Department of Statistics, University of Washington, Seattle, Washington 98195-4322.
Adobe Inc., San Jose, California 95110-2704.
G3 (Bethesda). 2019 May 7;9(5):1385-1391. doi: 10.1534/g3.119.0005.
For heritability estimation using a two-component random effects model, we provided formulas for the limiting distribution of the maximum likelihood estimate. These formulas are applicable even when the wrong measure of kinship is used to capture additive genetic correlation. When the model is correctly specified, we showed that the asymptotic sampling variance of heritability estimate is determined by both the study design and the extent of variation in the kinship measure that constitutes the additive genetic correlation matrix. When the correlation matrix is mis-specified, the extent of asymptotic bias depends additionally on how the fitted correlation matrix differs from the truth. In particular, we showed in a simulation study that estimating heritability using a population-based design and the classic GRM as the fitted correlation matrix can potentially contribute to the "missing heritability" problem.
对于使用两分量随机效应模型进行遗传力估计，我们提供了最大似然估计的极限分布的公式。即使使用错误的亲缘度量来捕捉加性遗传相关，这些公式也适用。当模型正确指定时，我们表明遗传力估计的渐近抽样方差取决于研究设计以及构成加性遗传相关矩阵的亲缘度量的变化程度。当相关矩阵被错误指定时，渐近偏差的程度还取决于拟合相关矩阵与真实情况的差异程度。特别是，我们在一项模拟研究中表明，使用基于人群的设计和经典 GRM 作为拟合相关矩阵来估计遗传力，可能会导致“遗传力缺失”问题。
