A unified method for rare variant analysis of gene-environment interactions

source extracted confidence: high 2026-04-09
#computer-simulation #gene-environment-interaction #genetic-variation #genotype #humans

A unified method for rare variant analysis of gene-environment interactions

Lim et al., 2020 | Stat Med | Meta Analysis

Citation

Lim Elise, Chen Han, ... Liu Ching-Ti. A unified method for rare variant analysis of gene-environment interactions. Stat Med. 2020-Mar-15;39(6):801-813. doi:10.1002/sim.8446

Abstract

Advanced technology in whole-genome sequencing has offered the opportunity to comprehensively investigate the genetic contribution, particularly rare variants, to complex traits. Several region-based tests have been developed to jointly model the marginal effect of rare variants, but methods to detect gene-environment (GE) interactions are underdeveloped. Identifying the modification effects of environmental factors on genetic risk poses a considerable challenge. To tackle this challenge, we develop a method to detect GE interactions for rare variants using generalized linear mixed effect model. The proposed method can accommodate either binary or continuous traits in related or unrelated samples. Under this model, genetic main effects, GE interactions, and sample relatedness are modeled as random effects. We adopt a kernel-based method to leverage the joint information across rare variants and implement variance component score tests to reduce the computational burden. Our simulation studies of continuous and binary traits show that the proposed method maintains correct type I error rates and appropriate power under various scenarios, such as genotype main effects and GE interaction effects in opposite directions and varying the proportion of causal variants in the model. We apply our method in the Framingham Heart Study to test GE interaction of smoking on body mass index or overweight status and replicate the Cholinergic Receptor Nicotinic Beta 4 gene association reported in previous large consortium meta-analysis of single nucleotide polymorphism-smoking interaction. Our proposed set-based GE test is computationally efficient and is applicable to both binary and continuous phenotypes, while appropriately accounting for familial or cryptic relatedness.

Key Findings

Our proposed set-based GE test is computationally efficient and is applicable to both binary and continuous phenotypes, while appropriately accounting for familial or cryptic relatedness.

Outcomes Measured

  • Requires manual extraction

Population

Field Value
Population See abstract
Sample Size See abstract
Age Range See abstract
Condition See abstract

MeSH Terms

  • Computer Simulation
  • Gene-Environment Interaction
  • Genetic Variation
  • Genotype
  • Humans
  • Linear Models
  • Models, Genetic
  • Phenotype

Evidence Classification

  • Level: Meta Analysis
  • Publication Types: Journal Article, Meta-Analysis, Research Support, N.I.H., Extramural
  • Vertical: niacin

Provenance

Source extracted via PubMed E-utilities API on 2026-04-09