Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits

Abstract

BackgroundDominance 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.ResultsEstimates 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.ConclusionsIn 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.

Genomic evaluation by including dominance effects and inbreeding depression for purebred and crossbred performance with an application in pigs

“…If there is directional dominance causing inbreeding depression [29], then inbreeding should be considered in the genetic evaluation models [30]. Otherwise, using pedigree or marker data, estimates of genetic parameters are inflated [30, 31]. In Vitezica et al [22], genomic inbreeding was fitted as a covariate and, in the current study, we prove this reasoning by using a parametric genomic model, such as a GBLUP.…”

Genomic estimation of additive and dominance effects and impact of accounting for dominance on accuracy of genomic evaluation in sheep populations

“…Using genomic data, Sun, VanRaden, Cole, and O'Connell (2014) reported the ratio of dominance to phenotypic variance for 8 production traits in dairy cattle breeds between 5% and 7%. Aliloo, Pryce, Gonzalez-Recio, Cocks, and Hayes (2016) similarly reported a small dominance variance for milk and fertility traits in Australian Holstein and Jersey dairy cattle. However, Ertl et al (2014) reported a range of low to very high ratio of dominance to total genetic variance for 9 milk production and conformation traits in Fleckvieh cattle and between 3.3% and 50.5% of the total genetic variance.…”

Estimation of additive and non‐additive genetic effects for fertility and reproduction traits in North American Holstein cattle using genomic information

“…However, relatively few studies have focused on estimating non-additive genetic effects. Non-additive genetic effects have been ignored due to incomplete pedigrees, computational limitations and requirement of individual phenotypes [i.e., estimated breeding values (EBVs) cannot be used as pseudo-phenotypes] and because selection acts additively (Aliloo, Pryce, González-Recio, Cocks, & Hayes, 2016). Nevertheless, it is suggested that including non-additive genetic effects (epistatic and dominance effects) could improve the accuracy of GS in complex traits (Nazarian & Gezan, 2016;Ober et al, 2015;Sun et al, 2014).…”

“…The range of estimated contributions of non-additive genetic variance to total genetic variance differs across studies (Aliloo et al, 2016;Ertl et al, 2014;Fernandez, Legarra, Martinez, Sánchez, & Baselga, 2017;Nishio & Satoh, 2014;Palucci, Schaeffer, Miglior, & Osborne, 2007;Su, Christensen, Ostersen, Henryon, & Lund, 2012;Sun et al, 2014). Inconsistencies may be explained by trait definitions, the size of the data sets used in the analyses, estimation of non-additive genetic relationships between individuals (using pedigree or genomic information), the models used to estimate yield deviations, preselection of genotyped animals, breed and population-specific differences, and confounding effect between additive and dominance variances (Vitezica, Varona, & Legarra, 2013).…”