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

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).

FGWAS: Functional genome wide association analysis

“…However, as in GWAS of AD, a question of interest is to test the null hypothesis of no association between functional phenotypes and the genotypes or genetic interactions (gene-environment), for example, genome-wide interaction analysis of relating SNPs to education level (Frost et al, 2016), case control conditions or memory scores (Yan et al, 2015). Meanwhile, detecting these interactions within genome-wide data can be challenging due to the loss in statistical power and computational efficiency.…”

Using Genetic Risk Score Approaches to Infer Whether an Environmental Factor Attenuates or Exacerbates the Adverse Influence of a Candidate Gene

“…and γ Int are asymptotically independent under the null hypothesis of no SNP-by-environment interaction, proved by corollary 1 of Dai et al (2012). A two-stage approach that first filters SNPs by a criterion independent of the test statistic ( γ Int estimated from model 7) under the null hypothesis, and then only uses SNPs that pass the filter, can maintain type I error rates and boost power (Bourgon et al, 2010;Frost et al, 2016). Empirically, our following simulation studies confirmed that GRS using internal weights is a valid approach in the sense that type I error rates match the nominal significance level.…”

A genome-by-environment interaction classifier for precision medicine: personal transcriptome response to rhinovirus identifies children prone to asthma exacerbations

“…Tissue-stimulation assays coupled with omics measurements provide a minimally invasive opportunity to understand an individual’s personal genome-by-environment response to a stimulus relevant to disease state or risk of progression. While cohort statistics can identify the most common stimulus-provoked molecular responses between clinical populations, 5 there are no unbiased tools for assessing a whole transcriptome response that can be scaled down to a single patient without requiring inference from reference sets based on well-powered, cross-patient comparisons. Tailoring patient treatment according to the results of a given in vivo or in vitro stimulation thus remains an unmet challenge.…”