were found to be distinct from the predicted somatic cell epimutations. Validation of the genome-wide germline predicted sites used two recently identified transgenerational sperm epimutation signature sets from the pesticides dichlorodiphenyltrichloroethane (DDT) and methoxychlor (MXC) exposure lineage F3 generation. Analysis of this positive validation data set showed a 100% prediction accuracy for all the DDT-MXC sperm epimutations. Observations further elucidate the genomic features associated with transgenerational germline epimutations and identify a genome

High-throughput crystal-optimization strategies in the South Paris Yeast Structural Genomics Project: one size fits all?

Science.gov (United States)

Leulliot, Nicolas; Trésaugues, Lionel; Bremang, Michael; Sorel, Isabelle; Ulryck, Nathalie; Graille, Marc; Aboulfath, Ilham; Poupon, Anne; Liger, Dominique; Quevillon-Cheruel, Sophie; Janin, Joël; van Tilbeurgh, Herman

2005-06-01

Crystallization has long been regarded as one of the major bottlenecks in high-throughput structural determination by X-ray crystallography. Structural genomics projects have addressed this issue by using robots to set up automated crystal screens using nanodrop technology. This has moved the bottleneck from obtaining the first crystal hit to obtaining diffraction-quality crystals, as crystal optimization is a notoriously slow process that is difficult to automatize. This article describes the high-throughput optimization strategies used in the Yeast Structural Genomics project, with selected successful examples.

Gaussian covariance graph models accounting for correlated marker effects in genome-wide prediction.

Science.gov (United States)

Martínez, C A; Khare, K; Rahman, S; Elzo, M A

2017-10-01

Several statistical models used in genome-wide prediction assume uncorrelated marker allele substitution effects, but it is known that these effects may be correlated. In statistics, graphical models have been identified as a useful tool for covariance estimation in high-dimensional problems and it is an area that has recently experienced a great expansion. In Gaussian covariance graph models (GCovGM), the joint distribution of a set of random variables is assumed to be Gaussian and the pattern of zeros of the covariance matrix is encoded in terms of an undirected graph G. In this study, methods adapting the theory of GCovGM to genome-wide prediction were developed (Bayes GCov, Bayes GCov-KR and Bayes GCov-H). In simulated data sets, improvements in correlation between phenotypes and predicted breeding values and accuracies of predicted breeding values were found. Our models account for correlation of marker effects and permit to accommodate general structures as opposed to models proposed in previous studies, which consider spatial correlation only. In addition, they allow incorporation of biological information in the prediction process through its use when constructing graph G, and their extension to the multi-allelic loci case is straightforward. © 2017 Blackwell Verlag GmbH.

Prediction of Cacao (Theobroma cacao) Resistance to Moniliophthora spp. Diseases via Genome-Wide Association Analysis and Genomic Selection.

Science.gov (United States)

McElroy, Michel S; Navarro, Alberto J R; Mustiga, Guiliana; Stack, Conrad; Gezan, Salvador; Peña, Geover; Sarabia, Widem; Saquicela, Diego; Sotomayor, Ignacio; Douglas, Gavin M; Migicovsky, Zoë; Amores, Freddy; Tarqui, Omar; Myles, Sean; Motamayor, Juan C

2018-01-01

Cacao ( Theobroma cacao ) is a globally important crop, and its yield is severely restricted by disease. Two of the most damaging diseases, witches' broom disease (WBD) and frosty pod rot disease (FPRD), are caused by a pair of related fungi: Moniliophthora perniciosa and Moniliophthora roreri , respectively. Resistant cultivars are the most effective long-term strategy to address Moniliophthora diseases, but efficiently generating resistant and productive new cultivars will require robust methods for screening germplasm before field testing. Marker-assisted selection (MAS) and genomic selection (GS) provide two potential avenues for predicting the performance of new genotypes, potentially increasing the selection gain per unit time. To test the effectiveness of these two approaches, we performed a genome-wide association study (GWAS) and GS on three related populations of cacao in Ecuador genotyped with a 15K single nucleotide polymorphism (SNP) microarray for three measures of WBD infection (vegetative broom, cushion broom, and chirimoya pod), one of FPRD (monilia pod) and two productivity traits (total fresh weight of pods and % healthy pods produced). GWAS yielded several SNPs associated with disease resistance in each population, but none were significantly correlated with the same trait in other populations. Genomic selection, using one population as a training set to estimate the phenotypes of the remaining two (composed of different families), varied among traits, from a mean prediction accuracy of 0.46 (vegetative broom) to 0.15 (monilia pod), and varied between training populations. Simulations demonstrated that selecting seedlings using GWAS markers alone generates no improvement over selecting at random, but that GS improves the selection process significantly. Our results suggest that the GWAS markers discovered here are not sufficiently predictive across diverse germplasm to be useful for MAS, but that using all markers in a GS framework holds

Prediction of Cacao (Theobroma cacao Resistance to Moniliophthora spp. Diseases via Genome-Wide Association Analysis and Genomic Selection

Directory of Open Access Journals (Sweden)

Michel S. McElroy

2018-03-01

Full Text Available Cacao (Theobroma cacao is a globally important crop, and its yield is severely restricted by disease. Two of the most damaging diseases, witches’ broom disease (WBD and frosty pod rot disease (FPRD, are caused by a pair of related fungi: Moniliophthora perniciosa and Moniliophthora roreri, respectively. Resistant cultivars are the most effective long-term strategy to address Moniliophthora diseases, but efficiently generating resistant and productive new cultivars will require robust methods for screening germplasm before field testing. Marker-assisted selection (MAS and genomic selection (GS provide two potential avenues for predicting the performance of new genotypes, potentially increasing the selection gain per unit time. To test the effectiveness of these two approaches, we performed a genome-wide association study (GWAS and GS on three related populations of cacao in Ecuador genotyped with a 15K single nucleotide polymorphism (SNP microarray for three measures of WBD infection (vegetative broom, cushion broom, and chirimoya pod, one of FPRD (monilia pod and two productivity traits (total fresh weight of pods and % healthy pods produced. GWAS yielded several SNPs associated with disease resistance in each population, but none were significantly correlated with the same trait in other populations. Genomic selection, using one population as a training set to estimate the phenotypes of the remaining two (composed of different families, varied among traits, from a mean prediction accuracy of 0.46 (vegetative broom to 0.15 (monilia pod, and varied between training populations. Simulations demonstrated that selecting seedlings using GWAS markers alone generates no improvement over selecting at random, but that GS improves the selection process significantly. Our results suggest that the GWAS markers discovered here are not sufficiently predictive across diverse germplasm to be useful for MAS, but that using all markers in a GS framework holds

Pan-Genome Analysis of Human Gastric Pathogen H. pylori: Comparative Genomics and Pathogenomics Approaches to Identify Regions Associated with Pathogenicity and Prediction of Potential Core Therapeutic Targets

DEFF Research Database (Denmark)

Ali, Amjad; Naz, Anam; Soares, Siomar C.

2015-01-01

-genome approach; the predicted conserved gene families (1,193) constitute similar to 77% of the average H. pylori genome and 45% of the global gene repertoire of the species. Reverse vaccinology strategies have been adopted to identify and narrow down the potential core-immunogenic candidates. Total of 28 nonhost....... Pan-genome analyses of the global representative H. pylori isolates consisting of 39 complete genomes are presented in this paper. Phylogenetic analyses have revealed close relationships among geographically diverse strains of H. pylori. The conservation among these genomes was further analyzed by pan...

GWAS and Genomic Prediction Based on Markers of SNP-CHIPS and Sequence Data in Cattle Populations

DEFF Research Database (Denmark)

Wu, Xiaoping

This thesis investigated the methods and models for genome wide association study and genomic prediction. The main conclusions are: 1) The power of QTL detection can be increased by increasing marker densities, and the Bayesian variable selection model together with the analysis of the QTL intens...

Public trust and 'ethics review' as a commodity: the case of Genomics England Limited and the UK's 100,000 genomes project.

Science.gov (United States)

Samuel, Gabrielle Natalie; Farsides, Bobbie

2018-06-01

The UK Chief Medical Officer's 2016 Annual Report, Generation Genome, focused on a vision to fully integrate genomics into all aspects of the UK's National Health Service (NHS). This process of integration, which has now already begun, raises a wide range of social and ethical concerns, many of which were discussed in the final Chapter of the report. This paper explores how the UK's 100,000 Genomes Project (100 kGP)-the catalyst for Generation Genome, and for bringing genomics into the NHS-is negotiating these ethical concerns. The UK's 100 kGP, promoted and delivered by Genomics England Limited (GEL), is an innovative venture aiming to sequence 100,000 genomes from NHS patients who have a rare disease, cancer, or an infectious disease. GEL has emphasised the importance of ethical governance and decision-making. However, some sociological critique argues that biomedical/technological organisations presenting themselves as 'ethical' entities do not necessarily reflect a space within which moral thinking occurs. Rather, the 'ethical work' conducted (and displayed) by organisations is more strategic, relating to the politics of the organisation and the need to build public confidence. We set out to explore whether GEL's ethical framework was reflective of this critique, and what this tells us more broadly about how genomics is being integrated into the NHS in response to the ethical and social concerns raised in Generation Genome. We do this by drawing on a series of 20 interviews with individuals associated with or working at GEL.

Field of genes: the politics of science and identity in the Estonian Genome Project.

Science.gov (United States)

Fletcher, Amy L

2004-04-01

This case study of the Estonian Genome Project (EGP) analyses the Estonian policy decision to construct a national human gene bank. Drawing upon qualitative data from newspaper articles and public policy documents, it focuses on how proponents use discourse to link the EGP to the broader political goal of securing Estonia's position within the Western/European scientific and cultural space. This dominant narrative is then situated within the analytical notion of the "brand state", which raises potentially negative political consequences for this type of market-driven genomic research. Considered against the increasing number of countries engaging in gene bank and/or gene database projects, this analysis of Estonia elucidates issues that cross national boundaries, while also illuminating factors specific to this small, post-Soviet state as it enters the global biocybernetic economy.

The GenABEL Project for statistical genomics.

Science.gov (United States)

Karssen, Lennart C; van Duijn, Cornelia M; Aulchenko, Yurii S

2016-01-01

Development of free/libre open source software is usually done by a community of people with an interest in the tool. For scientific software, however, this is less often the case. Most scientific software is written by only a few authors, often a student working on a thesis. Once the paper describing the tool has been published, the tool is no longer developed further and is left to its own device. Here we describe the broad, multidisciplinary community we formed around a set of tools for statistical genomics. The GenABEL project for statistical omics actively promotes open interdisciplinary development of statistical methodology and its implementation in efficient and user-friendly software under an open source licence. The software tools developed withing the project collectively make up the GenABEL suite, which currently consists of eleven tools. The open framework of the project actively encourages involvement of the community in all stages, from formulation of methodological ideas to application of software to specific data sets. A web forum is used to channel user questions and discussions, further promoting the use of the GenABEL suite. Developer discussions take place on a dedicated mailing list, and development is further supported by robust development practices including use of public version control, code review and continuous integration. Use of this open science model attracts contributions from users and developers outside the "core team", facilitating agile statistical omics methodology development and fast dissemination.

