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Classification and Clustering Methods for Multiple Environmental Factors in Gene-Environment Interaction Application to the Multi-Ethnic Study of Atherosclerosis
Journal article   Open access   Peer reviewed

Classification and Clustering Methods for Multiple Environmental Factors in Gene-Environment Interaction Application to the Multi-Ethnic Study of Atherosclerosis

Yi-An Ko, Bhramar Mukherjee, Jennifer A. Smith, Sharon L. R. Kardia, Matthew Allison and Ana V. Diez Roux
Epidemiology (Cambridge, Mass.), v 27(6), pp 870-878
01 Nov 2016
DOI: https://doi.org/10.1097/EDE.0000000000000548
PMID: 27479650
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://europepmc.org/articles/pmc5039086View
Accepted (AM),  Open

Abstract

Life Sciences & Biomedicine Public, Environmental & Occupational Health Science & Technology
There has been an increased interest in identifying gene-environment interaction (G x E) in the context of multiple environmental exposures. Most G x E studies analyze one exposure at a time, but we are exposed to multiple exposures in reality. Efficient analysis strategies for complex G x E with multiple environmental factors in a single model are still lacking. Using the data from the Multiethnic Study of Atherosclerosis, we illustrate a two-step approach for modeling G x E with multiple environmental factors. First, we utilize common clustering and classification strategies (e.g., k-means, latent class analysis, classification and regression trees, Bayesian clustering using Dirichlet Process) to define subgroups corresponding to distinct environmental exposure profiles. Second, we illustrate the use of an additive main effects and multiplicative interaction model, instead of the conventional saturated interaction model using product terms of factors, to study G x E with the data-driven exposure subgroups defined in the first step. We demonstrate useful analytical approaches to translate multiple environmental exposures into one summary class. These tools not only allow researchers to consider several environmental exposures in G x E analysis but also provide some insight into how genes modify the effect of a comprehensive exposure profile instead of examining effect modification for each exposure in isolation.

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14 Record Views
11 citations in Web of Science
11 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#13 Climate Action
#2 Zero Hunger

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Collaboration types
Domestic collaboration
Web of Science research areas
Public, Environmental & Occupational Health
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