Interactions between Polygenic Scores and Environments: Methodological and Conceptual Challenges.

Benjamin Domingue,Elliot Tucker-Drob,Sam Trejo,Emma Armstrong-Carter

doi:10.15195/v7.a19

Abstract

Interest in the study of gene–environment interaction has recently grown due to the sudden availability of molecular genetic data—in particular, polygenic scores—in many long-running longitudinal studies. Identifying and estimating statistical interactions comes with several analytic and inferential challenges; these challenges are heightened when used to integrate observational genomic and social science data. We articulate some of these key challenges, provide new perspectives on the study of gene–environment interactions, and end by offering some practical guidance for conducting research in this area. Given the sudden availability of well-powered polygenic scores, we anticipate a substantial increase in research testing for interaction between such scores and environments. The issues we discuss, if not properly addressed, may impact the enduring scientific value of gene–environment interaction studies.

Highlights

Interest in the study of gene–environment interaction has recently grown due to the sudden availability of molecular genetic data—in particular, polygenic scores—in many long-running longitudinal studies
polygenic scores (PGSs) are neither pure nor universal measures, they have still sparked substantial interest
We focus the current article on inferential and statistical issues pertaining to the samples in which the PGSs are constructed and analyzed

Summary

Introduction

Interest in the study of gene–environment interaction has recently grown due to the sudden availability of molecular genetic data—in particular, polygenic scores—in many long-running longitudinal studies. Population variation in these traits is attributable to many genetic variants, each individually exhibiting a relatively small effect This has led many researchers to forego the study of specific genetic variants in favor of genome-wide composite measures (Dudbridge 2013). Data sets such as the Health and Retirement Study (HRS; Ware et al 2017), Add Health (Braudt and Harris 2018), and the Wisconsin Longitudinal Study (Okbay, Benjamin, and Visscher 2018) are posting preconstructed scores for use by researchers, and catalogs of polygenic scores are being made available (Lambert et al 2020) This novel data resource may offer new and more robust avenues for exploration of GxE. We offer some guidelines for designing, implementing, and interpreting high-quality GxE research using PGSs

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Conclusion