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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Environmental Health Sciences (NIEHS)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21ES020811-01
Application #
8218656
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Mcallister, Kimberly A
Project Start
2012-07-18
Project End
2015-06-30
Budget Start
2012-07-18
Budget End
2013-06-30
Support Year
1
Fiscal Year
2012
Total Cost
$158,901
Indirect Cost
$38,837
Name
University of Michigan Ann Arbor
Department
None
Type
Schools of Public Health
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
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
Ferguson, Kelly K; McElrath, Thomas F; Ko, Yi-An et al. (2014) Variability in urinary phthalate metabolite levels across pregnancy and sensitive windows of exposure for the risk of preterm birth. Environ Int 70:118-24
Li, Shi; Mukherjee, Bhramar; Taylor, Jeremy M G et al. (2014) The role of environmental heterogeneity in meta-analysis of gene-environment interactions with quantitative traits. Genet Epidemiol 38:416-29
Boonstra, Philip S; Bondarenko, Irina; Park, Sung Kyun et al. (2014) Propensity score-based diagnostics for categorical response regression models. Stat Med 33:455-69
Park, Sung Kyun; Tao, Yebin; Meeker, John D et al. (2014) Environmental risk score as a new tool to examine multi-pollutants in epidemiologic research: an example from the NHANES study using serum lipid levels. PLoS One 9:e98632
Ahn, Jaeil; Johnson, Timothy D; Bhavnani, Darlene et al. (2014) A space-time point process model for analyzing and predicting case patterns of diarrheal disease in northwestern Ecuador. Spat Spatiotemporal Epidemiol 9:23-35
Ko, Yi-An; Mukherjee, Bhramar; Smith, Jennifer A et al. (2014) Testing departure from additivity in Tukey's model using shrinkage: application to a longitudinal setting. Stat Med 33:5177-91
Zhao, Xiaoyi; Sun, Zhichao; Ruan, Yanping et al. (2014) Personal black carbon exposure influences ambulatory blood pressure: air pollution and cardiometabolic disease (AIRCMD-China) study. Hypertension 63:871-7
Sun, Zhichao; Tao, Yebin; Li, Shi et al. (2013) Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons. Environ Health 12:85
Ko, Yi-An; Saha-Chaudhuri, Paramita; Park, Sung Kyun et al. (2013) Novel likelihood ratio tests for screening gene-gene and gene-environment interactions with unbalanced repeated-measures data. Genet Epidemiol 37:581-91

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