Genome-Wide Locations of Potential Epimutations Associated with Environmentally Induced Epigenetic Transgenerational Inheritance of Disease Using a Sequential Machine Learning Prediction Approach.

Directory of Open Access Journals (Sweden)

M Muksitul Haque

Full Text Available Environmentally induced epigenetic transgenerational inheritance of disease and phenotypic variation involves germline transmitted epimutations. The primary epimutations identified involve altered differential DNA methylation regions (DMRs. Different environmental toxicants have been shown to promote exposure (i.e., toxicant specific signatures of germline epimutations. Analysis of genomic features associated with these epimutations identified low-density CpG regions (<3 CpG / 100bp termed CpG deserts and a number of unique DNA sequence motifs. The rat genome was annotated for these and additional relevant features. The objective of the current study was to use a machine learning computational approach to predict all potential epimutations in the genome. A number of previously identified sperm epimutations were used as training sets. A novel machine learning approach using a sequential combination of Active Learning and Imbalance Class Learner analysis was developed. The transgenerational sperm epimutation analysis identified approximately 50K individual sites with a 1 kb mean size and 3,233 regions that had a minimum of three adjacent sites with a mean size of 3.5 kb. A select number of the most relevant genomic features were identified with the low density CpG deserts being a critical genomic feature of the features selected. A similar independent analysis with transgenerational somatic cell epimutation training sets identified a smaller number of 1,503 regions of genome-wide predicted sites and differences in genomic feature contributions. The predicted genome-wide germline (sperm epimutations were found to be distinct from the predicted somatic cell epimutations. Validation of the genome-wide germline predicted sites used two recently identified transgenerational sperm epimutation signature sets from the pesticides dichlorodiphenyltrichloroethane (DDT and methoxychlor (MXC exposure lineage F3 generation. Analysis of this positive validation

Understanding the Human Genome Project — A Fact Sheet | NIH MedlinePlus the Magazine

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... The Human Genome Project spurred a revolution in biotechnology innovation around the world and played a key ... the U.S. the global leader in the new biotechnology sector. In April 2003, researchers successfully completed the ...

Genome-wide comparative analysis of four Indian Drosophila species.

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Mohanty, Sujata; Khanna, Radhika

2017-12-01

Comparative analysis of multiple genomes of closely or distantly related Drosophila species undoubtedly creates excitement among evolutionary biologists in exploring the genomic changes with an ecology and evolutionary perspective. We present herewith the de novo assembled whole genome sequences of four Drosophila species, D. bipectinata, D. takahashii, D. biarmipes and D. nasuta of Indian origin using Next Generation Sequencing technology on an Illumina platform along with their detailed assembly statistics. The comparative genomics analysis, e.g. gene predictions and annotations, functional and orthogroup analysis of coding sequences and genome wide SNP distribution were performed. The whole genome of Zaprionus indianus of Indian origin published earlier by us and the genome sequences of previously sequenced 12 Drosophila species available in the NCBI database were included in the analysis. The present work is a part of our ongoing genomics project of Indian Drosophila species.

The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

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Lawrence N. Hudson; Joseph Wunderle M.; And Others

2016-01-01

The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to...

Prediction of Multiple-Trait and Multiple-Environment Genomic Data Using Recommender Systems

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Montesinos-López, Osval A.; Montesinos-López, Abelardo; Crossa, José; Montesinos-López, José C.; Mota-Sanchez, David; Estrada-González, Fermín; Gillberg, Jussi; Singh, Ravi; Mondal, Suchismita; Juliana, Philomin

2018-01-01

In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, although researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although some statistical models are usually mathematically elegant, many of them are also computationally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: item-based collaborative filtering (IBCF) and the matrix factorization algorithm (MF) in the context of multiple traits and multiple environments. The IBCF and MF methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique was slightly better in terms of prediction accuracy than the two conventional methods and the MF method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment–trait combinations) and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets. PMID:29097376

Prediction of Multiple-Trait and Multiple-Environment Genomic Data Using Recommender Systems.

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Montesinos-López, Osval A; Montesinos-López, Abelardo; Crossa, José; Montesinos-López, José C; Mota-Sanchez, David; Estrada-González, Fermín; Gillberg, Jussi; Singh, Ravi; Mondal, Suchismita; Juliana, Philomin

2018-01-04

In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, although researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although some statistical models are usually mathematically elegant, many of them are also computationally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: item-based collaborative filtering (IBCF) and the matrix factorization algorithm (MF) in the context of multiple traits and multiple environments. The IBCF and MF methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique was slightly better in terms of prediction accuracy than the two conventional methods and the MF method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment-trait combinations) and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets. Copyright © 2018 Montesinos-Lopez et al.

Prediction of Multiple-Trait and Multiple-Environment Genomic Data Using Recommender Systems

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Osval A. Montesinos-López

2018-01-01

Full Text Available In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, although researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although some statistical models are usually mathematically elegant, many of them are also computationally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: item-based collaborative filtering (IBCF and the matrix factorization algorithm (MF in the context of multiple traits and multiple environments. The IBCF and MF methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique was slightly better in terms of prediction accuracy than the two conventional methods and the MF method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment–trait combinations and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets.

Genetic Variance Partitioning and Genome-Wide Prediction with Allele Dosage Information in Autotetraploid Potato.

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Endelman, Jeffrey B; Carley, Cari A Schmitz; Bethke, Paul C; Coombs, Joseph J; Clough, Mark E; da Silva, Washington L; De Jong, Walter S; Douches, David S; Frederick, Curtis M; Haynes, Kathleen G; Holm, David G; Miller, J Creighton; Muñoz, Patricio R; Navarro, Felix M; Novy, Richard G; Palta, Jiwan P; Porter, Gregory A; Rak, Kyle T; Sathuvalli, Vidyasagar R; Thompson, Asunta L; Yencho, G Craig

2018-05-01

As one of the world's most important food crops, the potato ( Solanum tuberosum L.) has spurred innovation in autotetraploid genetics, including in the use of SNP arrays to determine allele dosage at thousands of markers. By combining genotype and pedigree information with phenotype data for economically important traits, the objectives of this study were to (1) partition the genetic variance into additive vs. nonadditive components, and (2) determine the accuracy of genome-wide prediction. Between 2012 and 2017, a training population of 571 clones was evaluated for total yield, specific gravity, and chip fry color. Genomic covariance matrices for additive ( G ), digenic dominant ( D ), and additive × additive epistatic ( G # G ) effects were calculated using 3895 markers, and the numerator relationship matrix ( A ) was calculated from a 13-generation pedigree. Based on model fit and prediction accuracy, mixed model analysis with G was superior to A for yield and fry color but not specific gravity. The amount of additive genetic variance captured by markers was 20% of the total genetic variance for specific gravity, compared to 45% for yield and fry color. Within the training population, including nonadditive effects improved accuracy and/or bias for all three traits when predicting total genotypic value. When six F 1 populations were used for validation, prediction accuracy ranged from 0.06 to 0.63 and was consistently lower (0.13 on average) without allele dosage information. We conclude that genome-wide prediction is feasible in potato and that it will improve selection for breeding value given the substantial amount of nonadditive genetic variance in elite germplasm. Copyright © 2018 by the Genetics Society of America.

Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in Human.

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Wu, Chengchao; Yao, Shixin; Li, Xinghao; Chen, Chujia; Hu, Xuehai

2017-02-16

DNA methylation plays a significant role in transcriptional regulation by repressing activity. Change of the DNA methylation level is an important factor affecting the expression of target genes and downstream phenotypes. Because current experimental technologies can only assay a small proportion of CpG sites in the human genome, it is urgent to develop reliable computational models for predicting genome-wide DNA methylation. Here, we proposed a novel algorithm that accurately extracted sequence complexity features (seven features) and developed a support-vector-machine-based prediction model with integration of the reported DNA composition features (trinucleotide frequency and GC content, 65 features) by utilizing the methylation profiles of embryonic stem cells in human. The prediction results from 22 human chromosomes with size-varied windows showed that the 600-bp window achieved the best average accuracy of 94.7%. Moreover, comparisons with two existing methods further showed the superiority of our model, and cross-species predictions on mouse data also demonstrated that our model has certain generalization ability. Finally, a statistical test of the experimental data and the predicted data on functional regions annotated by ChromHMM found that six out of 10 regions were consistent, which implies reliable prediction of unassayed CpG sites. Accordingly, we believe that our novel model will be useful and reliable in predicting DNA methylation.

A fast EM algorithm for BayesA-like prediction of genomic breeding values.

Directory of Open Access Journals (Sweden)

Xiaochen Sun

Full Text Available Prediction accuracies of estimated breeding values for economically important traits are expected to benefit from genomic information. Single nucleotide polymorphism (SNP panels used in genomic prediction are increasing in density, but the Markov Chain Monte Carlo (MCMC estimation of SNP effects can be quite time consuming or slow to converge when a large number of SNPs are fitted simultaneously in a linear mixed model. Here we present an EM algorithm (termed "fastBayesA" without MCMC. This fastBayesA approach treats the variances of SNP effects as missing data and uses a joint posterior mode of effects compared to the commonly used BayesA which bases predictions on posterior means of effects. In each EM iteration, SNP effects are predicted as a linear combination of best linear unbiased predictions of breeding values from a mixed linear animal model that incorporates a weighted marker-based realized relationship matrix. Method fastBayesA converges after a few iterations to a joint posterior mode of SNP effects under the BayesA model. When applied to simulated quantitative traits with a range of genetic architectures, fastBayesA is shown to predict GEBV as accurately as BayesA but with less computing effort per SNP than BayesA. Method fastBayesA can be used as a computationally efficient substitute for BayesA, especially when an increasing number of markers bring unreasonable computational burden or slow convergence to MCMC approaches.

The MedSeq Project: a randomized trial of integrating whole genome sequencing into clinical medicine.

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Vassy, Jason L; Lautenbach, Denise M; McLaughlin, Heather M; Kong, Sek Won; Christensen, Kurt D; Krier, Joel; Kohane, Isaac S; Feuerman, Lindsay Z; Blumenthal-Barby, Jennifer; Roberts, J Scott; Lehmann, Lisa Soleymani; Ho, Carolyn Y; Ubel, Peter A; MacRae, Calum A; Seidman, Christine E; Murray, Michael F; McGuire, Amy L; Rehm, Heidi L; Green, Robert C

2014-03-20

Whole genome sequencing (WGS) is already being used in certain clinical and research settings, but its impact on patient well-being, health-care utilization, and clinical decision-making remains largely unstudied. It is also unknown how best to communicate sequencing results to physicians and patients to improve health. We describe the design of the MedSeq Project: the first randomized trials of WGS in clinical care. This pair of randomized controlled trials compares WGS to standard of care in two clinical contexts: (a) disease-specific genomic medicine in a cardiomyopathy clinic and (b) general genomic medicine in primary care. We are recruiting 8 to 12 cardiologists, 8 to 12 primary care physicians, and approximately 200 of their patients. Patient participants in both the cardiology and primary care trials are randomly assigned to receive a family history assessment with or without WGS. Our laboratory delivers a genome report to physician participants that balances the needs to enhance understandability of genomic information and to convey its complexity. We provide an educational curriculum for physician participants and offer them a hotline to genetics professionals for guidance in interpreting and managing their patients' genome reports. Using varied data sources, including surveys, semi-structured interviews, and review of clinical data, we measure the attitudes, behaviors and outcomes of physician and patient participants at multiple time points before and after the disclosure of these results. The impact of emerging sequencing technologies on patient care is unclear. We have designed a process of interpreting WGS results and delivering them to physicians in a way that anticipates how we envision genomic medicine will evolve in the near future. That is, our WGS report provides clinically relevant information while communicating the complexity and uncertainty of WGS results to physicians and, through physicians, to their patients. This project will not only

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

Genome-Wide Polygenic Scores Predict Reading Performance throughout the School Years

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Selzam, Saskia; Dale, Philip S.; Wagner, Richard K.; DeFries, John C.; Cederlöf, Martin; O'Reilly, Paul F.; Krapohl, Eva; Plomin, Robert

2017-01-01

It is now possible to create individual-specific genetic scores, called genome-wide polygenic scores (GPS). We used a GPS for years of education ("EduYears") to predict reading performance assessed at UK National Curriculum Key Stages 1 (age 7), 2 (age 12) and 3 (age 14) and on reading tests administered at ages 7 and 12 in a UK sample…

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.

