Identifying significant gene-environment interactions using a combination of screening testing and hierarchical false discovery rate control

The Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

Although gene-environment (G× E) interactions play an important role in many biological systems, detecting these interactions within genome-wide data can be challenging due to the loss in statistical power incurred by multiple hypothesis correction. To address the challenge of poor power and the limitations of existing multistage methods, we recently developed a screening-testing approach for G× E interaction detection that combines elastic net penalized regression with joint estimation to support a single omnibus test for the presence of G× E interactions. In our original work on this technique, however, we did not assess type I error control or power and evaluated the method using just a single, small bladder cancer data set. In this paper, we extend the original method in two important directions and provide a more rigorous performance evaluation. First, we introduce a hierarchical false discovery rate approach to formally assess the significance of individual G× E interactions. Second, to support the analysis of truly genome-wide data sets, we incorporate a score statistic-based prescreening step to reduce the number of single nucleotide polymorphisms prior to fitting the first stage penalized regression model. To assess the statistical properties of our method, we compare the type I error rate and statistical power of our approach with competing techniques using both simple simulation designs as well as designs based on real disease architectures. Finally, we demonstrate the ability of our approach to identify biologically plausible SNP-education interactions relative to Alzheimer's disease status using genome-wide association study data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

Original languageEnglish (US)
Pages (from-to)544-557
Number of pages14
JournalGenetic epidemiology
Volume40
Issue number7
DOIs
StatePublished - Nov 1 2016

Keywords

  • gene-environment interactions
  • hierarchical FDR
  • penalized regression
  • screening testing

ASJC Scopus subject areas

  • Epidemiology
  • Genetics(clinical)

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