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A powerful and data‐adaptive test for rare‐variant–based gene‐environment interaction analysis
Author(s) -
Yang Tianzhong,
Chen Han,
Tang Hongwei,
Li Donghui,
Wei Peng
Publication year - 2018
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8037
Subject(s) - type i and type ii errors , exome , exome sequencing , computer science , score test , null hypothesis , statistics , statistical hypothesis testing , computational biology , biology , data mining , genetics , mutation , gene , mathematics
As whole-exome/genome sequencing data become increasingly available in genetic epidemiology research consortia, there is emerging interest in testing the interactions between rare genetic variants and environmental exposures that modify the risk of complex diseases. However, testing rare-variant-based gene-by-environment interactions (GxE) is more challenging than testing the genetic main effects due to the difficulty in correctly estimating the latter under the null hypothesis of no GxE effects and the presence of neutral variants. In response, we have developed a family of powerful and data-adaptive GxE tests, called "aGE" tests, in the framework of the adaptive powered score test, originally proposed for testing the genetic main effects. Using extensive simulations, we show that aGE tests can control the type I error rate in the presence of a large number of neutral variants or a nonlinear environmental main effect, and the power is more resilient to the inclusion of neutral variants than that of existing methods. We demonstrate the performance of the proposed aGE tests using Pancreatic Cancer Case-Control Consortium Exome Chip data. An R package "aGE" is available at http://github.com/ytzhong/projects/.