Improvement of Prediction Ability for Genomic Selection of Dairy Cattle by Including Dominance Effects

• Published in: PloS one 2014-08, Vol.9 (8), p.e103934-e103934
• Main Authors: Sun, Chuanyu, VanRaden, Paul M, Cole, John B, O'Connell, Jeffrey R
• Format: Article
• Language: English
• Subjects: additive effect; Agricultural production; Agriculture; Analysis; ancestry; Animal lactation; Animals; Autosomal dominant inheritance; Biology and Life Sciences; Bovidae; Cattle; Cattle - genetics; Chromosome 14; Chromosomes; Chromosomes, Mammalian - genetics; cows; Dairy cattle; Dairying; data collection; Dominance; dominance (genetics); Female; Genetic diversity; Genetic effects; Genetic Markers - genetics; Genetic variance; Genetics; Genome; Genomes; Genomics; genotype; Genotype & phenotype; Genotypes; Holstein; Hybrid Vigor - genetics; Inbreeding; Inheritance Patterns - genetics; Jersey; Laboratories; Likelihood Functions; Lipids - analysis; Male; marker-assisted selection; Mathematical models; Milk; Milk - chemistry; milk fat yield; Milk production; milk protein yield; Milk Proteins - analysis; Models, Genetic; Phenotype; Polymorphism, Single Nucleotide - genetics; Population; prediction; Prediction models; Pregnancy; pregnancy rate; Proteins; Quantitative Trait, Heritable; Selection, Genetic; Single nucleotide polymorphisms; Single-nucleotide polymorphism; somatic cells; Variance; Yield
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0103934

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.