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Tian Y, Lin Y, Qu C, Arndt V, Baurley JW, Berndt SI, Bien SA, Bishop DT, Brenner H, Buchanan DD, Budiarto A, Campbell PT, Carreras-Torres R, Casey G, Chan AT, Chen R, Chen X, Conti DV, Díez-Obrero V, Dimou N, Drew DA, Figueiredo JC, Gallinger S, Giles GG, Gruber SB, Gunter MJ, Harlid S, Harrison TA, Hidaka A, Hoffmeister M, Huyghe JR, Jenkins MA, Jordahl KM, Joshi AD, Keku TO, Kawaguchi E, Kim AE, Kundaje A, Larsson SC, Marchand LL, Lewinger JP, Li L, Moreno V, Morrison J, Murphy N, Nan H, Nassir R, Newcomb PA, Obón-Santacana M, Ogino S, Ose J, Pardamean B, Pellatt AJ, Peoples AR, Platz EA, Potter JD, Prentice RL, Rennert G, Ruiz-Narvaez EA, Sakoda LC, Schoen RE, Shcherbina A, Stern MC, Su YR, Thibodeau SN, Thomas DC, Tsilidis KK, van Duijnhoven FJB, Van Guelpen B, Visvanathan K, White E, Wolk A, Woods MO, Wu AH, Peters U, Gauderman WJ, Hsu L, Chang-Claude J. Genetic risk impacts the association of menopausal hormone therapy with colorectal cancer risk. Br J Cancer 2024:10.1038/s41416-024-02638-2. [PMID: 38561434 DOI: 10.1038/s41416-024-02638-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Menopausal hormone therapy (MHT), a common treatment to relieve symptoms of menopause, is associated with a lower risk of colorectal cancer (CRC). To inform CRC risk prediction and MHT risk-benefit assessment, we aimed to evaluate the joint association of a polygenic risk score (PRS) for CRC and MHT on CRC risk. METHODS We used data from 28,486 postmenopausal women (11,519 cases and 16,967 controls) of European descent. A PRS based on 141 CRC-associated genetic variants was modeled as a categorical variable in quartiles. Multiplicative interaction between PRS and MHT use was evaluated using logistic regression. Additive interaction was measured using the relative excess risk due to interaction (RERI). 30-year cumulative risks of CRC for 50-year-old women according to MHT use and PRS were calculated. RESULTS The reduction in odds ratios by MHT use was larger in women within the highest quartile of PRS compared to that in women within the lowest quartile of PRS (p-value = 2.7 × 10-8). At the highest quartile of PRS, the 30-year CRC risk was statistically significantly lower for women taking any MHT than for women not taking any MHT, 3.7% (3.3%-4.0%) vs 6.1% (5.7%-6.5%) (difference 2.4%, P-value = 1.83 × 10-14); these differences were also statistically significant but smaller in magnitude in the lowest PRS quartile, 1.6% (1.4%-1.8%) vs 2.2% (1.9%-2.4%) (difference 0.6%, P-value = 1.01 × 10-3), indicating 4 times greater reduction in absolute risk associated with any MHT use in the highest compared to the lowest quartile of genetic CRC risk. CONCLUSIONS MHT use has a greater impact on the reduction of CRC risk for women at higher genetic risk. These findings have implications for the development of risk prediction models for CRC and potentially for the consideration of genetic information in the risk-benefit assessment of MHT use.
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Affiliation(s)
- Yu Tian
- School of Public Health, Capital Medical University, Beijing, China
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Yi Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - James W Baurley
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
- BioRealm LLC, Walnut, CA, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephanie A Bien
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - D Timothy Bishop
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Hermann Brenner
- 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
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, 3010, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, 3010, Australia
- Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Arif Budiarto
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Peter T Campbell
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Robert Carreras-Torres
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute Dr Josep Trueta (IDIBGI), Salt, 17190, Girona, Spain
| | - Graham Casey
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Andrew T Chan
- 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
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Rui Chen
- School of Public Health, Capital Medical University, Beijing, China
| | - Xuechen Chen
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - David V Conti
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Virginia Díez-Obrero
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - David A Drew
- 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
| | - Jane C Figueiredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, ON, Canada
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Stephen B Gruber
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Sophia Harlid
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Akihisa Hidaka
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Kristina M Jordahl
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Amit D Joshi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Temitope O Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC, USA
| | - Eric Kawaguchi
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Andre E Kim
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Susanna C Larsson
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | | | - Juan Pablo Lewinger
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, VA, USA
| | - Victor Moreno
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine and health Sciences and Universitat de Barcelona Institute of Complex Systems (UBICS), University of Barcelona (UB), L'Hospitalet de Llobregat, Barcelona, Spain
| | - John Morrison
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Hongmei Nan
- Department of Global Health, Richard M. Fairbanks School of Public Health, Indianapolis, IN, USA
- Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indianapolis, Indianapolis, IN, USA
| | - Rami Nassir
- Department of Pathology, School of Medicine, Umm Al-Qura'a University, Mecca, Saudi Arabia
| | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Mireia Obón-Santacana
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Shuji Ogino
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Tokyo Medical and Dental University (Institute of Science Tokyo), Tokyo, Japan
| | - Jennifer Ose
- Huntsman Cancer Institute, Salt Lake City, UT, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
- Hochschule Hannover, University of Applied Sciences and Arts, Department III: Media, Information and Design, Hannover, Germany
| | - Bens Pardamean
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Andrew J Pellatt
- Department of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anita R Peoples
- Huntsman Cancer Institute, Salt Lake City, UT, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Elizabeth A Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - John D Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
- Research Centre for Hauora and Health, Massey University, Wellington, New Zealand
| | - Ross L Prentice
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Clalit National Cancer Control Center, Haifa, Israel
| | - Edward A Ruiz-Narvaez
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Lori C Sakoda
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Robert E Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Anna Shcherbina
- Biomedical Informatics Program, Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Mariana C Stern
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yu-Ru Su
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Stephen N Thibodeau
- Division of Laboratory Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Duncan C Thomas
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Konstantinos K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | | | - Bethany Van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Michael O Woods
- Memorial University of Newfoundland, Discipline of Genetics, St. John's, NL, Canada
| | - Anna H Wu
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - W James Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
- Department of Biostatistics, University of Washington, Seattle, WA, USA.
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- University Cancer Centre Hamburg (UCCH), University Medical Centre Hamburg-Eppendorf, Hamburg, Germany.
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2
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Bouras E, Kim AE, Lin Y, Morrison J, Du M, Albanes D, Barry EL, Baurley JW, Berndt SI, Bien SA, Bishop TD, Brenner H, Budiarto A, Burnett-Hartman A, Campbell PT, Carreras-Torres R, Casey G, Cenggoro TW, Chan AT, Chang-Claude J, Conti DV, Cotterchio M, Devall M, Diez-Obrero V, Dimou N, Drew DA, Figueiredo JC, Giles GG, Gruber SB, Gunter MJ, Harrison TA, Hidaka A, Hoffmeister M, Huyghe JR, Joshi AD, Kawaguchi ES, Keku TO, Kundaje A, Le Marchand L, Lewinger JP, Li L, Lynch BM, Mahesworo B, Männistö S, Moreno V, Murphy N, Newcomb PA, Obón-Santacana M, Ose J, Palmer JR, Papadimitriou N, Pardamean B, Pellatt AJ, Peoples AR, Platz EA, Potter JD, Qi L, Qu C, Rennert G, Ruiz-Narvaez E, Sakoda LC, Schmit SL, Shcherbina A, Stern MC, Su YR, Tangen CM, Thomas DC, Tian Y, Um CY, van Duijnhoven FJ, Van Guelpen B, Visvanathan K, Wang J, White E, Wolk A, Woods MO, Ulrich CM, Hsu L, Gauderman WJ, Peters U, Tsilidis KK. Genome-wide interaction analysis of folate for colorectal cancer risk. Am J Clin Nutr 2023; 118:881-891. [PMID: 37640106 PMCID: PMC10636229 DOI: 10.1016/j.ajcnut.2023.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 08/07/2023] [Accepted: 08/22/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Epidemiological and experimental evidence suggests that higher folate intake is associated with decreased colorectal cancer (CRC) risk; however, the mechanisms underlying this relationship are not fully understood. Genetic variation that may have a direct or indirect impact on folate metabolism can provide insights into folate's role in CRC. OBJECTIVES Our aim was to perform a genome-wide interaction analysis to identify genetic variants that may modify the association of folate on CRC risk. METHODS We applied traditional case-control logistic regression, joint 3-degree of freedom, and a 2-step weighted hypothesis approach to test the interactions of common variants (allele frequency >1%) across the genome and dietary folate, folic acid supplement use, and total folate in relation to risk of CRC in 30,550 cases and 42,336 controls from 51 studies from 3 genetic consortia (CCFR, CORECT, GECCO). RESULTS Inverse associations of dietary, total folate, and folic acid supplement with CRC were found (odds ratio [OR]: 0.93; 95% confidence interval [CI]: 0.90, 0.96; and 0.91; 95% CI: 0.89, 0.94 per quartile higher intake, and 0.82 (95% CI: 0.78, 0.88) for users compared with nonusers, respectively). Interactions (P-interaction < 5×10-8) of folic acid supplement and variants in the 3p25.2 locus (in the region of Synapsin II [SYN2]/tissue inhibitor of metalloproteinase 4 [TIMP4]) were found using traditional interaction analysis, with variant rs150924902 (located upstream to SYN2) showing the strongest interaction. In stratified analyses by rs150924902 genotypes, folate supplementation was associated with decreased CRC risk among those carrying the TT genotype (OR: 0.82; 95% CI: 0.79, 0.86) but increased CRC risk among those carrying the TA genotype (OR: 1.63; 95% CI: 1.29, 2.05), suggesting a qualitative interaction (P-interaction = 1.4×10-8). No interactions were observed for dietary and total folate. CONCLUSIONS Variation in 3p25.2 locus may modify the association of folate supplement with CRC risk. Experimental studies and studies incorporating other relevant omics data are warranted to validate this finding.
