Genomic analysis of dominance effects on milk production and conformation traits in Fleckvieh cattle
Genomic analysis of dominance effects on milk production and conformation traits in Fleckvieh cattle
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DOI：
10.1186/1297-9686-46-40
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发表时间：
2014-06-24
影响因子：
4.1
通讯作者：
Goetz, Kay-Uwe
中科院分区：
文献类型：
--
作者：
Ertl, Johann;Legarra, Andres;Goetz, Kay-Uwe
背景资料：基于谱系数据对奶牛显性方差的估计在不同性状之间差异很大，对于体型性状，其遗传方差占总遗传方差的50%，对于产奶性状，其遗传方差占总遗传方差的43%。利用牛SNP（单核苷酸多态性）基因型，显性方差可以在标记水平和动物水平上使用基因组显性效应关系矩阵进行估计。高密度基因分型Fleckvieh奶牛的产量偏差被用来评估交叉验证的准确性基因组预测与加性和显性模型。结果：利用1996头Fleckvieh奶牛的产量偏差，用加性和显性模型估计了9个产奶量和体型性状的方差分量，其方差分量占总遗传方差的3.3%~ 50.5%。REML和Gibbs抽样估计显示出良好的一致性。虽然显性方差的估计值的标准误是相当大的，牛奶，脂肪和蛋白质产量，体细胞评分和产奶力的显性方差的估计值显着差异0。预测育种值的交叉验证准确性较高，基因组模型比系谱模型。纳入显性效应并没有增加预测育种和总遗传值的准确性。用BLUP（最佳线性无偏预测）模型估计牛奶产量和蛋白质产量的加性和显性SNP效应，并用于计算推定后代的育种值和总遗传值的期望。选择总遗传值而不是育种值将导致更大的预期总遗传优势的后代，即14.8%的产奶量和27.8%的蛋白质产量和减少预期的加性遗传增益只有4.5%的产奶量和2.6%的蛋白质years.Conclusions：估计显性方差是显着的，为大多数分析性状。由于奶牛之间的小优势效应关系，个体优势偏差的预测是非常不准确的，在模型中包括优势并没有提高交叉验证研究中的预测准确性。利用显性方差在选择性交配是有前途的，并没有出现严重损害加性遗传增益。
Background: Estimates of dominance variance in dairy cattle based on pedigree data vary considerably across traits and amount to up to 50% of the total genetic variance for conformation traits and up to 43% for milk production traits. Using bovine SNP (single nucleotide polymorphism) genotypes, dominance variance can be estimated both at the marker level and at the animal level using genomic dominance effect relationship matrices. Yield deviations of high-density genotyped Fleckvieh cows were used to assess cross-validation accuracy of genomic predictions with additive and dominance models. The potential use of dominance variance in planned matings was also investigated.Results: Variance components of nine milk production and conformation traits were estimated with additive and dominance models using yield deviations of 1996 Fleckvieh cows and ranged from 3.3% to 50.5% of the total genetic variance. REML and Gibbs sampling estimates showed good concordance. Although standard errors of estimates of dominance variance were rather large, estimates of dominance variance for milk, fat and protein yields, somatic cell score and milkability were significantly different from 0. Cross-validation accuracy of predicted breeding values was higher with genomic models than with the pedigree model. Inclusion of dominance effects did not increase the accuracy of the predicted breeding and total genetic values. Additive and dominance SNP effects for milk yield and protein yield were estimated with a BLUP (best linear unbiased prediction) model and used to calculate expectations of breeding values and total genetic values for putative offspring. Selection on total genetic value instead of breeding value would result in a larger expected total genetic superiority in progeny, i.e. 14.8% for milk yield and 27.8% for protein yield and reduce the expected additive genetic gain only by 4.5% for milk yield and 2.6% for protein yield.Conclusions: Estimated dominance variance was substantial for most of the analyzed traits. Due to small dominance effect relationships between cows, predictions of individual dominance deviations were very inaccurate and including dominance in the model did not improve prediction accuracy in the cross-validation study. Exploitation of dominance variance in assortative matings was promising and did not appear to severely compromise additive genetic gain.