A Sequence Kernel Association Test for Dichotomous Traits in Family Samples under a Generalized Linear Mixed Model.
A Sequence Kernel Association Test for Dichotomous Traits in Family Samples under a Generalized Linear Mixed Model.
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作者：
Yan Q;Tiwari HK;Yi N;Gao G;Zhang K;Lin WY;Lou XY;Cui X;Liu N
现有的用于识别家庭样本中复杂疾病的多种罕见变异的方法不够有效。因此，我们的目标是开发一种新的基于集合的方法，用于家庭样本中二分性状的关联研究。我们引入了一个基于广义线性混合模型的框架，用于测试家庭样本中遗传变异与疾病的关联。我们提出的方法基于核机器回归，可以被视为序列核关联测试（SKAT 和 famSKAT）的扩展，适用于具有二分特征的家族数据（F-SKAT）。我们的模拟研究表明，当直接应用于家族数据时，原始 SKAT 会夸大 I 类错误率。相比之下，我们提出的 F-SKAT 具有正确的 I 类错误率。此外，在所有考虑的场景中，使用所有家庭数据的 F-SKAT 比仅使用家庭数据中无关个体的 SKAT 和使用所有家庭数据的另一种方法（缩写为 IL）具有更高的功效。我们提出了一种基于集合的关联测试，可用于分析具有二分表型的家族数据，同时处理具有相同或相反影响方向的遗传变异以及任何类型的家庭关系。
The existing methods for identifying multiple rare variants underlying complex diseases in family samples are underpowered. Therefore, we aim to develop a new set-based method for an association study of dichotomous traits in family samples. We introduce a framework for testing the association of genetic variants with diseases in family samples based on a generalized linear mixed model. Our proposed method is based on a kernel machine regression and can be viewed as an extension of the sequence kernel association test (SKAT and famSKAT) for application to familial data with dichotomous traits (F-SKAT). Our simulation studies show that the original SKAT has inflated Type I error rate when applied directly to familial data. By contrast, our proposed F-SKAT has the correct Type I error rate. Furthermore, in all of the considered scenarios, F-SKAT, which uses all family data, has higher power than both SKAT, which uses only unrelated individuals from the family data, and another method (abbreviated as IL) which uses all the family data. We propose a set-based association test that can be used to analyze familial data with dichotomous phenotypes, while handling genetic variants with the same or opposite directions of effects as well as any types of family relationships.