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Affiliation(s)
- Emmanouil Bouras
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Andre E Kim
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Yi Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - John Morrison
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Mengmeng Du
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Elizabeth L Barry
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, United States
| | - James W Baurley
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia; BioRealm LLC, Walnut, CA, United States
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Stephanie A Bien
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Timothy D Bishop
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, United Kingdom
| | - Hermann Brenner
- 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
| | - Arif Budiarto
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia; Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia
| | | | - Peter T Campbell
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Robert Carreras-Torres
- Unit of Biomarkers and Suceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, Spain; ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute (IDIBGI), Salt, Girona, Spain
| | - Graham Casey
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States
| | - Tjeng Wawan Cenggoro
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia; Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States; Broad Institute of Harvard and MIT, Cambridge, MA, United States; Department of Epidemiology, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, United States; Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, United States
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany
| | - David V Conti
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | | | - Matthew Devall
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, United States; Department of Public Health Sciences, Center for Public Health Genomics, Charlottesville, VA, United States
| | - Virginia Diez-Obrero
- Unit of Biomarkers and Suceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, Spain; ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
| | - Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - David A Drew
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Jane C Figueiredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Graham G Giles
- 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; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Stephen B Gruber
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA, United States
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Akihisa Hidaka
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Amit D Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States; Department of Epidemiology, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, United States
| | - Eric S Kawaguchi
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States; Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States
| | - Temitope O Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC, United States
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA, United States; Department of Computer Science, Stanford University, Stanford, CA, United States
| | | | - Juan Pablo Lewinger
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, VA, United States
| | - Brigid M Lynch
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Bharuno Mahesworo
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Satu Männistö
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
| | - Victor Moreno
- Unit of Biomarkers and Suceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, Spain; ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain; Department of Clinical Sciences, Faculty of Medicine and health Sciences and Universitat de Barcelona Institute of Complex Systems (UBICS), University of Barcelona (UB), L'Hospitalet de Llobregat, Barcelona, Spain
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States; School of Public Health, University of Washington, Seattle, WA, United States
| | - Mireia Obón-Santacana
- Unit of Biomarkers and Suceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, Spain; ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Jennifer Ose
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah; Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States
| | - Julie R Palmer
- Slone Epidemiology Center at Boston University, Boston, MA, United States
| | - Nikos Papadimitriou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Bens Pardamean
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Andrew J Pellatt
- Department of Cancer Medicine, MD Anderson Cancer Center, Houston, TX, United States
| | - Anita R Peoples
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah; Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States
| | - Elizabeth A Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - John D Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States; Research Centre for Hauora and Health, Massey University, Wellington, New Zealand
| | - Lihong Qi
- Department of Public Health Sciences, University of California Davis, Davis, CA, United States
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel; Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel; Clalit National Cancer Control Center, Haifa, Israel
| | - Edward Ruiz-Narvaez
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, United States
| | - Lori C Sakoda
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States; Division of Research, Kaiser Permanente Northern California, Oakland, CA, United States
| | - Stephanie L Schmit
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, United States; Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, OH, United States
| | - Anna Shcherbina
- Department of Genetics, Stanford University, Stanford, CA, United States; Department of Computer Science, Stanford University, Stanford, CA, United States
| | - Mariana C Stern
- Department of Population and Public Health Sciences and Norris Comprehensive Cancer Center, Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Yu-Ru Su
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Catherine M Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Duncan C Thomas
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Yu Tian
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; School of Public Health, Capital Medical University, Beijing, China
| | - Caroline Y Um
- Department of Population Science, American Cancer Society, Atlanta, GA, United States
| | - Franzel Jb van Duijnhoven
- Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, The Netherlands
| | - Bethany Van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden; Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Jun Wang
- Department of Population and Public Health Sciences and Norris Comprehensive Cancer Center, Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, United States
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Michael O Woods
- Memorial University of Newfoundland, Discipline of Genetics, St John's, Canada
| | - Cornelia M Ulrich
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah; Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States; Department of Biostatistics, University of Washington, Seattle, WA, United States
| | - W James Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States; Department of Epidemiology, University of Washington, Seattle, WA, United States.
| | - Konstantinos K Tsilidis
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece; Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, United Kingdom.
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3
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Yang Y, McMahan CS, Wang YB, Baurley JW, Park SS. SIGHR: Side information guided high-dimensional regression. Stat Methods Med Res 2023; 32:2270-2282. [PMID: 37823384 DOI: 10.1177/09622802231206475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
In this work, we develop a novel Bayesian regression framework that can be used to complete variable selection in high dimensional settings. Unlike existing techniques, the proposed approach can leverage side information to inform about the sparsity structure of the regression coefficients. This is accomplished by replacing the usual inclusion probability in the spike and slab prior with a binary regression model which assimilates this extra source of information. To facilitate model fitting, a computationally efficient and easy to implement Markov chain Monte Carlo posterior sampling algorithm is developed via carefully chosen priors and data augmentation steps. The finite sample performance of our methodology is assessed through numerical simulations, and we further illustrate our approach by using it to identify genetic markers associated with the nicotine metabolite ratio; a key biological marker associated with nicotine dependence and smoking cessation treatment.
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Affiliation(s)
- Yuan Yang
- School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, USA
| | - Christopher S McMahan
- School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, USA
| | - Yu-Bo Wang
- School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, USA
| | | | - Sung-Shim Park
- Population Sciences in the Pacific Program (Cancer Epidemiology), University of Hawai'i Cancer Center, Honolulu, HI, USA
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4
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Witkiewitz K, Kirouac M, Baurley JW, McMahan CS. Patterns of drinking behavior around a treatment episode for alcohol use disorder: Predictions from pre-treatment measures. Alcohol Clin Exp Res (Hoboken) 2023; 47:2138-2148. [PMID: 38226755 DOI: 10.1111/acer.15183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 08/26/2023] [Accepted: 08/27/2023] [Indexed: 01/17/2024]
Abstract
BACKGROUND Alcohol use disorder (AUD) has been described as a chronic disease given the high rates that affected individuals have in returning to drinking after a change attempt. Many studies have characterized predictors of aggregated alcohol use (e.g., percent heavy drinking days) following treatment for AUD. However, to inform future research on predicting drinking as an AUD outcome measure, a better understanding is needed of the patterns of drinking that surround a treatment episode and which clinical measures predict patterns of drinking. METHODS We analyzed data from the Project MATCH and COMBINE studies (MATCH: n = 1726; 24.3% female, 20.0% non-White; COMBINE: n = 1383; 30.9% female, 23.2% non-White). Daily drinking was measured in the 90 days prior to treatment, 90 days (MATCH) and 120 days (COMBINE) during treatment, and 365 days following treatment. Gradient boosting machine learning methods were used to explore baseline predictors of drinking patterns. RESULTS Drinking patterns during a prior time period were the most consistent predictors of future drinking patterns. Social network drinking, AUD severity, mental health symptoms, and constructs based on the addiction cycle (incentive salience, negative emotionality, and executive function) were associated with patterns of drinking prior to treatment. Addiction cycle constructs, AUD severity, purpose in life, social network, legal history, craving, and motivation were associated with drinking during the treatment period and following treatment. CONCLUSIONS There is heterogeneity in drinking patterns around an AUD treatment episode. This study provides novel information about variables that may be important to measure to improve the prediction of drinking patterns during and following treatment. Future research should consider which patterns of drinking they aim to predict and which period of drinking is most important to predict. The current findings could guide the selection of predictor variables and generate hypotheses for those predictors.
