Meta-Analyses to Investigate Gene-Environment Interactions in Neuroepidemiology

Abstract

Background: Most chronic neurological diseases are caused by a combination of multiple genetic and environmental factors. Increasingly, gene-environment interactions (GxE) are being examined, providing opportunities to combine studies systematically using meta-analysis. Methods: Systematic review of the literature on how to examine GxE using observational study designs, and how to conduct a meta-analysis of studies on GxE. Results: Most methods and challenges related to a standard meta-analysis apply to a GxE meta-analysis. There are, however, some substantive differences. With GxE, there is the capability of using a case-only design. Research on GxE interactions may be more prone to publication bias, since interactions are usually not the primary hypothesis and only ‘exciting' significant GxE findings are reported out of a range of secondary analyses. In disease aetiology research, there has been debate whether to measure interaction on a multiplicative or additive scale. There are some significant challenges associated with measuring interaction on an additive scale, and thus the uptake of the measures of additive interaction has been limited. As a result, the methods of analysing interaction have been less consistent and reporting has been highly variable. We suggest using the STROBE/STREGA reporting guidelines to allow evaluation of interaction on both scales. Conclusions: We identified a number of differences of a GxE meta-analysis over a standard meta-analysis. Awareness of these issues is important. Using established reporting guidelines for GxE studies is recommended. The development of consortia for neurological disorders that include both genetic and environmental data might offer benefits for GxE meta-analyses in the future.

How do age and major risk factors for mortality interact over the life-course? Implications for health disparities research and public health policy

“…Subsequent work formalized the basic mathematics behind statistical interactions raising general awareness of the importance of scale to the interpretation of statistical interactions (Brown, 1986; Rothman et al, 1980; Walter & Holford, 1978). Recently, this issue has been central to debates on how to interpret gene by environment interactions (Gauderman et al, 2017; Ritz et al, 2017; Schwartz, 2006; van der Mei, Otahal, Taylor, & Winzenberg, 2014) and how to interpret whether the magnitude of health disparities are increasing or decreasing over time (Harper et al, 2008; Harper & Lynch, 2005, 2007; Moonesinghe & Beckles, 2015). A main lesson from these debates is that choice of scale is often consequential to conclusions.…”

Genetic Epidemiology and Insights into Interactive Genetic and Environmental Effects in Autism Spectrum Disorders

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“…To allow this to happen for gene-environment interaction studies, we recommend using STROBE/STREGA reporting guidelines, as many studies in the past were inconsistent in their methods of analysing interactions and reporting was also highly variable, as we have described previously. 56…”