Shrinkage estimation of the realized relationship matrix.
Shrinkage estimation of the realized relationship matrix.
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DOI：
10.1534/g3.112.004259
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发表时间：
2012-11
期刊：
影响因子：
--
通讯作者：
Jannink JL
中科院分区：
文献类型：
--
作者：
Endelman JB;Jannink JL
加性关系矩阵在育种值的混合模型预测中起着重要作用。对于基因型矩阵X（列中的基因座），乘积XX′被广泛用作实现关系矩阵，但该矩阵的标度是模糊的。我们的第一个目标是得到一个适当的比例，使平均对角元素等于1+f，其中f是当前种群的近亲繁殖系数。其结果是一个公式，涉及的协方差矩阵的采样基因组位点，这必须估计与标记。我们的第二个目标是调查是否收缩估计的协方差矩阵可以提高育种值（GEBV）预测的准确性与低密度标记。使用相对于均方误差最佳的收缩强度的分析公式，模拟显示，收缩可以显着提高非结构化群体中的GEBV准确性，但仅适用于表型化品系;对于未表型化品系没有益处。收缩的精度增益随遗传力增加，但在高遗传力（> 0.6），这种好处是无关紧要的，因为表型精度是可比的。这些趋势在具有后代测试估计育种值的商业猪群体中得到证实。对于表型准确度为0.58的匿名性状，当使用来自60 K阵列的384个标记的随机组时，收缩将平均GEBV准确度从0.56增加到0.62（SE < 0.00）。我们的结论是，当中等精度的表型和低密度标记的基因组选择的候选人，关系矩阵的收缩估计可以提高遗传增益。
The additive relationship matrix plays an important role in mixed model prediction of breeding values. For genotype matrix X (loci in columns), the product XX′ is widely used as a realized relationship matrix, but the scaling of this matrix is ambiguous. Our first objective was to derive a proper scaling such that the mean diagonal element equals 1+f, where f is the inbreeding coefficient of the current population. The result is a formula involving the covariance matrix for sampling genomic loci, which must be estimated with markers. Our second objective was to investigate whether shrinkage estimation of this covariance matrix can improve the accuracy of breeding value (GEBV) predictions with low-density markers. Using an analytical formula for shrinkage intensity that is optimal with respect to mean-squared error, simulations revealed that shrinkage can significantly increase GEBV accuracy in unstructured populations, but only for phenotyped lines; there was no benefit for unphenotyped lines. The accuracy gain from shrinkage increased with heritability, but at high heritability (> 0.6) this benefit was irrelevant because phenotypic accuracy was comparable. These trends were confirmed in a commercial pig population with progeny-test-estimated breeding values. For an anonymous trait where phenotypic accuracy was 0.58, shrinkage increased the average GEBV accuracy from 0.56 to 0.62 (SE < 0.00) when using random sets of 384 markers from a 60K array. We conclude that when moderate-accuracy phenotypes and low-density markers are available for the candidates of genomic selection, shrinkage estimation of the relationship matrix can improve genetic gain.
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影响因子：
3.3
作者：
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通讯作者：
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DOI：
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发表时间：
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期刊：
G3 (Bethesda, Md.)
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