Abstract
BackgroundIn animal breeding, genetic variance for complex traits is often estimated using linear mixed models that incorporate information from single nucleotide polymorphism (SNP) markers using a realized genomic relationship matrix. In such models, individual genetic markers are weighted equally and genomic variation is treated as a “black box.” This approach is useful for selecting animals with high genetic potential, but it does not generate or utilise knowledge of the biological mechanisms underlying trait variation. Here we propose a linear mixed-model approach that can evaluate the collective effects of sets of SNPs and thereby open the “black box.” The described genomic feature best linear unbiased prediction (GFBLUP) model has two components that are defined by genomic features.ResultsWe analysed data on average daily gain, feed efficiency, and lean meat percentage from 3,085 Duroc boars, along with genotypes from a 60 K SNP chip. In addition information on known quantitative trait loci (QTL) from the animal QTL database was integrated in the GFBLUP as a genomic feature. Our results showed that the most significant QTL categories were indeed biologically meaningful. Additionally, for high heritability traits, prediction accuracy was improved by the incorporation of biological knowledge in prediction models. A simulation study using the real genotypes and simulated phenotypes demonstrated challenges regarding detection of causal variants in low to medium heritability traits.ConclusionsThe GFBLUP model showed increased predictive ability when enough causal variants were included in the genomic feature to explain over 10 % of the genomic variance, and when dilution by non-causal markers was minimal. In the observed data set, predictive ability was increased by the inclusion of prior QTL information obtained outside the training data set, but only for the trait with highest heritability.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-015-0322-9) contains supplementary material, which is available to authorized users.
Highlights
In animal breeding, genetic variance for complex traits is often estimated using linear mixed models that incorporate information from single nucleotide polymorphism (SNP) markers using a realized genomic relationship matrix
To attain insight into the biological mechanisms causing trait variation, we identified genomic features that were enriched for associated SNPs
Power to detect marker sets with causal variants We investigated the effects of the five different quantitative trait loci (QTL), population, or trait-specific factors in terms of the power to detect marker sets including causal variants
Summary
Genetic variance for complex traits is often estimated using linear mixed models that incorporate information from single nucleotide polymorphism (SNP) markers using a realized genomic relationship matrix In such models, individual genetic markers are weighted and genomic variation is treated as a “black box.”. Extensions of the standard GBLUP modelling approach have been proposed to incorporate available information regarding causal marker distribution along the genome or biological mechanisms underlying trait variation [6,7,8] Such approaches may increase prediction accuracy in populations with low levels of genetic relatedness, but not in populations with highly related individuals (e.g. inbred mice stocks [7]). These associations represent novel insights into the genetic mechanisms underlying a trait, and may be used to develop more accurate genomic feature BLUP (GFBLUP) models
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