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Affiliation(s)
- Katie Witkiewitz
- Center on Alcohol, Substance Use, and Addictions (CASAA), University of New Mexico, Albuquerque, New Mexico, USA
| | - Megan Kirouac
- Center on Alcohol, Substance Use, and Addictions (CASAA), University of New Mexico, Albuquerque, New Mexico, USA
| | | | - Christopher S McMahan
- School of Mathematical and Statistical Sciences, Clemson University, Clemson, South Carolina, USA
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5
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Dimou N, Kim AE, Flanagan O, Murphy N, Diez-Obrero V, Shcherbina A, Aglago EK, Bouras E, Campbell PT, Casey G, Gallinger S, Gruber SB, Jenkins MA, Lin Y, Moreno V, Ruiz-Narvaez E, Stern MC, Tian Y, Tsilidis KK, Arndt V, Barry EL, Baurley JW, Berndt SI, Bézieau S, Bien SA, Bishop DT, Brenner H, Budiarto A, Carreras-Torres R, Cenggoro TW, Chan AT, Chang-Claude J, Chanock SJ, Chen X, Conti DV, Dampier CH, Devall M, Drew DA, Figueiredo JC, Giles GG, Gsur A, Harrison TA, Hidaka A, Hoffmeister M, Huyghe JR, Jordahl K, Kawaguchi E, Keku TO, Larsson SC, Le Marchand L, Lewinger JP, Li L, Mahesworo B, Morrison J, Newcomb PA, Newton CC, Obon-Santacana M, Ose J, Pai RK, Palmer JR, Papadimitriou N, Pardamean B, Peoples AR, Pharoah PDP, Platz EA, Potter JD, Rennert G, Scacheri PC, Schoen RE, Su YR, Tangen CM, Thibodeau SN, Thomas DC, Ulrich CM, Um CY, van Duijnhoven FJB, Visvanathan K, Vodicka P, Vodickova L, White E, Wolk A, Woods MO, Qu C, Kundaje A, Hsu L, Gauderman WJ, Gunter MJ, Peters U. Probing the diabetes and colorectal cancer relationship using gene - environment interaction analyses. Br J Cancer 2023; 129:511-520. [PMID: 37365285 PMCID: PMC10403521 DOI: 10.1038/s41416-023-02312-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 05/10/2023] [Accepted: 06/06/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Diabetes is an established risk factor for colorectal cancer. However, the mechanisms underlying this relationship still require investigation and it is not known if the association is modified by genetic variants. To address these questions, we undertook a genome-wide gene-environment interaction analysis. METHODS We used data from 3 genetic consortia (CCFR, CORECT, GECCO; 31,318 colorectal cancer cases/41,499 controls) and undertook genome-wide gene-environment interaction analyses with colorectal cancer risk, including interaction tests of genetics(G)xdiabetes (1-degree of freedom; d.f.) and joint testing of Gxdiabetes, G-colorectal cancer association (2-d.f. joint test) and G-diabetes correlation (3-d.f. joint test). RESULTS Based on the joint tests, we found that the association of diabetes with colorectal cancer risk is modified by loci on chromosomes 8q24.11 (rs3802177, SLC30A8 - ORAA: 1.62, 95% CI: 1.34-1.96; ORAG: 1.41, 95% CI: 1.30-1.54; ORGG: 1.22, 95% CI: 1.13-1.31; p-value3-d.f.: 5.46 × 10-11) and 13q14.13 (rs9526201, LRCH1 - ORGG: 2.11, 95% CI: 1.56-2.83; ORGA: 1.52, 95% CI: 1.38-1.68; ORAA: 1.13, 95% CI: 1.06-1.21; p-value2-d.f.: 7.84 × 10-09). DISCUSSION These results suggest that variation in genes related to insulin signaling (SLC30A8) and immune function (LRCH1) may modify the association of diabetes with colorectal cancer risk and provide novel insights into the biology underlying the diabetes and colorectal cancer relationship.
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Affiliation(s)
- Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France.
| | - Andre E Kim
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Orlagh Flanagan
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Virginia Diez-Obrero
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona, 08908, Spain
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona, 08908, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health, Barcelona, 08908, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, 08908, Spain
| | - Anna Shcherbina
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Elom K Aglago
- School of Public Health, Imperial College London, London, United Kingdom
| | - Emmanouil Bouras
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Peter T Campbell
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Graham Casey
- Department of Public Health Sciences, Center for Public Health Genomics, Charlottesville, VA, USA
| | - Steven Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, ON, Canada
| | - Stephen B Gruber
- Center for Precision Medicine, Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Yi Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Victor Moreno
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, 08908, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- ONCOBEL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
| | - Edward Ruiz-Narvaez
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Mariana C Stern
- Department of Population and Public Health Sciences & USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yu Tian
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- School of Public Health, Capital Medical University, Beijing, China
| | - Kostas K Tsilidis
- School of Public Health, Imperial College London, London, United Kingdom
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Elizabeth L Barry
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - James W Baurley
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
- BioRealm LLC, Walnut, CA, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stéphane Bézieau
- Nantes Université, CHU Nantes, Service de Génétique médicale, F-44000, Nantes, France
| | - Stephanie A Bien
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - D Timothy Bishop
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Hermann Brenner
- 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
| | - Arif Budiarto
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
- Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia
| | - Robert Carreras-Torres
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 8908, Barcelona, Spain
| | - Tjeng Wawan Cenggoro
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, 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
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Xuechen Chen
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - David V Conti
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Christopher H Dampier
- Department of Public Health Sciences, Center for Public Health Genomics, Charlottesville, VA, USA
- Department of General Surgery, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Matthew Devall
- Department of Family Medicine, University of Virginia, Charlottesville, VA, USA
| | - David A Drew
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jane C Figueiredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Andrea Gsur
- Center for Cancer Research, Medical University of Vienna, Vienna, Austria
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Akihisa Hidaka
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Kristina Jordahl
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Eric Kawaguchi
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Temitope O Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC, USA
| | - Susanna C Larsson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | | | - Juan Pablo Lewinger
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, VA, USA
| | - Bharuno Mahesworo
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - John Morrison
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- School of Public Health, University of Washington, Seattle, WA, USA
| | - Christina C Newton
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Mireia Obon-Santacana
- Unit of Nutrition, Environment and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology (ICO-IDIBELL), Avda Gran Via Barcelona 199-203, 08908L'Hospitalet de Llobregat, Barcelona, Spain
| | - Jennifer Ose
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UH, USA
| | - Rish K Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Julie R Palmer
- Slone Epidemiology Center at Boston University, Boston, MA, USA
| | - Nikos Papadimitriou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Bens Pardamean
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Anita R Peoples
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UH, USA
| | - Paul D P Pharoah
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Elizabeth A Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - John D Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
- Research Centre for Hauora and Health, Massey University, Wellington, New Zealand
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Clalit National Cancer Control Center, Haifa, Israel
| | - Peter C Scacheri
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Robert E Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Yu-Ru Su
- Biostatistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Catherine M Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Stephen N Thibodeau
- Division of Laboratory Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Duncan C Thomas
- Department of Population and Public Health Sciences & USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Cornelia M Ulrich
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UH, USA
| | - Caroline Y Um
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | | | - Kala Visvanathan
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
- Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic
| | - Ludmila Vodickova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
- Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Michael O Woods
- Memorial University of Newfoundland, Discipline of Genetics, St. John's, NL, Canada
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - W James Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
- School of Public Health, Imperial College London, London, United Kingdom
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
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6
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Aglago EK, Kim A, Lin Y, Qu C, Evangelou M, Ren Y, Morrison J, Albanes D, Arndt V, Barry EL, Baurley JW, Berndt SI, Bien SA, Bishop DT, Bouras E, Brenner H, Buchanan DD, Budiarto A, Carreras-Torres R, Casey G, Cenggoro TW, Chan AT, Chang-Claude J, Chen X, Conti DV, Devall M, Diez-Obrero V, Dimou N, Drew D, Figueiredo JC, Gallinger S, Giles GG, Gruber SB, Gsur A, Gunter MJ, Hampel H, Harlid S, Hidaka A, Harrison TA, Hoffmeister M, Huyghe JR, Jenkins MA, Jordahl K, Joshi AD, Kawaguchi ES, Keku TO, Kundaje A, Larsson SC, Marchand LL, Lewinger JP, Li L, Lynch BM, Mahesworo B, Mandic M, Obón-Santacana M, Moreno V, Murphy N, Nan H, Nassir R, Newcomb PA, Ogino S, Ose J, Pai RK, Palmer JR, Papadimitriou N, Pardamean B, Peoples AR, Platz EA, Potter JD, Prentice RL, Rennert G, Ruiz-Narvaez E, Sakoda LC, Scacheri PC, Schmit SL, Schoen RE, Shcherbina A, Slattery ML, Stern MC, Su YR, Tangen CM, Thibodeau SN, Thomas DC, Tian Y, Ulrich CM, van Duijnhoven FJB, Van Guelpen B, Visvanathan K, Vodicka P, Wang J, White E, Wolk A, Woods MO, Wu AH, Zemlianskaia N, Hsu L, Gauderman WJ, Peters U, Tsilidis KK, Campbell PT. A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk. Cancer Res 2023; 83:2572-2583. [PMID: 37249599 PMCID: PMC10391330 DOI: 10.1158/0008-5472.can-22-3713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/25/2023] [Accepted: 05/24/2023] [Indexed: 05/31/2023]
Abstract
Colorectal cancer risk can be impacted by genetic, environmental, and lifestyle factors, including diet and obesity. Gene-environment interactions (G × E) can provide biological insights into the effects of obesity on colorectal cancer risk. Here, we assessed potential genome-wide G × E interactions between body mass index (BMI) and common SNPs for colorectal cancer risk using data from 36,415 colorectal cancer cases and 48,451 controls from three international colorectal cancer consortia (CCFR, CORECT, and GECCO). The G × E tests included the conventional logistic regression using multiplicative terms (one degree of freedom, 1DF test), the two-step EDGE method, and the joint 3DF test, each of which is powerful for detecting G × E interactions under specific conditions. BMI was associated with higher colorectal cancer risk. The two-step approach revealed a statistically significant G×BMI interaction located within the Formin 1/Gremlin 1 (FMN1/GREM1) gene region (rs58349661). This SNP was also identified by the 3DF test, with a suggestive statistical significance in the 1DF test. Among participants with the CC genotype of rs58349661, overweight and obesity categories were associated with higher colorectal cancer risk, whereas null associations were observed across BMI categories in those with the TT genotype. Using data from three large international consortia, this study discovered a locus in the FMN1/GREM1 gene region that interacts with BMI on the association with colorectal cancer risk. Further studies should examine the potential mechanisms through which this locus modifies the etiologic link between obesity and colorectal cancer. SIGNIFICANCE This gene-environment interaction analysis revealed a genetic locus in FMN1/GREM1 that interacts with body mass index in colorectal cancer risk, suggesting potential implications for precision prevention strategies.