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Bandeira E Sousa, Massaine; Cuevas, Jaime; de Oliveira Couto, Evellyn Giselly; Pérez-Rodríguez, Paulino; Jarquín, Diego; Fritsche-Neto, Roberto; Burgueño, Juan; Crossa, Jose

2017-06-07

Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied. Copyright © 2017 Bandeira e Sousa et al.

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction

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Massaine Bandeira e Sousa

2017-06-01

Full Text Available Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1 single-environment, main genotypic effect model (SM; (2 multi-environment, main genotypic effects model (MM; (3 multi-environment, single variance G×E deviation model (MDs; and (4 multi-environment, environment-specific variance G×E deviation model (MDe. Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB, and a nonlinear kernel Gaussian kernel (GK. The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets, having different numbers of maize hybrids evaluated in different environments for grain yield (GY, plant height (PH, and ear height (EH. Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied.

PREDICTS: Projecting Responses of Ecological Diversity in Changing Terrestrial Systems

Directory of Open Access Journals (Sweden)

Georgina Mace

2012-12-01

Full Text Available The PREDICTS project (www.predicts.org.uk is a three-year NERC-funded project to model and predict at a global scale how local terrestrial diversity responds to human pressures such as land use, land cover, pollution, invasive species and infrastructure. PREDICTS is a collaboration between Imperial College London, the UNEP World Conservation Monitoring Centre, Microsoft Research Cambridge, UCL and the University of Sussex. In order to meet its aims, the project relies on extensive data describing the diversity and composition of biological communities at a local scale. Such data are collected on a vast scale through the committed efforts of field ecologists. If you have appropriate data that you would be willing to share with us, please get in touch (enquiries@predicts.org.uk. All contributions will be acknowledged appropriately and all data contributors will be included as co-authors on an open-access paper describing the database.

ECLogger: Cross-Project Catch-Block Logging Prediction Using Ensemble of Classifiers

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Sangeeta Lal

2017-01-01

Full Text Available Background: Software developers insert log statements in the source code to record program execution information. However, optimizing the number of log statements in the source code is challenging. Machine learning based within-project logging prediction tools, proposed in previous studies, may not be suitable for new or small software projects. For such software projects, we can use cross-project logging prediction. Aim: The aim of the study presented here is to investigate cross-project logging prediction methods and techniques. Method: The proposed method is ECLogger, which is a novel, ensemble-based, cross-project, catch-block logging prediction model. In the research We use 9 base classifiers were used and combined using ensemble techniques. The performance of ECLogger was evaluated on on three open-source Java projects: Tomcat, CloudStack and Hadoop. Results: ECLogger Bagging, ECLogger AverageVote, and ECLogger MajorityVote show a considerable improvement in the average Logged F-measure (LF on 3, 5, and 4 source -> target project pairs, respectively, compared to the baseline classifiers. ECLogger AverageVote performs best and shows improvements of 3.12% (average LF and 6.08% (average ACC – Accuracy. Conclusion: The classifier based on ensemble techniques, such as bagging, average vote, and majority vote outperforms the baseline classifier. Overall, the ECLogger AverageVote model performs best. The results show that the CloudStack project is more generalizable than the other projects.

A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks.

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Hojung Nam

2014-09-01

Full Text Available Altered metabolism in cancer cells has been viewed as a passive response required for a malignant transformation. However, this view has changed through the recently described metabolic oncogenic factors: mutated isocitrate dehydrogenases (IDH, succinate dehydrogenase (SDH, and fumarate hydratase (FH that produce oncometabolites that competitively inhibit epigenetic regulation. In this study, we demonstrate in silico predictions of oncometabolites that have the potential to dysregulate epigenetic controls in nine types of cancer by incorporating massive scale genetic mutation information (collected from more than 1,700 cancer genomes, expression profiling data, and deploying Recon 2 to reconstruct context-specific genome-scale metabolic models. Our analysis predicted 15 compounds and 24 substructures of potential oncometabolites that could result from the loss-of-function and gain-of-function mutations of metabolic enzymes, respectively. These results suggest a substantial potential for discovering unidentified oncometabolites in various forms of cancers.

Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers.

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Guosheng Su

Full Text Available Non-additive genetic variation is usually ignored when genome-wide markers are used to study the genetic architecture and genomic prediction of complex traits in human, wild life, model organisms or farm animals. However, non-additive genetic effects may have an important contribution to total genetic variation of complex traits. This study presented a genomic BLUP model including additive and non-additive genetic effects, in which additive and non-additive genetic relation matrices were constructed from information of genome-wide dense single nucleotide polymorphism (SNP markers. In addition, this study for the first time proposed a method to construct dominance relationship matrix using SNP markers and demonstrated it in detail. The proposed model was implemented to investigate the amounts of additive genetic, dominance and epistatic variations, and assessed the accuracy and unbiasedness of genomic predictions for daily gain in pigs. In the analysis of daily gain, four linear models were used: 1 a simple additive genetic model (MA, 2 a model including both additive and additive by additive epistatic genetic effects (MAE, 3 a model including both additive and dominance genetic effects (MAD, and 4 a full model including all three genetic components (MAED. Estimates of narrow-sense heritability were 0.397, 0.373, 0.379 and 0.357 for models MA, MAE, MAD and MAED, respectively. Estimated dominance variance and additive by additive epistatic variance accounted for 5.6% and 9.5% of the total phenotypic variance, respectively. Based on model MAED, the estimate of broad-sense heritability was 0.506. Reliabilities of genomic predicted breeding values for the animals without performance records were 28.5%, 28.8%, 29.2% and 29.5% for models MA, MAE, MAD and MAED, respectively. In addition, models including non-additive genetic effects improved unbiasedness of genomic predictions.

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...

Computational prediction of over-annotated protein-coding genes in the genome of Agrobacterium tumefaciens strain C58

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Yu, Jia-Feng; Sui, Tian-Xiang; Wang, Hong-Mei; Wang, Chun-Ling; Jing, Li; Wang, Ji-Hua

2015-12-01

Agrobacterium tumefaciens strain C58 is a type of pathogen that can cause tumors in some dicotyledonous plants. Ever since the genome of A. tumefaciens strain C58 was sequenced, the quality of annotation of its protein-coding genes has been queried continually, because the annotation varies greatly among different databases. In this paper, the questionable hypothetical genes were re-predicted by integrating the TN curve and Z curve methods. As a result, 30 genes originally annotated as “hypothetical” were discriminated as being non-coding sequences. By testing the re-prediction program 10 times on data sets composed of the function-known genes, the mean accuracy of 99.99% and mean Matthews correlation coefficient value of 0.9999 were obtained. Further sequence analysis and COG analysis showed that the re-annotation results were very reliable. This work can provide an efficient tool and data resources for future studies of A. tumefaciens strain C58. Project supported by the National Natural Science Foundation of China (Grant Nos. 61302186 and 61271378) and the Funding from the State Key Laboratory of Bioelectronics of Southeast University.

Predicting Hybrid Performances for Quality Traits through Genomic-Assisted Approaches in Central European Wheat

KAUST Repository

Liu, Guozheng

2016-07-06

Bread-making quality traits are central targets for wheat breeding. The objectives of our study were to (1) examine the presence of major effect QTLs for quality traits in a Central European elite wheat population, (2) explore the optimal strategy for predicting the hybrid performance for wheat quality traits, and (3) investigate the effects of marker density and the composition and size of the training population on the accuracy of prediction of hybrid performance. In total 135 inbred lines of Central European bread wheat (Triticum aestivum L.) and 1,604 hybrids derived from them were evaluated for seven quality traits in up to six environments. The 135 parental lines were genotyped using a 90k single-nucleotide polymorphism array. Genome-wide association mapping initially suggested presence of several quantitative trait loci (QTLs), but cross-validation rather indicated the absence of major effect QTLs for all quality traits except of 1000-kernel weight. Genomic selection substantially outperformed marker-assisted selection in predicting hybrid performance. A resampling study revealed that increasing the effective population size in the estimation set of hybrids is relevant to boost the accuracy of prediction for an unrelated test population.

Predicting Hybrid Performances for Quality Traits through Genomic-Assisted Approaches in Central European Wheat.

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Guozheng Liu

Full Text Available Bread-making quality traits are central targets for wheat breeding. The objectives of our study were to (1 examine the presence of major effect QTLs for quality traits in a Central European elite wheat population, (2 explore the optimal strategy for predicting the hybrid performance for wheat quality traits, and (3 investigate the effects of marker density and the composition and size of the training population on the accuracy of prediction of hybrid performance. In total 135 inbred lines of Central European bread wheat (Triticum aestivum L. and 1,604 hybrids derived from them were evaluated for seven quality traits in up to six environments. The 135 parental lines were genotyped using a 90k single-nucleotide polymorphism array. Genome-wide association mapping initially suggested presence of several quantitative trait loci (QTLs, but cross-validation rather indicated the absence of major effect QTLs for all quality traits except of 1000-kernel weight. Genomic selection substantially outperformed marker-assisted selection in predicting hybrid performance. A resampling study revealed that increasing the effective population size in the estimation set of hybrids is relevant to boost the accuracy of prediction for an unrelated test population.

Predicting Hybrid Performances for Quality Traits through Genomic-Assisted Approaches in Central European Wheat

KAUST Repository

Liu, Guozheng; Zhao, Yusheng; Gowda, Manje; Longin, C. Friedrich H.; Reif, Jochen C.; Mette, Michael F.

2016-01-01

Bread-making quality traits are central targets for wheat breeding. The objectives of our study were to (1) examine the presence of major effect QTLs for quality traits in a Central European elite wheat population, (2) explore the optimal strategy for predicting the hybrid performance for wheat quality traits, and (3) investigate the effects of marker density and the composition and size of the training population on the accuracy of prediction of hybrid performance. In total 135 inbred lines of Central European bread wheat (Triticum aestivum L.) and 1,604 hybrids derived from them were evaluated for seven quality traits in up to six environments. The 135 parental lines were genotyped using a 90k single-nucleotide polymorphism array. Genome-wide association mapping initially suggested presence of several quantitative trait loci (QTLs), but cross-validation rather indicated the absence of major effect QTLs for all quality traits except of 1000-kernel weight. Genomic selection substantially outperformed marker-assisted selection in predicting hybrid performance. A resampling study revealed that increasing the effective population size in the estimation set of hybrids is relevant to boost the accuracy of prediction for an unrelated test population.

