A meta‐analysis approach with filtering for identifying gene‐level gene–environment interactions

Abstract

There is a growing recognition that gene-environment interaction (GxE) plays a pivotal role in the development and progression of complex diseases. Despite a wealth of genetic data on various complex diseases/traits generated from association and sequencing studies, detecting GxE via genome-wide analysis remains challenging due to power issues. In genome-wide GxE studies, a common strategy to improve power is to first conduct a filtering test and retain only the genetic variants that pass the filtering step for subsequent GxE analyses. Two-stage, multi-stage, and unified tests have been proposed to jointly consider the filtering statistics in GxE tests. However, such GxE tests based on data from a single study may still be underpowered. Meanwhile, large-scale consortia have been formed to borrow strength across studies and populations. In this work, motivated by existing single-study GxE tests with filtering and the needs for meta-analysis GxE approaches based on consortia data, we propose a meta-analysis framework for detecting gene-based GxE effects, and introduce meta-analysis-based filtering statistics in the gene-level GxE tests. Simulations demonstrate the advantages of the proposed method – the ofGEM test. We apply the proposed tests to existing data from two breast cancer consortia to identify the genes harboring genetic variants with age-dependent penetrance (i.e., gene-age interaction effects). We develop an R software package for the proposed meta-analysis tests.

Impact of Gene–Environment Interactions on Cancer Development

“…Also, human exposures and biological impacts can fluctuate impressively from the period of conception through adulthood. Hence, G × E studies have the potential to induce a non-linear association because the impacts of environmental factors on biological systems are pleiotropic in nature and likely to induce overlapping effects on malignant growth [109,110]. Since exposures are often spatially, temporally, and socially dependent, investigators conducting research on G × E interactions agree that exposure effects and disease outcomes differ across genotypes and geographic settings.…”

Genomic summary statistics and meta-analysis for set-based gene-environment interaction tests in large-scale sequencing studies

“…For set-based GEI tests and joint tests, however, very few meta-analysis methods have been developed. Among them, ofGEM (22) introduces filtering statistics based on meta-analysis, but it is only applicable to unrelated samples and no joint tests for genetic main effects and GEI effects were proposed. A recent study proposed extending the rareGE framework (8) to meta-analysis, which can also be conducted for unrelated samples only (23).…”

Hereditary and breastfeeding factors are positively associated with the aetiology of mammary gland hyperplasia: a case–control study

“… 5 Heredity–environment interactions have the potential to illustrate the biologic causes of disease, distinguish individuals for whom risk factors are most related and develop precision medicine. 6 However, few researchers have explored the interaction between a family history of breast cancer and HMG. Furthermore, existing studies include only a single statistical method to study the interaction between a family history of breast cancer and HMG, lacking the internal validation and decreased statistical power to identify underlying heredity–environment interactions.…”