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Affiliation(s)
- Elom K. Aglago
- Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, United Kingdom
| | - Andre Kim
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Yi Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Marina Evangelou
- Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, United Kingdom
| | - Yu Ren
- Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, United Kingdom
| | - John Morrison
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Elizabeth L. Barry
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
| | - James W. Baurley
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
- BioRealm LLC, Walnut, California
| | - Sonja I. Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Stephanie A. Bien
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - D. Timothy Bishop
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, United Kingdom
| | - Emmanouil Bouras
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Hermann Brenner
- 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
| | - Daniel D. Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia
- Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Arif Budiarto
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
- Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia
| | - Robert Carreras-Torres
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute (IDIBGI), Salt, Girona, Spain
| | - Graham Casey
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia
| | - Tjeng Wawan Cenggoro
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Andrew T. Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany
| | - Xuechen Chen
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - David V. Conti
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Matthew Devall
- Department of Family Medicine, University of Virginia, Charlottesville, Virginia
| | - Virginia Diez-Obrero
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - David Drew
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Jane C. Figueiredo
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Steven Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Graham G. Giles
- 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, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Stephen B. Gruber
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte California
| | - Andrea Gsur
- Center for Cancer Research, Medical University of Vienna, Vienna, Austria
| | - Marc J. Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Heather Hampel
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte California
| | - Sophia Harlid
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
| | - Akihisa Hidaka
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Tabitha A. Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jeroen R. Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Mark A. Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Kristina Jordahl
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Amit D. Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Eric S. Kawaguchi
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Temitope O. Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, North Carolina
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, California
- Department of Computer Science, Stanford University, Stanford, California
| | - Susanna C. Larsson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | | | - Juan Pablo Lewinger
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, Virginia
| | - Brigid M. Lynch
- 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, Australia
- Physical Activity Laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia
| | - Bharuno Mahesworo
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Marko Mandic
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Mireia Obón-Santacana
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Victor Moreno
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Hongmei Nan
- Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indianapolis, Indiana
- IU Melvin and Bren Simon Cancer Center, Indiana University, Indianapolis, Indiana
| | - Rami Nassir
- Department of Pathology, School of Medicine, Umm Al-Qura'a University, Mecca, Saudi Arabia
| | - Polly A. Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
| | - Shuji Ogino
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jennifer Ose
- Huntsman Cancer Institute, Salt Lake City, Utah
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah
| | - Rish K. Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, Arizona
| | - Julie R. Palmer
- Department of Medicine, Boston University School of Medicine, Slone Epidemiology Center, Boston University, Boston, Massachusetts
| | - Nikos Papadimitriou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Bens Pardamean
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Anita R. Peoples
- Huntsman Cancer Institute, Salt Lake City, Utah
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah
| | - Elizabeth A. Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - John D. Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
- Research Centre for Hauora and Health, Massey University, Wellington, New Zealand
| | - Ross L. Prentice
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Clalit National Cancer Control Center, Haifa, Israel
| | - Edward Ruiz-Narvaez
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan
| | - Lori C. Sakoda
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Peter C. Scacheri
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio
| | | | - Robert E. Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Anna Shcherbina
- Department of Genetics, Stanford University, Stanford, California
- Department of Computer Science, Stanford University, Stanford, California
| | - Martha L. Slattery
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah
| | - Mariana C. Stern
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Yu-Ru Su
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Catherine M. Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Stephen N. Thibodeau
- Division of Laboratory Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Duncan C. Thomas
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Yu Tian
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- School of Public Health, Capital Medical University, Beijing, China
| | - Cornelia M. Ulrich
- Huntsman Cancer Institute, Salt Lake City, Utah
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah
| | - Franzel JB van Duijnhoven
- Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, the Netherlands
| | - Bethany Van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
- Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic
| | - Jun Wang
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Michael O. Woods
- Memorial University of Newfoundland, Discipline of Genetics, St. John's, Canada
| | - Anna H. Wu
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Natalia Zemlianskaia
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Biostatistics, University of Washington, Seattle, Washington
| | - W. James Gauderman
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
| | - Konstantinos K. Tsilidis
- Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, United Kingdom
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Peter T. Campbell
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York
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7
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Pardamean CI, Sudigyo D, Budiarto A, Mahesworo B, Hidayat AA, Baurley JW, Pardamean B. Changing Colorectal Cancer Trends in Asians: Epidemiology and Risk Factors. Oncol Rev 2023; 17:10576. [PMID: 37284188 PMCID: PMC10241074 DOI: 10.3389/or.2023.10576] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 05/11/2023] [Indexed: 06/08/2023] Open
Abstract
Once an infrequent disease in parts of Asia, the rate of colorectal cancer in recent decades appears to be steadily increasing. Colorectal cancer represents one of the most important causes of cancer mortality worldwide, including in many regions in Asia. Rapid changes in socioeconomic and lifestyle habits have been attributed to the notable increase in the incidence of colorectal cancers in many Asian countries. Through published data from the International Agency for Cancer Research (IARC), we utilized available continuous data to determine which Asian nations had a rise in colorectal cancer rates. We found that East and South East Asian countries had a significant rise in colorectal cancer rates. Subsequently, we summarized here the known genetics and environmental risk factors for colorectal cancer among populations in this region as well as approaches to screening and early detection that have been considered across various countries in the region.
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8
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Carreras-Torres R, Kim AE, Lin Y, Diez-Obrero V, Bien SA, Qu C, Wang J, Dimou N, Aglago EK, Albanes D, Arndt V, Baurley JW, Berndt SI, Bézieau S, Bishop DT, Bouras E, Brenner H, Budiarto A, Campbell PT, Casey G, Chan AT, Chang-Claude J, Chen X, Conti DV, Dampier CH, Devall MAM, Drew DA, Figueiredo JC, Gallinger S, Giles GG, Gruber SB, Gsur A, Gunter MJ, Harrison TA, Hidaka A, Hoffmeister M, Huyghe JR, Jenkins MA, Jordahl KM, Kawaguchi E, Keku TO, Kundaje A, Le Marchand L, Lewinger JP, Li L, Mahesworo B, Morrison JL, Murphy N, Nan H, Nassir R, Newcomb PA, Obón-Santacana M, Ogino S, Ose J, Pai RK, Palmer JR, Papadimitriou N, Pardamean B, Peoples AR, Pharoah PDP, Platz EA, Rennert G, Ruiz-Narvaez E, Sakoda LC, Scacheri PC, Schmit SL, Schoen RE, Shcherbina A, Slattery ML, Stern MC, Su YR, Tangen CM, Thomas DC, Tian Y, Tsilidis KK, Ulrich CM, van Duijnhoven FJB, Van Guelpen B, Visvanathan K, Vodicka P, Cenggoro TW, Weinstein SJ, White E, Wolk A, Woods MO, Hsu L, Peters U, Moreno V, Gauderman WJ. Genome-wide Interaction Study with Smoking for Colorectal Cancer Risk Identifies Novel Genetic Loci Related to Tumor Suppression, Inflammation, and Immune Response. Cancer Epidemiol Biomarkers Prev 2023; 32:315-328. [PMID: 36576985 PMCID: PMC9992283 DOI: 10.1158/1055-9965.epi-22-0763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 09/19/2022] [Accepted: 12/19/2022] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Tobacco smoking is an established risk factor for colorectal cancer. However, genetically defined population subgroups may have increased susceptibility to smoking-related effects on colorectal cancer. METHODS A genome-wide interaction scan was performed including 33,756 colorectal cancer cases and 44,346 controls from three genetic consortia. RESULTS Evidence of an interaction was observed between smoking status (ever vs. never smokers) and a locus on 3p12.1 (rs9880919, P = 4.58 × 10-8), with higher associated risk in subjects carrying the GG genotype [OR, 1.25; 95% confidence interval (CI), 1.20-1.30] compared with the other genotypes (OR <1.17 for GA and AA). Among ever smokers, we observed interactions between smoking intensity (increase in 10 cigarettes smoked per day) and two loci on 6p21.33 (rs4151657, P = 1.72 × 10-8) and 8q24.23 (rs7005722, P = 2.88 × 10-8). Subjects carrying the rs4151657 TT genotype showed higher risk (OR, 1.12; 95% CI, 1.09-1.16) compared with the other genotypes (OR <1.06 for TC and CC). Similarly, higher risk was observed among subjects carrying the rs7005722 AA genotype (OR, 1.17; 95% CI, 1.07-1.28) compared with the other genotypes (OR <1.13 for AC and CC). Functional annotation revealed that SNPs in 3p12.1 and 6p21.33 loci were located in regulatory regions, and were associated with expression levels of nearby genes. Genetic models predicting gene expression revealed that smoking parameters were associated with lower colorectal cancer risk with higher expression levels of CADM2 (3p12.1) and ATF6B (6p21.33). CONCLUSIONS Our study identified novel genetic loci that may modulate the risk for colorectal cancer of smoking status and intensity, linked to tumor suppression and immune response. IMPACT These findings can guide potential prevention treatments.
