Using gene-environment interactions to explore pathways for colorectal cancer risk

Emmanouil Bouras¹, Ren Yu², Andre E Kim³, Georgios Markozannes⁴, Neil Murphy⁵, Demetrius Albanes⁶, Laura N Anderson⁷, Elizabeth L Barry⁸, Sonja I Berndt⁶, D Timothy Bishop⁹, Hermann Brenner¹⁰, Andrea Burnett-Hartman¹¹, Peter T Campbell¹², Robert Carreras-Torres¹³, Andrew T Chan¹⁴, Iona Cheng¹⁵, Matthew A Devall¹⁶, Virginia Diez-Obrero¹⁷, Niki Dimou⁵, David A Drew¹⁸, Stephen B Gruber¹⁹, Andrea Gsur²⁰, Michael Hoffmeister²¹, Li Hsu²², Jeroen R Huyghe²³, Eric Kawaguchi³, Temitope O Keku²⁴, Anshul Kundaje²⁵, Sébastien Küry²⁶, Loïc Le Marchand²⁷, Juan Pablo Lewinger³, Li Li²⁸, Brigid M Lynch²⁹, Victor Moreno¹⁷, John L Morrison³, Christina C Newton³⁰, Mireia Obón-Santacana³¹, Julie R Palmer³², Nikos Papadimitriou⁵, Andrew J Pellatt³³, Anita R Peoples³⁰, Paul D P Pharoah³⁴, Elizabeth A Platz³⁵, Conghui Qu²³, Edward Ruiz-Narvaez³⁶, Joel Sanchez Mendez³⁷, Robert E Schoen³⁸, Mariana C Stern³⁷, Claire E Thomas²³, Yu Tian³⁹, Caroline Y Um³⁰, Kala Visvanathan³⁵, Pavel Vodicka⁴⁰, Veronika Vymetalkova⁴⁰, Emily White⁴¹, Alicja Wolk⁴², Michael O Woods⁴³, Anna H Wu³⁷, Marc J Gunter⁴⁴, W James Gauderman³, Ulrike Peters⁴¹, Marina Evangelou², Konstantinos K Tsilidis⁴⁵

Affiliations

¹ Department of Epidemiology and Biostatistics, School of Public Health-Faculty of Medicine, Imperial College London, London, UK; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece. Electronic address: ebouras@imperial.ac.uk.
² Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London, UK.
³ Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
⁴ Department of Epidemiology and Biostatistics, School of Public Health-Faculty of Medicine, Imperial College London, London, UK; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
⁵ Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France.
⁶ Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
⁷ Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
⁸ Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
⁹ Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK.
¹⁰ Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
¹¹ Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, USA.
¹² Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
¹³ Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08908, Barcelona, Spain; Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute (IDIBGI), Salt, 17190, Girona, Spain.
¹⁴ Broad Institute of Harvard and MIT, Cambridge, MA, USA; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
¹⁵ Department of Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, CA, USA.
¹⁶ Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA; Department of Public Health Sciences, Center for Public Health Genomics, Charlottesville, VA, USA.
¹⁷ Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08908, Barcelona, Spain; Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Barcelona, 08908, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029, Madrid, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, 08908, Spain.
¹⁸ Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
¹⁹ Department of Medical Oncology & Therapeutics Research and Center for Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA.
²⁰ Center for Cancer Research, Medical University of Vienna, Vienna, Austria.
²¹ Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
²² Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Biostatistics, University of Washington, Seattle, WA, USA.
²³ Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
²⁴ Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC, USA.
²⁵ Department of Computer Science, Stanford University, Stanford, CA, USA; Department of Genetics, Stanford University, Stanford, CA, USA.
²⁶ Service de Génétique Médicale, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France.
²⁷ Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA.
²⁸ Department of Family Medicine, University of Virginia, Charlottesville, VA, USA.
²⁹ Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
³⁰ Department of Population Science, American Cancer Society, Atlanta, GA, USA.
³¹ Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08908, Barcelona, Spain; Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Barcelona, 08908, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029, Madrid, Spain.
³² Slone Epidemiology Center, at Boston University, Boston, MA, USA.
³³ Intermountain Health, Salt Lake City, UT, USA.
³⁴ Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
³⁵ Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
³⁶ Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA.
³⁷ Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
³⁸ Departments of Medicine and Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA.
³⁹ Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; School of Public Health, Capital Medical University, Beijing, China.
⁴⁰ Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic; Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic; Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic.
⁴¹ Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA.
⁴² Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
⁴³ Memorial University of Newfoundland, Discipline of Genetics, St. John's, Canada.
⁴⁴ Department of Epidemiology and Biostatistics, School of Public Health-Faculty of Medicine, Imperial College London, London, UK; Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France.
⁴⁵ Department of Epidemiology and Biostatistics, School of Public Health-Faculty of Medicine, Imperial College London, London, UK; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece. Electronic address: k.tsilidis@imperial.ac.uk.

PMID: 41076992
PMCID: PMC12547926
DOI: 10.1016/j.ebiom.2025.105964

Using gene-environment interactions to explore pathways for colorectal cancer risk

Emmanouil Bouras et al. EBioMedicine. 2025 Nov.

. 2025 Nov:121:105964.

doi: 10.1016/j.ebiom.2025.105964. Epub 2025 Oct 11.