Predicting Hybrid Performances for Quality Traits through Genomic-Assisted Approaches in Central European Wheat

Science.gov (United States)

Liu, Guozheng; Zhao, Yusheng; Gowda, Manje; Longin, C. Friedrich H.; Reif, Jochen C.; Mette, Michael F.

2016-01-01

Bread-making quality traits are central targets for wheat breeding. The objectives of our study were to (1) examine the presence of major effect QTLs for quality traits in a Central European elite wheat population, (2) explore the optimal strategy for predicting the hybrid performance for wheat quality traits, and (3) investigate the effects of marker density and the composition and size of the training population on the accuracy of prediction of hybrid performance. In total 135 inbred lines of Central European bread wheat (Triticum aestivum L.) and 1,604 hybrids derived from them were evaluated for seven quality traits in up to six environments. The 135 parental lines were genotyped using a 90k single-nucleotide polymorphism array. Genome-wide association mapping initially suggested presence of several quantitative trait loci (QTLs), but cross-validation rather indicated the absence of major effect QTLs for all quality traits except of 1000-kernel weight. Genomic selection substantially outperformed marker-assisted selection in predicting hybrid performance. A resampling study revealed that increasing the effective population size in the estimation set of hybrids is relevant to boost the accuracy of prediction for an unrelated test population. PMID:27383841

Multitrait, Random Regression, or Simple Repeatability Model in High-Throughput Phenotyping Data Improve Genomic Prediction for Wheat Grain Yield.

Science.gov (United States)

Sun, Jin; Rutkoski, Jessica E; Poland, Jesse A; Crossa, José; Jannink, Jean-Luc; Sorrells, Mark E

2017-07-01

High-throughput phenotyping (HTP) platforms can be used to measure traits that are genetically correlated with wheat ( L.) grain yield across time. Incorporating such secondary traits in the multivariate pedigree and genomic prediction models would be desirable to improve indirect selection for grain yield. In this study, we evaluated three statistical models, simple repeatability (SR), multitrait (MT), and random regression (RR), for the longitudinal data of secondary traits and compared the impact of the proposed models for secondary traits on their predictive abilities for grain yield. Grain yield and secondary traits, canopy temperature (CT) and normalized difference vegetation index (NDVI), were collected in five diverse environments for 557 wheat lines with available pedigree and genomic information. A two-stage analysis was applied for pedigree and genomic selection (GS). First, secondary traits were fitted by SR, MT, or RR models, separately, within each environment. Then, best linear unbiased predictions (BLUPs) of secondary traits from the above models were used in the multivariate prediction models to compare predictive abilities for grain yield. Predictive ability was substantially improved by 70%, on average, from multivariate pedigree and genomic models when including secondary traits in both training and test populations. Additionally, (i) predictive abilities slightly varied for MT, RR, or SR models in this data set, (ii) results indicated that including BLUPs of secondary traits from the MT model was the best in severe drought, and (iii) the RR model was slightly better than SR and MT models under drought environment. Copyright © 2017 Crop Science Society of America.

The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

OpenAIRE

Hudson, L. N.; Newbold, T.; Contu, S.; Hill, S. L.; Lysenko, I.; De Palma, A.; Phillips, H. R.; Alhusseini, T. I.; Bedford, F. E.; Bennett, D. J.; Booth, H.; Burton, V. J.; Chng, C. W.; Choimes, A.; Correia, D. L.

2017-01-01

The PREDICTS project-Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)-has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make free...

Genomic Prediction with Pedigree and Genotype × Environment Interaction in Spring Wheat Grown in South and West Asia, North Africa, and Mexico

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Sivakumar Sukumaran

2017-02-01

Full Text Available Developing genomic selection (GS models is an important step in applying GS to accelerate the rate of genetic gain in grain yield in plant breeding. In this study, seven genomic prediction models under two cross-validation (CV scenarios were tested on 287 advanced elite spring wheat lines phenotyped for grain yield (GY, thousand-grain weight (GW, grain number (GN, and thermal time for flowering (TTF in 18 international environments (year-location combinations in major wheat-producing countries in 2010 and 2011. Prediction models with genomic and pedigree information included main effects and interaction with environments. Two random CV schemes were applied to predict a subset of lines that were not observed in any of the 18 environments (CV1, and a subset of lines that were not observed in a set of the environments, but were observed in other environments (CV2. Genomic prediction models, including genotype × environment (G×E interaction, had the highest average prediction ability under the CV1 scenario for GY (0.31, GN (0.32, GW (0.45, and TTF (0.27. For CV2, the average prediction ability of the model including the interaction terms was generally high for GY (0.38, GN (0.43, GW (0.63, and TTF (0.53. Wheat lines in site-year combinations in Mexico and India had relatively high prediction ability for GY and GW. Results indicated that prediction ability of lines not observed in certain environments could be relatively high for genomic selection when predicting G×E interaction in multi-environment trials.

Human Genome Project discoveries: Dialectics and rhetoric in the science of genetics

Science.gov (United States)

Robidoux, Charlotte A.

The Human Genome Project (HGP), a $437 million effort that began in 1990 to chart the chemical sequence of our three billion base pairs of DNA, was completed in 2003, marking the 50th anniversary that proved the definitive structure of the molecule. This study considered how dialectical and rhetorical arguments functioned in the science, political, and public forums over a 20-year period, from 1980 to 2000, to advance human genome research and to establish the official project. I argue that Aristotle's continuum of knowledge--which ranges from the probable on one end to certified or demonstrated knowledge on the other--provides useful distinctions for analyzing scientific reasoning. While contemporary scientific research seeks to discover certified knowledge, investigators generally employ the hypothetico-deductive or scientific method, which often yields probable rather than certain findings, making these dialectical in nature. Analysis of the discourse describing human genome research revealed the use of numerous rhetorical figures and topics. Persuasive and probable reasoning were necessary for scientists to characterize unknown genetic phenomena, to secure interest in and funding for large-scale human genome research, to solve scientific problems, to issue probable findings, to convince colleagues and government officials that the findings were sound and to disseminate information to the public. Both government and private venture scientists drew on these tools of reasoning to promote their methods of mapping and sequencing the genome. The debate over how to carry out sequencing was rooted in conflicting values. Scientists representing the academic tradition valued a more conservative method that would establish high quality results, and those supporting private industry valued an unconventional approach that would yield products and profits more quickly. Values in turn influenced political and public forum arguments. Agency representatives and investors sided

Ethical challenges and innovations in the dissemination of genomic data: the experience of the PERSPECTIVE project

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Lévesque E

2015-08-01

Full Text Available Emmanuelle Lévesque,1 Bartha Maria Knoppers,1 Jacques Simard,2 1Department of Human Genetics, Centre for Genomics and Policy, McGill University, Montréal, 2Genomics Centre, CHU de Québec Research Center, Department of Molecular Medicine, Laval University, Québec City, QC, Canada Abstract: The importance of making genomic data available for future research is now widely recognized among the scientific community and policymakers. In this era of shared responsibility for data dissemination, improved patient care through research depends on the development of powerful and secure data-sharing systems. As part of the concerted effort to share research resources, the project entitled Personalized Risk Stratification for Prevention and Early Detection of Breast Cancer (PERSPECTIVE makes effective data sharing through the development of a data-sharing framework, one of its goals. The secondary uses of data from PERSPECTIVE for future research promise to enhance our knowledge of breast cancer etiologies without duplicating data-gathering efforts. Despite its benefit for research, we recognize the ethical challenges of data sharing on the local, national, and international levels. The effective management of ethical approvals for projects spanning across jurisdictions, the return of results to research participants, and research incentives and recognition for data production, are but a few pressing issues that need to be properly addressed. We discuss how we managed these issues and suggest how ongoing innovations might help to facilitate data sharing in future genomic research projects. Keywords: data sharing, research ethics, cancer

Resource allocation for maximizing prediction accuracy and genetic gain of genomic selection in plant breeding: a simulation experiment.

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Lorenz, Aaron J

2013-03-01

Allocating resources between population size and replication affects both genetic gain through phenotypic selection and quantitative trait loci detection power and effect estimation accuracy for marker-assisted selection (MAS). It is well known that because alleles are replicated across individuals in quantitative trait loci mapping and MAS, more resources should be allocated to increasing population size compared with phenotypic selection. Genomic selection is a form of MAS using all marker information simultaneously to predict individual genetic values for complex traits and has widely been found superior to MAS. No studies have explicitly investigated how resource allocation decisions affect success of genomic selection. My objective was to study the effect of resource allocation on response to MAS and genomic selection in a single biparental population of doubled haploid lines by using computer simulation. Simulation results were compared with previously derived formulas for the calculation of prediction accuracy under different levels of heritability and population size. Response of prediction accuracy to resource allocation strategies differed between genomic selection models (ridge regression best linear unbiased prediction [RR-BLUP], BayesCπ) and multiple linear regression using ordinary least-squares estimation (OLS), leading to different optimal resource allocation choices between OLS and RR-BLUP. For OLS, it was always advantageous to maximize population size at the expense of replication, but a high degree of flexibility was observed for RR-BLUP. Prediction accuracy of doubled haploid lines included in the training set was much greater than of those excluded from the training set, so there was little benefit to phenotyping only a subset of the lines genotyped. Finally, observed prediction accuracies in the simulation compared well to calculated prediction accuracies, indicating these theoretical formulas are useful for making resource allocation

The fishes of Genome 10K

KAUST Repository

Bernardi, Giacomo

2012-09-01

The Genome 10K project aims to sequence the genomes of 10,000 vertebrates, representing approximately one genome for each vertebrate genus. Since fishes (cartilaginous fishes, ray-finned fishes and lobe-finned fishes) represent more than 50% of extant vertebrates, it is planned to target 4,000 fish genomes. At present, nearly 60 fish genomes are being sequenced at various public funded labs, and under a Genome 10K and BGI pilot project. An additional 100 fishes have been identified for sequencing in the next phase of Genome 10K project. © 2012 Elsevier B.V.

The fishes of Genome 10K

KAUST Repository

Bernardi, Giacomo; Wiley, Edward O.; Mansour, Hicham; Miller, Michael R.; Ortí , Guillermo; Haussler, David H.; O'Brien, Stephen J O; Ryder, Oliver A.; Venkatesh, Byrappa

2012-01-01

The Genome 10K project aims to sequence the genomes of 10,000 vertebrates, representing approximately one genome for each vertebrate genus. Since fishes (cartilaginous fishes, ray-finned fishes and lobe-finned fishes) represent more than 50% of extant vertebrates, it is planned to target 4,000 fish genomes. At present, nearly 60 fish genomes are being sequenced at various public funded labs, and under a Genome 10K and BGI pilot project. An additional 100 fishes have been identified for sequencing in the next phase of Genome 10K project. © 2012 Elsevier B.V.

