TY - JOUR
T1 - Interactions between polygenic scores and environments
T2 - Methodological and conceptual challenges
AU - Domingue, Benjamin W.
AU - Trejo, Sam
AU - Armstrong-Carter, Emma
AU - Tucker-Drob, Elliot M.
N1 - Funding Information:
This work has been supported by the Russell Sage Foundation and the Ford Foundation (grant 96-17-04). S.T. was supported by the National Science Foundation (grant DGE-1656518) and the Institute of Education Sciences (grant R305B140009). E.M.T.-D. was supported by the National Institutes of Health (grants R01AG054628, R01MH120219, and R01HD083613) and by the Jacobs Foundation. Any opinions expressed are those of the authors alone and should not be construed as representing the opinions of any foundation. The authors would like to thank Jason Boardman and Jason Fletcher for comments on an early draft of this article.
Publisher Copyright:
© 2020 The Author(s).
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Gene-environment interaction
KW - Polygenic score
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U2 - 10.15195/V7.A19
DO - 10.15195/V7.A19
M3 - Article
AN - SCOPUS:85092005371
SN - 2330-6696
VL - 7
SP - 465
EP - 486
JO - Sociological Science
JF - Sociological Science
ER -