Authors

Affiliations

¹ Department of Epidemiology and Biostatistics, School of Public Health-Faculty of Medicine, Imperial College London, London, UK; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece. Electronic address: ebouras@imperial.ac.uk.
² Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London, UK.
³ Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
⁴ Department of Epidemiology and Biostatistics, School of Public Health-Faculty of Medicine, Imperial College London, London, UK; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
⁵ Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France.
⁶ Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
⁷ Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
⁸ Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
⁹ Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK.
¹⁰ Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
¹¹ Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, USA.
¹² Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
¹³ Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08908, Barcelona, Spain; Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute (IDIBGI), Salt, 17190, Girona, Spain.
¹⁴ Broad Institute of Harvard and MIT, Cambridge, MA, USA; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
¹⁵ Department of Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, CA, USA.
¹⁶ Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA; Department of Public Health Sciences, Center for Public Health Genomics, Charlottesville, VA, USA.
¹⁷ Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08908, Barcelona, Spain; Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Barcelona, 08908, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029, Madrid, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, 08908, Spain.
¹⁸ Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
¹⁹ Department of Medical Oncology & Therapeutics Research and Center for Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA.
²⁰ Center for Cancer Research, Medical University of Vienna, Vienna, Austria.
²¹ Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
²² Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Biostatistics, University of Washington, Seattle, WA, USA.
²³ Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
²⁴ Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC, USA.
²⁵ Department of Computer Science, Stanford University, Stanford, CA, USA; Department of Genetics, Stanford University, Stanford, CA, USA.
²⁶ Service de Génétique Médicale, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France.
²⁷ Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA.
²⁸ Department of Family Medicine, University of Virginia, Charlottesville, VA, USA.
²⁹ Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
³⁰ Department of Population Science, American Cancer Society, Atlanta, GA, USA.
³¹ Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08908, Barcelona, Spain; Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Barcelona, 08908, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029, Madrid, Spain.
³² Slone Epidemiology Center, at Boston University, Boston, MA, USA.
³³ Intermountain Health, Salt Lake City, UT, USA.
³⁴ Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
³⁵ Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
³⁶ Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA.
³⁷ Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
³⁸ Departments of Medicine and Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA.
³⁹ Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; School of Public Health, Capital Medical University, Beijing, China.
⁴⁰ Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic; Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic; Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic.
⁴¹ Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA.
⁴² Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
⁴³ Memorial University of Newfoundland, Discipline of Genetics, St. John's, Canada.
⁴⁴ Department of Epidemiology and Biostatistics, School of Public Health-Faculty of Medicine, Imperial College London, London, UK; Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France.
⁴⁵ Department of Epidemiology and Biostatistics, School of Public Health-Faculty of Medicine, Imperial College London, London, UK; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece. Electronic address: k.tsilidis@imperial.ac.uk.

PMID: 41076992
PMCID: PMC12547926
DOI: 10.1016/j.ebiom.2025.105964

Abstract

Background: Colorectal cancer (CRC) is a significant public health concern, highlighting the critical need for identifying novel intervention targets for its prevention.

Methods: We conducted genome-wide interaction analyses for 15 exposures with established or putative CRC risk [body mass index (BMI), height, physical activity, smoking, type 2 diabetes, use of menopausal hormone therapy, non-steroidal anti-inflammatory drugs, and intake of alcohol, calcium, fibre, folate, fruits, processed meat, red meat, and vegetables], and used interaction estimates to explore pathways and genes underlying CRC risk. The adaptive combination of Bayes Factors (ADABF), and over-representation analysis (ORA) were used for pathway analyses, and findings were further investigated using publicly available resources [hallmarks of cancer, Open Targets Platform (OTP)].

Findings: A total of 1973 pathways using ADABF, and 840 pathways using ORA, out of the 2950 analysed, were enriched (P < 0.05) for at least one exposure, as well as 1227 genes within the enriched pathways. Data were available for 811/1227 coding genes in the OTP, 241 of which were supported by strong relative abundance of prior evidence (overall OTP score > 0.05). Fifty percent of the genes (617/1227) mapped to at least one hallmark of cancer, most of which (388/617) pertained to the Sustaining Proliferative Signalling hallmark. Our findings reflect previously established pathways for CRC risk and highlight the emerging importance of several less studied genes. Common pathways were found for several combinations of exposures, potentially suggesting common underlying mechanisms.

Interpretation: The results of the present analysis provide a basis for further functional research. If confirmed, they may help elucidate the etiological associations between risk factors and CRC risk and ultimately inform personalized prevention strategies.

Funding: This study was funded by Cancer Research UK (CRUK; grant number:PPRCPJT∖100005) and World Cancer Research Fund International (WCRF; IIG_FULL_2020_022). Funding for grant IIG_FULL_2020_022 was obtained from Wereld Kanker Onderzoek Fonds (WKOF) as part of the World Cancer Research Fund International grant programme. Full funding details for the individual consortia are provided in the acknowledgements.

Keywords: Colorectal cancer; Gene-environment interactions; Mechanisms; Molecular pathways; Pathway analysis.