Genome-wide prediction methods in highly diverse and heterozygous species: proof-of-concept through simulation in grapevine.

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Agota Fodor

Full Text Available Nowadays, genome-wide association studies (GWAS and genomic selection (GS methods which use genome-wide marker data for phenotype prediction are of much potential interest in plant breeding. However, to our knowledge, no studies have been performed yet on the predictive ability of these methods for structured traits when using training populations with high levels of genetic diversity. Such an example of a highly heterozygous, perennial species is grapevine. The present study compares the accuracy of models based on GWAS or GS alone, or in combination, for predicting simple or complex traits, linked or not with population structure. In order to explore the relevance of these methods in this context, we performed simulations using approx 90,000 SNPs on a population of 3,000 individuals structured into three groups and corresponding to published diversity grapevine data. To estimate the parameters of the prediction models, we defined four training populations of 1,000 individuals, corresponding to these three groups and a core collection. Finally, to estimate the accuracy of the models, we also simulated four breeding populations of 200 individuals. Although prediction accuracy was low when breeding populations were too distant from the training populations, high accuracy levels were obtained using the sole core-collection as training population. The highest prediction accuracy was obtained (up to 0.9 using the combined GWAS-GS model. We thus recommend using the combined prediction model and a core-collection as training population for grapevine breeding or for other important economic crops with the same characteristics.

Genome Improvement at JGI-HAGSC

Energy Technology Data Exchange (ETDEWEB)

Grimwood, Jane; Schmutz, Jeremy J.; Myers, Richard M.

2012-03-03

Since the completion of the sequencing of the human genome, the Joint Genome Institute (JGI) has rapidly expanded its scientific goals in several DOE mission-relevant areas. At the JGI-HAGSC, we have kept pace with this rapid expansion of projects with our focus on assessing, assembling, improving and finishing eukaryotic whole genome shotgun (WGS) projects for which the shotgun sequence is generated at the Production Genomic Facility (JGI-PGF). We follow this by combining the draft WGS with genomic resources generated at JGI-HAGSC or in collaborator laboratories (including BAC end sequences, genetic maps and FLcDNA sequences) to produce an improved draft sequence. For eukaryotic genomes important to the DOE mission, we then add further information from directed experiments to produce reference genomic sequences that are publicly available for any scientific researcher. Also, we have continued our program for producing BAC-based finished sequence, both for adding information to JGI genome projects and for small BAC-based sequencing projects proposed through any of the JGI sequencing programs. We have now built our computational expertise in WGS assembly and analysis and have moved eukaryotic genome assembly from the JGI-PGF to JGI-HAGSC. We have concentrated our assembly development work on large plant genomes and complex fungal and algal genomes.

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

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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.

Amino acid changes in disease-associated variants differ radically from variants observed in the 1000 genomes project dataset.

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Tjaart A P de Beer

Full Text Available The 1000 Genomes Project data provides a natural background dataset for amino acid germline mutations in humans. Since the direction of mutation is known, the amino acid exchange matrix generated from the observed nucleotide variants is asymmetric and the mutabilities of the different amino acids are very different. These differences predominantly reflect preferences for nucleotide mutations in the DNA (especially the high mutation rate of the CpG dinucleotide, which makes arginine mutability very much higher than other amino acids rather than selection imposed by protein structure constraints, although there is evidence for the latter as well. The variants occur predominantly on the surface of proteins (82%, with a slight preference for sites which are more exposed and less well conserved than random. Mutations to functional residues occur about half as often as expected by chance. The disease-associated amino acid variant distributions in OMIM are radically different from those expected on the basis of the 1000 Genomes dataset. The disease-associated variants preferentially occur in more conserved sites, compared to 1000 Genomes mutations. Many of the amino acid exchange profiles appear to exhibit an anti-correlation, with common exchanges in one dataset being rare in the other. Disease-associated variants exhibit more extreme differences in amino acid size and hydrophobicity. More modelling of the mutational processes at the nucleotide level is needed, but these observations should contribute to an improved prediction of the effects of specific variants in humans.

Prospects of Genomic Prediction in the USDA Soybean Germplasm Collection: Historical Data Creates Robust Models for Enhancing Selection of Accessions

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Diego Jarquin

2016-08-01

Full Text Available The identification and mobilization of useful genetic variation from germplasm banks for use in breeding programs is critical for future genetic gain and protection against crop pests. Plummeting costs of next-generation sequencing and genotyping is revolutionizing the way in which researchers and breeders interface with plant germplasm collections. An example of this is the high density genotyping of the entire USDA Soybean Germplasm Collection. We assessed the usefulness of 50K single nucleotide polymorphism data collected on 18,480 domesticated soybean (Glycine max accessions and vast historical phenotypic data for developing genomic prediction models for protein, oil, and yield. Resulting genomic prediction models explained an appreciable amount of the variation in accession performance in independent validation trials, with correlations between predicted and observed reaching up to 0.92 for oil and protein and 0.79 for yield. The optimization of training set design was explored using a series of cross-validation schemes. It was found that the target population and environment need to be well represented in the training set. Second, genomic prediction training sets appear to be robust to the presence of data from diverse geographical locations and genetic clusters. This finding, however, depends on the influence of shattering and lodging, and may be specific to soybean with its presence of maturity groups. The distribution of 7608 nonphenotyped accessions was examined through the application of genomic prediction models. The distribution of predictions of phenotyped accessions was representative of the distribution of predictions for nonphenotyped accessions, with no nonphenotyped accessions being predicted to fall far outside the range of predictions of phenotyped accessions.

Exploring the genetic architecture and improving genomic prediction accuracy for mastitis and milk production traits in dairy cattle by mapping variants to hepatic transcriptomic regions responsive to intra-mammary infection.

Science.gov (United States)

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

2017-05-12

A better understanding of the genetic architecture of complex traits can contribute to improve genomic prediction. We hypothesized that genomic variants associated with mastitis and milk production traits in dairy cattle are enriched in hepatic transcriptomic regions that are responsive to intra-mammary infection (IMI). Genomic markers [e.g. single nucleotide polymorphisms (SNPs)] from those regions, if included, may improve the predictive ability of a genomic model. We applied a genomic feature best linear unbiased prediction model (GFBLUP) to implement the above strategy by considering the hepatic transcriptomic regions responsive to IMI as genomic features. GFBLUP, an extension of GBLUP, includes a separate genomic effect of SNPs within a genomic feature, and allows differential weighting of the individual marker relationships in the prediction equation. Since GFBLUP is computationally intensive, we investigated whether a SNP set test could be a computationally fast way to preselect predictive genomic features. The SNP set test assesses the association between a genomic feature and a trait based on single-SNP genome-wide association studies. We applied these two approaches to mastitis and milk production traits (milk, fat and protein yield) in Holstein (HOL, n = 5056) and Jersey (JER, n = 1231) cattle. We observed that a majority of genomic features were enriched in genomic variants that were associated with mastitis and milk production traits. Compared to GBLUP, the accuracy of genomic prediction with GFBLUP was marginally improved (3.2 to 3.9%) in within-breed prediction. The highest increase (164.4%) in prediction accuracy was observed in across-breed prediction. The significance of genomic features based on the SNP set test were correlated with changes in prediction accuracy of GFBLUP (P layers of biological knowledge to provide novel insights into the biological basis of complex traits, and to improve the accuracy of genomic prediction. The SNP set

Intrinsic disorder in Viral Proteins Genome-Linked: experimental and predictive analyses

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Van Dorsselaer Alain

2009-02-01

Full Text Available Abstract Background VPgs are viral proteins linked to the 5' end of some viral genomes. Interactions between several VPgs and eukaryotic translation initiation factors eIF4Es are critical for plant infection. However, VPgs are not restricted to phytoviruses, being also involved in genome replication and protein translation of several animal viruses. To date, structural data are still limited to small picornaviral VPgs. Recently three phytoviral VPgs were shown to be natively unfolded proteins. Results In this paper, we report the bacterial expression, purification and biochemical characterization of two phytoviral VPgs, namely the VPgs of Rice yellow mottle virus (RYMV, genus Sobemovirus and Lettuce mosaic virus (LMV, genus Potyvirus. Using far-UV circular dichroism and size exclusion chromatography, we show that RYMV and LMV VPgs are predominantly or partly unstructured in solution, respectively. Using several disorder predictors, we show that both proteins are predicted to possess disordered regions. We next extend theses results to 14 VPgs representative of the viral diversity. Disordered regions were predicted in all VPg sequences whatever the genus and the family. Conclusion Based on these results, we propose that intrinsic disorder is a common feature of VPgs. The functional role of intrinsic disorder is discussed in light of the biological roles of VPgs.

Genome-Wide Polygenic Scores Predict Reading Performance Throughout the School Years.

Science.gov (United States)

Selzam, Saskia; Dale, Philip S; Wagner, Richard K; DeFries, John C; Cederlöf, Martin; O'Reilly, Paul F; Krapohl, Eva; Plomin, Robert

2017-07-04

It is now possible to create individual-specific genetic scores, called genome-wide polygenic scores (GPS). We used a GPS for years of education ( EduYears ) to predict reading performance assessed at UK National Curriculum Key Stages 1 (age 7), 2 (age 12) and 3 (age 14) and on reading tests administered at ages 7 and 12 in a UK sample of 5,825 unrelated individuals. EduYears GPS accounts for up to 5% of the variance in reading performance at age 14. GPS predictions remained significant after accounting for general cognitive ability and family socioeconomic status. Reading performance of children in the lowest and highest 12.5% of the EduYears GPS distribution differed by a mean growth in reading ability of approximately two school years. It seems certain that polygenic scores will be used to predict strengths and weaknesses in education.

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

The Human Genome Project and Eugenics: Identifying the Impact on Individuals with Mental Retardation.

Science.gov (United States)

Kuna, Jason

2001-01-01

This article explores the impact of the mapping work of the Human Genome Project on individuals with mental retardation and the negative effects of genetic testing. The potential to identify disabilities and the concept of eugenics are discussed, along with ethical issues surrounding potential genetic therapies. (Contains references.) (CR)

Increasing Prediction the Original Final Year Project of Student Using Genetic Algorithm

Science.gov (United States)

Saragih, Rijois Iboy Erwin; Turnip, Mardi; Sitanggang, Delima; Aritonang, Mendarissan; Harianja, Eva

2018-04-01

Final year project is very important forgraduation study of a student. Unfortunately, many students are not seriouslydidtheir final projects. Many of studentsask for someone to do it for them. In this paper, an application of genetic algorithms to predict the original final year project of a studentis proposed. In the simulation, the data of the final project for the last 5 years is collected. The genetic algorithm has several operators namely population, selection, crossover, and mutation. The result suggest that genetic algorithm can do better prediction than other comparable model. Experimental results of predicting showed that 70% was more accurate than the previous researched.

Increased prediction accuracy in wheat breeding trials using a marker × environment interaction genomic selection model.

