Genomic correlation: harnessing the benefit of combining two unrelated populations for genomic selection.

Laercio R Porto-Neto,John M Henshall,Sean M Mcwilliam,Antonio Reverter,Sigrid A Lehnert,William Barendse

doi:10.1186/s12711-015-0162-0

Abstract

BackgroundThe success of genomic selection in animal breeding hinges on the availability of a large reference population on which genomic-based predictions of additive genetic or breeding values are built. Here, we explore the benefit of combining two unrelated populations into a single reference population.MethodsThe datasets consisted of 1829 Brahman and 1973 Tropical Composite cattle with measurements on five phenotypes relevant to tropical adaptation and genotypes for 71,726 genome-wide single nucleotide polymorphisms (SNPs). The underlying genomic correlation for the same phenotype across the two breeds was explored on the basis of consistent linkage disequilibrium (LD) phase and marker effects in both breeds.ResultsThe proportion of genetic variance explained by the entire set of SNPs ranged from 37.5 to 57.6 %. Estimated genomic correlations were drastically affected by the process used to select SNPs and went from near 0 to more than 0.80 for most traits when using the set of SNPs with significant effects and the same LD phase in the two breeds. We found that, by carefully selecting the subset of SNPs, the missing heritability can be largely recovered and accuracies in genomic predictions can be improved six-fold. However, the increases in accuracy might come at the expense of large biases.ConclusionsOur results offer hope for the effective implementation of genomic selection schemes in situations where the number of breeds is large, the sample size within any single breed is small and the breeding objective includes many phenotypes.

Highlights

The success of genomic selection in animal breeding hinges on the availability of a large reference population on which genomic-based predictions of additive genetic or breeding values are built
When large reference populations are not available for a particular breed, it has been proposed that the combined data from different breeds be used to generate genomic breeding values in order to increase the effective size of the reference population [4,5,6]
We showed that building a genomic relationship matrix (GRM) by using a carefully selected set of single nucleotide polymorphisms (SNPs), either with the same or different linkage disequilibrium (LD) phases between SNP alleles and the quantitative trait locus (QTL), and with SNP effects on the extremes of the distribution, resulted in partial recovery of the missing heritability

Summary

Introduction

The success of genomic selection in animal breeding hinges on the availability of a large reference population on which genomic-based predictions of additive genetic or breeding values are built. To improve the accuracy of genomic selection, large reference populations are usually recommended for estimating genome-based predictions of additive genetic effects or breeding values [1,2,3]. Since genomic prediction that uses genome-wide single nucleotide polymorphisms (SNPs) is almost entirely based on linkage disequilibrium (LD) between the SNP alleles and the causative allele(s) at the quantitative trait locus (QTL), combining data with inconsistent phase relationships between the SNP alleles and the causative alleles will serve to destroy evidence of associations. It should be noted that while consistency of phase of allele effects is aligned with a positive genetic correlation between phenotypes of the two breeds and might be seen as a proxy for positive genetic correlation, it is not equivalent to a positive genetic correlation

Methods

Results

Discussion

Conclusion