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Conflict of interest statement

Declaration of interests EB was supported by grants from Cancer Research UK (CRUK; grants number: PPRCPJT∖100005), and World Cancer Research Fund International (WCRF; IIG_FULL_2020_022). RY was supported by Cancer Research UK (CRUK; grants number: PPRCPJT∖100005). ELB was supported by the National Cancer Institute, National Institutes of Health. SB was supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH. ATC was supported by grants from the National Institutes of Health, Stand Up to Cancer, Freenome Holdings, and American Cancer Society; Received payment for expert testimony by Boehringer Ingelheim; Participation on a Data Safety Monitoring Board or Advisory Board for Pfizer Inc; Held leadership or fiduciary role in the American Gastroenterological Association, and National Cancer Institute. DAD was supported by a National Institutes of Health grant. ESK was supported by grants from the National Cancer Institute [R01 CA201407, P01 CA196569]; Holds stocks or stock options from Abbvie (ABBV), and Pfizer (PFE). TOK was supported by a National Cancer Institute grant [R01CA066635, 1996–2008]. AK was supported by a National Institutes of Health grant [R01CA273198]; Was a consultant with Bristol Myers Squibb, Inari Agriculture, and Arcadia Science; Holds stocks or stock options from TensorBio Inc, SerImmune Inc., Illumina, Deep Genomics, ImmunAI. BML was a consultant with Elsevier; Was supported for attending meetings and/or travel by the International Epidemiological Association; Held leadership or fiduciary role in the International Epidemiological Association Council. VM was supported by grants from the Instituto de Salud Carlos III, Programa FORTALECE del Ministerio de Ciencia e Innovación through the project number FORT23/00032, Spanish Association Against Cancer (AECC) Scientific Foundation grant GCTRA18022MORE, and Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP) action Genrisk. JLM was supported by grants from the National Cancer Institute [R01 CA201407, P01 CA196569, R01 CA273198]. JRP was supported by a National Institutes of Health grant. AJP Was a consultant with Abbvie. PDPP was supported by a Cancer Research UK grant. EAP held leadership or fiduciary role (unpaid), as an elected member of the American Association for Cancer Research Board of Directors. RES was supported by grants from the National Cancer Institute NCI EDRN Grant (U01-CA152753), and Exact Sciences, Freenome, Immunovia; Was a consultant with Guardant, Freenome, Exact Sciences; Participated on a Data Safety Monitoring Board or Advisory Board in VA CONFIRM trial, NCI Cancer Screening Research Network. CET is an epidemiology contractor with Pfizer, unrelated to the present study. VV was supported by a Czech Science Foundation 21-04607X grant, unrelated to the submitted work. AW was supported by a Swedish Cancer Foundation grant. WJG was supported by grants from the National Cancer Institute [R01 CA201407, P01 CA196569, R01 CA273198]. UP was supported by grants from the National Institute of Health, V Foundation, and Goldman Sachs Foundation; Was a consultant with AbbVie; UP's family is holding individual stocks for the following companies: Amazon, ARM Holdings PLC, BioNTech, BYD Company Limited, Crowdstrike Holdings Inc, CureVac, Google/Alphabet, Microsoft Corp, NVIDIA Corp, Stellantis. ME was supported by Cancer Research UK (CRUK; grants number: PPRCPJT∖100005). KKT was supported by grants from Cancer Research UK (CRUK; grants number: PPRCPJT∖100005), and World Cancer Research Fund International (WCRF; IIG_FULL_2020_022).The remaining authors have no competing interests to declare.

Figures

**Fig. 1**
Project overview. ADABF: adaptive combination of Bayes factors; BF: Bayes factors; CCFR: Colon Cancer Family Registry Cohort; CRC: Colorectal cancer; GECCO: Genetics and Epidemiology of Colorectal Cancer Consortium; KEGG: Kyoto Encyclopedia of Genes and Genomes; ORA: Over-representation analysis; PID: Pathway Interaction Database; SNPs: Single nucleotide polymorphisms.

