Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits
Iona M. MacLeod,Iona M. MacLeod,Phil J. Bowman,Phil J. Bowman,C. J. Vander Jagt,Mekonnen Haile-Mariam,Kathryn E. Kemper,Amanda J. Chamberlain,Chris Schrooten,Ben J. Hayes,Ben J. Hayes,Michael E. Goddard,Michael E. Goddard +12 more
Reads0
Chats0
TLDR
The results suggest that the new BayesRC method was equal to or more powerful than BayesR for detecting candidate causal variants and for genomic prediction of milk traits.Abstract:
Dense SNP genotypes are often combined with complex trait phenotypes to map causal variants, study genetic architecture and provide genomic predictions for individuals with genotypes but no phenotype. A single method of analysis that jointly fits all genotypes in a Bayesian mixture model (BayesR) has been shown to competitively address all 3 purposes simultaneously. However, BayesR and other similar methods ignore prior biological knowledge and assume all genotypes are equally likely to affect the trait. While this assumption is reasonable for SNP array genotypes, it is less sensible if genotypes are whole-genome sequence variants which should include causal variants. We introduce a new method (BayesRC) based on BayesR that incorporates prior biological information in the analysis by defining classes of variants likely to be enriched for causal mutations. The information can be derived from a range of sources, including variant annotation, candidate gene lists and known causal variants. This information is then incorporated objectively in the analysis based on evidence of enrichment in the data. We demonstrate the increased power of BayesRC compared to BayesR using real dairy cattle genotypes with simulated phenotypes. The genotypes were imputed whole-genome sequence variants in coding regions combined with dense SNP markers. BayesRC increased the power to detect causal variants and increased the accuracy of genomic prediction. The relative improvement for genomic prediction was most apparent in validation populations that were not closely related to the reference population. We also applied BayesRC to real milk production phenotypes in dairy cattle using independent biological priors from gene expression analyses. Although current biological knowledge of which genes and variants affect milk production is still very incomplete, our results suggest that the new BayesRC method was equal to or more powerful than BayesR for detecting candidate causal variants and for genomic prediction of milk traits. BayesRC provides a novel and flexible approach to simultaneously improving the accuracy of QTL discovery and genomic prediction by taking advantage of prior biological knowledge. Approaches such as BayesRC will become increasing useful as biological knowledge accumulates regarding functional regions of the genome for a range of traits and species.read more
Citations
More filters
Journal Article
Molecular dissection of a quantitative trait locus: a phenylalanine-to-tyrosine substitution in the transmembrane domain of the bovine growth hormone receptor is associated with a major effect on milk yield and composition.
Wouter Coppieters,Sarah C. Blott,Michel Georges,Richard J. Spelman,Latifa Karim,Frédéric Farnir,Anne Lisbeth Schmidt,Bernard Grisart,Anne Cornet,Sirja Moisio,Patricia Simon-Assmann,Nadine Cambisano,Sharon A Küntzel,Talitha C. Ford,Chang-Joo Kim,Jerry Wong,Russell G. Snell,Gwang Sik Kim,Dave Johnson,Paulette Berzi,Johanna Vilkki +20 more
TL;DR: Using a denser chromosome 20 marker map and exploiting linkage disequilibrium using two distinct approaches, strong evidence is provided that a chromosome segment including the gene coding for the growth hormone receptor accounts for at least part of the chromosome 20 QTL effect.
Journal ArticleDOI
Harnessing genomics to fast-track genetic improvement in aquaculture.
Ross D. Houston,Tim P. Bean,Daniel J. Macqueen,Manu Kumar Gundappa,Ye Hwa Jin,Tom L. Jenkins,Sarah Louise C Selly,Samuel A.M. Martin,Jamie R. Stevens,Eduarda M. Santos,Andrew Davie,Diego Robledo +11 more
TL;DR: The authors review how genomics is being applied to aquaculture species at all stages of the domestication process to optimize selective breeding and how combining genomic selection with biotechnological innovations, such as genome editing and surrogate broodstock technologies, may further expedite genetic improvement in Aquaculture.
Journal ArticleDOI
Harnessing genomic information for livestock improvement
TL;DR: Genomic information of increasing complexity (including genomic, epigenomic, transcriptomic and microbiome data), combined with technological advances for its cost-effective collection and use, will make a major contribution to tackling the looming food crisis.
Journal ArticleDOI
Accelerating crop genetic gains with genomic selection.
TL;DR: The lessons learned from implementing GS in livestock and the impact of GS on crop breeding are reviewed, and important features for the success of GS under different breeding scenarios are discussed.
Journal ArticleDOI
Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture
TL;DR: This work advocates the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution, and illustrates how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits.
References
More filters
Journal ArticleDOI
PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses
Shaun Purcell,Shaun Purcell,Benjamin M. Neale,Benjamin M. Neale,Kathe Todd-Brown,Lori Thomas,Manuel A. R. Ferreira,David Bender,David Bender,Julian Maller,Julian Maller,Pamela Sklar,Pamela Sklar,Paul I.W. de Bakker,Paul I.W. de Bakker,Mark J. Daly,Mark J. Daly,Pak C. Sham +17 more
TL;DR: This work introduces PLINK, an open-source C/C++ WGAS tool set, and describes the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation, which focuses on the estimation and use of identity- by-state and identity/descent information in the context of population-based whole-genome studies.
Journal ArticleDOI
Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps
TL;DR: It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.
Journal ArticleDOI
SIFT: predicting amino acid changes that affect protein function
Pauline C. Ng,Steven Henikoff +1 more
TL;DR: SIFT is a program that predicts whether an amino acid substitution affects protein function so that users can prioritize substitutions for further study and can distinguish between functionally neutral and deleterious amino acid changes in mutagenesis studies and on human polymorphisms.
Journal ArticleDOI
Common SNPs explain a large proportion of the heritability for human height
Jian Yang,Beben Benyamin,Brian P. McEvoy,Scott D. Gordon,Anjali K. Henders,Dale R. Nyholt,Pamela A. F. Madden,Andrew C. Heath,Nicholas G. Martin,Grant W. Montgomery,Michael E. Goddard,Peter M. Visscher +11 more
TL;DR: Evidence is provided that the remaining heritability is due to incomplete linkage disequilibrium between causal variants and genotyped SNPs, exacerbated by causal variants having lower minor allele frequency than the SNPs explored to date.