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Statistical Approaches to Gene X Environment Interactions for Complex Phenotypes$
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Michael Windle

Print publication date: 2016

Print ISBN-13: 9780262034685

Published to MIT Press Scholarship Online: May 2017

DOI: 10.7551/mitpress/9780262034685.001.0001

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Complex Phenotypes and the Use of Polygenic Scores to Investigate Gene × Environment Interactions for Substance Use

Complex Phenotypes and the Use of Polygenic Scores to Investigate Gene × Environment Interactions for Substance Use

Chapter:
(p.219) 10 Complex Phenotypes and the Use of Polygenic Scores to Investigate Gene × Environment Interactions for Substance Use
Source:
Statistical Approaches to Gene X Environment Interactions for Complex Phenotypes
Author(s):

Michael Windle

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262034685.003.0010

The chapter suggests the need of a “second-generation” candidate gene approach to adapt to first-generation limitations and to strengthen efforts to study GE interactions. The sample sizes associated with many phenotypes and areas of study in the literature (e.g., clinical trials, neuroimaging studies) are unlikely to yield sample sizes in the area of GWA and NGS studies (i.e., 200,000-300,000 participants). However, by building upon prior limitations in the candidate gene literature, using findings from GWA and NGS studies and meta-analyses, using multiple methods of analyses (e.g., gene expression analysis; methylation analysis), and using theory and prior substantive research to guide hypothesis testing, progress can be made on G X E interactions for complex phenotypes. Several illustrative path models were provided in this chapter to provide a visual frame for how we have approached G X E interactions in the past, and how, going forward, we might proceed to investigate multiple polygenic by multiple environmental models. This level of complexity may be necessary to advance the field to address the many exciting research questions of interest, as well as the challenges that confront us as we attempt to move this knowledge from discovery to practice.

Keywords:   Polygenic scores, candidate gene studies, multiple G x E path models

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