**Fig. 2**
Summary of enriched genes (P < 0.05) and genes commonly enriched across exposures using the adaptive combination of Bayes factors approach. A. Commonly enriched genes across different exposures. B. Total genes enriched per exposure (top), and number overlapping genes across exposures, by Open targets platform (OTP) score. BMI: Body mass index; NSAIDs: non-steroidal anti-inflammatory drugs. **List of gene abbreviations on the plot:** ABCB1: ATP binding cassette subfamily B member 1; ACSM3: acyl-CoA synthetase medium chain family member 3; AGO1: argonaute RISC component 1; AGO3: argonaute RISC catalytic component 3; APOE: apolipoprotein E; ARNTL: basic helix-loop-helix ARNT like 1; BCL2L1: BCL2 like 1; C2: complement C2; C3: complement C3; C4A: complement C4A (Chido/Rodgers blood group); C4B: complement C4B (Chido/Rodgers blood group); CCR4: C–C motif chemokine receptor 4; CD55: CD55 molecule (Cromer blood group); CDKN1C: cyclin dependent kinase inhibitor 1C; CFB: complement factor B; CLDN11: claudin 11; COX4I2: cytochrome c oxidase subunit 4I2; CYP24A1: cytochrome P450 family 24 subfamily A member 1; CYP4B1: cytochrome P450 family 4 subfamily B member 1; EGFR: epidermal growth factor receptor; ERBB4: erb-b2 receptor tyrosine kinase 4; ESR1: estrogen receptor 1; FGF3: fibroblast growth factor 3; FGG: fibrinogen gamma chain; FTMT: ferritin mitochondrial; GABBR2: gamma-aminobutyric acid type B receptor subunit 2; GRIN2B: glutamate ionotropic receptor NMDA type subunit 2B; HLA-B: major histocompatibility complex, class I, B; HLA-C: major histocompatibility complex, class I, C; HLA-DMA: major histocompatibility complex, class II, DM alpha; HLA-DMB: major histocompatibility complex, class II, DM beta; HLA-DQB1: major histocompatibility complex, class II, DQ beta 1; HSPA1A: heat shock protein family A (Hsp70) member 1A; HSPA1B: heat shock protein family A (Hsp70) member 1B; IFNG: interferon gamma; INSR: insulin receptor; ITGA6: integrin subunit alpha 6; ITPR2: inositol 1,4,5-trisphosphate receptor type 2; JAK1: Janus kinase 1; LDLR: low density lipoprotein receptor; LEPR: leptin receptor; MEF2C: myocyte enhancer factor 2C; MT1M: metallothionein 1M; NCOR2: nuclear receptor corepressor 2; OR1J4: olfactory receptor family 1 subfamily J member 4; OR1L8: olfactory receptor family 1 subfamily L member 8; OR1N1: olfactory receptor family 1 subfamily N member 1; OR1N2: olfactory receptor family 1 subfamily N member 2; PARP1: poly(ADP-ribose) polymerase 1; PDCD1LG2: programmed cell death 1 ligand 2; PDGFRA: platelet derived growth factor receptor alpha; POU5F1: POU class 5 homeobox 1; PRKG1: protein kinase cGMP-dependent 1; RARB: retinoic acid receptor beta; RBBP8: RB binding protein 8, endonuclease; RBFOX1: RNA binding fox-1 homolog 1; RPS6KA2: ribosomal protein S6 kinase A2; SDC1: syndecan 1; SERPINE1: serpin family E member 1; SGK1: serum/glucocorticoid regulated kinase 1; ST6GALNAC3: ST6 N-acetylgalactosaminide alpha-2,6-sialyltransferase 3; STAT3: signal transducer and activator of transcription 3; STAT5A: signal transducer and activator of transcription 5A; STAT5B: signal transducer and activator of transcription 5B; TAS2R14: taste 2 receptor member 14; TAS2R19: taste 2 receptor member 19; TAS2R20: taste 2 receptor member 20; TAS2R30: taste 2 receptor member 30; TAS2R31: taste 2 receptor member 31; TAS2R42: taste 2 receptor member 42; TAS2R43: taste 2 receptor member 43; TAS2R46: taste 2 receptor member 46; TAS2R50: taste 2 receptor member 50; TFPI: tissue factor pathway inhibitor; TJP1: tight junction protein 1; UCP1: uncoupling protein 1; UGT1A10: UDP glucuronosyltransferase family 1 member A10; UGT1A3: UDP glucuronosyltransferase family 1 member A3; UGT1A4: UDP glucuronosyltransferase family 1 member A4; UGT1A5: UDP glucuronosyltransferase family 1 member A5; UGT1A6: UDP glucuronosyltransferase family 1 member A6; UGT1A7: UDP glucuronosyltransferase family 1 member A7; UGT1A8: UDP glucuronosyltransferase family 1 member A8; UGT1A9: UDP glucuronosyltransferase family 1 member A9; WWP1: WW domain containing E3 ubiquitin protein ligase 1.

**Fig. 3**
Summary of enriched (P < 0.05) pathways. A. Total number of enriched pathways per gene-set library, exposure, and method used. Color is analogous to the percent of pathways enriched relative to pathways assessed. B. Overlapping of enriched pathways across exposures, per method used. C. Top enriched pathway per gene-set library, and exposure. Circles represent associated ADABF pathways with the size analogous to the percent of enriched genes within the pathway, and triangles represent enriched ORA pathways. The top ADABF pathways were defined by the total number of enriched genes within the pathway, while top ORA pathways based on the minimum q-value. ADABF: adaptive combination of Bayes factors; KEGG: Kyoto Encyclopedia of Genes and Genomes; ORA: Over-representation analysis; PID: Pathway Interaction Database; SNPs: Single nucleotide polymorphisms; WP: WikiPathways.

