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

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.

Genomic prediction using preselected DNA variants from a GWAS with whole-genome sequence data in Holstein–Friesian cattle

“…Also, bias was improved when tested in the independent population of French Holsteins. An extensive study using sequence data, and using across-breed QTL and genomic prediction was performed by van den Berg [41]. Using multibreed information, increases in reliability of up to 10% were found for all the breeds, but they were sensitive to the selection of variants and the model used.…”

Meta-analysis of sequence-based association studies across three cattle breeds reveals 25 QTL for fat and protein percentages in milk at nucleotide resolution

“…However, most associated variants resided in non-coding regions of the genome and a functional characterization of such variants was not attempted in our study. Nevertheless, including the trait-associated sequence variants of our meta-analysis in genomic predictions may improve the reliability of genomic predictions for dairy traits in cattle also for breeds other than BV, FV and HOL [46][47][48].…”

Multi-breed genomic prediction using Bayes R with sequence data and dropping variants with a small effect

“…Several stimulation studies [ 6 , 7 ] indicate that, rather than analysing all sequence variants together, preselecting variants that are close to the causative mutations can lead to increased prediction accuracy. In dairy cattle [ 8 , 9 ] and Drosophila [ 10 ], substantial increases in accuracy were obtained when several tens, hundreds or thousands variants were selected based on a genome-wide association study (GWAS) and used for prediction in addition to genome-wide SNPs.…”