Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits.
Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits.
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DOI：
10.1186/s12711-016-0186-0
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发表时间：
2016-02-01
期刊：
影响因子：
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通讯作者：
Hayes BJ
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文献类型：
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作者：
Aliloo H;Pryce JE;González-Recio O;Cocks BG;Hayes BJ
显性效应可能导致奶牛复杂性状的遗传变异，特别是与适合度密切相关的性状，如繁殖力。然而，传统的遗传评价一般忽略显性效应，只考虑加性遗传效应。密集单核苷酸多态(SNPs)图谱的建立为在SNP和动物水平上研究显性在复杂性状数量变异中的作用提供了机会。在动物基因组评估中纳入显性效应也有助于提高对未来表型预测的准确性。在这项研究中，我们估计了澳大利亚荷斯坦和泽西奶牛的繁殖力和产奶量性状的加性和显性方差分量。通过五次交叉验证，比较了只考虑加性效应(加性)的模型和既考虑加性效应又考虑显性效应(加性效应+显性效应)的模型的预测能力。在荷斯坦奶牛和泽西奶牛中，SNPs捕获的生产性状的显性变异相对于表型变异的比例估计分别高达3.8%和7.1%，而在繁殖力方面，它们在荷斯坦奶牛中等于1.2%，在泽西奶牛中非常接近于零。我们发现，在模型中包含主导地位并不总是有利的。基于最大似然比检验，两个品种的牛奶、脂肪和蛋白质产量的加性+加性显性模型比加性模型更好地拟合了数据。然而，关于通过五次交叉验证评估的表型预测，包括模型中的显性效应，提高了仅对荷斯坦奶牛产脂量的准确性。表型对遗传值和预测均方误差的回归系数表明，加性+显性模型对部分性状的预测能力优于加性模型。在两个品种中，显性效应对所有产奶量性状都有显著影响(P<0.01)，但对繁殖力没有显著影响。通过在基因组评估模型中加入显性效应，表型预测的准确性略有提高。因此，它可以帮助更好地识别表现优异的个体，并对淘汰决策有用。
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. 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. 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.