**Fig. 4**
Network of genes identified using the adaptive combination of Bayes factors (ADABF) within pathways that were highlighted by both the ADABF and over-representation analysis (ORA). The plot displays pathways containing genes commonly enriched across any combination of exposures. Human Leukocyte Antigen (HLA) genes were not included in the network. BMI: Body mass index; NSAIDs: non-steroidal anti-inflammatory drugs; OTP: open targets platform. **List of gene abbreviations on the plot:** ABL2: ABL proto-oncogene 2, non-receptor tyrosine kinase; ACTL6B: actin like 6B; ALAD: aminolevulinate dehydratase; ALDH9A1: aldehyde dehydrogenase 9 family member A1; ANO10: anoctamin 10; ANO4: anoctamin 4; AP2A1: adaptor related protein complex 2 subunit alpha 1; ARAP2: ArfGAP with RhoGAP domain, ankyrin repeat and PH domain 2; ARFGAP2: ADP ribosylation factor GTPase activating protein 2; ARHGEF3: Rho guanine nucleotide exchange factor 3; ARL2: ADP ribosylation factor like GTPase 2; ARNT2: aryl hydrocarbon receptor nuclear translocator 2; ATP10A: ATPase phospholipid transporting 10A (putative); ATP1B3: ATPase Na+/K+ transporting subunit beta 3; ATP6V1B1: ATPase H+ transporting V1 subunit B1; BAIAP2L1: BAR/IMD domain containing adaptor protein 2 like 1; ARNTL: basic helix-loop-helix ARNT like 1; BRAF: B-Raf proto-oncogene, serine/threonine kinase; CACNA1A: calcium voltage-gated channel subunit alpha1 A; CACNA1D: calcium voltage-gated channel subunit alpha1 D; CALML6: calmodulin like 6; CAMK1: calcium/calmodulin dependent protein kinase I; CCNT2: cyclin T2; CDH18: cadherin 18; CDK4: cyclin dependent kinase 4; CDK6: cyclin dependent kinase 6; CGB5: chorionic gonadotropin subunit beta 5; CGB8: chorionic gonadotropin subunit beta 8; CHN2: chimerin 2; CHRM4: cholinergic receptor muscarinic 4; CLASP1: cytoplasmic linker associated protein 1; CNTN6: contactin 6; COL14A1: collagen type XIV alpha 1 chain; COL23A1: collagen type XXIII alpha 1 chain; COL25A1: collagen type XXV alpha 1 chain; COL2A1: collagen type II alpha 1 chain; COL4A2: collagen type IV alpha 2 chain; COL4A3: collagen type IV alpha 3 chain; COX4I2: cytochrome c oxidase subunit 4I2; COX6B2: cytochrome c oxidase subunit 6B2; CPSF7: cleavage and polyadenylation specific factor 7; CREB3L1: cAMP responsive element binding protein 3 like 1; CREBBP: CREB binding protein; CRH: corticotropin releasing hormone; CRIM1: cysteine rich transmembrane BMP regulator 1; CYP4B1: cytochrome P450 family 4 subfamily B member 1; DAB1: DAB adaptor protein 1; DAZL: deleted in azoospermia like; DBNL: drebrin like; DCC: DCC netrin 1 receptor; DEK: DEK proto-oncogene; DLG2: discs large MAGUK scaffold protein 2; DMRT1: doublesex and mab-3 related transcription factor 1; DNM3: dynamin 3; DSCAML1: DS cell adhesion molecule like 1; EARS2: glutamyl-tRNA synthetase 2, mitochondrial; EFNA5: ephrin A5; EGFR: epidermal growth factor receptor; EIF4EBP2: eukaryotic translation initiation factor 4E binding protein 2; ERBB4: erb-b2 receptor tyrosine kinase 4; ESR1: estrogen receptor 1; EXOC2: exocyst complex component 2; EXOC3: exocyst complex component 3; FAM20C: FAM20C golgi associated secretory pathway kinase; FAU: FAU ubiquitin like and ribosomal protein S30 fusion; FLI1: Fli-1 proto-oncogene, ETS transcription factor; FOXO3: forkhead box O3; FTMT: ferritin mitochondrial; GABBR2: gamma-aminobutyric acid type B receptor subunit 2; GALNT12: polypeptide N-acetylgalactosaminyltransferase 12; GALNT13: polypeptide N-acetylgalactosaminyltransferase 13; GALNTL6: polypeptide N-acetylgalactosaminyltransferase like 6; GLRA3: glycine receptor alpha 3; GRIK2: glutamate ionotropic receptor kainate type subunit 2; GRIN2A: glutamate ionotropic receptor NMDA type subunit 2A; GRIP1: glutamate receptor interacting protein 1; GRM7: glutamate metabotropic receptor 7; GUCA1A: guanylate cyclase activator 1A; HCST: hematopoietic cell signal transducer; HOXD11: homeobox D11; HS3ST4: heparan sulfate-glucosamine 3-sulfotransferase 4; HS3ST5: heparan sulfate-glucosamine 3-sulfotransferase 5; HSPA1L: heat shock protein family A (Hsp70) member 1 like; ITPR2: inositol 1,4,5-trisphosphate receptor type 2; KALRN: kalirin RhoGEF kinase; KAZN: kazrin, periplakin interacting protein; KCNAB2: potassium voltage-gated channel subfamily A regulatory beta subunit 2; KCNE4: potassium voltage-gated channel subfamily E regulatory subunit 4; KIF5A: kinesin family member 5A; KIT: KIT proto-oncogene, receptor tyrosine kinase; KLF4: KLF transcription factor 4; KRT38: keratin 38; KRT39: keratin 39; KRT40: keratin 40; KRT71: keratin 71; KRT74: keratin 74; KRT86: keratin 86; KRTAP10-10: keratin associated protein 10-10; KRTAP10-4: keratin associated protein 10-4; KRTAP10-9: keratin associated protein 10-9; KRTAP12-1: keratin associated protein 12-1; KRTAP12-2: keratin associated protein 12-2; KRTAP3-2: keratin associated protein 3-2; KRTAP3-3: keratin associated protein 3-3; KRTAP5-6: keratin associated protein 5-6; KSR2: kinase suppressor of ras 2; LAT: linker for activation of T cells; LCK: LCK proto-oncogene, Src family tyrosine kinase; LILRA1: leukocyte immunoglobulin like receptor A1; LILRA2: leukocyte immunoglobulin like receptor A2; LIN7B: