Increasing Predictive Ability Using Dominance in Genomic Selection

Abstract Text:

This study investigated the dominance for Holstein and Jersey milk, protein, and fat yields; somatic cell score, productive life, and daughter pregnancy rate. Additive and dominance variance components were estimated. Predictive abilities between three models with both additive and dominance effects (M_AD1, M_AD2 and M_AD3) and a model with additive effect only (M_A) were assessed using 10-fold cross-validation. The M_AD1 model estimated dominance values; M_AD2 estimated dominance deviations with a different dominance relationship matrix. M_AD3 enlarges dataset by including cows whose genotype probabilities were derived using genotyped ancestors. Dominance from M_AD1 accounted for 5 and 7% of total variance for Holstein and Jersey yield traits, respectively. Heritability estimates were lower for dominance and higher for additive effects with M_AD2 than with M_AD1. M_AD1 and M_AD2 increased prediction accuracy relative to the M_A model for yield traits. M_AD3model did not further improve prediction. 

Keywords:

prediction

dominance

genomic selection