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Affiliation(s)
- Robert Carreras-Torres
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
- Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute (IDIBGI), Salt, 17190, Girona, Spain
| | - Andre E Kim
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Yi Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Virginia Diez-Obrero
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Stephanie A Bien
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Jun Wang
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Elom K Aglago
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - James W Baurley
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Stéphane Bézieau
- Service de Génétique Médicale, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France
| | - D Timothy Bishop
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Emmanouil Bouras
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Hermann Brenner
- 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
| | - Arif Budiarto
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Peter T Campbell
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, Georgia, USA
| | - Graham Casey
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, USA
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Xuechen Chen
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - David V Conti
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Christopher H Dampier
- Department of General Surgery, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Matthew AM Devall
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, USA
| | - David A Drew
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jane C Figueiredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Steven Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Stephen B Gruber
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Andrea Gsur
- Institute of Cancer Research, Department of Medicine I, Medical University Vienna, Vienna, Austria
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Akihisa Hidaka
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Kristina M Jordahl
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Eric Kawaguchi
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Temitope O Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Anshul Kundaje
- Department of Genetics, Department of Computer Science, Stanford University, Stanford, California, USA
| | | | - Juan Pablo Lewinger
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, Virginia, USA
| | - Bharuno Mahesworo
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - John L Morrison
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Hongmei Nan
- Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, USA
| | - Rami Nassir
- Department of Pathology, School of Medicine, Umm Al-Qura’a University, Saudi Arabia
| | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Mireia Obón-Santacana
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Shuji Ogino
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA; Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Jennifer Ose
- Huntsman Cancer Institute, Salt Lake City, Utah, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Rish K Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, Arizona, USA
| | - Julie R Palmer
- Slone Epidemiology Center at Boston University, Boston, MA, USA
| | - Nikos Papadimitriou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Bens Pardamean
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | | | - Paul D P Pharoah
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Elizabeth A Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
| | - Edward Ruiz-Narvaez
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | - Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
| | - Peter C Scacheri
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Stephanie L Schmit
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, Ohio, USA
- Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, Ohio, USA
| | - Robert E Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Anna Shcherbina
- Biomedical Informatics Program, Dept. of Biomedical Data Sciences, Stanford University
| | - Martha L Slattery
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Mariana C Stern
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Yu-Ru Su
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Catherine M Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Duncan C Thomas
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Yu Tian
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- School of Public Health, Capital Medical University, Beijing, China
| | - Konstantinos K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Cornelia M Ulrich
- Huntsman Cancer Institute, Salt Lake City, Utah, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Franzel JB van Duijnhoven
- Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, The Netherlands
| | - Bethany Van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, and Biomedical Center, Medical Faculty, Pilsen, Czech Republic
| | - Tjeng Wawan Cenggoro
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Stephanie J Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Michael O Woods
- Memorial University of Newfoundland, Discipline of Genetics, St. John's, Canada
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- School of Public Health, University of Washington, Seattle, Washington, USA
| | - Victor Moreno
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - W James Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
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9
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Leventhal AM, Conti DV, Ray LA, Baurley JW, Bello MS, Cho J, Zhang Y, Pester MS, Lebovitz L, Budiarto A, Mahesworo B, Pardamean B. A genetic association study of tobacco withdrawal endophenotypes in African Americans. Exp Clin Psychopharmacol 2022; 30:673-681. [PMID: 34279980 PMCID: PMC8928755 DOI: 10.1037/pha0000492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Genome-wide association (GWA) genetic epidemiology research has identified several variants modestly associated with brief self-report smoking measures, predominately in European Americans. GWA research has not applied intensive laboratory-based measures of smoking endophenotypes in African Americans-a population with disproportionately low quit smoking rates and high tobacco-related disease risk. This genetic epidemiology study of non-Hispanic African Americans tested associations of 89 genetic variants identified in previous GWA research and exploratory GWAs with 24 laboratory-derived tobacco withdrawal endophenotypes. African American cigarette smokers (N = 528; ≥10 cig/day; 36.2% female) completed two counterbalanced visits following either 16-hr of tobacco deprivation or ad libitum smoking. At both visits, self-report and behavioral measures across six unique "sub-phenotype" domains within the tobacco withdrawal syndrome were assessed (Urge/Craving, Negative Affect, Low Positive Affect, Cognition, Hunger, and Motivation to Resume Smoking). Results of the candidate variant analysis found two significant small-magnitude associations. The rs11915747 alternate allele in the CAD2M gene region was associated with .09 larger deprivation-induced changes in reported impulsivity (0-4 scale). The rs2471711alternate allele in the AC097480.1/AC097480.2 gene region was associated with 0.26 lower deprivation-induced changes in confusion (0-4 scale). For both variants, associations were opposite in direction to previous research. Individual genetic variants may exert only weak influences on tobacco withdrawal in African Americans. Larger sample sizes of non-European ancestry individuals might be needed to investigate both known and novel loci that may be ancestry-specific. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Affiliation(s)
- Adam M. Leventhal
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California
- Department of Psychology, University of Southern California
| | - David V. Conti
- Department of Psychology, University of Southern California
| | - Lara A. Ray
- Department of Psychology, University of California, Los Angeles
| | | | | | - Junhan Cho
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California
| | - Yi Zhang
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California
| | | | - Lucas Lebovitz
- Keck School of Medicine, University of Southern California
| | - Arif Budiarto
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Bharuno Mahesworo
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Bens Pardamean
- BioRealm LLC, California
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
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10
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Baurley JW, Bergen AW, Ervin CM, Park SSL, Murphy SE, McMahan CS. Predicting nicotine metabolism across ancestries using genotypes. BMC Genomics 2022; 23:663. [PMID: 36131240 PMCID: PMC9490935 DOI: 10.1186/s12864-022-08884-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 09/09/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND There is a need to match characteristics of tobacco users with cessation treatments and risks of tobacco attributable diseases such as lung cancer. The rate in which the body metabolizes nicotine has proven an important predictor of these outcomes. Nicotine metabolism is primarily catalyzed by the enzyme cytochrone P450 (CYP2A6) and CYP2A6 activity can be measured as the ratio of two nicotine metabolites: trans-3'-hydroxycotinine to cotinine (NMR). Measurements of these metabolites are only possible in current tobacco users and vary by biofluid source, timing of collection, and protocols; unfortunately, this has limited their use in clinical practice. The NMR depends highly on genetic variation near CYP2A6 on chromosome 19 as well as ancestry, environmental, and other genetic factors. Thus, we aimed to develop prediction models of nicotine metabolism using genotypes and basic individual characteristics (age, gender, height, and weight). RESULTS We identified four multiethnic studies with nicotine metabolites and DNA samples. We constructed a 263 marker panel from filtering genome-wide association scans of the NMR in each study. We then applied seven machine learning techniques to train models of nicotine metabolism on the largest and most ancestrally diverse dataset (N=2239). The models were then validated using the other three studies (total N=1415). Using cross-validation, we found the correlations between the observed and predicted NMR ranged from 0.69 to 0.97 depending on the model. When predictions were averaged in an ensemble model, the correlation was 0.81. The ensemble model generalizes well in the validation studies across ancestries, despite differences in the measurements of NMR between studies, with correlations of: 0.52 for African ancestry, 0.61 for Asian ancestry, and 0.46 for European ancestry. The most influential predictors of NMR identified in more than two models were rs56113850, rs11878604, and 21 other genetic variants near CYP2A6 as well as age and ancestry. CONCLUSIONS We have developed an ensemble of seven models for predicting the NMR across ancestries from genotypes and age, gender and BMI. These models were validated using three datasets and associate with nicotine dosages. The knowledge of how an individual metabolizes nicotine could be used to help select the optimal path to reducing or quitting tobacco use, as well as, evaluating risks of tobacco use.
