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

Abstract

Objective: 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. Methods: 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). Results: 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. Conclusion: 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.