Family-based gene-environment interaction using sequence kernel association test (FGE-SKAT) for complex quantitative traits

Chao-Yu Guo,Hsin-Chou Yang,Reng-Hong Wang

doi:10.1038/s41598-021-86871-2

Abstract

After the genome-wide association studies (GWAS) era, whole-genome sequencing is highly engaged in identifying the association of complex traits with rare variations. A score-based variance-component test has been proposed to identify common and rare genetic variants associated with complex traits while quickly adjusting for covariates. Such kernel score statistic allows for familial dependencies and adjusts for random confounding effects. However, the etiology of complex traits may involve the effects of genetic and environmental factors and the complex interactions between genes and the environment. Therefore, in this research, a novel method is proposed to detect gene and gene-environment interactions in a complex family-based association study with various correlated structures. We also developed an R function for the Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT), which is freely available as supplementary material for easy GWAS implementation to unveil such family-based joint effects. Simulation studies confirmed the validity of the new strategy and the superior statistical power. The FGE-SKAT was applied to the whole genome sequence data provided by Genetic Analysis Workshop 18 (GAW18) and discovered concordant and discordant regions compared to the methods without considering gene by environment interactions.

Highlights

After the genome-wide association s tudies[1,2,3,4,5,6], common genetic markers associated with complex diseases and quantitative traits have been successfully identified
Genome-wide association studies have focused on the genetic association of common variants with complex diseases
A rare variation is usually defined as the minor allele frequency (MAF) < 0.5%

Summary

Introduction

After the genome-wide association s tudies[1,2,3,4,5,6], common genetic markers associated with complex diseases and quantitative traits have been successfully identified. Robust and efficient statistical methods for the association between rare variants and complex diseases and traits are desired. A conventional association test uses one genetic marker at one time to identify common variations that are associated with a disease or trait. The genomic region-based assessment considers multiple variants and traits, such as the collapsing m ethod[10] and the sequence kernel association test (SKAT)[11], a flexible and efficient regression method for the associations between genomic regions and quantitative traits with consideration of covariates. If the subjects are correlated with family structures, the fast family-based SKAT (FFBSKAT) was developed to avoid invalid r esults[12,13]

Objectives

Methods

Results