Genomic evaluation of binary traits in dairy cattle by considering genotype × environment interactions

Abstract

Aim of study: To assess genotype by environment (G×E) interaction via single- and multi-trait animal models for binary traits in dairy cattle. Area of study: University of Tabriz, Tabriz, Iran. Material and methods: Phenotypic and genomic data were simulated considering a binary trait in four environments as different correlated traits. Heritabilities of 0.05, 0.10, 0.15, and 0.20 were considered to mimic the genetic variation of the binary trait in different environments. Eight scenarios resulted from combining the number of QTLs (60 or 300), LD level (high or low), and incidence of the binary trait (10% or 30%) were simulated to compare the accuracy of predictions. For all scenarios, 1667 markers per chromosome (depicting a 50K SNP chip) were randomly spaced over 30 chromosomes. Multi-trait animal models were applied to take account of G×E interaction and to predict the genomic breeding value in different environments. Prediction accuracies obtained from the single- and multi-trait animal models were compared. Main results: In the models with G×E interaction, the largest accuracy of 0.401 was obtained in high LD scenario with 60 QTLs, and incidence of 30% for the fourth environment. The lowest accuracy of 0.190 was achieved in low LD scenario with 300 QTLs and incidence of 10% for the first environment. Research highlights: Genomic selection with high prediction accuracy can be possible by considering the G×E interaction during the genetic improvement programs in dairy cattle