Genomic prediction for numerically small breeds, using models with pre-selected and differentially weighted markers

Biaty Raymond,Roel F Veerkamp,Yvonne C J Wientjes,Chris Schrooten,Aniek C Bouwman,Jeanine Houwing-Duistermaat

doi:10.1186/s12711-018-0419-5

Abstract

BackgroundGenomic prediction (GP) accuracy in numerically small breeds is limited by the small size of the reference population. Our objective was to test a multi-breed multiple genomic relationship matrices (GRM) GP model (MBMG) that weighs pre-selected markers separately, uses the remaining markers to explain the remaining genetic variance that can be explained by markers, and weighs information of breeds in the reference population by their genetic correlation with the validation breed.MethodsGenotype and phenotype data were used on 595 Jersey bulls from New Zealand and 5503 Holstein bulls from the Netherlands, all with deregressed proofs for stature. Different sets of markers were used, containing either pre-selected markers from a meta-genome-wide association analysis on stature, remaining markers or both. We implemented a multi-breed bivariate GREML model in which we fitted either a single multi-breed GRM (MBSG), or two distinct multi-breed GRM (MBMG), one made with pre-selected markers and the other with remaining markers. Accuracies of predicting stature for Jersey individuals using the multi-breed models (Holstein and Jersey combined reference population) was compared to those obtained using either the Jersey (within-breed) or Holstein (across-breed) reference population. All the models were subsequently fitted in the analysis of simulated phenotypes, with a simulated genetic correlation between breeds of 1, 0.5, and 0.25.ResultsThe MBMG model always gave better prediction accuracies for stature compared to MBSG, within-, and across-breed GP models. For example, with MBSG, accuracies obtained by fitting 48,912 unselected markers (0.43), 357 pre-selected markers (0.38) or a combination of both (0.43), were lower than accuracies obtained by fitting pre-selected and unselected markers in separate GRM in MBMG (0.49). This improvement was further confirmed by results from a simulation study, with MBMG performing on average 23% better than MBSG with all markers fitted.ConclusionsWith the MBMG model, it is possible to use information from numerically large breeds to improve prediction accuracy of numerically small breeds. The superiority of MBMG is mainly due to its ability to use information on pre-selected markers, explain the remaining genetic variance and weigh information from a different breed by the genetic correlation between breeds.

Highlights

Genomic prediction (GP) accuracy in numerically small breeds is limited by the small size of the refer‐ ence population
Estimated genetic parameters In the analysis using deregressed proofs (DRP) for stature, we estimated h2DRP for both Dutch Holstein bulls (DH) and New Zealand Jersey bulls (NZJ) using the MBSG and MBMG models, which in this case reflect the proportion of the explained variance of DRP
When the genomic relationship matrices (GRM) based on the TOP 133 single nucleotide polymorphisms (SNPs) was fitted in MBSG, estimated h2DRP were low, i.e. 0.26 and 0.27 for DH and NZJ, respectively

Summary

Introduction

Genomic prediction (GP) accuracy in numerically small breeds is limited by the small size of the refer‐ ence population. The accuracy of genomic prediction (GP) depends on the size of the reference population. Accuracy of GP is limited in numerically small populations [1, 2]. In practice, it has been shown that across-breed GP does not result in significant improvement in prediction accuracy, as compared with within-breed GP [3,4,5,6]. Across-breed GP can result even in negative prediction accuracies [3, 4]. One of the suggested reasons for poor prediction accuracies across breeds is that breeds differ in patterns of linkage disequilibrium (LD) between quantitative trait loci (QTL) and markers. In contrast to these expectations, Van den Berg et al [9] and Raymond et al [10] showed that increasing the density of markers up to whole-genome sequence does not improve accuracy of GP across breeds

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Conclusion