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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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Set-Based Gene × Environment Interaction Tests for Complex Diseases with Application to Genome-Wide Association and Sequencing Studies

Set-Based Gene × Environment Interaction Tests for Complex Diseases with Application to Genome-Wide Association and Sequencing Studies

Chapter:
(p.53) 4 Set-Based Gene × Environment Interaction Tests for Complex Diseases with Application to Genome-Wide Association and Sequencing Studies
Source:
Statistical Approaches to Gene X Environment Interactions for Complex Phenotypes
Author(s):

Shuo Jiao

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262034685.003.0004

This chapter presents set-based approaches that focus on identifying G X E interactions rather than set-based approaches that are based primarily on detecting G main effects (e.g., via marginal effects). The author reviews both his own research and the development of his Set Based Gene EnviRonment InterAction test (SBERIA), as well as another set-based G X E approach referred to as GESAT. GESAT extends the variance component test of the SNP-set Kernel Association Test (SKAT) to evaluate G x E effects while incorporating the main SNP effects as covariates. While both of these approaches (SBERIA and GESAT) have outperformed other benchmark methods (e.g., likelihood ratio test) and have been demonstrated to retain the appropriate Type 1 error rate, in this chapter the author conducts simulation studies to compare findings for SBERIA and GESAT approaches, and identifies associated strengths and limitations of the respective methods.

Keywords:   Set-based approaches, Set Based Gene EnviRonment InterAction test (SBERIA), SNP-set Kernel Association Test (SKAT), Simulation studies

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