Multitrait genome-wide association best linear unbiased prediction of genetic values.

Background: The GWABLUP (Genome-Wide Association based Best Linear Unbiased Prediction) approach used GWA analysis results to differentially weigh the SNPs in genomic prediction, and was found to improve the reliabilities of genomic predictions. However, the proposed multitrait GWABLUP method assumed that the SNP weights were the same across the traits. Here we extended and validated the multitrait GWABLUP method towards using trait specific SNP weights.

Results: In a 3-trait dairy data set, multitrait GWAS estimates of SNP effects and their standard errors were translated into trait specific likelihood ratios for the SNPs having trait effects, and posterior probabilities using the GWABLUP approach. This produced trait specific prior (co)variance matrices for each SNP, which were applied in a SNP-BLUP model for genomic predictions, implemented in the APEX linear model suite. In a validation population, the trait specific SNP weights resulted in more reliable predictions for all three traits. Especially, for somatic cell count, which was hardly related to the other traits, the use of the same weights across all traits was harming genomic predictions. The use of trait specific SNP weights overcame this problem.

Conclusions: In multitrait GWABLUP analyses of ~ 30,000 reference population cows, trait specific SNP weights resulted in up to 13% more reliable genomic predictions than unweighted SNP-BLUP, and improved genomic predictions for all three studied traits.