Colorectal cancer (CRC) is a complex disease with both genetic (G) and environmental (E) risk factors contributing to susceptibility. Genome-wide GxE interaction scans (GWIS) can help identify novel susceptibility loci and biologically meaningful GxE interactions that point to new carcinogenic mechanisms. Limited statistical power remains a primary concern in GxE analyses. To maximize the statistical power in a GWIS, it is essential to have the largest possible sample size by pooling resources across studies. In this project, we will combine the resources of three existing CRC consortia (approximately 53,600 cases and 52,400 controls of European descent): the Colorectal Cancer Family Registry (CCFR), the Colorectal Cancer Transdisciplinary (CORECT) Study, and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) for interaction testing with 8 environmental and lifestyle factors: alcohol, calcium, folate, hormone replacement therapy (HRT), non- steroidal anti-inflammatory drugs (NSAIDs), red meat, processed meat, and smoking. To improve statistical power and enhance our ability to discover true GxE associations, we will as part of Aim 1 incorporate functional genomics data in two forms: (1) enhancer/promoter profiles derived from ChIPseq and DNase I hypersensitive sites (DHS) data publicly available from Roadmap or from our own experiments in normal colon tissue; and (2) our newly generated RNA-Seq results from normal colon biopsies with detailed environmental and lifestyle risk factor information, and gene expression measured in normal human 3D colon organoids (?mini guts?) in response to environmental exposures.
In Aim 2 we will use our novel statistical methods that can incorporate the CR and E-specific functional genomics data generated in Aim 1 to discover new GxE interaction for CRC with rare and common single nucleotide variants (down to MAF 0.1%) in up to 53,600 cases and 52,400 controls. To narrow in on the underlying causal variant(s) for any identified novel GxE interaction, we will conduct fine-mapping analyses using a trans-ethnic meta-analysis (23,500 non-European and 106,000 European). To follow-up on identified significant GxE interactions, we will functionally validate our strongest GxE interactions (including previously published findings) to provide support for the novel GxE interactions such as knock down in CRC cell lines and normal human 3D colon epithelial organoids. Our large and well-characterized study population, combined with our experienced research team, and integration of functional genomics data into our novel statistical methods provide opportunities to better understand how genetic and environmental risk factors, combined, contribute to individual risk of CRC. Discovering GxE interactions will provide insight into the underlying mechanisms that drive gene-CRC associations impacted by established environmental risk factors. Since genetic profiles are fixed, modifying environmental exposures to alter deleterious effects of alleles remains an important preventive strategy.

Public Health Relevance

This study will incorporate functional genomics data and apply novel statistical methods to investigate interaction between rare and common variants across the genome and eight environmental and lifestyle risk factors for CRC. Our findings will contribute to identifying carcinogenic mechanisms and, in turn, potential targets for future interventions. They also can help improve upon current strategies for preventing CRC.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Cancer, Heart, and Sleep Epidemiology B Study Section (CHSB)
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Mechanic, Leah E
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Fred Hutchinson Cancer Research Center
United States
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Moss, Lilit C; Gauderman, William J; Lewinger, Juan Pablo et al. (2018) Using Bayes model averaging to leverage both gene main effects and G?×? E interactions to identify genomic regions in genome-wide association studies. Genet Epidemiol :
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
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
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