Functionally prioritised whole-genome sequence variants improve the accuracy of genomic 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. 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 combined with a standard 50k single nucleotide polymorphism (SNP) panel used by the dairy industry.

Methods: Over 40,000 dairy cattle with genotype and phenotype data were analysed. The phenotypes used to measure an individual's heat tolerance were defined as the rate of decline in milk production traits with rising temperature and humidity. We used Holstein and Jersey cows to select sequence variants linked to heat tolerance. The prioritised sequence variants were the most significant SNPs passing a GWAS p-value threshold selected based on sliding 100-kb windows along each chromosome. We used a bull reference set to develop the genomic prediction equations, which were then validated in an independent set of Holstein, Jersey, and crossbred cows. Prediction analyses were performed using the BayesR, BayesRC, and GBLUP methods.

Results: The accuracy of genomic prediction for heat tolerance improved by up to 0.07, 0.05, and 0.10 units in Holstein, Jersey, and crossbred cows, respectively, when sets of selected sequence markers from Holstein cows were added to the 50k SNP panel. However, in some scenarios, the prediction accuracy decreased unexpectedly with the largest drop of - 0.10 units for the heat tolerance fat yield trait observed in Jersey cows when 50k plus pre-selected SNPs from Holstein cows were used. Using pre-selected SNPs discovered on a combined set of Holstein and Jersey cows generally improved the accuracy, especially in the Jersey validation. In addition, combining Holstein and Jersey bulls in the reference set generally improved prediction accuracy in most scenarios compared to using only Holstein bulls as the reference set.

Conclusions: Informative sequence markers can be prioritised to improve the genomic prediction of heat tolerance in different breeds. In addition to providing biological insight, these variants could also have a direct application for developing customized SNP arrays or can be used via imputation in current industry SNP panels.

Fig. 1

Overview of the analyses with the three study scenarios. ‘Scenario 1’: the QTL discovery set was comprised of a subset of 20,623 older Holstein cows (born in 2012 or earlier); the reference set included only Holstein bulls (N = 3323) that were not sires of cows in the discovery set; validation sets included Holsteins, Jersey, and crossbred cows. ‘Scenario 2’: the QTL discovery set was comprised of a combined set of Holsteins (N = 20,623) and Jersey cows (N = 5143); the reference set included only Holstein bulls (N = 3323; as described for “Scenario 1”) that were not sires of the Holstein cows in the discovery set; validation sets included Holstein (N = 1223), Jersey (N = 6338) and crossbred (N = 790) cows. ‘Scenario 3’: the QTL discovery set included only Holstein cows (N = 20,623; as described for “Scenario 1”); the reference set included a combined set of Holsteins (N = 3323) and Jersey (N = 852) bulls); validation sets included Holstein (N = 1223), Jersey (N = 431) and crossbred (N = 790) cows

Fig. 2

Accuracy of genomic predictions (Holstein only reference) using either 50k SNP data (colored grey) or 50k + a range of ‘top SNPs’ sets (selected from the Holstein QTL discovery set). The ‘top SNPs’ were selected from single-trait GWAS (colored blue) and multi-trait meta-analysis (colored orange) at a less stringent cut-off threshold of − log10(p-value) ≥ 2 [~ 9000 SNPs] and at a more stringent p-value of − log10(p-value) ≥ 3 [~ 2000 SNPs]. Accuracy of predictions are provided for three cow validation sets: a Holsteins, N = 1223), b Jersey, N = 6338), and c Holstein–Jersey crossbreds, N = 790). The traits analysed are heat tolerance milk (HTMYslope), fat (HTFYslope), and protein (HTPYslope) yield slopes. The genomic predictions were generated using either BayesR (50k SNP set) or BayesRC (50k + top SNPs). Vertical lines represent the standard errors calculated from two random validation subsets

Fig. 3

Accuracy and dispersion bias in Holstein (N = 1223), Jersey (N = 1195) and crossbred (N = 790) cows when using 50k + ‘top SNPs’ selected from the multi-breed (Holstein + Jersey) QTL discovery set. Holstein bulls (N = 3323) were used as the reference set for genomic predictions. The ‘top SNPs’ were selected based on a single-trait GWAS cut-off of [− log10(p-value) ≥ 2]. The traits analysed are heat tolerance milk (HTMYslope), fat (HTFYslope), and protein (HTPYslope) yield. Vertical lines represent the standard errors calculated from two random validation subsets

Fig. 4

Accuracy and bias of genomic predictions in Holstein (N = 1223), Jersey (N = 431) and crossbred (N = 790) cows when using the multi-breed reference set (Holstein and Jersey bulls; N = 4175). The selected ‘top SNPs’ used in the BayesRC were from the Holstein cow discovery set (N = 20,623) based on the single-trait GWAS cut-off of [− log10(p-value) ≥ 2]. The traits analysed are heat tolerance milk (HTMYslope), fat (HTFYslope), and protein (HTPYslope) yield slopes. Vertical lines represent the standard errors calculated from two random validation subsets