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Affiliation(s)
- James W. Baurley
- grid.427493.fBioRealm LLC, 340 S Lemon Ave, Suite 1931, 91789 Walnut, CA USA
| | - Andrew W. Bergen
- grid.427493.fBioRealm LLC, 340 S Lemon Ave, Suite 1931, 91789 Walnut, CA USA ,grid.280332.80000 0001 2110 136XOregon Research Institute, 3800 Sports Way, 97477 Springfield, OR USA
| | - Carolyn M. Ervin
- grid.427493.fBioRealm LLC, 340 S Lemon Ave, Suite 1931, 91789 Walnut, CA USA
| | - Sung-shim Lani Park
- grid.410445.00000 0001 2188 0957University of Hawaii, 701 Ilalo Street, 96813 Honolulu, HI USA
| | - Sharon E. Murphy
- grid.17635.360000000419368657University of Minnesota, 2231 6th St SE, 55455 Minneapolis, MN USA
| | - Christopher S. McMahan
- grid.26090.3d0000 0001 0665 0280Clemson University, 220 Parkway Drive, 29634 Clemson, SC USA
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11
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Tian Y, Kim AE, Bien SA, Lin Y, Qu C, Harrison TA, Carreras-Torres R, Díez-Obrero V, Dimou N, Drew DA, Hidaka A, Huyghe JR, Jordahl KM, Morrison J, Murphy N, Obón-Santacana M, Ulrich CM, Ose J, Peoples AR, Ruiz-Narvaez EA, Shcherbina A, Stern MC, Su YR, van Duijnhoven FJB, Arndt V, Baurley JW, Berndt SI, Bishop DT, Brenner H, Buchanan DD, Chan AT, Figueiredo JC, Gallinger S, Gruber SB, Harlid S, Hoffmeister M, Jenkins MA, Joshi AD, Keku TO, Larsson SC, Le Marchand L, Li L, Giles GG, Milne RL, Nan H, Nassir R, Ogino S, Budiarto A, Platz EA, Potter JD, Prentice RL, Rennert G, Sakoda LC, Schoen RE, Slattery ML, Thibodeau SN, Van Guelpen B, Visvanathan K, White E, Wolk A, Woods MO, Wu AH, Campbell PT, Casey G, Conti DV, Gunter MJ, Kundaje A, Lewinger JP, Moreno V, Newcomb PA, Pardamean B, Thomas DC, Tsilidis KK, Peters U, Gauderman WJ, Hsu L, Chang-Claude J. Genome-Wide Interaction Analysis of Genetic Variants With Menopausal Hormone Therapy for Colorectal Cancer Risk. J Natl Cancer Inst 2022; 114:1135-1148. [PMID: 35512400 PMCID: PMC9360460 DOI: 10.1093/jnci/djac094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 02/17/2022] [Accepted: 04/26/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND The use of menopausal hormone therapy (MHT) may interact with genetic variants to influence colorectal cancer (CRC) risk. METHODS We conducted a genome-wide, gene-environment interaction between single nucleotide polymorphisms and the use of any MHT, estrogen only, and combined estrogen-progestogen therapy with CRC risk, among 28 486 postmenopausal women (11 519 CRC patients and 16 967 participants without CRC) from 38 studies, using logistic regression, 2-step method, and 2- or 3-degree-of-freedom joint test. A set-based score test was applied for rare genetic variants. RESULTS The use of any MHT, estrogen only and estrogen-progestogen were associated with a reduced CRC risk (odds ratio [OR] = 0.71, 95% confidence interval [CI] = 0.64 to 0.78; OR = 0.65, 95% CI = 0.53 to 0.79; and OR = 0.73, 95% CI = 0.59 to 0.90, respectively). The 2-step method identified a statistically significant interaction between a GRIN2B variant rs117868593 and MHT use, whereby MHT-associated CRC risk was statistically significantly reduced in women with the GG genotype (OR = 0.68, 95% CI = 0.64 to 0.72) but not within strata of GC or CC genotypes. A statistically significant interaction between a DCBLD1 intronic variant at 6q22.1 (rs10782186) and MHT use was identified by the 2-degree-of-freedom joint test. The MHT-associated CRC risk was reduced with increasing number of rs10782186-C alleles, showing odds ratios of 0.78 (95% CI = 0.70 to 0.87) for TT, 0.68 (95% CI = 0.63 to 0.73) for TC, and 0.66 (95% CI = 0.60 to 0.74) for CC genotypes. In addition, 5 genes in rare variant analysis showed suggestive interactions with MHT (2-sided P < 1.2 × 10-4). CONCLUSION Genetic variants that modify the association between MHT and CRC risk were identified, offering new insights into pathways of CRC carcinogenesis and potential mechanisms involved.
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Affiliation(s)
- Yu Tian
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- School of Public Health, Capital Medical University, Beijing, China
| | - Andre E Kim
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Stephanie A Bien
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Yi Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Robert Carreras-Torres
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L’Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Virginia Díez-Obrero
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L’Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - David A Drew
- 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
| | - Akihisa Hidaka
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Kristina M Jordahl
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA
| | - John Morrison
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Mireia Obón-Santacana
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L’Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Cornelia M Ulrich
- Huntsman Cancer Institute, Salt Lake City, UT, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Jennifer Ose
- Huntsman Cancer Institute, Salt Lake City, UT, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Anita R Peoples
- Huntsman Cancer Institute, Salt Lake City, UT, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Edward A Ruiz-Narvaez
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Anna Shcherbina
- Biomedical Informatics Program, Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Mariana C Stern
- Division of Biostatistics, Department of Population and Public Health Sciences & USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yu-Ru Su
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | | | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - James W Baurley
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
- BioRealm LLC, Walnut, CA, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - D Timothy Bishop
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Hermann Brenner
- 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
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia
- Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Andrew T Chan
- 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
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Jane C Figueiredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Stephen B Gruber
- Division of Biostatistics, Department of Population and Public Health Sciences & USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sophia Harlid
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Amit D Joshi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Temitope O Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC, USA
| | - Susanna C Larsson
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | | | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, VA, USA
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Hongmei Nan
- Department of Global Health, Richard M. Fairbanks School of Public Health, Indianapolis, IN, USA
- Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indianapolis, IN, USA
| | - Rami Nassir
- Department of Pathology, School of Medicine, Umm Al-Qura’a University, Mecca, Saudi Arabia
| | - Shuji Ogino
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Arif Budiarto
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Elizabeth A Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - John D Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Research Centre for Hauora and Health, Massey University, Wellington, New Zealand
| | - Ross L Prentice
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Clalit National Cancer Control Center, Haifa, Israel
| | - Lori C Sakoda
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Robert E Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Martha L Slattery
- Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
| | - Stephen N Thibodeau
- Division of Laboratory Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Bethany Van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Michael O Woods
- Memorial University of Newfoundland, Discipline of Genetics, St. John’s, NL,Canada
| | - Anna H Wu
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Peter T Campbell
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Graham Casey
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - David V Conti
- Division of Biostatistics, Department of Population and Public Health Sciences & USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Juan Pablo Lewinger
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Victor Moreno
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L’Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA
| | - Bens Pardamean
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Duncan C Thomas
- Division of Biostatistics, Department of Population and Public Health Sciences & USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Konstantinos K Tsilidis
- Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA
| | - W James Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences & USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Cancer Centre Hamburg (UCCH), University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
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12
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Baurley JW, Claus ED, Witkiewitz K, McMahan CS. A Bayesian mixed effects support vector machine for learning and predicting daily substance use disorder patterns. Am J Drug Alcohol Abuse 2022; 48:413-421. [PMID: 35196194 DOI: 10.1080/00952990.2021.2024839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 12/27/2021] [Accepted: 12/29/2021] [Indexed: 06/14/2023]
Abstract
Background: Substance use disorder (SUD) is a heterogeneous disorder. Adapting machine learning algorithms to allow for the parsing of intrapersonal and interpersonal heterogeneity in meaningful ways may accelerate the discovery and implementation of clinically actionable interventions in SUD research.Objectives: Inspired by a study of heavy drinkers that collected daily drinking and substance use (ABQ DrinQ), we develop tools to estimate subject-specific risk trajectories of heavy drinking; estimate and perform inference on patient characteristics and time-varying covariates; and present results in easy-to-use Jupyter notebooks. Methods: We recast support vector machines (SVMs) into a Bayesian model extended to handle mixed effects. We then apply these methods to ABQ DrinQ to model alcohol use patterns. ABQ DrinQ consists of 190 heavy drinkers (44% female) with 109,580 daily observations. Results: We identified male gender (point estimate; 95% credible interval: -0.25;-0.29,-0.21), older age (-0.03;-0.03,-0.03), and time varying usage of nicotine (1.68;1.62,1.73), cannabis (0.05;0.03,0.07), and other drugs (1.16;1.01,1.35) as statistically significant factors of heavy drinking behavior. By adopting random effects to capture the subject-specific longitudinal trajectories, the algorithm outperforms traditional SVM (classifies 84% of heavy drinking days correctly versus 73%). Conclusions: We developed a mixed effects variant of SVM and compare it to the traditional formulation, with an eye toward elucidating the importance of incorporating random effects to account for underlying heterogeneity in SUD data. These tools and examples are packaged into a repository for researchers to explore. Understanding patterns and risk of substance use could be used for developing individualized interventions.
