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Accepted (AM)Open Access (License Unspecified), Open
Journal article
Set-Based Tests for the Gene-Environment Interaction in Longitudinal Studies
Journal of the American Statistical Association, v 112(519), pp 966-978
01 Jan 2017
PMID: 29780190
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We propose a generalized score type test for set-based inference for the gene-environment interaction with longitudinally measured quantitative traits. The test is robust to misspecification of within subject correlation structure and has enhanced power compared to existing alternatives. Unlike tests for marginal genetic association, set-based tests for the gene-environment interaction face the challenges of a potentially misspecified and high-dimensional main effect model under the null hypothesis. We show that our proposed test is robust to main effect misspecification of environmental exposure and genetic factors under the gene-environment independence condition. When genetic and environmental factors are dependent, the method of sieves is further proposed to eliminate potential bias due to a misspecified main effect of a continuous environmental exposure. A weighted principal component analysis approach is developed to perform dimension reduction when the number of genetic variants in the set is large relative to the sample size. The methods are motivated by an example from the Multi-Ethnic Study of Atherosclerosis (MESA), investigating interaction between measures of neighborhood environment and genetic regions on longitudinal measures of blood pressure over a study period of about seven years with four exams. Supplementary materials for this article are available online.
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Details
- Title
- Set-Based Tests for the Gene-Environment Interaction in Longitudinal Studies
- Creators
- Zihuai He - University of Michigan–Ann ArborMin Zhang - University of Michigan–Ann ArborSeunggeun Lee - University of Michigan–Ann ArborJennifer A. Smith - University of Michigan–Ann ArborSharon L. R. Kardia - University of Michigan–Ann ArborV. Diez Roux - Drexel Univ, Dept Epidemiol, Philadelphia, PA 19104 USABhramar Mukherjee - University of Michigan–Ann Arbor
- Publication Details
- Journal of the American Statistical Association, v 112(519), pp 966-978
- Publisher
- Amer Statistical Assoc
- Number of pages
- 13
- Grant note
- R00HL113164; HL101161 / NIH/NHLBI; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Heart Lung & Blood Institute (NHLBI) ES020811 / NIH/NIEHS; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Environmental Health Sciences (NIEHS) N02-HL-64278 / NHLBI; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Heart Lung & Blood Institute (NHLBI) 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; CTSAUL1-RR-024156 / MESA 2P60MD002249 / NIMHHD Grant MESA DMS 1406712 / NSF; National Science Foundation (NSF) National Heart, Lung, and BloodInstitute (NHLBI); United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Heart Lung & Blood Institute (NHLBI)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Urban Health Collaborative
- Web of Science ID
- WOS:000416611500011
- Scopus ID
- 2-s2.0-85032476865
- Other Identifier
- 991019168547904721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Statistics & Probability