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

Human Heredity
Qi YanNianjun Liu

Abstract

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 family data with dichotomous traits (F-SKAT). 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. 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 relat...Continue Reading

Citations

Jun 23, 2016·Scientific Reports·Qi YanWei Chen
Dec 7, 2018·Alzheimer's & Dementia : the Journal of the Alzheimer's Association·Xiaoling ZhangLindsay A Farrer
Feb 23, 2020·Neurogenetics·Tanya Ramdal TechloThomas Folkmann Hansen
Aug 31, 2020·Genetic Epidemiology·Asuman S Turkmen, Shili Lin
Dec 21, 2018·Neurology. Genetics·Gary W BeechamUNKNOWN Alzheimer's Disease Sequencing Project
Nov 14, 2018·Statistics in Biosciences·Clemontina A DavenportJung-Ying Tzeng
Apr 25, 2018·Annals of Clinical and Translational Neurology·Badri N VardarajanLi-San Wang

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