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Affiliation(s)
| | - Eric D Claus
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA USA
| | - Katie Witkiewitz
- Department of Psychology, University of New Mexico, Albuquerque, NM, USA
| | - Christopher S McMahan
- School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, USA
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13
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Jordahl KM, Shcherbina A, Kim AE, Su YR, Lin Y, Wang J, Qu C, Albanes D, Arndt V, Baurley JW, Berndt SI, Bien SA, Bishop DT, Bouras E, Brenner H, Buchanan DD, Budiarto A, Campbell PT, Carreras-Torres R, Casey G, Cenggoro TW, Chan AT, Conti DV, Dampier CH, Devall MA, Díez-Obrero V, Dimou N, Drew DA, Figueiredo JC, Gallinger S, Giles GG, Gruber SB, Gsur A, Gunter MJ, Hampel H, Harlid S, Harrison TA, Hidaka A, Hoffmeister M, Huyghe JR, Jenkins MA, Joshi AD, Keku TO, Larsson SC, Le Marchand L, Lewinger JP, Li L, Mahesworo B, Moreno V, Morrison JL, Murphy N, Nan H, Nassir R, Newcomb PA, Obón-Santacana M, Ogino S, Ose J, Pai RK, Palmer JR, Papadimitriou N, Pardamean B, Peoples AR, Pharoah PDP, Platz EA, Potter JD, Prentice RL, Rennert G, Ruiz-Narvaez E, Sakoda LC, Scacheri PC, Schmit SL, Schoen RE, Slattery ML, Stern MC, Tangen CM, Thibodeau SN, Thomas DC, Tian Y, Tsilidis KK, Ulrich CM, van Duijnhoven FJB, Van Guelpen B, Visvanathan K, Vodicka P, White E, Wolk A, Woods MO, Wu AH, Zemlianskaia N, Chang-Claude J, Gauderman WJ, Hsu L, Kundaje A, Peters U. Beyond GWAS of Colorectal Cancer: Evidence of Interaction with Alcohol Consumption and Putative Causal Variant for the 10q24.2 Region. Cancer Epidemiol Biomarkers Prev 2022; 31:1077-1089. [PMID: 35438744 PMCID: PMC9081195 DOI: 10.1158/1055-9965.epi-21-1003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 10/30/2021] [Accepted: 02/15/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Currently known associations between common genetic variants and colorectal cancer explain less than half of its heritability of 25%. As alcohol consumption has a J-shape association with colorectal cancer risk, nondrinking and heavy drinking are both risk factors for colorectal cancer. METHODS Individual-level data was pooled from the Colon Cancer Family Registry, Colorectal Transdisciplinary Study, and Genetics and Epidemiology of Colorectal Cancer Consortium to compare nondrinkers (≤1 g/day) and heavy drinkers (>28 g/day) with light-to-moderate drinkers (1-28 g/day) in GxE analyses. To improve power, we implemented joint 2df and 3df tests and a novel two-step method that modifies the weighted hypothesis testing framework. We prioritized putative causal variants by predicting allelic effects using support vector machine models. RESULTS For nondrinking as compared with light-to-moderate drinking, the hybrid two-step approach identified 13 significant SNPs with pairwise r2 > 0.9 in the 10q24.2/COX15 region. When stratified by alcohol intake, the A allele of lead SNP rs2300985 has a dose-response increase in risk of colorectal cancer as compared with the G allele in light-to-moderate drinkers [OR for GA genotype = 1.11; 95% confidence interval (CI), 1.06-1.17; OR for AA genotype = 1.22; 95% CI, 1.14-1.31], but not in nondrinkers or heavy drinkers. Among the correlated candidate SNPs in the 10q24.2/COX15 region, rs1318920 was predicted to disrupt an HNF4 transcription factor binding motif. CONCLUSIONS Our study suggests that the association with colorectal cancer in 10q24.2/COX15 observed in genome-wide association study is strongest in nondrinkers. We also identified rs1318920 as the putative causal regulatory variant for the region. IMPACT The study identifies multifaceted evidence of a possible functional effect for rs1318920.
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Affiliation(s)
- Kristina M Jordahl
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Anna Shcherbina
- Department of Genetics, Stanford University, Stanford, California
- Department of Computer Science, Stanford University, Stanford, California
| | - Andre E Kim
- Division of Biostatistics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Yu-Ru Su
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Yi Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Jun Wang
- Division of Biostatistics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - James W Baurley
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
- BioRealm LLC, Walnut, California
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Stephanie A Bien
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - D Timothy Bishop
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, United Kingdom
| | - Emmanouil Bouras
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Hermann Brenner
- 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
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia
- Genetic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Arif Budiarto
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
- Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia
| | - Peter T Campbell
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, Georgia
| | - Robert Carreras-Torres
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
| | - Graham Casey
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia
| | - Tjeng Wawan Cenggoro
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - David V Conti
- Department of Preventive Medicine and USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Christopher H Dampier
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia
| | - Matthew A Devall
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia
| | - Virginia Díez-Obrero
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Niki Dimou
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France
| | - David A Drew
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Jane C Figueiredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Steven Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Graham G Giles
- 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
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Stephen B Gruber
- Department of Preventive Medicine and USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Andrea Gsur
- Institute of Cancer Research, Department of Medicine I, Medical University Vienna, Vienna, Austria
| | - Marc J Gunter
- Nutrition and Metabolism Section, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Heather Hampel
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Sophia Harlid
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Akihisa Hidaka
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Amit D Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Temitope O Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, North Carolina
| | - Susanna C Larsson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | | | - Juan Pablo Lewinger
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, Virginia
| | - Bharuno Mahesworo
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Victor Moreno
- Oncology Data Analytics Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- ONCOBEL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
| | - John L Morrison
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Neil Murphy
- Nutrition and Metabolism Section, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Hongmei Nan
- Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indianapolis, Indiana
- IU Melvin and Bren Simon Cancer Center, Indiana University, Indianapolis, Indiana
| | - Rami Nassir
- Department of Pathology, School of Medicine, Umm Al-Qura'a University, Saudi Arabia
| | - Polly A Newcomb
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Mireia Obón-Santacana
- Unit of Nutrition, Environment and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology (ICO-IDIBELL), Avda Gran Via Barcelona 199-203, 08908L'Hospitalet de Llobregat, Barcelona, Spain
| | - Shuji Ogino
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Jennifer Ose
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah
| | - Rish K Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, Arizona
| | - Julie R Palmer
- Slone Epidemiology Center at Boston University, Boston, Massachusetts
| | - Nikos Papadimitriou
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France
| | - Bens Pardamean
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Anita R Peoples
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah
- Huntsman Cancer Institute, Salt Lake City, Utah
| | - Paul D P Pharoah
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Elizabeth A Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - John D Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Ross L Prentice
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Clalit National Cancer Control Center, Haifa, Israel
| | - Edward Ruiz-Narvaez
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan
| | - Lori C Sakoda
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Peter C Scacheri
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio
| | - Stephanie L Schmit
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, Ohio
- Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, Ohio
| | - Robert E Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Martha L Slattery
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah
| | - Mariana C Stern
- Department of Preventive Medicine and USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Catherine M Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Stephen N Thibodeau
- Division of Laboratory Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Duncan C Thomas
- Department of Preventive Medicine and USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Yu Tian
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- School of Public Health, Capital Medical University, Beijing, China
| | - Konstantinos K Tsilidis
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
- Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, United Kingdom
| | - Cornelia M Ulrich
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah
- Huntsman Cancer Institute, Salt Lake City, Utah
| | | | - Bethany Van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
- Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic
| | - Emily White
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Michael O Woods
- Memorial University of Newfoundland, Discipline of Genetics, St. John's, Canada
| | - Anna H Wu
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Natalia Zemlianskaia
- Department of Preventive Medicine and USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany
| | - W James Gauderman
- Department of Preventive Medicine and USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Biostatistics, University of Washington, Seattle, Washington
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, California
- Department of Computer Science, Stanford University, Stanford, California
| | - Ulrike Peters
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
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St. Ville M, Bergen AW, Baurley JW, Bible JD, McMahan CS. Assessing Opioid Use Disorder Treatments in Trials Subject to Non-Adherence via a Functional Generalized Linear Mixed-Effects Model. Int J Environ Res Public Health 2022; 19:ijerph19095456. [PMID: 35564851 PMCID: PMC9104047 DOI: 10.3390/ijerph19095456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 04/21/2022] [Accepted: 04/27/2022] [Indexed: 12/10/2022]
Abstract
The opioid crisis in the United States poses a major threat to public health due to psychiatric and infectious disease comorbidities and death due to opioid use disorder (OUD). OUD is characterized by patterns of opioid misuse leading to persistent heavy use and overdose. The standard of care for treatment of OUD is medication-assisted treatment, in combination with behavioral therapy. Medications for opioid use disorder have been shown to improve OUD outcomes, including reduction and prevention of overdose. However, understanding the effectiveness of such medications has been limited due to non-adherence to assigned dose levels by study patients. To overcome this challenge, herein we develop a model that views dose history as a time-varying covariate. Proceeding in this fashion allows the model to estimate dose effect while accounting for lapses in adherence. The proposed model is used to conduct a secondary analysis of data collected from six efficacy and safety trials of buprenorphine maintenance treatment. This analysis provides further insight into the time-dependent treatment effects of buprenorphine and how different dose adherence patterns relate to risk of opioid use.
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Affiliation(s)
- Madeleine St. Ville
- School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634, USA; (M.S.V.); (J.D.B.)
| | - Andrew W. Bergen
- Oregon Research Institute, Eugene, OR 97403, USA;
- BioRealm, LLC, Walnut, CA 91789, USA;
| | | | - Joe D. Bible
- School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634, USA; (M.S.V.); (J.D.B.)
| | - Christopher S. McMahan
- School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634, USA; (M.S.V.); (J.D.B.)
