Improved Variance Component Score Tests of Gene-Environment Interactions

• Activity Number: 489 - Methods Development for Mediation and Interaction in Post-GWAS Data
• Type: Contributed
• Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): NANXUN MA* and Michael C. Wu and Jing Ma
• Companies: University of Washington and Fred Hutchinson Cancer Research Center and Fred Hutch Cancer Research Center
• Keywords: Kernel Machine; Bias Correction; Ridge Regression; Gene-environment Interaction; Variance Component Test

Abstract

Variance component (VC) based score tests are a popular and powerful class of methods for assessing interactions between groups of genomic markers and environmental exposures. However, many existing methods tend to give inflated type I errors due to challenges in estimating the null model which contains main effects for the genomic markers. Least squares based estimation fails especially when the number of genetic markers increases, and alternative ridge regression gives biased estimates due to the penalty function. To overcome these difficulties, we propose an improved VC based score test that estimates the main effects under the null hypothesis using bias­corrected ridge regression. To construct the test statistic, we adapt the classical kernel association test under a linear kernel but use a novel empirical corrected projection matrices corresponding to the bias­corrected ridge regression. We also give p­values from asymptotic mixture chi­ squared distribution. Simulations and real data analysis demonstrate that proposed bias­corrected interaction tests improve the type I error control compared with current methods, while still maintaining power.