We aim to develop novel statistical methods to address some of the major problems facing cancer genetic epidemiologists in the post-GWAS era and to illustrate their use for discovery of novel biology in various colorectal cancer (CRC) studies. These methods leverage prior biological knowledge to inform integrative genomics analyses (Project 1), use phylogenetic information to infer gene function as inputs to our epidemiologic modeling projects (Project 2), model the role of the microbiome and the exposome in cancer risk (Project 3), and exploit intra-tumor heterogeneity to learn about somatic tumor evolution and how this process is modified by the internal environment (Project 4). These four projects will be supported by an administrative core and three shared resource cores on functional annotation, high performance computing, and software development. The entire program is motivated by an overall objective of providing tools for evaluating the impact of potential preventive or therapeutic interventions based on modifiable risk factors. Specifically, the aims of the overall program are (1) to develop statistical analysis methods to integrate multiple types of omics data that describe both constitutional and acquired genomic variation as well as measures of the external and internal environment into comprehensive risk prediction models, leveraging external information; (2) to apply these methods to various studies of CRC etiology and prognosis to uncover novel associations and to develop predictive models that would have translational significance for possible primary, secondary, and tertiary interventions; and (3) to establish an infrastructure (administrative, bioinformatic, computational, software) to support the various research projects and facilitate making our methods accessible to the broader scientific community. This will be achieved by a combination of theoretical developments, simulation studies closely keyed to real data projects, applications to several studies of CRC, and distribution of software for use by outside investigators. Beyond applications to colorectal cancer, our methods will be broadly applicable to other cancer types and many other chronic diseases.
OVERALL PROGRAM NARRATIVE The overall goal of the proposed research program is the integration of different types of information to build comprehensive statistical models for cancer causes and prognosis. Motivated by various studies of colorectal cancer, we will develop novel statistical methods that use prior biological knowledge to inform integrative genomics analyses, that use phylogenetic information to infer gene function, that incorporate high-dimensional data on the internal environment (the microbiome and the exposome), and that model tumor evolution from data on somatic changes within and between tumors. Beyond colon cancer, our methods will be broadly applicable to other cancer types and many other chronic diseases.
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| Liu, Jie; Liang, Gangning; Siegmund, Kimberly D et al. (2018) Data integration by multi-tuning parameter elastic net regression. BMC Bioinformatics 19:369 |
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| Ritchie, Marylyn D; Davis, Joe R; Aschard, Hugues et al. (2017) Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions. Am J Epidemiol 186:771-777 |
| Patel, Chirag J; Kerr, Jacqueline; Thomas, Duncan C et al. (2017) Opportunities and Challenges for Environmental Exposure Assessment in Population-Based Studies. Cancer Epidemiol Biomarkers Prev 26:1370-1380 |
| Thomas, Paul D (2017) The Gene Ontology and the Meaning of Biological Function. Methods Mol Biol 1446:15-24 |
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| Marconett, Crystal N; Zhou, Beiyun; Sunohara, Mitsuhiro et al. (2017) Cross-Species Transcriptome Profiling Identifies New Alveolar Epithelial Type I Cell-Specific Genes. Am J Respir Cell Mol Biol 56:310-321 |
| Ritz, Beate R; Chatterjee, Nilanjan; Garcia-Closas, Montserrat et al. (2017) Lessons Learned From Past Gene-Environment Interaction Successes. Am J Epidemiol 186:778-786 |
| 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 |
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