Functionally prioritised whole-genome sequence variants improve the accuracy ofgenomic prediction for heat tolerance

Abstract

Background Heat tolerance is a trait of economic importance in the context of warm climates and the effects of global warming on livestock, production, reproduction, health, and well-being. It is desirable to improve the prediction accuracy for heat tolerance to help accelerate the genetic gain for this trait. This study investigated the improvement in prediction accuracy for heat tolerance when selected sets of sequence variants from a large genome-wide association study (GWAS) were incorporated into a standard 50k SNP panel used by the industry. Methods Over 40,000 dairy cattle (Holsteins, Jersey, and crossbreds) with genotype and phenotype data were analysed. The phenotypes used to measure an individual’s heat tolerance were defined as the rate of milk production decline (slope traits for the yield of milk, fat, and protein) with a rising temperature-humidity index. We used Holstein and Jersey cows to select sequence variants linked to heat tolerance based on GWAS. We then investigated the accuracy of prediction when sets of these pre-selected sequence variants were added to the 50k industry SNP array used routinely for genomic evaluations in Australia. We used a bull reference set to develop the genomic prediction equations and then validated them in an independent set of Holsteins, Jersey, and crossbred cows. The genomic prediction analyses were performed using BayesR and BayesRC methods. Results The accuracy of genomic prediction for heat tolerance improved by up to 7%, 5%, and 10% in Holsteins, Jersey, and crossbred cows, respectively, when sets of selected sequence markers from Holsteins (i.e., single-breed QTL discovery set) were added to the 50k industry SNP panel. Using pre-selected sequence variants identified based on a combined set of Holstein and Jersey cows in a multi-breed QTL discovery, a set of 6,132 to 6,422 SNPs generally improved accuracy, especially in the Jersey validation set. Combining Holstein and Jersey bulls (multi-breed) in the reference set improved prediction accuracy compared to using only Holstein bulls in the reference set. Conclusion Informative sequence markers can be prioritised to improve the genetic prediction of heat tolerance in different breeds, and these variants, in addition to providing biological insight, have direct application in the development of customized SNP arrays or can be utilised via imputation into current SNP sets.