Science.gov (United States)

Lopez-Cruz, Marco; Crossa, Jose; Bonnett, David; Dreisigacker, Susanne; Poland, Jesse; Jannink, Jean-Luc; Singh, Ravi P; Autrique, Enrique; de los Campos, Gustavo

2015-02-06

Genomic selection (GS) models use genome-wide genetic information to predict genetic values of candidates of selection. Originally, these models were developed without considering genotype × environment interaction(G×E). Several authors have proposed extensions of the single-environment GS model that accommodate G×E using either covariance functions or environmental covariates. In this study, we model G×E using a marker × environment interaction (M×E) GS model; the approach is conceptually simple and can be implemented with existing GS software. We discuss how the model can be implemented by using an explicit regression of phenotypes on markers or using co-variance structures (a genomic best linear unbiased prediction-type model). We used the M×E model to analyze three CIMMYT wheat data sets (W1, W2, and W3), where more than 1000 lines were genotyped using genotyping-by-sequencing and evaluated at CIMMYT's research station in Ciudad Obregon, Mexico, under simulated environmental conditions that covered different irrigation levels, sowing dates and planting systems. We compared the M×E model with a stratified (i.e., within-environment) analysis and with a standard (across-environment) GS model that assumes that effects are constant across environments (i.e., ignoring G×E). The prediction accuracy of the M×E model was substantially greater of that of an across-environment analysis that ignores G×E. Depending on the prediction problem, the M×E model had either similar or greater levels of prediction accuracy than the stratified analyses. The M×E model decomposes marker effects and genomic values into components that are stable across environments (main effects) and others that are environment-specific (interactions). Therefore, in principle, the interaction model could shed light over which variants have effects that are stable across environments and which ones are responsible for G×E. The data set and the scripts required to reproduce the analysis are

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.

Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks.

Science.gov (United States)

Wang, Yiheng; Liu, Tong; Xu, Dong; Shi, Huidong; Zhang, Chaoyang; Mo, Yin-Yuan; Wang, Zheng

2016-01-22

The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named "DeepMethyl" to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/.

Genomic prediction based on data from three layer lines using non-linear regression models

NARCIS (Netherlands)

Huang, H.; Windig, J.J.; Vereijken, A.; Calus, M.P.L.

2014-01-01

Background - Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods. Methods - In an attempt to alleviate

High Genomic Instability Predicts Survival in Metastatic High-Risk Neuroblastoma

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Sara Stigliani

2012-09-01

Full Text Available We aimed to identify novel molecular prognostic markers to better predict relapse risk estimate for children with high-risk (HR metastatic neuroblastoma (NB. We performed genome- and/or transcriptome-wide analyses of 129 stage 4 HR NBs. Children older than 1 year of age were categorized as “short survivors” (dead of disease within 5 years from diagnosis and “long survivors” (alive with an overall survival time ≥ 5 years. We reported that patients with less than three segmental copy number aberrations in their tumor represent a molecularly defined subgroup with a high survival probability within the current HR group of patients. The complex genomic pattern is a prognostic marker independent of NB-associated chromosomal aberrations, i.e., MYCN amplification, 1p and 11q losses, and 17q gain. Integrative analysis of genomic and expression signatures demonstrated that fatal outcome is mainly associated with loss of cell cycle control and deregulation of Rho guanosine triphosphates (GTPases functioning in neuritogenesis. Tumors with MYCN amplification show a lower chromosome instability compared to MYCN single-copy NBs (P = .0008, dominated by 17q gain and 1p loss. Moreover, our results suggest that the MYCN amplification mainly drives disruption of neuronal differentiation and reduction of cell adhesion process involved in tumor invasion and metastasis. Further validation studies are warranted to establish this as a risk stratification for patients.

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.

The Human Genome Project and Mental Retardation: An Educational Program. Final Progress Report

Energy Technology Data Exchange (ETDEWEB)

Davis, Sharon

1999-05-03

The Arc, a national organization on mental retardation, conducted an educational program for members, many of whom have a family member with a genetic condition causing mental retardation. The project informed members about the Human Genome scientific efforts, conducted training regarding ethical, legal and social implications and involved members in issue discussions. Short reports and fact sheets on genetic and ELSI topics were disseminated to 2,200 of the Arc's leaders across the country and to other interested individuals. Materials produced by the project can e found on the Arc's web site, TheArc.org.

Functional food ingredients against colorectal cancer. An example project integrating functional genomics, nutrition and health

NARCIS (Netherlands)

Stierum, R.; Burgemeister, R.; Helvoort, van A.; Peijnenburg, A.; Schütze, K.; Seidelin, M.; Vang, O.; Ommen, van B.

2001-01-01

Functional Food Ingredients Against Colorectal Cancer is one of the first European Union funded Research Projects at the cross-road of functional genomics [comprising transcriptomics, the measurement of the expression of all messengers RNA (mRNAs) and proteomics, the measurement of expression/state

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

Directory of Open Access Journals (Sweden)

Anubhav Jain

2013-07-01

Full Text Available Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org, a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorganic materials. This open dataset can be accessed through multiple channels for both interactive exploration and data mining. The Materials Project also seeks to create open-source platforms for developing robust, sophisticated materials analyses. Future efforts will enable users to perform ‘‘rapid-prototyping’’ of new materials in silico, and provide researchers with new avenues for cost-effective, data-driven materials design.

Genome sequencing and annotation of multidrug resistant Mycobacterium tuberculosis (MDR-TB PR10 strain

Directory of Open Access Journals (Sweden)

Mohd Zakihalani A. Halim

2016-03-01

Full Text Available Here, we report the draft genome sequence and annotation of a multidrug resistant Mycobacterium tuberculosis strain PR10 (MDR-TB PR10 isolated from a patient diagnosed with tuberculosis. The size of the draft genome MDR-TB PR10 is 4.34 Mbp with 65.6% of G + C content and consists of 4637 predicted genes. The determinants were categorized by RAST into 400 subsystems with 4286 coding sequences and 50 RNAs. The whole genome shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession number CP010968. Keywords: Mycobacterium tuberculosis, Genome, MDR, Extrapulmonary

The Epilepsy Phenome/Genome Project (EPGP) informatics platform.

Science.gov (United States)

Nesbitt, Gerry; McKenna, Kevin; Mays, Vickie; Carpenter, Alan; Miller, Kevin; Williams, Michael

2013-04-01

The Epilepsy Phenome/Genome Project (EPGP) is a large-scale, multi-institutional, collaborative network of 27 epilepsy centers throughout the U.S., Australia, and Argentina, with the objective of collecting detailed phenotypic and genetic data on a large number of epilepsy participants. The goals of EPGP are (1) to perform detailed phenotyping on 3750 participants with specific forms of non-acquired epilepsy and 1500 parents without epilepsy, (2) to obtain DNA samples on these individuals, and (3) to ultimately genotype the samples in order to discover novel genes that cause epilepsy. To carry out the project, a reliable and robust informatics platform was needed for standardized electronic data collection and storage, data quality review, and phenotypic analysis involving cases from multiple sites. EPGP developed its own suite of web-based informatics applications for participant tracking, electronic data collection (using electronic case report forms/surveys), data management, phenotypic data review and validation, specimen tracking, electroencephalograph and neuroimaging storage, and issue tracking. We implemented procedures to train and support end-users at each clinical site. Thus far, 3780 study participants have been enrolled and 20,957 web-based study activities have been completed using this informatics platform. Over 95% of respondents to an end-user satisfaction survey felt that the informatics platform was successful almost always or most of the time. The EPGP informatics platform has successfully and effectively allowed study management and efficient and reliable collection of phenotypic data. Our novel informatics platform met the requirements of a large, multicenter research project. The platform has had a high level of end-user acceptance by principal investigators and study coordinators, and can serve as a model for new tools to support future large scale, collaborative research projects collecting extensive phenotypic data. Copyright © 2012

Developing a stochastic traffic volume prediction model for public-private partnership projects

Science.gov (United States)

Phong, Nguyen Thanh; Likhitruangsilp, Veerasak; Onishi, Masamitsu

2017-11-01

Transportation projects require an enormous amount of capital investment resulting from their tremendous size, complexity, and risk. Due to the limitation of public finances, the private sector is invited to participate in transportation project development. The private sector can entirely or partially invest in transportation projects in the form of Public-Private Partnership (PPP) scheme, which has been an attractive option for several developing countries, including Vietnam. There are many factors affecting the success of PPP projects. The accurate prediction of traffic volume is considered one of the key success factors of PPP transportation projects. However, only few research works investigated how to predict traffic volume over a long period of time. Moreover, conventional traffic volume forecasting methods are usually based on deterministic models which predict a single value of traffic volume but do not consider risk and uncertainty. This knowledge gap makes it difficult for concessionaires to estimate PPP transportation project revenues accurately. The objective of this paper is to develop a probabilistic traffic volume prediction model. First, traffic volumes were estimated following the Geometric Brownian Motion (GBM) process. Monte Carlo technique is then applied to simulate different scenarios. The results show that this stochastic approach can systematically analyze variations in the traffic volume and yield more reliable estimates for PPP projects.

In the Beginning was the Genome: Genomics and the Bi-textuality of Human Existence.

Science.gov (United States)

Zwart, H A E Hub

2018-04-01

This paper addresses the cultural impact of genomics and the Human Genome Project (HGP) on human self-understanding. Notably, it addresses the claim made by Francis Collins (director of the HGP) that the genome is the language of God and the claim made by Max Delbrück (founding father of molecular life sciences research) that Aristotle must be credited with having predicted DNA as the soul that organises bio-matter. From a continental philosophical perspective I will argue that human existence results from a dialectical interaction between two types of texts: the language of molecular biology and the language of civilisation; the language of the genome and the language of our socio-cultural, symbolic ambiance. Whereas the former ultimately builds on the alphabets of genes and nucleotides, the latter is informed by primordial texts such as the Bible and the Quran. In applied bioethics deliberations on genomics, science is easily framed as liberating and progressive, religious world-views as conservative and restrictive (Zwart 1993). This paper focusses on the broader cultural ambiance of the debate to discern how the bi-textuality of human existence is currently undergoing a transition, as not only the physiological, but also the normative dimension is being reframed in biomolecular and terabyte terms.

Genome Sequence of the Freshwater Yangtze Finless Porpoise.

Science.gov (United States)

Yuan, Yuan; Zhang, Peijun; Wang, Kun; Liu, Mingzhong; Li, Jing; Zheng, Jingsong; Wang, Ding; Xu, Wenjie; Lin, Mingli; Dong, Lijun; Zhu, Chenglong; Qiu, Qiang; Li, Songhai

2018-04-16

The Yangtze finless porpoise ( Neophocaena asiaeorientalis ssp. asiaeorientalis ) is a subspecies of the narrow-ridged finless porpoise ( N. asiaeorientalis ). In total, 714.28 gigabases (Gb) of raw reads were generated by whole-genome sequencing of the Yangtze finless porpoise, using an Illumina HiSeq 2000 platform. After filtering the low-quality and duplicated reads, we assembled a draft genome of 2.22 Gb, with contig N50 and scaffold N50 values of 46.69 kilobases (kb) and 1.71 megabases (Mb), respectively. We identified 887.63 Mb of repetitive sequences and predicted 18,479 protein-coding genes in the assembled genome. The phylogenetic tree showed a relationship between the Yangtze finless porpoise and the Yangtze River dolphin, which diverged approximately 20.84 million years ago. In comparisons with the genomes of 10 other mammals, we detected 44 species-specific gene families, 164 expanded gene families, and 313 positively selected genes in the Yangtze finless porpoise genome. The assembled genome sequence and underlying sequence data are available at the National Center for Biotechnology Information under BioProject accession number PRJNA433603.

