Supplementary Material for: A Sequence Kernel Association Test for Dichotomous Traits in Family Samples under a Generalized Linear Mixed Model

<b><i>Objective:</i></b> 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. <b><i>Methods:</i></b> 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 family data with dichotomous traits (F-SKAT). <b><i>Results:</i></b> Our simulation studies show that the original SKAT has inflated type I error rates when applied directly to family 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, which uses all family data. <b><i>Conclusion:</i></b> We propose a set-based association test that can be used to analyze family data with dichotomous phenotypes while handling genetic variants with the same or opposite directions of effects as well as any types of family relationships.

<b><i>研究目标：</i></b> 当前用于在家系样本中鉴定复杂疾病潜在多重罕见变异的方法检验效能不足。因此，本研究旨在开发一种全新的基于基因集的方法，用于家系样本中二分类性状的关联研究。<b><i>研究方法：</i></b> 本研究提出了一种基于广义线性混合模型（generalized linear mixed model）的家系样本遗传变异与疾病关联检验框架。所提方法依托核机器回归（kernel machine regression）构建，可作为序列核关联检验（sequence kernel association test，SKAT与famSKAT）的扩展方法，适配带有二分类性状的家系数据，本方法命名为F-SKAT。<b><i>研究结果：</i></b> 模拟研究结果表明，直接将原始SKAT应用于家系数据时，会出现一类错误率（type I error rate）膨胀的问题。与之相对，本研究提出的F-SKAT能够维持准确的一类错误率。此外，在所有考察的场景中，使用全部家系数据的F-SKAT，其检验效能均高于仅使用家系中非相关个体的SKAT，以及另一种同样使用全部家系数据的分析方法。<b><i>研究结论：</i></b> 本研究提出了一种基于基因集的关联检验方法，可用于分析带有二分类表型的家系数据，同时能够处理效应方向相同或相反的遗传变异，以及任意类型的家系亲缘关系。