Classification and clustering methods for multiple environmental factors in gene-environment interaction: Application to the multi-ethnic study of atherosclerosis

• Type of Material: Article
• Authors: Yi-An Ko, Emory University; Bhramar Mukherjee, University of Michigan; Jennifer A. Smith, University of Michigan; Sharon L.R. Kardia, University of Michigan; Matthew Allison, University of California San Diego; Ana V. Diez Roux, Drexel University
• Language: English
• Date: 2016-11-01
• Publisher: Lippincott, Williams & Wilkins
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2016 Wolters Kluwer Health, Inc.
• Final Published Version (URL): http://dx.doi.org/10.1097/EDE.0000000000000548
• Title of Journal or Parent Work: Epidemiology
• ISSN: 1044-3983
• Volume: 27
• Issue: 6
• Start Page: 870
• End Page: 878
• Grant/Funding Information: Research of BM and YK was supported by NIH/NIEHS grant ES-20811 and NSF grant DMS-1406712.; Genotyping was performed at Affymetrix (Santa Clara, California, USA) and the Broad Institute of Harvard and MIT (Boston, Massachusetts, USA) using the Affymetrix Genome-Wide Human SNP Array 6.0.; Funding for SHARe genotyping was provided by NHLBI Contract N02-HL-64278.; This project was conducted in part with funding from HL101161 from NHLBI and 2P60MD002249 from NIMHD.; Support for MESA is provided by contracts N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169 and CTSA UL1-RR-024156 and UL1-TR-001079.; The National Heart, Lung and Blood Institute (NHLBI) is the primary MESA funding source.; MESA and the MESA SHARe project are conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators.
• Research Categories: Biology, Biostatistics; Biology, Genetics; Health Sciences, Epidemiology

Abstract

There has been an increased interest in identifying gene-environment interaction (G × E) in the context of multiple environmental exposures. Most G × E studies analyze one exposure at a time, but we are exposed to multiple exposures in reality. Efficient analysis strategies for complex G × 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 × 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 × 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 × 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.