Abstract
Dominance may be an important source of non-additive genetic variance for many traits of dairy cattle. However, nearly all prediction models for dairy cattle have included only additive effects because of the limited number of cows with both genotypes and phenotypes. The role of dominance in the Holstein and Jersey breeds was investigated for eight traits: milk, fat, and protein yields; productive life; daughter pregnancy rate; somatic cell score; fat percent and protein percent. Additive and dominance variance components were estimated and then used to estimate additive and dominance effects of single nucleotide polymorphisms (SNPs). The predictive abilities of three models with both additive and dominance effects and a model with additive effects only were assessed using ten-fold cross-validation. One procedure estimated dominance values, and another estimated dominance deviations; calculation of the dominance relationship matrix was different for the two methods. The third approach enlarged the dataset by including cows with genotype probabilities derived using genotyped ancestors. For yield traits, dominance variance accounted for 5 and 7% of total variance for Holsteins and Jerseys, respectively; using dominance deviations resulted in smaller dominance and larger additive variance estimates. For non-yield traits, dominance variances were very small for both breeds. For yield traits, including additive and dominance effects fit the data better than including only additive effects; average correlations between estimated genetic effects and phenotypes showed that prediction accuracy increased when both effects rather than just additive effects were included. No corresponding gains in prediction ability were found for non-yield traits. Including cows with derived genotype probabilities from genotyped ancestors did not improve prediction accuracy. The largest additive effects were located on chromosome 14 near DGAT1 for yield traits for both breeds; those SNPs also showed the largest dominance effects for fat yield (both breeds) as well as for Holstein milk yield.
Highlights
Simulations and validation studies using real data have indicated that genomic selection can provide remarkably high accuracy of predicted breeding values (BV) of individuals without their own records or without progeny records [1], [2], which offers the opportunity to select individuals as parents of the generation accurately at an early stage of life
For both Holsteins and Jersey yield traits, MAD had lower additive heritabilities and higher dominance heritabilities than MAD2, but the sum of additive and dominance variances were similar for both models
Based on MAD and MAD2, dominance variance accounted for 5% and slightly less than 4%, respectively, of phenotypic variance for Holstein yield traits and 7% and 5.5% of Jersey yield traits
Summary
Simulations and validation studies using real data have indicated that genomic selection can provide remarkably high accuracy of predicted breeding values (BV) of individuals without their own records or without progeny records [1], [2], which offers the opportunity to select individuals as parents of the generation accurately at an early stage of life. The expression is naturally limited to females and estimated BV (EBV) or de-regressed EBV obtained from routine evaluations [10] are used as phenotypes in most applications of genomic selection Such data allow only the estimation of allele substitution effects, and distinguishing between additive and dominance effects is not possible. Sun et al [11] estimated dominance variance using only cows that had genotypes and phenotypes for milk yield in the U.S national database but did not test predictive ability for a model that included a dominance effect
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