Impacts of both reference population size and inclusion of a residual polygenic effect on the accuracy of genomic prediction

Zengting Liu 1 Franz R Seefried 1 Friedrich Reinhardt 1 Stephan Rensing 1 Georg Thaller 0 Reinhard Reents 1 0 Christian-Albert- University, Institute of Animal Breeding and Husbandry , 24908 Kiel , Germany 1 vit w.V. , Heideweg 1, 27283 Verden/Aller , Germany Background: The purpose of this work was to study the impact of both the size of genomic reference populations and the inclusion of a residual polygenic effect on dairy cattle genetic evaluations enhanced with genomic information. Methods: Direct genomic values were estimated for German Holstein cattle with a genomic BLUP model including a residual polygenic effect. A total of 17,429 genotyped Holstein bulls were evaluated using the phenotypes of 44 traits. The Interbull genomic validation test was implemented to investigate how the inclusion of a residual polygenic effect impacted genomic estimated breeding values. Results: As the number of reference bulls increased, both the variance of the estimates of single nucleotide polymorphism effects and the reliability of the direct genomic values of selection candidates increased. Fitting a residual polygenic effect in the model resulted in less biased genome-enhanced breeding values and decreased the correlation between direct genomic values and estimated breeding values of sires in the reference population. Conclusions: Genetic evaluation of dairy cattle enhanced with genomic information is highly effective in increasing reliability, as well as using large genomic reference populations. We found that fitting a residual polygenic effect reduced the bias in genome-enhanced breeding values, decreased the correlation between direct genomic values and sire's estimated breeding values and made genome-enhanced breeding values more consistent in mean and variance as is the case for pedigree-based estimated breeding values. - Background With the availability of the bovine genome sequence and the development of high-density arrays of single nucleotide polymorphism (SNP) markers, the accuracy of genetic predictions has improved compared to conventional breeding value estimations based on phenotypic data and pedigree [1-9]. In order to model genetic variation for quantitative traits, Meuwissen et al. [10] have proposed a genetic evaluation model that includes a large number of SNP markers simultaneously. This genomic model assumes that, all the loci that affect the trait are in linkage disequilibrium (LD) with at least one SNP marker and thus marker genotypes can be used as predictors for breeding values. A main advantage of the availability of * Correspondence: 1vit w.V., Heideweg 1, 27283 Verden/Aller, Germany Full list of author information is available at the end of the article genome-enhanced breeding values (GEBV) in dairy cattle comes from the improved accuracy in pre-selecting animals for breeding. Therefore, more and more countries have been implementing genomic evaluations in dairy cattle breeding. The genomic BLUP model, which has been used to include high-density SNP data in most of the dairy cattle applications [11-17], assumes that all SNP contribute equally to the genetic variance, because field data results support the infinitesimal model [11,15,18]. The reliability of genomic predictions strongly depends on the number of genotyped bulls in the reference population that is used to estimate SNP effects [15,18]. The increase in genomic reliability appears to be approximately linearly correlated with the number of reference bulls [15]. However, little is known on how the size of reference populations impacts the estimation of SNP effects. A German national genomic dataset has been used to study this question. Genomic models [10,15-17,19] usually assume that a given SNP marker chip, such as the Illumina Bovine54K (Illumina Inc., San Diego, CA), explains all the genetic variation of a trait, and as a consequence no residual polygenic effect (RPG) is typically fitted in genomic prediction [10,15-17,19]. Fitting the RPG effect can account for the fact that SNP markers may not explain all the genetic variance [13,20,21]. Including the RPG effect in the genomic model can also render the estimates of SNP effect less biased and more persistent over generations [22]. To investigate the impact of including an RPG effect on genomic prediction, a larger dataset from the EuroGenomics reference population [18] was used. The objectives of this study were to investigate (1) the impact of the size of a genomic reference population using German reference bulls on the estimation of SNP effects and on direct genomic values (DGV) and (2) the impact of including an RPG effect on the accuracy of genomic prediction using EuroGenomics reference bulls. Methods German national genomic and phenotypic data Holstein bulls from the German national genomic reference population originating partially from the national genome project GenoTrack and partially from routinely genotyped populations, were genotyped using the Illumina Bov (...truncated)