Predicting Software Projects Cost Estimation Based on Mining Historical Data

OpenAIRE

Najadat, Hassan; Alsmadi, Izzat; Shboul, Yazan

2012-01-01

In this research, a hybrid cost estimation model is proposed to produce a realistic prediction model that takes into consideration software project, product, process, and environmental elements. A cost estimation dataset is built from a large number of open source projects. Those projects are divided into three domains: communication, finance, and game projects. Several data mining techniques are used to classify software projects in terms of their development complexity. Data mining techniqu...

A post-assembly genome-improvement toolkit (PAGIT) to obtain annotated genomes from contigs.

Science.gov (United States)

Swain, Martin T; Tsai, Isheng J; Assefa, Samual A; Newbold, Chris; Berriman, Matthew; Otto, Thomas D

2012-06-07

Genome projects now produce draft assemblies within weeks owing to advanced high-throughput sequencing technologies. For milestone projects such as Escherichia coli or Homo sapiens, teams of scientists were employed to manually curate and finish these genomes to a high standard. Nowadays, this is not feasible for most projects, and the quality of genomes is generally of a much lower standard. This protocol describes software (PAGIT) that is used to improve the quality of draft genomes. It offers flexible functionality to close gaps in scaffolds, correct base errors in the consensus sequence and exploit reference genomes (if available) in order to improve scaffolding and generating annotations. The protocol is most accessible for bacterial and small eukaryotic genomes (up to 300 Mb), such as pathogenic bacteria, malaria and parasitic worms. Applying PAGIT to an E. coli assembly takes ∼24 h: it doubles the average contig size and annotates over 4,300 gene models.

Prediction of maize phenotype based on whole-genome single nucleotide polymorphisms using deep belief networks

Science.gov (United States)

Rachmatia, H.; Kusuma, W. A.; Hasibuan, L. S.

2017-05-01

Selection in plant breeding could be more effective and more efficient if it is based on genomic data. Genomic selection (GS) is a new approach for plant-breeding selection that exploits genomic data through a mechanism called genomic prediction (GP). Most of GP models used linear methods that ignore effects of interaction among genes and effects of higher order nonlinearities. Deep belief network (DBN), one of the architectural in deep learning methods, is able to model data in high level of abstraction that involves nonlinearities effects of the data. This study implemented DBN for developing a GP model utilizing whole-genome Single Nucleotide Polymorphisms (SNPs) as data for training and testing. The case study was a set of traits in maize. The maize dataset was acquisitioned from CIMMYT’s (International Maize and Wheat Improvement Center) Global Maize program. Based on Pearson correlation, DBN is outperformed than other methods, kernel Hilbert space (RKHS) regression, Bayesian LASSO (BL), best linear unbiased predictor (BLUP), in case allegedly non-additive traits. DBN achieves correlation of 0.579 within -1 to 1 range.

Computational prediction of cAMP receptor protein (CRP binding sites in cyanobacterial genomes

Directory of Open Access Journals (Sweden)

Su Zhengchang

2009-01-01

Full Text Available Abstract Background Cyclic AMP receptor protein (CRP, also known as catabolite gene activator protein (CAP, is an important transcriptional regulator widely distributed in many bacteria. The biological processes under the regulation of CRP are highly diverse among different groups of bacterial species. Elucidation of CRP regulons in cyanobacteria will further our understanding of the physiology and ecology of this important group of microorganisms. Previously, CRP has been experimentally studied in only two cyanobacterial strains: Synechocystis sp. PCC 6803 and Anabaena sp. PCC 7120; therefore, a systematic genome-scale study of the potential CRP target genes and binding sites in cyanobacterial genomes is urgently needed. Results We have predicted and analyzed the CRP binding sites and regulons in 12 sequenced cyanobacterial genomes using a highly effective cis-regulatory binding site scanning algorithm. Our results show that cyanobacterial CRP binding sites are very similar to those in E. coli; however, the regulons are very different from that of E. coli. Furthermore, CRP regulons in different cyanobacterial species/ecotypes are also highly diversified, ranging from photosynthesis, carbon fixation and nitrogen assimilation, to chemotaxis and signal transduction. In addition, our prediction indicates that crp genes in modern cyanobacteria are likely inherited from a common ancestral gene in their last common ancestor, and have adapted various cellular functions in different environments, while some cyanobacteria lost their crp genes as well as CRP binding sites during the course of evolution. Conclusion The CRP regulons in cyanobacteria are highly diversified, probably as a result of divergent evolution to adapt to various ecological niches. Cyanobacterial CRPs may function as lineage-specific regulators participating in various cellular processes, and are important in some lineages. However, they are dispensable in some other lineages. The

Predicting Defects Using Information Intelligence Process Models in the Software Technology Project.

Science.gov (United States)

Selvaraj, Manjula Gandhi; Jayabal, Devi Shree; Srinivasan, Thenmozhi; Balasubramanie, Palanisamy

2015-01-01

A key differentiator in a competitive market place is customer satisfaction. As per Gartner 2012 report, only 75%-80% of IT projects are successful. Customer satisfaction should be considered as a part of business strategy. The associated project parameters should be proactively managed and the project outcome needs to be predicted by a technical manager. There is lot of focus on the end state and on minimizing defect leakage as much as possible. Focus should be on proactively managing and shifting left in the software life cycle engineering model. Identify the problem upfront in the project cycle and do not wait for lessons to be learnt and take reactive steps. This paper gives the practical applicability of using predictive models and illustrates use of these models in a project to predict system testing defects thus helping to reduce residual defects.

Thorough analysis of unorthodox ABO deletions called by the 1000 Genomes project.

Science.gov (United States)

Möller, M; Hellberg, Å; Olsson, M L

2018-02-01

ABO remains the clinically most important blood group system, but despite earlier extensive research, significant findings are still being made. The vast majority of catalogued ABO null alleles are based on the c.261delG polymorphism. Apart from c.802G>A, other mechanisms for O alleles are rare. While analysing the data set from the 1000 Genomes (1000G) project, we encountered two previously uncharacterized deletions, which needed further exploration. The Erythrogene database, complemented with bioinformatics software, was used to analyse ABO in 2504 individuals from 1000G. DNA samples from selected 1000G donors and African blood donors were examined by allele-specific PCR and Sanger sequencing to characterize predicted deletions. A 5821-bp deletion encompassing exons 5-7 was called in twenty 1000G individuals, predominantly Africans. This allele was confirmed and its exact deletion point defined by bioinformatic analyses and in vitro experiments. A PCR assay was developed, and screening of African samples revealed three donors heterozygous for this deletion, which was thereby phenotypically established as an O allele. Analysis of upstream genetic markers indicated an ancestral origin from ABO*O.01.02. We estimate this deletion as the 3rd most common mechanism behind O alleles. A 24-bp deletion was called in nine individuals and showed greater diversity regarding ethnic distribution and allelic background. It could neither be confirmed by in silico nor in vitro experiments. A previously uncharacterized ABO deletion among Africans was comprehensively mapped and a genotyping strategy devised. The false prediction of another deletion emphasizes the need for cautious interpretation of NGS data and calls for strict validation routines. © 2017 International Society of Blood Transfusion.

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...

Predictive Power Estimation Algorithm (PPEA--a new algorithm to reduce overfitting for genomic biomarker discovery.

Directory of Open Access Journals (Sweden)

Jiangang Liu

Full Text Available Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA, which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1 PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2 the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3 using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4 more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses.

An integrative approach to predicting the functional effects of small indels in non-coding regions of the human genome.

Science.gov (United States)

Ferlaino, Michael; Rogers, Mark F; Shihab, Hashem A; Mort, Matthew; Cooper, David N; Gaunt, Tom R; Campbell, Colin

2017-10-06

Small insertions and deletions (indels) have a significant influence in human disease and, in terms of frequency, they are second only to single nucleotide variants as pathogenic mutations. As the majority of mutations associated with complex traits are located outside the exome, it is crucial to investigate the potential pathogenic impact of indels in non-coding regions of the human genome. We present FATHMM-indel, an integrative approach to predict the functional effect, pathogenic or neutral, of indels in non-coding regions of the human genome. Our method exploits various genomic annotations in addition to sequence data. When validated on benchmark data, FATHMM-indel significantly outperforms CADD and GAVIN, state of the art models in assessing the pathogenic impact of non-coding variants. FATHMM-indel is available via a web server at indels.biocompute.org.uk. FATHMM-indel can accurately predict the functional impact and prioritise small indels throughout the whole non-coding genome.

The human genome project: Information management, access, and regulation. Technical progress report, 1 April--31 August 1993

Energy Technology Data Exchange (ETDEWEB)

McInerney, J.D.; Micikas, L.B.

1993-09-10

Efforts are described to prepare educational materials including computer based as well as conventional type teaching materials for training interested high school and elementary students in aspects of Human Genome Project.

Prediction of human population responses to toxic compounds by a collaborative competition.

Science.gov (United States)

Eduati, Federica; Mangravite, Lara M; Wang, Tao; Tang, Hao; Bare, J Christopher; Huang, Ruili; Norman, Thea; Kellen, Mike; Menden, Michael P; Yang, Jichen; Zhan, Xiaowei; Zhong, Rui; Xiao, Guanghua; Xia, Menghang; Abdo, Nour; Kosyk, Oksana; Friend, Stephen; Dearry, Allen; Simeonov, Anton; Tice, Raymond R; Rusyn, Ivan; Wright, Fred A; Stolovitzky, Gustavo; Xie, Yang; Saez-Rodriguez, Julio

2015-09-01

The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.

HGVA: the Human Genome Variation Archive

OpenAIRE

Lopez, Javier; Coll, Jacobo; Haimel, Matthias; Kandasamy, Swaathi; Tarraga, Joaquin; Furio-Tari, Pedro; Bari, Wasim; Bleda, Marta; Rueda, Antonio; Gr?f, Stefan; Rendon, Augusto; Dopazo, Joaquin; Medina, Ignacio

2017-01-01

Abstract High-profile genomic variation projects like the 1000 Genomes project or the Exome Aggregation Consortium, are generating a wealth of human genomic variation knowledge which can be used as an essential reference for identifying disease-causing genotypes. However, accessing these data, contrasting the various studies and integrating those data in downstream analyses remains cumbersome. The Human Genome Variation Archive (HGVA) tackles these challenges and facilitates access to genomic...

Genomic Diversity in the Genus of Aspergillus

DEFF Research Database (Denmark)

Rasmussen, Jane Lind Nybo

, sections and genus of Aspergillus. The work uncovers a large genomic diversity across all studied groups of species. The genomic diversity was especially evident on the section level, where the proteins shared by all species only represents ⇠55% of the proteome. This number decreases even further, to 38......, sections Nigri, Usti and Cavericolus, clade Tubingensis, and species A. niger. It lastly uses these results to predict genetic traits that take part in fungal speciation. Within a few years the Aspergillus whole-genus sequencing project will have published all currently-accepted Aspergillus genomes......Aspergillus is a highly important genus of saprotrophic filamentous fungi. It is a very diverse genus that is inextricably intertwined with human a↵airs on a daily basis, holding species relevant to plant and human pathology, enzyme and bulk chemistry production, food and beverage biotechnology...