lin-7 homolog B, crumbs cell polarity complex component; LIPK: lipase family member K; LIPN: lipase family member N; LMO7: LIM domain 7; LRFN4: leucine rich repeat and fibronectin type III domain containing 4; LRRC4C: leucine rich repeat containing 4C; LRRTM4: leucine rich repeat transmembrane neuronal 4; LTBP1: latent transforming growth factor beta binding protein 1; MAFB: MAF bZIP transcription factor B; MAP2K4: mitogen-activated protein kinase kinase 4; MAPK1: mitogen-activated protein kinase 1; MED25: mediator complex subunit 25; MED27: mediator complex subunit 27; MED6: mediator complex subunit 6; MED7: mediator complex subunit 7; MET: MET proto-oncogene, receptor tyrosine kinase; MGST2: microsomal glutathione S-transferase 2; MYC: MYC proto-oncogene, bHLH transcription factor; MYLK3: myosin light chain kinase 3; MYRIP: myosin VIIA and Rab interacting protein; NCALD: neurocalcin delta; NCOR2: nuclear receptor corepressor 2; NDST2: N-deacetylase and N-sulfotransferase 2; NFE2L2: NFE2 like bZIP transcription factor 2; NFKB2: nuclear factor kappa B subunit 2; NOTCH2: notch receptor 2; NOX3: NADPH oxidase 3; NRG1: neuregulin 1; NTRK1: neurotrophic receptor tyrosine kinase 1; OPRM1: opioid receptor mu 1; PDCD1LG2: programmed cell death 1 ligand 2; PDE11A: phosphodiesterase 11A; PDGFRA: platelet derived growth factor receptor alpha; PDGFRB: platelet derived growth factor receptor beta; PECR: peroxisomal trans-2-enoyl-CoA reductase; PHF21A: PHD finger protein 21A; PILRA: paired immunoglobin like type 2 receptor alpha; PILRB: paired immunoglobin like type 2 receptor beta; PIP4K2C: phosphatidylinositol-5-phosphate 4-kinase type 2 gamma; PIP5K1C: phosphatidylinositol-4-phosphate 5-kinase type 1 gamma; PKP4: plakophilin 4; PLEKHG1: pleckstrin homology and RhoGEF domain containing G1; PLEKHG4: pleckstrin homology and RhoGEF domain containing G4; PLXNB2: plexin B2; POMGNT1: protein O-linked mannose N-acetylglucosaminyltransferase 1 (beta 1,2-); PON2: paraoxonase 2; PPFIA3: PTPRF interacting protein alpha 3; PPP2R5B: protein phosphatase 2 regulatory subunit B'beta; PPP3CA: protein phosphatase 3 catalytic subunit alpha; PREX2: phosphatidylinositol-3,4,5-trisphosphate dependent Rac exchange factor 2; PSMB3: proteasome 20S subunit beta 3; PSMD11: proteasome 26S subunit, non-ATPase 11; PSMD13: proteasome 26S subunit, non-ATPase 13; PTGS2: prostaglandin-endoperoxide synthase 2; PUM2: pumilio RNA binding family member 2; RAD51B: RAD51 paralog B; RAF1: Raf-1 proto-oncogene, serine/threonine kinase; RARA: retinoic acid receptor alpha; RASGRP4: RAS guanyl releasing protein 4; RB1: RB transcriptional corepressor 1; RBL2: RB transcriptional corepressor like 2; RBPJ: recombination signal binding protein for immunoglobulin kappa J region; RGS6: regulator of G protein signalling 6; RPL37A: ribosomal protein L37a; RPS18: ribosomal protein S18; RPS6KA2: ribosomal protein S6 kinase A2; RPS9: ribosomal protein S9; RUNX1: RUNX family transcription factor 1; RUNX1T1: RUNX1 partner transcriptional co-repressor 1; SCN4B: sodium voltage-gated channel beta subunit 4; SDHA: succinate dehydrogenase complex flavoprotein subunit A; SDK1: sidekick cell adhesion molecule 1; SDK2: sidekick cell adhesion molecule 2; SEMA5A: semaphorin 5A; SHC2: SHC adaptor protein 2; SIGLEC11: sialic acid binding Ig like lectin 11; SMARCA4: SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4; SMPD2: sphingomyelin phosphodiesterase 2; SOX5: SRY-box transcription factor 5; SPRED2: sprouty related EVH1 domain containing 2; SRGAP2: SLIT-ROBO Rho GTPase activating protein 2; ST6GALNAC3: ST6 N-acetylgalactosaminide alpha-2,6-sialyltransferase 3; STAM2: signal transducing adaptor molecule 2; STAT3: signal transducer and activator of transcription 3; STAT5B: signal transducer and activator of transcription 5B; SULT6B1: sulfotransferase family 6B member 1; SYK: spleen associated tyrosine kinase; SYN3: synapsin III; TAS2R19: taste 2 receptor member 19; TAS2R20: taste 2 receptor member 20; TAS2R30: taste 2 receptor member 30; TAS2R31: taste 2 receptor member 31; TAS2R41: taste 2 receptor member 41; TAS2R42: taste 2 receptor member 42; TAS2R43: taste 2 receptor member 43; TAS2R46: taste 2 receptor member 46; TAS2R50: taste 2 receptor member 50; TBC1D1: TBC1 domain family member 1; TGS1: trimethylguanosine synthase 1; THSD4: thrombospondin type 1 domain containing 4; TLL1: tolloid like 1; TNR: tenascin R; TP63: tumour protein p63; TRAF1: TNF receptor associated factor 1; TREML1: triggering receptor expressed on myeloid cells like 1; TRPM3: transient receptor potential cation channel subfamily M member 3; TSC2: TSC complex subunit 2; UCN: urocortin; UGT1A3: UDP glucuronosyltransferase family 1 member A3; UGT1A4: UDP glucuronosyltransferase family 1 member A4; UGT1A5: UDP glucuronosyltransferase family 1 member A5; UQCRQ: ubiquinol-cytochrome c reductase complex III subunit VII; UTRN: utrophin; VEGFA: vascular endothelial growth factor A; VPS45: vacuolar protein sorting 45 homolog; WNK4: WNK lysine deficient protein kinase 4; WNT8A: Wnt family member 8A; WWTR1: WW domain containing transcription regulator 1; XYLT2: xylosyltransferase 2; ZSWIM8: zinc finger SWIM-type containing 8.

