Abstract
BackgroundWhole-genome sequence (WGS) data are increasingly available on large numbers of individuals in animal and plant breeding and in human genetics through second-generation resequencing technologies, 1000 genomes projects, and large-scale genotype imputation from lower marker densities. Here, we present a computationally fast implementation of a variable selection genomic prediction method, that could handle WGS data on more than 35,000 individuals, test its accuracy for across-breed predictions and assess its quantitative trait locus (QTL) mapping precision.MethodsThe Monte Carlo Markov chain (MCMC) variable selection model (Bayes GC) fits simultaneously a genomic best linear unbiased prediction (GBLUP) term, i.e. a polygenic effect whose correlations are described by a genomic relationship matrix (G), and a Bayes C term, i.e. a set of single nucleotide polymorphisms (SNPs) with large effects selected by the model. Computational speed is improved by a Metropolis–Hastings sampling that directs computations to the SNPs, which are, a priori, most likely to be included into the model. Speed is also improved by running many relatively short MCMC chains. Memory requirements are reduced by storing the genotype matrix in binary form. The model was tested on a WGS dataset containing Holstein, Jersey and Australian Red cattle. The data contained 4,809,520 genotypes on 35,549 individuals together with their milk, fat and protein yields, and fat and protein percentage traits.ResultsThe prediction accuracies of the Jersey individuals improved by 1.5% when using across-breed GBLUP compared to within-breed predictions. Using WGS instead of 600 k SNP-chip data yielded on average a 3% accuracy improvement for Australian Red cows. QTL were fine-mapped by locating the SNP with the highest posterior probability of being included in the model. Various QTL known from the literature were rediscovered, and a new SNP affecting milk production was discovered on chromosome 20 at 34.501126 Mb. Due to the high mapping precision, it was clear that many of the discovered QTL were the same across the five dairy traits.ConclusionsAcross-breed Bayes GC genomic prediction improved prediction accuracies compared to GBLUP. The combination of across-breed WGS data and Bayesian genomic prediction proved remarkably effective for the fine-mapping of QTL.
Highlights
Whole-genome sequence (WGS) data are increasingly available on large numbers of individuals in animal and plant breeding and in human genetics through second-generation resequencing technologies, 1000 genomes projects, and large-scale genotype imputation from lower marker densities
Accuracy of prediction declines if the target population is not closely related to the training population because the linkage disequilibrium (LD) between markers and causal variants differs between populations
quantitative trait locus (QTL) mapping Figure 1 shows the Manhattan plot of the variances of local genomic breeding value estimates (GEBV) for fat percentage calculated in 250-kb regions across the genome, as an indicator for the genetic variance contained in the regions [23], which indicates whether the region contains important QTL
Summary
Whole-genome sequence (WGS) data are increasingly available on large numbers of individuals in animal and plant breeding and in human genetics through second-generation resequencing technologies, 1000 genomes projects, and large-scale genotype imputation from lower marker densities. Meuwissen et al Genet Sel Evol (2021) 53:19 numbers of individuals in animal and plant breeding, and in humans This is due to cost-effective second-generation resequencing technologies, in combination with 1000 genomes projects (e.g. for humans [1]; plants [2]; and livestock [3]). A method of genomic prediction that maintains higher accuracy when the training and target populations are not closely related is desirable Part of such a method would exploit high-density marker or whole-genome sequence (WGS) data because markers that are close to the causal variants, or the causal variants themselves, are included in the data [7]. To make effective use of such high-density markers, a method of variable selection is needed so that the causal variants or markers in high LD with them dominate the prediction
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