The accuracy of genomic prediction for mink was compared for single-trait and multiple-trait GBLUP models and Bayesian models that allowed for heterogeneous (co)variance structure over the genome. The mink population consisted of 2,103 brown minks genotyped with the method of genotyping by sequencing. Four live grading traits and four traits on dried pelts for size and quality were analysed. GWAS analysis detected significant SNPs for all the traits. The single-trait Bayesian model resulted in higher accuracies for the genomic predictions than the single-trait GBLUP model, especially for the traits measured on dried pelts. We expected the multiple-trait models to be superior to the single trait models since the multiple-trait model can make use of information when traits are correlated. However, we did not find a general improvement in accuracies with the multiple-trait models compared to the single-trait models. Keywords: GWAS, GBLUP, BayesAS, heterogeneous (co)variances

Trine Villumsen, Guosheng Su, Zexi Cai, Bernt Guldbrandtsen, Torben Asp, Goutam Sahana, Mogens S Lund

Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Methods and Tools - Prediction 2, , 618, 2018
Download Full PDF BibTEX Citation Endnote Citation Search the Proceedings

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.