A powerful and data-adaptive test for rare-variant–based gene-environment interaction analysis

Tianzhong Yang, Han Chen, Hongwei Tang, Donghui Li, Peng Wei

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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/.

Original languageEnglish (US)
Pages (from-to)1230-1244
Number of pages15
JournalStatistics in Medicine
Issue number7
StatePublished - Mar 30 2019
Externally publishedYes

Bibliographical note

Funding Information:
This research was supported by the National Institutes of Health (NIH) under grant R01CA169122; Peng Wei was supported by the NIH under grants R01HL116720 and R21HL126032; Han Chen was supported by the NIH under grants R00HL130593 and U01HL120393. The authors are grateful to two anonymous reviewers for their many helpful and constructive comments that improved the presentation of this paper. The authors thank Ms Lee Ann Chastain for editorial assistance. The authors acknowledge the Texas Advanced Computing Center at The University of Texas at Austin for providing HPC resources that contributed to the research results reported within this paper. The authors declare no conflict of interest.


  • data-adaptive hypothesis testing
  • gene-environment interaction
  • model misspecification
  • rare variant

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural

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