Genomic prediction based on selected variants from imputed whole-genome sequence data in Australian sheep populations

Abstract

BackgroundWhole-genome sequence (WGS) data could contain information on genetic variants at or in high linkage disequilibrium with causative mutations that underlie the genetic variation of polygenic traits. Thus far, genomic prediction accuracy has shown limited increase when using such information in dairy cattle studies, in which one or few breeds with limited diversity predominate. The objective of our study was to evaluate the accuracy of genomic prediction in a multi-breed Australian sheep population of relatively less related target individuals, when using information on imputed WGS genotypes.MethodsBetween 9626 and 26,657 animals with phenotypes were available for nine economically important sheep production traits and all had WGS imputed genotypes. About 30% of the data were used to discover predictive single nucleotide polymorphism (SNPs) based on a genome-wide association study (GWAS) and the remaining data were used for training and validation of genomic prediction. Prediction accuracy using selected variants from imputed sequence data was compared to that using a standard array of 50k SNP genotypes, thereby comparing genomic best linear prediction (GBLUP) and Bayesian methods (BayesR/BayesRC). Accuracy of genomic prediction was evaluated in two independent populations that were each lowly related to the training set, one being purebred Merino and the other crossbred Border Leicester x Merino sheep.ResultsA substantial improvement in prediction accuracy was observed when selected sequence variants were fitted alongside 50k genotypes as a separate variance component in GBLUP (2GBLUP) or in Bayesian analysis as a separate category of SNPs (BayesRC). From an average accuracy of 0.27 in both validation sets for the 50k array, the average absolute increase in accuracy across traits with 2GBLUP was 0.083 and 0.073 for purebred and crossbred animals, respectively, whereas with BayesRC it was 0.102 and 0.087. The average gain in accuracy was smaller when selected sequence variants were treated in the same category as 50k SNPs. Very little improvement over 50k prediction was observed when using all WGS variants.ConclusionsAccuracy of genomic prediction in diverse sheep populations increased substantially by using variants selected from whole-genome sequence data based on an independent multi-breed GWAS, when compared to genomic prediction using standard 50K genotypes.

Use of gene expression and whole-genome sequence information to improve the accuracy of genomic prediction for carcass traits in Hanwoo cattle

“…To select the GWAS discovery dataset, the more genetically diverse animals were chosen, resulting in a lower degree of LD in the discovery set and therefore a higher resolution of the QTL regions identified. Previous studies have shown that SNPs that are located near QTL detected in multibreed datasets tend to be more useful in improving accuracy of prediction [8,45], and this is expected to be due to the lower LD that exists in multibreed datasets. In such a case, detection of QTL would require a higher marker density, but fewer markers would be in LD with the QTL across a set of more diverse individuals, hence allowing more precise mapping of the QTL.…”

“…In addition, a larger improvement in accuracy of prediction was observed for BFT (0.06) and CWT (0.04) when BayesR with pre-selected SNPs (BayesR-GWAS) was used. In sheep, the use of top SNPs in combination with BayesR increased the accuracy by 0.09 in Merino and 0.06 in crossbreds [8].…”

“…Brøndum et al [ 55 ] reported that the use of top-SNPs together with the 50 k SNP chip in a GBLUP model increased the accuracy of prediction by 5% for production traits in dairy cattle. In a study based on sheep data, the use of two matrices (50 k and significant variants from the GWAS) to estimate GBV led to an increase in accuracy of prediction by on average 6.2 and 4.1% for purebred and crossbred sheep, respectively [ 8 ].…”

“…To compare the different prediction methods with the selected SNPs, we decided to use a k-means strategy for selecting the validation and reference sets. This strategy decreased the relatedness between training and validation sets, which likely resulted in a greater benefit from using pre-selected SNPs from prediction [8] and therefore allowed a better comparison of methods. Moreover, in breeding programs, often the animals for which breeding values need to be predicted are relatively less related to the nucleus, and such a situation can be simulated with the k-means strategy.…”

“…In dairy cattle [4,6,7] and sheep [8], pre-selection of SNPs from whole-genome sequence data based on either candidate genes or genome-wide association studies (GWAS) have resulted in small to moderate improvements in accuracy of prediction. GWAS in Hanwoo have provided information about the genetic architecture of carcass traits [9][10][11][12][13][14] and sensory traits [15].…”