? ? Unlike monogenic diseases, both genetic and environmental factors play essential and interactive roles in controlling risk to complex diseases such as coronary heart disease, asthma, cancer and psychiatric disorders. A principal challenge facing genome-wide association (GWA) scans and more focused investigations is the difficulty in detecting true genetic signals embedded in considerable statistical and technical variation, and the multiplicity associated with examining a large number of markers. Motivated by these issues and opportunities, we will develop a suite of complementary methods to address gene environment (GxE) interactions at many levels, including GWA studies, gene localization via linkage, and candidate gene approaches. For each level we will develop new techniques that fill gaps in currently available methods or develop and evaluate new strategic approaches. The research proposed here specifically focuses on enhancing methods to identify GxE interactions in the search for biologically important genes controlling risk. We propose to address the following specific aims: (1) Develop and evaluate new statistical methods to prioritize genes through proper ranking in genome-wide association studies that address GxE interactions; (2) Develop and evaluate new statistical methods to localize causal genes as part of linkage and fine mapping studies while considering GxE interactions; (3) Develop and evaluate new statistical methods to identify higher order interactions between environmental variables and SNPs in candidate gene studies; (4) Adapt existing and develop new statistical methods to address imprecise and missing environmental and genetic measurements; and (5) Develop and disseminate efficient algorithms for GxE analyses, and apply these methods in several ongoing genetic studies of complex diseases. Our novel statistical approaches will accommodate multiplicity, identify and incorporate measurement uncertainty, take advantage of genomic structure and the wealth of information from related linkage, fine mapping, and candidate gene studies, and structure inferences through biologically relevant statistical models. They will make efficient use of available information and report findings in a scientifically relevant framework. Our proposed research will confer scientific benefits, many with public health implications, of improved characterization of the interaction between genes and environmental factors controlling risk to complex diseases. In particular, quantifying GxE interaction can help identify high risk groups for focused intervention and prevention, and improve our understanding of etiologic mechanisms for specific diseases. (End of Abstract) ? ? ?

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
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Special Emphasis Panel (ZHL1-CSR-D (S1))
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Paltoo, Dina
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Johns Hopkins University
Biostatistics & Other Math Sci
Schools of Public Health
United States
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