Set-Based Gene-Environment Interaction Tests with Adaptive Filtering

• Activity Number: 618
• Type: Contributed
• Date/Time: Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Qianying Liu*+ and Lin Chen and Dan Nicolae
• Companies: University of Chicago and University of Chicago and University of Chicago
• Keywords: set-based tests ; adaptive filtering ; gene-environment interaction ; genome-wide search

Abstract

Complex diseases arise as the consequences of genetic, environmental risk factors and their interactions. Despite the established role of gene-environment interactions (GxE) in disease etiology, there are only a limited number of GxE being identified via genome-wide searches, due to power issues. To improve the power of detecting GxE, we propose a set-based test with two unified steps -- filtering and testing. We propose to first conduct a filtering test on each variant in a set (e.g. a protein coding gene) to eliminate the variants that are less likely to have GxE, and then construct a set-based test statistic for the retaining variants. We derive the exact distribution of the overall set-based test statistic and approximate its power function. We obtain the optimal filtering threshold by maximizing the power function, and show that the optimal filtering threshold depends on many factors and needs to be chosen adaptively for each gene in genome-wide gene-based analyses. The proposed method can be applied to both quantitative and binary outcomes. We demonstrate with simulations and real data application that the proposed test outperforms existing methods for GxE in the literature.