Heritability is the proportion of phenotypic variance in a population that is attributable to individual genotypes. Heritability is considered an important measure in both evolutionary biology and in medicine, and is routinely estimated and reported in genetic epidemiology studies. In population-based genome-wide association studies (GWAS), mixed models are used to estimate variance components, from which a heritability estimate is obtained. The estimated heritability is the proportion of the model’s total variance that is due to the genetic relatedness matrix (kinship measured from genotypes). Current practice is to use bootstrapping, which is slow, or normal asymptotic approximation to estimate the precision of the heritability estimate; however, this approximation fails to hold near the boundaries of the parameter space or when the sample size is small. In this paper we propose to estimate variance components via a Haseman-Elston regression, find the asymptotic distribution of the variance components and proportions of variance, and use them to construct confidence intervals (CIs). Our method is further developed to obtain unbiased variance components estimators and construct CIs by meta-analyzing information from multiple studies. We demonstrate our approach on data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

中文翻译：

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通过 Haseman-Elston 回归的遗传性置信区间

遗传力是群体中可归因于个体基因型的表型变异的比例。遗传力被认为是进化生物学和医学中的重要衡量标准，并且在遗传流行病学研究中经常被估计和报告。在基于人群的全基因组关联研究 (GWAS) 中，混合模型用于估计方差分量，从中获得遗传力估计。估计的遗传力是由遗传相关矩阵（从基因型测量的亲属关系）引起的模型总方差的比例。当前的做法是使用自举（速度较慢）或正态渐近近似来估计遗传力估计的精度；然而，这种近似在参数空间的边界附近或样本量较小时无法成立。在本文中，我们建议通过 Haseman-Elston 回归估计方差分量，找到方差分量的渐近分布和方差比例，并使用它们来构建置信区间 (CI)。我们的方法得到进一步发展，以获得无偏方差分量估计量，并通过元分析来自多项研究的信息来构建 CI。我们展示了我们对来自西班牙裔社区健康研究/拉丁美洲人研究 (HCHS/SOL) 数据的方法。我们的方法得到进一步发展，以获得无偏方差分量估计量，并通过元分析来自多项研究的信息来构建 CI。我们展示了我们对来自西班牙裔社区健康研究/拉丁美洲人研究 (HCHS/SOL) 数据的方法。我们的方法得到进一步发展，以获得无偏方差分量估计量，并通过元分析来自多项研究的信息来构建 CI。我们展示了我们对来自西班牙裔社区健康研究/拉丁美洲人研究 (HCHS/SOL) 数据的方法。