HGVA: the Human Genome Variation Archive.

Science.gov (United States)

Lopez, Javier; Coll, Jacobo; Haimel, Matthias; Kandasamy, Swaathi; Tarraga, Joaquin; Furio-Tari, Pedro; Bari, Wasim; Bleda, Marta; Rueda, Antonio; Gräf, Stefan; Rendon, Augusto; Dopazo, Joaquin; Medina, Ignacio

2017-07-03

High-profile genomic variation projects like the 1000 Genomes project or the Exome Aggregation Consortium, are generating a wealth of human genomic variation knowledge which can be used as an essential reference for identifying disease-causing genotypes. However, accessing these data, contrasting the various studies and integrating those data in downstream analyses remains cumbersome. The Human Genome Variation Archive (HGVA) tackles these challenges and facilitates access to genomic data for key reference projects in a clean, fast and integrated fashion. HGVA provides an efficient and intuitive web-interface for easy data mining, a comprehensive RESTful API and client libraries in Python, Java and JavaScript for fast programmatic access to its knowledge base. HGVA calculates population frequencies for these projects and enriches their data with variant annotation provided by CellBase, a rich and fast annotation solution. HGVA serves as a proof-of-concept of the genome analysis developments being carried out by the University of Cambridge together with UK's 100 000 genomes project and the National Institute for Health Research BioResource Rare-Diseases, in particular, deploying open-source for Computational Biology (OpenCB) software platform for storing and analyzing massive genomic datasets. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

The whole genome sequences and experimentally phased haplotypes of over 100 personal genomes.

Science.gov (United States)

Mao, Qing; Ciotlos, Serban; Zhang, Rebecca Yu; Ball, Madeleine P; Chin, Robert; Carnevali, Paolo; Barua, Nina; Nguyen, Staci; Agarwal, Misha R; Clegg, Tom; Connelly, Abram; Vandewege, Ward; Zaranek, Alexander Wait; Estep, Preston W; Church, George M; Drmanac, Radoje; Peters, Brock A

2016-10-11

Since the completion of the Human Genome Project in 2003, it is estimated that more than 200,000 individual whole human genomes have been sequenced. A stunning accomplishment in such a short period of time. However, most of these were sequenced without experimental haplotype data and are therefore missing an important aspect of genome biology. In addition, much of the genomic data is not available to the public and lacks phenotypic information. As part of the Personal Genome Project, blood samples from 184 participants were collected and processed using Complete Genomics' Long Fragment Read technology. Here, we present the experimental whole genome haplotyping and sequencing of these samples to an average read coverage depth of 100X. This is approximately three-fold higher than the read coverage applied to most whole human genome assemblies and ensures the highest quality results. Currently, 114 genomes from this dataset are freely available in the GigaDB repository and are associated with rich phenotypic data; the remaining 70 should be added in the near future as they are approved through the PGP data release process. For reproducibility analyses, 20 genomes were sequenced at least twice using independent LFR barcoded libraries. Seven genomes were also sequenced using Complete Genomics' standard non-barcoded library process. In addition, we report 2.6 million high-quality, rare variants not previously identified in the Single Nucleotide Polymorphisms database or the 1000 Genomes Project Phase 3 data. These genomes represent a unique source of haplotype and phenotype data for the scientific community and should help to expand our understanding of human genome evolution and function.

Exploring Other Genomes: Bacteria.

Science.gov (United States)

Flannery, Maura C.

2001-01-01

Points out the importance of genomes other than the human genome project and provides information on the identified bacterial genomes Pseudomonas aeuroginosa, Leprosy, Cholera, Meningitis, Tuberculosis, Bubonic Plague, and plant pathogens. Considers the computer's use in genome studies. (Contains 14 references.) (YDS)

The 2008 update of the Aspergillus nidulans genome annotation: A community effort

DEFF Research Database (Denmark)

Wortman, Jennifer Russo; Gilsenan, Jane Mabey; Joardar, Vinita

2009-01-01

The identification and annotation of protein-coding genes is one of the primary goals of whole-genome sequencing projects, and the accuracy of predicting the primary protein products of gene expression is vital to the interpretation of the available data and the design of downstream functional ap...

The 2008 update of the Aspergillus nidulans genome annotation : a community effort

NARCIS (Netherlands)

Wortman, Jennifer Russo; Gilsenan, Jane Mabey; Joardar, Vinita; Deegan, Jennifer; Clutterbuck, John; Andersen, Mikael R; Archer, David; Bencina, Mojca; Braus, Gerhard; Coutinho, Pedro; von Döhren, Hans; Doonan, John; Driessen, Arnold J M; Durek, Pawel; Espeso, Eduardo; Fekete, Erzsébet; Flipphi, Michel; Estrada, Carlos Garcia; Geysens, Steven; Goldman, Gustavo; de Groot, Piet W J; Hansen, Kim; Harris, Steven D; Heinekamp, Thorsten; Helmstaedt, Kerstin; Henrissat, Bernard; Hofmann, Gerald; Homan, Tim; Horio, Tetsuya; Horiuchi, Hiroyuki; James, Steve; Jones, Meriel; Karaffa, Levente; Karányi, Zsolt; Kato, Masashi; Keller, Nancy; Kelly, Diane E; Kiel, Jan A K W; Kim, Jung-Mi; van der Klei, Ida J; Klis, Frans M; Kovalchuk, Andriy; Krasevec, Nada; Kubicek, Christian P; Liu, Bo; Maccabe, Andrew; Meyer, Vera; Mirabito, Pete; Miskei, Márton; Mos, Magdalena; Mullins, Jonathan; Nelson, David R; Nielsen, Jens; Oakley, Berl R; Osmani, Stephen A; Pakula, Tiina; Paszewski, Andrzej; Paulsen, Ian; Pilsyk, Sebastian; Pócsi, István; Punt, Peter J; Ram, Arthur F J; Ren, Qinghu; Robellet, Xavier; Robson, Geoff; Seiboth, Bernhard; van Solingen, Piet; Specht, Thomas; Sun, Jibin; Taheri-Talesh, Naimeh; Takeshita, Norio; Ussery, Dave; vanKuyk, Patricia A; Visser, Hans; van de Vondervoort, Peter J I; de Vries, Ronald P; Walton, Jonathan; Xiang, Xin; Xiong, Yi; Zeng, An Ping; Brandt, Bernd W; Cornell, Michael J; van den Hondel, Cees A M J J; Visser, Jacob; Oliver, Stephen G; Turner, Geoffrey

The identification and annotation of protein-coding genes is one of the primary goals of whole-genome sequencing projects, and the accuracy of predicting the primary protein products of gene expression is vital to the interpretation of the available data and the design of downstream functional

The 2008 update of the Aspergillus nidulans genome annotation : A community effort

NARCIS (Netherlands)

Wortman, Jennifer Russo; Gilsenan, Jane Mabey; Joardar, Vinita; Deegan, Jennifer; Clutterbuck, John; Andersen, Mikael R.; Archer, David; Bencina, Mojca; Braus, Gerhard; Coutinho, Pedro; von Doehren, Hans; Doonan, John; Driessen, Arnold J. M.; Durek, Pawel; Espeso, Eduardo; Fekete, Erzsebet; Flipphi, Michel; Garcia Estrada, Carlos; Geysens, Steven; Goldman, Gustavo; de Groot, Piet W. J.; Hansen, Kim; Harris, Steven D.; Heinekamp, Thorsten; Helmstaedt, Kerstin; Henrissat, Bernard; Hofmann, Gerald; Homan, Tim; Horio, Tetsuya; Horiuchi, Hiroyuki; James, Steve; Jones, Meriel; Karaffa, Levente; Karanyi, Zsolt; Kato, Masashi; Keller, Nancy; Kelly, Diane E.; Kiel, Jan A. K. W.; Kim, Jung-Mi; van der Klei, Ida J.; Klis, Frans M.; Kovalchuk, Andriy; Krasevec, Nada; Kubicek, Christian P.; Liu, Bo; MacCabe, Andrew; Meyer, Vera; Mirabito, Pete; Miskei, Marton; Mos, Magdalena; Mullins, Jonathan; Nelson, David R.; Nielsen, Jens; Oakley, Berl R.; Osmani, Stephen A.; Pakula, Tiina; Paszewski, Andrzej; Paulsen, Ian; Pilsyk, Sebastian; Pocsi, Istvan; Punt, Peter J.; Ram, Arthur F. J.; Ren, Qinghu; Robellet, Xavier; Robson, Geoff; Seiboth, Bernhard; van Solingen, Piet; Specht, Thomas; Sun, Jibin; Taheri-Talesh, Naimeh; Takeshita, Norio; Ussery, Dave; Vankuyk, Patricia A.; Visser, Hans; de Vondervoort, Peter J. I. van; Walton, Jonathan; Xiang, Xin; Xiong, Yi; Zeng, An Ping; Brandt, Bernd W.; Cornell, Michael J.; van den Hondel, Cees A. M. J. J.; Visser, Jacob; Oliver, Stephen G.; Turner, Geoffrey; Kraševec, Nada; Kuyk, Patricia A. van; Döhren, D.H.; van Seilboth, B; de Vries, R.

The identification and annotation of protein-coding genes is one of the primary goals of whole-genome sequencing projects, and the accuracy of predicting the primary protein products of gene expression is vital to the interpretation of the available data and the design of downstream functional

Genomic Prediction Within and Across Biparental Families: Means and Variances of Prediction Accuracy and Usefulness of Deterministic Equations

Directory of Open Access Journals (Sweden)

Pascal Schopp

2017-11-01

Full Text Available A major application of genomic prediction (GP in plant breeding is the identification of superior inbred lines within families derived from biparental crosses. When models for various traits were trained within related or unrelated biparental families (BPFs, experimental studies found substantial variation in prediction accuracy (PA, but little is known about the underlying factors. We used SNP marker genotypes of inbred lines from either elite germplasm or landraces of maize (Zea mays L. as parents to generate in silico 300 BPFs of doubled-haploid lines. We analyzed PA within each BPF for 50 simulated polygenic traits, using genomic best linear unbiased prediction (GBLUP models trained with individuals from either full-sib (FSF, half-sib (HSF, or unrelated families (URF for various sizes (Ntrain of the training set and different heritabilities (h2 . In addition, we modified two deterministic equations for forecasting PA to account for inbreeding and genetic variance unexplained by the training set. Averaged across traits, PA was high within FSF (0.41–0.97 with large variation only for Ntrain < 50 and h2 < 0.6. For HSF and URF, PA was on average ∼40–60% lower and varied substantially among different combinations of BPFs used for model training and prediction as well as different traits. As exemplified by HSF results, PA of across-family GP can be very low if causal variants not segregating in the training set account for a sizeable proportion of the genetic variance among predicted individuals. Deterministic equations accurately forecast the PA expected over many traits, yet cannot capture trait-specific deviations. We conclude that model training within BPFs generally yields stable PA, whereas a high level of uncertainty is encountered in across-family GP. Our study shows the extent of variation in PA that must be at least reckoned with in practice and offers a starting point for the design of training sets composed of multiple BPFs.

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