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.
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.
|Wang, Xin; Mukherjee, Bhramar; Park, Sung Kyun (2018) Associations of cumulative exposure to heavy metal mixtures with obesity and its comorbidities among U.S. adults in NHANES 2003-2014. Environ Int 121:683-694|
|Liu, Gang; Mukherjee, Bhramar; Lee, Seunggeun et al. (2018) Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence. Am J Epidemiol 187:366-377|
|Chen, Yin-Hsiu; Mukherjee, Bhramar; Adar, Sara D et al. (2018) Robust distributed lag models using data adaptive shrinkage. Biostatistics 19:461-478|
|Cheng, Wenting; Taylor, Jeremy M G; Vokonas, Pantel S et al. (2018) Improving estimation and prediction in linear regression incorporating external information from an established reduced model. Stat Med 37:1515-1530|
|Gauderman, W James; Mukherjee, Bhramar; Aschard, Hugues et al. (2017) Update on the State of the Science for Analytical Methods for Gene-Environment Interactions. Am J Epidemiol 186:762-770|
|Chen, Yin-Hsiu; Mukherjee, Bhramar (2017) A New Variance Component Score Test for Testing Distributed Lag Functions with Applications in Time Series Analysis. Stat Probab Lett 123:122-127|
|Park, Sung Kyun; Zhao, Zhangchen; Mukherjee, Bhramar (2017) Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES. Environ Health 16:102|
|McAllister, Kimberly; Mechanic, Leah E; Amos, Christopher et al. (2017) Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. Am J Epidemiol 186:753-761|
|He, Zihuai; Lee, Seunggeun; Zhang, Min et al. (2017) Rare-variant association tests in longitudinal studies, with an application to the Multi-Ethnic Study of Atherosclerosis (MESA). Genet Epidemiol 41:801-810|
|Estes, Jason P; Rice, John D; Li, Shi et al. (2017) Meta-analysis of gene-environment interaction exploiting gene-environment independence across multiple case-control studies. Stat Med 36:3895-3909|
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