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 propos

SUBMITTER: Yan Q 

PROVIDER: S-EPMC4825859 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature