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Grant Details

Grant Number: 5R01CA201407-04 Interpret this number
Primary Investigator: Peters, Ulrike
Organization: Fred Hutchinson Cancer Research Center
Project Title: Using Functional Genomics to Inform Gene Environment Interactions for Colorectal Cancer
Fiscal Year: 2019
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Abstract

PROJECT SUMMARY/ABSTRACT 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.

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Publications

Genetic overlap between autoimmune diseases and non-Hodgkin lymphoma subtypes.
Authors: Din L. , Sheikh M. , Kosaraju N. , Smedby K.E. , Bernatsky S. , Berndt S.I. , Skibola C.F. , Nieters A. , Wang S. , McKay J.D. , et al. .
Source: Genetic epidemiology, 2019-08-13; , .
EPub date: 2019-08-13.
PMID: 31407831
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Correction to: Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer.
Authors: Bien S.A. , Su Y.R. , Conti D.V. , Harrison T.A. , Qu C. , Guo X. , Lu Y. , Albanes D. , Auer P.L. , Banbury B.L. , et al. .
Source: Human genetics, 2019 Jul; 138(7), p. 789-791.
PMID: 31254090
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Moving from one to many: insights from the growing list of pleiotropic cancer risk genes.
Authors: Bien S.A. , Peters U. .
Source: British journal of cancer, 2019 Jun; 120(12), p. 1087-1089.
EPub date: 2019-05-21.
PMID: 31110328
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Meta-analysis of 16 studies of the association of alcohol with colorectal cancer.
Authors: McNabb S. , Harrison T.A. , Albanes D. , Berndt S.I. , Brenner H. , Caan B.J. , Campbell P.T. , Cao Y. , Chang-Claude J. , Chan A. , et al. .
Source: International journal of cancer, 2019-04-29; , .
EPub date: 2019-04-29.
PMID: 31037736
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Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer.
Authors: Bien S.A. , Su Y.R. , Conti D.V. , Harrison T.A. , Qu C. , Guo X. , Lu Y. , Albanes D. , Auer P.L. , Banbury B.L. , et al. .
Source: Human genetics, 2019 Apr; 138(4), p. 307-326.
EPub date: 2019-02-28.
PMID: 30820706
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A Unified Model for the Analysis of Gene-Environment Interaction.
Authors: Gauderman W.J. , Kim A. , Conti D.V. , Morrison J. , Thomas D.C. , Vora H. , Lewinger J.P. .
Source: American journal of epidemiology, 2019-04-01; 188(4), p. 760-767.
PMID: 30649161
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Shared heritability and functional enrichment across six solid cancers.
Authors: Jiang X. , Finucane H.K. , Schumacher F.R. , Schmit S.L. , Tyrer J.P. , Han Y. , Michailidou K. , Lesseur C. , Kuchenbaecker K.B. , Dennis J. , et al. .
Source: Nature communications, 2019-01-25; 10(1), p. 431.
EPub date: 2019-01-25.
PMID: 30683880
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Discovery of common and rare genetic risk variants for colorectal cancer.
Authors: Huyghe J.R. , Bien S.A. , Harrison T.A. , Kang H.M. , Chen S. , Schmit S.L. , Conti D.V. , Qu C. , Jeon J. , Edlund C.K. , et al. .
Source: Nature genetics, 2019 01; 51(1), p. 76-87.
EPub date: 2018-12-03.
PMID: 30510241
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Using Bayes model averaging to leverage both gene main effects and G ×  E interactions to identify genomic regions in genome-wide association studies.
Authors: Moss L.C. , Gauderman W.J. , Lewinger J.P. , Conti D.V. .
Source: Genetic epidemiology, 2019 03; 43(2), p. 150-165.
EPub date: 2018-11-19.
PMID: 30456811
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Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases.
Authors: McAllister K. , Mechanic L.E. , Amos C. , Aschard H. , Blair I.A. , Chatterjee N. , Conti D. , Gauderman W.J. , Hsu L. , Hutter C.M. , et al. .
Source: American journal of epidemiology, 2017-10-01; 186(7), p. 753-761.
PMID: 28978193
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Update on the State of the Science for Analytical Methods for Gene-Environment Interactions.
Authors: Gauderman W.J. , Mukherjee B. , Aschard H. , Hsu L. , Lewinger J.P. , Patel C.J. , Witte J.S. , Amos C. , Tai C.G. , Conti D. , et al. .
Source: American journal of epidemiology, 2017-10-01; 186(7), p. 762-770.
PMID: 28978192
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Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions.
Authors: Ritchie M.D. , Davis J.R. , Aschard H. , Battle A. , Conti D. , Du M. , Eskin E. , Fallin M.D. , Hsu L. , Kraft P. , et al. .
Source: American journal of epidemiology, 2017-10-01; 186(7), p. 771-777.
PMID: 28978191
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Lessons Learned From Past Gene-Environment Interaction Successes.
Authors: Ritz B.R. , Chatterjee N. , Garcia-Closas M. , Gauderman W.J. , Pierce B.L. , Kraft P. , Tanner C.M. , Mechanic L.E. , McAllister K. .
Source: American journal of epidemiology, 2017-10-01; 186(7), p. 778-786.
PMID: 28978190
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