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He Z, Zhang M, Lee S, et al. Set-Based Tests for the Gene-Environment Interaction in Longitudinal Studies. J Am Stat Assoc. 2016;112(519):966-978doi: 10.1080/01621459.2016.1252266.
He, Z., Zhang, M., Lee, S., Smith, J. A., Kardia, S. L. R., Diez Roux, A. V., & Mukherjee, B. (2017). Set-Based Tests for the Gene-Environment Interaction in Longitudinal Studies. Journal of the American Statistical Association, 112(519), 966-978. https://doi.org/10.1080/01621459.2016.1252266
He, Zihuai, et al. "Set-Based Tests for the Gene-Environment Interaction in Longitudinal Studies." Journal of the American Statistical Association vol. 112,519 (2017): 966-978. doi: https://doi.org/10.1080/01621459.2016.1252266
He Z, Zhang M, Lee S, Smith JA, Kardia SLR, Diez Roux AV, Mukherjee B. Set-Based Tests for the Gene-Environment Interaction in Longitudinal Studies. J Am Stat Assoc. 2017;112(519):966-978. doi: 10.1080/01621459.2016.1252266. Epub 2016 Dec 16. PMID: 29780190; PMCID: PMC5954413.
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J Am Stat Assoc. 2017;112(519):966-978. doi: 10.1080/01621459.2016.1252266. Epub 2016 Dec 16.

Set-Based Tests for the Gene-Environment Interaction in Longitudinal Studies.

Journal of the American Statistical Association

Zihuai He, Min Zhang, Seunggeun Lee, Jennifer A Smith, Sharon L R Kardia, Ana V Diez Roux, Bhramar Mukherjee

Affiliations

  1. Department of Biostatistics, University of Michigan.
  2. Department of Epidemiology, University of Michigan.
  3. Department of Epidemiology, Drexel University.

PMID: 29780190 PMCID: PMC5954413 DOI: 10.1080/01621459.2016.1252266

Abstract

We propose a generalized score type test for set-based inference for 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 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 4 exams.

Keywords: Gene-environment independence; Generalized score test; MESA neighborhood study; Model misspecification; Robustness

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