Methods for Investigating Gene-Environment Interactions in Candidate Pathway and Genome-Wide Association Studies

Abstract

Despite the considerable enthusiasm about the yield of novel and replicated discoveries of genetic associations from the new generation of genome-wide association studies (GWAS), the proportion of the heritability of most complex diseases that have been studied to date remains small. Some of this "dark matter" could be due to gene-environment (G × E) interactions or more complex pathways involving multiple genes and exposures. We review the basic epidemiologic study design and statistical analysis approaches to studying G × E interactions individually and then consider more comprehensive approaches to studying entire pathways or GWAS data. In addition to the usual issues in genetic association studies, particular care is needed in exposure assessment, and very large sample sizes are required. Although hypothesis-driven, pathway-based and agnostic GWA study approaches are generally viewed as opposite poles, we suggest that the two can be usefully married using hierarchical modeling strategies that exploit external pathway knowledge in mining genome-wide data.

Assessing Gene-Environment Interactions for Common and Rare Variants with Binary Traits Using Gene-Trait Similarity Regression

“…A screening by joint tests may lead to increased flexibility and power to detect a signal because some genes can exhibit negligible marginal effects but strong effects among particular exposure groups (Kraft et al 2007;Thomas 2010). If the joint test is rejected, a G3E test can then be used to identify whether the effects of the genetic variables are modified by the environmental variables.…”

“…Studies of gene-environment (G3E) interactions can facilitate our understanding of genetic heterogeneity under different environmental exposures (Kraft et al 2007;Van Os and Rutten 2009), help to identify high-risk subgroups in the population (Murcray et al 2009), provide insight into the biological mechanisms of complex diseases (Thomas 2010), and improve the ability to discover susceptible genes that interact with other factors but exhibit little marginal effect (Thomas 2010). However, finding significant G3E interactions is not an easy task.…”

Genome-wide gene–environment interactions on quantitative traits using family data

“…1 In this article, we discuss an agnostic approach that evaluates interaction between single-nucleotide polymorphisms (SNPs) and environmental exposures on quantitative traits. In particular, we are interested in the following statistical model:…”

“…1 Such interactions could lead to strategies for targeting interventions toward the people who are most likely to benefit from them. For example, many drugs have unintended side effects or variable effectiveness across people.…”

Genetic Research on Sleep, Sleep Disturbances and Associated Difficulties

“…В настоящее время предложены новые подходы к изучению Г×С -так называемые полногеномные анализы Г×С (Thomas, 2010;Winham & Biernacka, 2013). Основной особенностью данных подходов явля-ется включение фактора(ов) среды в полногеномный анализ ассоциаций, что позволяет не только выявить эффекты взаимодействий, но и эффекты генов на конкретный признак, которые могут маскироваться такими взаи-модействиями.…”