On the use of whole-genome sequence data for across-breed genomic prediction and fine-scale mapping of QTL

Abstract

Background 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. 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. Methods The 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. Results The 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. Conclusions Across-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.

Genomic evaluation with multibreed and crossbred data

“…They concluded that the prediction reliabilities for one breed based on the reference population of another breed are low, and combining reference populations does not increase reliabilities over a single breed. Similar conclusions were reached in beef (Kachman et al, 2013), sheep (Moghaddar et al, 2014(Moghaddar et al, , 2019, and other dairy populations, even when using highly specialized techniques (Raymond et al, 2018;Karaman et al, 2021;Meuwissen et al, 2021). Some insight on why across-breed predictions are poor can be gained from a study by Steyn et al (2019), who simulated 5 different populations.…”

“…Using putative sequence information had a positive but still very small impact on across-breed reliabilities (Meuwissen et al, 2021). In the latter study, relatively large populations of Australian cattle were used to identify putative QTN and evaluate their effect on reliability within and across breeds.…”

Genomic prediction with whole-genome sequence data in intensely selected pig lines

“…This is a very active area of research and multiple novel methodologies have been proposed over the last years. Some examples are a combination of subsampling and Gibbs sampling [79], and a model that simultaneously fits a GBLUP term for a polygenic effect and a BayesC term for variants with large effects selected by the model (BayesGC) [26]. Testing alternative models and methods for genomic prediction was out of the scope of this report.…”

“…and a model that simultaneously fits a GBLUP term for a polygenic effect and a BayesC term for variants with large effects selected by the model (BayesGC) [26].…”

“…Other studies found small, and often unstable, improvements (e.g., from 1 to 5% or no improvement depending on prediction method [17][18][19], or trait-dependent results [19,20]). With genomic prediction across populations, the identification of causal variants from WGS can improve prediction accuracy [21][22][23][24], especially for numerically small populations or for populations that are not represented in the training [21,[23][24][25][26][27].…”