Advance articles

Genetic and environmental factors are both known to contribute to susceptibility to complex diseases. Therefore, the study of gene-environment (GxE) interactions has been a focus of research for several years. In this manuscript, select examples of GxE from the literature are described to highlight different approaches and underlying principles related to the success of these studies. These examples were broadly categorized as studies of: single metabolism genes, genes in complex metabolism pathways, ranges of exposure levels, functional approaches and model systems, and pharmacogenomics. Some studies illustrated the success of studying exposure metabolism for which candidate genes can be identified. Moreover, some GxE successes depended on the availability of high quality exposure assessment and longitudinal measures, study populations with a wide range of exposure levels, and the inclusion of ethnically and geographically diverse populations. In several examples, large population sizes were required to detect GxE interactions. Other examples illustrated the impact of accurately defining scale of the interactions (i.e., additive or multiplicative). Lastly, model systems and functional approaches provided insights into GxE in several examples. Future studies may benefit from these lessons learned.

A growing knowledge base of genetic and environmental information has greatly enabled the study of disease risk factors. However, the computational complexity and statistical burden of testing all variants by all environments has required novel study designs and hypothesis-driven approaches. We discuss how incorporating biological knowledge from model organisms, functional genomics, and integrative approaches can empower the discovery of novel gene-by-environment (GxE) interactions and discuss specific methodological considerations with each approach. We consider specific examples where the application of these approaches has uncovered GxE effects relevant to drug response and immunity, and highlight how such improvements enable a great understanding of the pathogenesis of disease and the realization of precision medicine.

The analysis of gene-environment (GxE) interaction may hold the key for further understanding the etiology of many complex traits. The current availability of high-volume genetic data, the wide range in types of environmental data that can be measured, and the formation of consortia of multiple studies provide new opportunities to identify GxE interactions, but also new analytic challenges. In this paper, we summarize several statistical approaches that can be used to test for GxE interaction in a genomewide association study. These include both traditional models of GxE interaction in a case-control or quantitative trait study, as well as alternative approaches that can provide substantially greater power. The latest methods for analyzing GxE with gene sets and with data in a consortium setting are summarized, as are issues that arise due to the complexity of environmental data. We provide some speculation on why detecting GxE interactions in a genomewide association study has thus far been difficult. We conclude with a description of software programs that can be used to implement most of the methods described in the paper.

Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (GxE) in large-scale human population studies. There are currently opportunities in this field, particularly with respect to the incorporation of -omics and next-generation sequencing data and continual improvement in measures of environmental exposures implicated in complex disease outcomes. A workshop held on October 17–18, 2014 by the National Institute of Environmental Health Sciences and the National Cancer Institute in conjunction with the annual American Society of Human Genetics meeting explored new approaches and tools developed in recent years for GxE interaction discovery. This paper will highlight current and critical issues and themes in GxE research discussed that need additional consideration including the topics of improved data analytical methods, environmental exposure assessment, and incorporation of functional data and annotations.