Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits
journal contributionposted on 09.05.2022, 01:08 authored by H Aliloo, Jennie PryceJennie Pryce, O Gonzalez-Recio, Benjamin CocksBenjamin Cocks, BJ Hayes
Background: Dominance effects may contribute to genetic variation of complex traits in dairy cattle, especially for traits closely related to fitness such as fertility. However, traditional genetic evaluations generally ignore dominance effects and consider additive genetic effects only. Availability of dense single nucleotide polymorphisms (SNPs) panels provides the opportunity to investigate the role of dominance in quantitative variation of complex traits at both the SNP and animal levels. Including dominance effects in the genomic evaluation of animals could also help to increase the accuracy of prediction of future phenotypes. In this study, we estimated additive and dominance variance components for fertility and milk production traits of genotyped Holstein and Jersey cows in Australia. The predictive abilities of a model that accounts for additive effects only (additive), and a model that accounts for both additive and dominance effects (additive + dominance) were compared in a fivefold cross-validation. Results: Estimates of the proportion of dominance variation relative to phenotypic variation that is captured by SNPs, for production traits, were up to 3.8 and 7.1 % in Holstein and Jersey cows, respectively, whereas, for fertility, they were equal to 1.2 % in Holstein and very close to zero in Jersey cows. We found that including dominance in the model was not consistently advantageous. Based on maximum likelihood ratio tests, the additive + dominance model fitted the data better than the additive model, for milk, fat and protein yields in both breeds. However, regarding the prediction of phenotypes assessed with fivefold cross-validation, including dominance effects in the model improved accuracy only for fat yield in Holstein cows. Regression coefficients of phenotypes on genetic values and mean squared errors of predictions showed that the predictive ability of the additive + dominance model was superior to that of the additive model for some of the traits. Conclusions: In both breeds, dominance effects were significant (P < 0.01) for all milk production traits but not for fertility. Accuracy of prediction of phenotypes was slightly increased by including dominance effects in the genomic evaluation model. Thus, it can help to better identify highly performing individuals and be useful for culling decisions.
The authors would like to thank Dairy Futures Cooperative Research Centre (DFCRC, Melbourne, Australia) for funding this research and Australian Dairy Herd Improvement Scheme (ADHIS, Melbourne, Australia) for providing the phenotypes used in this study. In addition, we would like to acknowledge Dr Mekonnen Haile-Mariam (Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources, AgriBio, 5 Ring Road, Bundoora, VIC. 3083, Australia) for his advice on the computational aspects of this research.
JournalGenetics Selection Evolution
Pagination11p. (p. 1-11)
Rights Statement© 2016 Aliloo et al. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Science & TechnologyLife Sciences & BiomedicineAgriculture, Dairy & Animal ScienceGenetics & HeredityAgricultureNONADDITIVE GENETIC-VARIATIONINBREEDING DEPRESSIONMETHOD-RVARIANCEHOLSTEINCATTLEPREDICTIONSSELECTIONYIELDAnimalsAustraliaBreedingCattleDairyingFemaleFertilityGenes, DominantGenomicsGenotypeLactationLikelihood FunctionsLipidsMaleMilkModels, GeneticPhenotypePolymorphism, Single NucleotidePregnancyQuantitative Trait, HeritableSelection, GeneticDairy & Animal Science