**Fig. 5**
Summary of the enriched genes and pathways using the adaptive combination of Bayes factors (ADABF) approach. Only the genes with an Open Targets Platform (OTP) overall association score >0.2, and pertinent pathways with >5 enriched genes with OTP>0.2 are plotted. A. Enriched genes and pathways. X-axis (top) presents enriched genes. Y-axis of the main panel shows the enriched pathways, and the tiles represent the exposures [B: Body mass Index (BMI); S: Smoking: N: Non-Steroidal Anti-inflammatory Drugs (NSAIDs)]. Tile color is analogous to the -log10 ADABF P-value of pertinent gene. B. Summary of the evidence from the OTP. Each pie-chart represents a gene and illustrates the relative contribution of individual components that form the overall OTP association score, and their size is analogous to the OTP overall association score. Five sub-components of OTP are plotted, cancerGeneCensus: representing genes containing mutations that have been causally implicated in cancer, chembl: representing bioactive molecules with drug-like properties, europepmc: representing gene-colorectal cancer (CRC) co-occurrences in the literature, expressionAtlas: representing differentially expressed genes in CRC, impc: representing the similarity between a mouse model and a human CRC on each gene. C. Summary of the Hallmarks of cancer. The bottom panel shows the hallmark capabilities in which each gene has been implicated in based on various grouping schemes. **List of gene abbreviations on the plot:** ABL2: ABL proto-oncogene 2, non-receptor tyrosine kinase; ALK: ALK receptor tyrosine kinase; ARHGEF10: Rho guanine nucleotide exchange factor 10; ARHGEF10L: Rho guanine nucleotide exchange factor 10 like; BCR: BCR activator of RhoGEF and GTPase; BRAF: B-Raf proto-oncogene, serine/threonine kinase; BTG2: BTG anti-proliferation factor 2; CACNA1D: calcium voltage-gated channel subunit alpha1 D; CCDC6: coiled-coil domain containing 6; CCR4: C–C motif chemokine receptor 4; CDK4: cyclin dependent kinase 4; CDK6: cyclin dependent kinase 6; CDKN2C: cyclin dependent kinase inhibitor 2C; COL2A1: collagen type II alpha 1 chain; CREB3L1: cAMP responsive element binding protein 3 like 1; CREBBP: CREB binding protein; CTNNA2: catenin alpha 2; DAXX: death domain associated protein; DCC: DCC netrin 1 receptor; DDB2: damage specific DNA binding protein 2; DEK: DEK proto-oncogene; DGCR8: DGCR8 microprocessor complex subunit; DLC1: DLC1 Rho GTPase activating protein; EGFR: epidermal growth factor receptor; ERBB4: erb-b2 receptor tyrosine kinase 4; ESR1: estrogen receptor 1; FIP1L1: factor interacting with PAPOLA and CPSF1; FLI1: Fli-1 proto-oncogene, ETS transcription factor; FOXO3: forkhead box O3; FZD3: frizzled class receptor 3; GRIN2A: glutamate ionotropic receptor NMDA type subunit 2A; INSR: insulin receptor; KAT6B: lysine acetyltransferase 6B; KIAA1549: KIAA1549; KIT: KIT proto-oncogene, receptor tyrosine kinase; KLF4: KLF transcription factor 4; KRT8: keratin 8; LCK: LCK proto-oncogene, Src family tyrosine kinase; MAFB: MAF bZIP transcription factor B; MAML2: mastermind like transcriptional coactivator 2; MAP2K4: mitogen-activated protein kinase kinase 4; MAPK1: mitogen-activated protein kinase 1; MECOM: MDS1 and EVI1 complex locus; MET: MET proto-oncogene, receptor tyrosine kinase; MLLT3: MLLT3 super elongation complex subunit; MUC16: mucin 16, cell surface associated; MYC: MYC proto-oncogene, bHLH transcription factor; NBEA: neurobeachin; NCOR2: nuclear receptor corepressor 2; NDUFA10: NADH:ubiquinone oxidoreductase subunit A10; NDUFA3: NADH:ubiquinone oxidoreductase subunit A3; NDUFA4: NDUFA4 mitochondrial complex associated; NDUFA8: NADH:ubiquinone oxidoreductase subunit A8; NDUFAB1: NADH:ubiquinone