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

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

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

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
Volume38
Issue number7
DOIs
StatePublished - Mar 30 2019

Fingerprint

Gene-environment Interaction
Adaptive Test
Exome
Gene-Environment Interaction
Genetic Testing
Main Effect
Genetic Research
Testing
Molecular Epidemiology
Environmental Exposure
Pancreatic Neoplasms
Genetic Epidemiology
Genome
Case-control
Type I Error Rate
Score Test
Interaction
Null hypothesis
Sequencing
Cancer

Keywords

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

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Cite this

A powerful and data-adaptive test for rare-variant–based gene-environment interaction analysis. / Yang, Tianzhong; Chen, Han; Tang, Hong Wei; Li, Donghui; Wei, Peng.

In: Statistics in Medicine, Vol. 38, No. 7, 30.03.2019, p. 1230-1244.

Research output: Contribution to journalArticle

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