Estimating total narrow-sense heritability in admixed populations remains an open question. In this work, we used extensive simulations to evaluate existing linear mixed-model frameworks for estimating total narrow-sense heritability in two population-based cohorts from Greenland, and compared the results with data from unadmixed individuals from Denmark. When our analysis focused on Greenlandic sib pairs, and under the assumption that shared environment among siblings has a negligible effect, the model with two relationship matrices, one capturing identity by descent and one capturing identity by state, returned heritability estimates close to the true simulated value, while using each of the two matrices alone led to downward biases. When phenotypes correlated with ancestry, heritability estimates were inflated. Based on these observations, we propose a PCA-based adjustment that recovers the true simulated heritability. We use this knowledge to estimate the heritability of ten quantitative traits from the two Greenlandic cohorts, and report differences such as lower heritability for height in Greenlanders compared with Europeans. In conclusion, narrow-sense heritability in admixed populations is best estimated when using a mixture of genetic relationship matrices on individuals with at least one first-degree relative included in the sample.

中文翻译：

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使用来自混合人群的家庭数据估计狭义遗传力

估计混合人群的总狭义遗传力仍然是一个悬而未决的问题。在这项工作中，我们使用广泛的模拟来评估现有的线性混合模型框架，以估计来自格陵兰岛的两个基于人群的队列的总狭义遗传力，并将结果与​​来自丹麦的未混合个体的数据进行比较。当我们的分析集中在格陵兰同胞对时，并假设兄弟姐妹之间共享环境的影响可以忽略不计，具有两个关系矩阵的模型，一个通过血统捕获身份，一个通过状态捕获身份，返回的遗传力估计值接近真实模拟值，而单独使用两个矩阵中的每一个都会导致向下偏差。当表型与祖先相关时，遗传力估计值被夸大了。基于这些观察，我们提出了一种基于 PCA 的调整，以恢复真实的模拟遗传力。我们使用这些知识来估计来自两个格陵兰人队列的 10 个数量性状的遗传力，并报告了格陵兰人与欧洲人相比身高遗传力较低等差异。总之，当对样本中至少包含一个一级亲属的个体使用遗传关系矩阵的混合时，最好估计混合种群的狭义遗传力。