Sequence variants selected from a multi-breed GWAS can improve the reliability of genomic predictions in dairy cattle

Irene Van Den Berg,Didier Boichard,Mogens S Lund

doi:10.1186/s12711-016-0259-0

Abstract

BackgroundSequence data can potentially increase the reliability of genomic predictions, because such data include causative mutations instead of relying on linkage disequilibrium (LD) between causative mutations and prediction variants. However, the location of the causative mutations is not known, and the presence of many variants that are in low LD with the causative mutations may reduce prediction reliability. Our objective was to investigate whether the use of variants at quantitative trait loci (QTL) that are identified in a multi-breed genome-wide association study (GWAS) for milk, fat and protein yield would increase the reliability of within- and multi-breed genomic predictions in Holstein, Jersey and Danish Red cattle. A wide range of scenarios that test different strategies to select prediction markers, for both within-breed and multi-breed prediction, were compared.ResultsFor all breeds and traits, the use of variants selected from a multi-breed GWAS resulted in substantial increases in prediction reliabilities compared to within-breed prediction using a 50 K SNP array. Reliabilities depended highly on the choice of the prediction markers, and the scenario that led to the highest reliability varied between breeds and traits. While genomic correlations across breeds were low for genome-wide sequence variants, the effects of the QTL variants that yielded the highest reliabilities were highly correlated across breeds.ConclusionsOur results show that the use of sequence variants, which are located near peaks of QTL that are detected in a multi-breed GWAS, can increase reliability of genomic predictions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0259-0) contains supplementary material, which is available to authorized users.

Highlights

Sequence data can potentially increase the reliability of genomic predictions, because such data include causative mutations instead of relying on linkage disequilibrium (LD) between causative mutations and prediction variants
Our objective was to investigate whether the use of variants at quantitative trait loci (QTL) that are selected from a multi-breed genome-wide association study (GWAS) for milk, fat and protein yields would increase the reliability of within- and multi-breed genomic predictions in three dairy cattle breeds that range from very related populations to unrelated breeds
Prediction reliability increased substantially for all breeds and traits when sequence variants selected from a GWAS were used for genomic prediction

Summary

Introduction

Sequence data can potentially increase the reliability of genomic predictions, because such data include causative mutations instead of relying on linkage disequilibrium (LD) between causative mutations and prediction variants. Our objective was to investigate whether the use of variants at quantitative trait loci (QTL) that are identified in a multi-breed genome-wide association study (GWAS) for milk, fat and protein yield would increase the reliability of within- and multi-breed genomic predictions in Holstein, Jersey and Danish Red cattle. In a simulation study [20], reliability of genomic predictions decreased faster across breeds than within breeds as the distance between prediction variants and causative mutations increased. The true causative mutations are unknown, with a few exceptions [21], a large number of quantitative trait loci (QTL) regions have been detected in dairy cattle [22,23,24,25,26,27], and this information could be used to select sequence variants for genomic prediction. Multi-breed GWAS results in more precise QTL mapping for variants that are shared across breeds [11, 28, 29]

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