oxidoreductase subunit AB1; NDUFB2: NADH:ubiquinone oxidoreductase subunit B2; NDUFB7: NADH:ubiquinone oxidoreductase subunit B7; NDUFB9: NADH:ubiquinone oxidoreductase subunit B9; NDUFS1: NADH:ubiquinone oxidoreductase core subunit S1; NDUFS3: NADH:ubiquinone oxidoreductase core subunit S3; NDUFV1: NADH:ubiquinone oxidoreductase core subunit V1; NFE2L2: NFE2 like bZIP transcription factor 2; NFKB2: nuclear factor kappa B subunit 2; NOTCH2: notch receptor 2; NRG1: neuregulin 1; NTRK1: neurotrophic receptor tyrosine kinase 1; OPRM1: opioid receptor mu 1; PABPC1: poly(A) binding protein cytoplasmic 1; PARP1: poly(ADP-ribose) polymerase 1; PAX5: paired box 5; PAX8: paired box 8; PDE10A: phosphodiesterase 10A; PDE1C: phosphodiesterase 1C; PDE4D: phosphodiesterase 4D; PDE8A: phosphodiesterase 8A; PDGFRA: platelet derived growth factor receptor alpha; PDGFRB: platelet derived growth factor receptor beta; POU5F1: POU class 5 homeobox 1; PPP6C: protein phosphatase 6 catalytic subunit; PRDM1: PR/SET domain 1; PRDM16: PR/SET domain 16; PREX2: phosphatidylinositol-3,4,5-trisphosphate dependent Rac exchange factor 2; PTGS2: prostaglandin-endoperoxide synthase 2; PTPRD: protein tyrosine phosphatase receptor type D; RAD51D: RAD51 paralog D; RAF1: Raf-1 proto-oncogene, serine/threonine kinase; RARA: retinoic acid receptor alpha; RB1: RB transcriptional corepressor 1; RBFOX1: RNA binding fox-1 homolog 1; RGS7: regulator of G protein signalling 7; ROBO2: roundabout guidance receptor 2; RUNX1: RUNX family transcription factor 1; RUNX1T1: RUNX1 partner transcriptional co-repressor 1; S100A7: S100 calcium binding protein A7; SDHA: succinate dehydrogenase complex flavoprotein subunit A; SDHD: succinate dehydrogenase complex subunit D; SGK1: serum/glucocorticoid regulated kinase 1; SMARCA4: SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4; SMARCB1: SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily b, member 1; SRGAP3: SLIT-ROBO Rho GTPase activating protein 3; STAT3: signal transducer and activator of transcription 3; STAT5B: signal transducer and activator of transcription 5B; SYK: spleen associated tyrosine kinase; TCF12: transcription factor 12; TCF7: transcription factor 7; TFPT: TCF3 fusion partner; TOP2A: DNA topoisomerase II alpha; TP63: tumour protein p63; TSC2: TSC complex subunit 2; VDR: vitamin D receptor; VEGFA: vascular endothelial growth factor A; WWTR1: WW domain containing transcription regulator 1.

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References

1. Sung H., Ferlay J., Siegel R.L., et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–249. - PubMed
1. Morgan E., Arnold M., Gini A., et al. Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN. Gut. 2023;72(2):338–344. - PubMed
1. World Cancer Research Fund/American Institute for Cancer Research Continuous Update Project Expert Report 2018. Diet, nutrition, physical activity and colorectal cancer. dietandcancerreport.org Available at:
1. O'Sullivan D.E., Sutherland R.L., Town S., et al. Risk factors for early-onset colorectal cancer: a systematic review and meta-analysis. Clin Gastroenterol Hepatol. 2022;20(6):1229–1240.e5. - PubMed
1. Yang T., Li X., Montazeri Z., et al. Gene-environment interactions and colorectal cancer risk: An umbrella review of systematic reviews and meta-analyses of observational studies. Int J Cancer. 2019;145(9):2315–2329. - PMC - PubMed

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Using gene-environment interactions to explore pathways for colorectal cancer risk

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Using gene-environment interactions to explore pathways for colorectal cancer risk

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