A chi-square test for detecting multiple joint genetic variants in genome-wide association studies

Abstract

As a result of genotyping technologies, genome-wide association studies (GWAS) have been widely used to identify genetic variants associated with common complex traits. While most GWAS have focused on associations with single genetic variants, the investigation of multiple joint genetic variants is essential for understanding genetic architecture of complex traits because common complex traits are associated with multiple genetic variants. However, it is not easy to conduct the multiple joint genetic variants analysis and to identify high order interactions using a number of genetic variants in GWAS. In this study, we propose a stepwise method based on the Chi-square test in order to identify causal joint multiple genetic variants in GWAS. Through simulation studies, we examine the properties of the stepwise method and then apply the proposed method to a GWA data for detecting joint multiple genetic variants for age-related macular degeneration.