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Estimating dominance genetic variances for growth traits in American Angus males using genomic models.

TitleEstimating dominance genetic variances for growth traits in American Angus males using genomic models.
Publication TypeJournal Article
Year of Publication2020
AuthorsGarcia-Baccino, CA, Lourenco, DAL, Miller, S, Cantet, RJC, Vitezica, ZG
JournalJ Anim Sci
Volume98
Issue1
Date Published2020 Jan 01
ISSN1525-3163
KeywordsAnimals, Breeding, Cattle, Genes, Dominant, Genetic Variation, Genomics, Genotype, Male, Models, Genetic, Pedigree, Phenotype, Polymorphism, Single Nucleotide
Abstract

Estimates of dominance variance for growth traits in beef cattle based on pedigree data vary considerably across studies, and the proportion of genetic variance explained by dominance deviations remains largely unknown. The potential benefits of including nonadditive genetic effects in the genomic model combined with the increasing availability of large genomic data sets have recently renewed the interest in including nonadditive genetic effects in genomic evaluation models. The availability of genomic information enables the computation of covariance matrices of dominant genomic relationships among animals, similar to matrices of additive genomic relationships, and in a more straightforward manner than the pedigree-based dominance relationship matrix. Data from 19,357 genotyped American Angus males were used to estimate additive and dominant variance components for 3 growth traits: birth weight, weaning weight, and postweaning gain, and to evaluate the benefit of including dominance effects in beef cattle genomic evaluations. Variance components were estimated using 2 models: the first one included only additive effects (MG) and the second one included both additive and dominance effects (MGD). The dominance deviation variance ranged from 3% to 8% of the additive variance for all 3 traits. Gibbs sampling and REML estimates showed good concordance. Goodness of fit of the models was assessed by a likelihood ratio test. For all traits, MG fitted the data as well as MGD as assessed either by the likelihood ratio test or by the Akaike information criterion. Predictive ability of both models was assessed by cross-validation and did not improve when including dominance effects in the model. There was little evidence of nonadditive genetic variation for growth traits in the American Angus male population as only a small proportion of genetic variation was explained by nonadditive effects. A genomic model including the dominance effect did not improve the model fit. Consequently, including nonadditive effects in the genomic evaluation model is not beneficial for growth traits in the American Angus male population.

DOI10.1093/jas/skz384
Alternate JournalJ. Anim. Sci.
PubMed ID31867623
PubMed Central IDPMC6978891