Abstract
BackgroundBayesian methods allow prediction of genomic breeding values (GEBVs) using high-density single nucleotide polymorphisms (SNPs) covering the whole genome with effective shrinkage of SNP effects using appropriate priors. In this study we applied a modification of the well-known BayesA and BayesB methods to estimate the proportion of SNPs with zero effects (π) and a common variance for non-zero effects. The method, termed BayesCπ, was used to predict the GEBVs of the last generation of the QTLMAS2010 data. The accuracy of GEBVs from various methods was estimated by the correlation with phenotypes in the last generation. The methods were BayesCPi and BayesB with different π values, both with and without polygenic effects, and best linear unbiased prediction using an animal model with a genomic or numerator relationship matrix. Positions of quantitative trait loci (QTLs) were identified based on the variances of GEBVs for windows of 10 consecutive SNPs. We also proposed a novel approach to set significance thresholds for claiming QTL in this specific case by using pedigree-based simulation of genotypes. All analyses were focused on detecting and evaluating QTL with additive effects.ResultsThe accuracy of GEBVs was highest for BayesCπ, but the accuracy of BayesB with π equal to 0.99 was similar to that of BayesCπ. The accuracy of BayesB dropped with a decrease in π. Including polygenic effects into the model only had marginal effects on accuracy and bias of predictions. The number of QTL identified was 15 when based on a stringent 10% chromosome-wise threshold and increased to 21 when a 20% chromosome-wise threshold was used.ConclusionsThe BayesCπ method without polygenic effects was identified to be the best method for the QTLMAS2010 dataset, because it had highest accuracy and least bias. The significance criterion based on variance of 10-SNP windows allowed detection of more than half of the QTL, with few false positives.
Highlights
Bayesian methods allow prediction of genomic breeding values (GEBVs) using high-density single nucleotide polymorphisms (SNPs) covering the whole genome with effective shrinkage of SNP effects using appropriate priors
Each QTL is expected to be in linkage disequilibrium (LD) with at least one SNP because of the high marker density, the effects of all QTL are expected to be captured by SNPs
In this study we aimed to: (i) identify the Bayesian approach that most accurately predicts GEBV for the QTLMAS2010 data; (ii) develop a new criterion based on the 10-SNP window variance for QTL detection to concentrate signals from high density SNP panels; and (iii) set significance thresholds for the window variance criterion to claim QTL when pedigree relationships exist among individuals
Summary
Bayesian methods allow prediction of genomic breeding values (GEBVs) using high-density single nucleotide polymorphisms (SNPs) covering the whole genome with effective shrinkage of SNP effects using appropriate priors. Bayesian methods enable prediction of the effects of high-density SNPs covering the whole genome, even when the number of SNPs is much larger than the number of individuals with phenotypic and genotypic records. We applied a modification of BayesB, BayesCπ [2], where a single effect variance is common to all SNPs with non-zero effects, and the probability that a SNP has zero effect, π, is treated as unknown. This modification aims at overcoming the drawbacks of BayesB pointed out by Gianola et al.[3], that the fullconditional posterior distribution is dominated by the prior and not by the data
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