A Powerful and Data-Adaptive Test for Rare Variant-Based GxE Analysis

• Activity Number: 300 - Gene-Gene and Gene-Environment Interactions
• Type: Contributed
• Date/Time: Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Tianzhong Yang* and Han Chen and Donghui Li and Peng Wei
• Companies: University of Texas Health Science Center at Houston and University of Texas Health Science Center at Houston and The University of Texas MD Anderson Cancer Center and The University of Texas MD Anderson Cancer Center
• Keywords: rare variants ; Gene-environment interaction ; aSPU ; Misspecification

Abstract

As whole exome/genome sequencing data become increasingly available in large genetic epidemiology research consortia, there is an emerging interest in testing the interaction between rare genetic variants and environmental exposures. However, testing rare variant-based GxE is more challenging than testing genetic main effects due to the difficulty in correctly estimating the latter under the null hypothesis of no GxE effects and the presence of neural variants. In response, we developed a powerful and data-adaptive GxE tests, called aGE, in the framework of aSPU test (Pan et al., 2014), originally proposed for testing the main effects of rare variants. Using extensive simulations, we compared our proposed test with rareGE (Chen et al., 2014) and iSKAT (Lin et al., 2015). We found that the proposed test could control the Type I error in the presence of misspecification of the main model. In addition, our test was more resilient to inclusion of neutral variants and more powerful under a variety of scenarios. Finally, we demonstrated the performance of the proposed aGE test using the ExomeChip dataset of pancreatic cancer.