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A two-step approach to testing overall effect of gene–environment interaction for multiple phenotypes
Author(s) -
Arunabha Majumdar,
Kathryn S. Burch,
Tanushree Haldar,
Sriram Sankararaman,
Bogdan Paşaniuc,
W. James Gauderman,
John S. Witte
Publication year - 2020
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btaa1083
Subject(s) - univariate , phenotype , computer science , pleiotropy , multivariate statistics , computational biology , statistics , genetics , biology , mathematics , gene , machine learning
While gene-environment (GxE) interactions contribute importantly to many different phenotypes, detecting such interactions requires well-powered studies and has proven difficult. To address this, we combine two approaches to improve GxE power: simultaneously evaluating multiple phenotypes and using a two-step analysis approach. Previous work shows that the power to identify a main genetic effect can be improved by simultaneously analyzing multiple related phenotypes. For a univariate phenotype, two-step methods produce higher power for detecting a GxE interaction compared to single step analysis. Therefore, we propose a two-step approach to test for an overall GxE effect for multiple phenotypes.

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