|SUN, CHUANYU - National Association Of Animal Breeders|
|O'CONNELL, JEFFREY - University Of Maryland|
Submitted to: PLOS ONE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/4/2014
Publication Date: 8/1/2014
Publication URL: http://handle.nal.usda.gov/10113/60896
Citation: Sun, C., Van Raden, P.M., Cole, J.B., O'Connell, J.R. 2014. Improvement of prediction ability for genomic selection of dairy cattle by including dominance effects. PLoS One. 9(8):e103934.
Interpretive Summary: Many cows in the United States now have both genotypes and phenotypes, allowing investigation of models that predict both additive and dominant genomic effects. Additive and dominance variance components were estimated for six traits and two cattle breeds, and then predictive abilities of three different models with both additive and dominance effects and a model with only additive effects were compared using ten-fold cross-validation. Dominance variance accounted for 5 and 7% of total variance for yield traits of 30,482 Holsteins and 8,321 Jerseys, respectively. Models with additive and dominance effects fit the data better than including only additive effects; average correlations between estimated genetic effects and phenotypes also showed that prediction accuracy increased when both effects were included. For both breeds, the largest additive and dominant effects were located near a major gene (DGAT1) for yield traits on chromosome 14. Models with dominance effects can increase the accuracy of predicted genomic breeding values and also allow predicting specific combining abilities for use in mating programs.
Technical Abstract: Dominance can 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 six traits: milk, fat, and protein yields; productive life; daughter pregnancy rate; and somatic cell score. 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 data set 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.