This proposal is in response to the Funding Opportunity Announcement PAR- 11-032 on Methods and Approaches for Detection of Gene- Environment Interactions in Human Disease (R21). The proposal will be led by multiple PIs, Dr. Bhramar Mukherjee at the Department of Biostatistics, University of Michigan and Dr. Jinbo Chen at the Department of Biostatistics and Epidemiology, University of Pennsylvania. Dr. Stephen B. Gruber, Dr. Sung Kyun Park and Dr. Naisyin Wang from the University of Michigan are key clinical and methodological consultants on the project. In this proposal, we will have two specific aims: (i) Evaluate efficient two-phase design and analysis choices in the post genomewide association studies (GWAS) era where additional genotyping or biomarker data is collected on a prioritized selection of a sub-sample of study subjects in an existing study base. This includes the possibility of using supplementary data on cases and controls with only genetic or environmental data. The methods are guided by modern retrospective likelihood framework. (ii) Develop methods for screening of interaction in cohort studies using a novel technique developed by the PIs called "Principal Interactions Analysis". This method is based on a parsimonious low rank representation of the interaction matrix after fitting additive main effects of gene and environment. The proposal plans to extend this method to longitudinal studies to capture time- varying effects of interaction. Visual diagnostics to identify time-windows of critical importance will be developed as a byproduct. The planned work in this important proposal will meaningfully contribute to the mission of this FOA, and advance study design and analytical techniques for studying G x E effects. The proposal will involve active collaboration between Dr. Chen and Dr. Mukherjee, their doctoral/post-doctoral trainees and foster collaboration between two peer institutions: University of Pennsylvania and University of Michigan. The proposal lies in the intersection of statistics, medicine, epidemiology and human genetics in terms of methodology development. The broader impact is better understanding of disease etiology and identify potentials for targeted intervention strategies.

Public Health Relevance

This proposal is submitted in response to the Funding Opportunity Announcement PAR- 11-032 on Methods and Approaches for Detection of Gene-Environment Interactions in Human Disease (R21). The proposal is lead by multiple PD/PI, Dr. Bhramar Mukherjee at the University of Michigan and Dr. Jinbo Chen at the University of Pennsylvania. Synergism of genes and environment plays an important role in the etiology of complex diseases. This proposal addresses important study design and analytical challenges for efficient detection of gene-environment interactions in epidemiological studies. In the first specific aim, we consider design and analytical methods associated with strategies for selection of cases/controls for additional genotyping or collection of biomarker data in existing study bases. In the second specific aim, we consider a highly novel strategy for exploring interactions in longitudinal studies, based on a singular value decomposition of the residual interaction contrast matrix after removing additive effects. We call this analysis Principal Interactions Analysis, due to its similarity with Principal Components Analysis. The proposal is expected to contribute significantly to the existing literature on G x E studies.

National Institute of Health (NIH)
National Institute of Environmental Health Sciences (NIEHS)
Exploratory/Developmental Grants (R21)
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Biomedical Computing and Health Informatics Study Section (BCHI)
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Mcallister, Kimberly A
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University of Michigan Ann Arbor
Schools of Public Health
Ann Arbor
United States
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Chen, Yin-Hsiu; Mukherjee, Bhramar; Ferguson, Kelly K et al. (2016) Mediation Formula for a Binary Outcome and a Time-Varying Exposure and Mediator, Accounting for Possible Exposure-Mediator Interaction. Am J Epidemiol 184:157-9
Boonstra, Philip S; Mukherjee, Bhramar; Gruber, Stephen B et al. (2016) Tests for Gene-Environment Interactions and Joint Effects With Exposure Misclassification. Am J Epidemiol 183:237-47
Ko, Yi-An; Mukherjee, Bhramar; Smith, Jennifer A et al. (2016) Classification and Clustering Methods for Multiple Environmental Factors in Gene-Environment Interaction: Application to the Multi-Ethnic Study of Atherosclerosis. Epidemiology 27:870-8
Li, Huilin; Chen, Jinbo (2016) Efficient unified rare variant association test by modeling the population genetic distribution in case-control studies. Genet Epidemiol 40:579-590
Chen, Lu; Weinberg, Clarice R; Chen, Jinbo (2016) Using family members to augment genetic case-control studies of a life-threatening disease. Stat Med 35:2815-30
Shen, Yuanyuan; Cai, Tianxi; Chen, Yu et al. (2015) Retrospective likelihood-based methods for analyzing case-cohort genetic association studies. Biometrics 71:960-8
Stenzel, Stephanie L; Ahn, Jaeil; Boonstra, Philip S et al. (2015) The impact of exposure-biased sampling designs on detection of gene-environment interactions in case-control studies with potential exposure misclassification. Eur J Epidemiol 30:413-23
He, Zihuai; Zhang, Min; Lee, Seunggeun et al. (2015) Set-based tests for genetic association in longitudinal studies. Biometrics 71:606-15
Tao, Yebin; Sánchez, Brisa N; Mukherjee, Bhramar (2015) Latent variable models for gene-environment interactions in longitudinal studies with multiple correlated exposures. Stat Med 34:1227-41
Chen, Yin-Hsiu; Ferguson, Kelly K; Meeker, John D et al. (2015) Statistical methods for modeling repeated measures of maternal environmental exposure biomarkers during pregnancy in association with preterm birth. Environ Health 14:9

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