The Genetic Correlation Between Traits Using Genome-Wide SNP Information

Genetic association between traits, and correlated responses resulting from those associations, are generally predicted using genetic correlations. However, depending on the distribution(s) of gene actions that underlie the genetic covariance asymmetry of correlated responses might occur (Bohren et al. 1966). Genome-wide association studies with denser marker genotypes might be useful to investigate the makeup of the genetic covariance between traits. Therefore, the objective was to investigate the makeup of the genetic covariances between milk production traits in more detail. Phenotypic records of 1737 heifers of research farms in four different countries were used after homogenizing and adjusting for management effects. All cows had a genotype for 37,590 SNPs. A Bayes Stochastic Search Variable Selection model was used to estimate the SNP effects. About 0.5 – 1.0% of the SNPs had a significant effect on one or more traits; however, the SNPs without a significant effect explained most of the genetic variances and covariances of the traits. Ten regions were found with an effect on multiple traits, in one of these regions the DGAT1 gene was previously reported with an effect on multiple traits. DGAT1 explained up to 41% of the variances of four traits and explained a major part of the correlation between fat yield and fat% and contributes to asymmetry in correlated response between fat yield and fat%. No further regions were found with a dominant effect on the covariance between traits. Based on our results, the genetic correlations modeled using the infinitesimal model, are expected to be good predictors of the genetic association between traits.