- Correspondence:
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15
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Bergen AW, McMahan CS, McGee S, Ervin CM, Tindle HA, Le Marchand L, Murphy SE, Stram DO, Patel YM, Park SL, Baurley JW. Multiethnic Prediction of Nicotine Biomarkers and Association With Nicotine Dependence. Nicotine Tob Res 2021; 23:2162-2169. [PMID: 34313775 PMCID: PMC8757310 DOI: 10.1093/ntr/ntab124] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 06/11/2021] [Indexed: 01/16/2023]
Abstract
Introduction The nicotine metabolite ratio and nicotine equivalents are measures of metabolism rate and intake. Genome-wide prediction of these nicotine biomarkers in multiethnic samples will enable tobacco-related biomarker, behavioral, and exposure research in studies without measured biomarkers. Aims and Methods We screened genetic variants genome-wide using marginal scans and applied statistical learning algorithms on top-ranked genetic variants, age, ethnicity and sex, and, in additional modeling, cigarettes per day (CPD), (in additional modeling) to build prediction models for the urinary nicotine metabolite ratio (uNMR) and creatinine-standardized total nicotine equivalents (TNE) in 2239 current cigarette smokers in five ethnic groups. We predicted these nicotine biomarkers using model ensembles and evaluated external validity using dependence measures in 1864 treatment-seeking smokers in two ethnic groups. Results The genomic regions with the most selected and included variants for measured biomarkers were chr19q13.2 (uNMR, without and with CPD) and chr15q25.1 and chr10q25.3 (TNE, without and with CPD). We observed ensemble correlations between measured and predicted biomarker values for the uNMR and TNE without (with CPD) of 0.67 (0.68) and 0.65 (0.72) in the training sample. We observed inconsistency in penalized regression models of TNE (with CPD) with fewer variants at chr15q25.1 selected and included. In treatment-seeking smokers, predicted uNMR (without CPD) was significantly associated with CPD and predicted TNE (without CPD) with CPD, time-to-first-cigarette, and Fagerström total score. Conclusions Nicotine metabolites, genome-wide data, and statistical learning approaches developed novel robust predictive models for urinary nicotine biomarkers in multiple ethnic groups. Predicted biomarker associations helped define genetically influenced components of nicotine dependence. Implications We demonstrate development of robust models and multiethnic prediction of the uNMR and TNE using statistical and machine learning approaches. Variants included in trained models for nicotine biomarkers include top-ranked variants in multiethnic genome-wide studies of smoking behavior, nicotine metabolites, and related disease. Association of the two predicted nicotine biomarkers with Fagerström Test for Nicotine Dependence items supports models of nicotine biomarkers as predictors of physical dependence and nicotine exposure. Predicted nicotine biomarkers may facilitate tobacco-related disease and treatment research in samples with genomic data and limited nicotine metabolite or tobacco exposure data.
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Affiliation(s)
- Andrew W Bergen
- Oregon Research Institute, Eugene, OR, USA.,BioRealm, LLC, Walnut, CA, USA
| | - Christopher S McMahan
- School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, USA
| | | | | | - Hilary A Tindle
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.,Veterans Health Administration-Tennessee Valley Healthcare System, Geriatric Research, Education and Clinical Center (GRECC), Nashville, TN, USA
| | - Loïc Le Marchand
- Cancer Epidemiology and University of Hawaii Cancer Center, University of Hawai'i, Honolulu, HI, USA
| | - Sharon E Murphy
- Biochemistry, Molecular Biology, and Biophysics and Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA
| | - Daniel O Stram
- Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yesha M Patel
- Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sungshim L Park
- Cancer Epidemiology and University of Hawaii Cancer Center, University of Hawai'i, Honolulu, HI, USA
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Baurley JW, Kjærsgaard A, Zwick ME, Cronin-Fenton DP, Collin LJ, Damkier P, Hamilton-Dutoit S, Lash TL, Ahern TP. Bayesian Pathway Analysis for Complex Interactions. Am J Epidemiol 2020; 189:1610-1622. [PMID: 32639515 DOI: 10.1093/aje/kwaa130] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 06/23/2020] [Accepted: 06/30/2020] [Indexed: 12/24/2022] Open
Abstract
Modern epidemiologic studies permit investigation of the complex pathways that mediate effects of social, behavioral, and molecular factors on health outcomes. Conventional analytical approaches struggle with high-dimensional data, leading to high likelihoods of both false-positive and false-negative inferences. Herein, we describe a novel Bayesian pathway analysis approach, the algorithm for learning pathway structure (ALPS), which addresses key limitations in existing approaches to complex data analysis. ALPS uses prior information about pathways in concert with empirical data to identify and quantify complex interactions within networks of factors that mediate an association between an exposure and an outcome. We illustrate ALPS through application to a complex gene-drug interaction analysis in the Predictors of Breast Cancer Recurrence (ProBe CaRe) Study, a Danish cohort study of premenopausal breast cancer patients (2002-2011), for which conventional analyses severely limit the quality of inference.
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Ahern TP, Collin LJ, Baurley JW, Kjærsgaard A, Nash R, Maliniak ML, Damkier P, Zwick ME, Isett RB, Christiansen PM, Ejlertsen B, Lauridsen KL, Christensen KB, Silliman RA, Sørensen HT, Tramm T, Hamilton-Dutoit S, Lash TL, Cronin-Fenton D. Metabolic Pathway Analysis and Effectiveness of Tamoxifen in Danish Breast Cancer Patients. Cancer Epidemiol Biomarkers Prev 2020; 29:582-590. [PMID: 31932415 PMCID: PMC7060091 DOI: 10.1158/1055-9965.epi-19-0833] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 11/15/2019] [Accepted: 12/20/2019] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Tamoxifen and its metabolites compete with estrogen to occupy the estrogen receptor. The conventional dose of adjuvant tamoxifen overwhelms estrogen in this competition, reducing breast cancer recurrence risk by nearly half. Phase I metabolism generates active tamoxifen metabolites, and phase II metabolism deactivates them. No earlier pharmacogenetic study has comprehensively evaluated the metabolism and transport pathways, and no earlier study has included a large population of premenopausal women. METHODS We completed a cohort study of 5,959 Danish nonmetastatic premenopausal breast cancer patients, in whom 938 recurrences occurred, and a case-control study of 541 recurrent cases in a cohort of Danish predominantly postmenopausal breast cancer patients, all followed for 10 years. We collected formalin-fixed paraffin-embedded tumor blocks and genotyped 32 variants in 15 genes involved in tamoxifen metabolism or transport. We estimated conventional associations for each variant and used prior information about the tamoxifen metabolic path to evaluate the importance of metabolic and transporter pathways. RESULTS No individual variant was notably associated with risk of recurrence in either study population. Both studies showed weak evidence of the importance of phase I metabolism in the clinical response to adjuvant tamoxifen therapy. CONCLUSIONS Consistent with prior knowledge, our results support the role of phase I metabolic capacity in clinical response to tamoxifen. Nonetheless, no individual variant substantially explained the modest phase I effect on tamoxifen response. IMPACT These results are consistent with guidelines recommending against genotype-guided prescribing of tamoxifen, and for the first time provide evidence supporting these guidelines in premenopausal women.
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Affiliation(s)
- Thomas P Ahern
- Department of Surgery, Larner College of Medicine at The University of Vermont, Burlington, Vermont
| | - Lindsay J Collin
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia
| | | | - Anders Kjærsgaard
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
| | - Rebecca Nash
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Maret L Maliniak
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Per Damkier
- Department of Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Michael E Zwick
- Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia
- Emory Integrated Genomics Core, Emory University, Atlanta, Georgia
| | - R Benjamin Isett
- Emory Integrated Genomics Core, Emory University, Atlanta, Georgia
| | - Peer M Christiansen
- Breast Unit, Aarhus University Hospital/Randers Regional Hospital, Aarhus, Denmark
- Danish Breast Cancer Group, Copenhagen University Hospital, Copenhagen, Denmark
| | - Bent Ejlertsen
- Danish Breast Cancer Group, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Oncology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | | | | | - Rebecca A Silliman
- Boston University School of Medicine, Boston University, Boston, Massachusetts
| | - Henrik T Sørensen
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
| | - Trine Tramm
- Department of Pathology, Aarhus University Hospital, Aarhus, Denmark
| | | | - Timothy L Lash
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia.
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
- Winship Cancer Institute, Emory University, Atlanta, Georgia
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Stepan JG, Lovecchio FC, Premkumar A, Kahlenberg CA, Albert TJ, Baurley JW, Nwachukwu BU. Development of an Institutional Opioid Prescriber Education Program and Opioid-Prescribing Guidelines: Impact on Prescribing Practices. J Bone Joint Surg Am 2019; 101:5-13. [PMID: 30601411 DOI: 10.2106/jbjs.17.01645] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
BACKGROUND Our institution developed a 1-hour mandatory narcotics-prescribing education program as well as postoperative opioid-prescribing guidelines in response to the opioid epidemic. The purpose of this study was to evaluate the effect of this hospital-wide opioid education and subsequent institution of postoperative opioid guidelines on opioid-prescribing practices after ambulatory surgery. METHODS This retrospective study was performed at 1 academic orthopaedic hospital. In November 2016, a 1-hour mandatory opioid education program was completed by all hospital prescribers. Postoperative opioid guidelines were then developed and were disseminated in February 2017. All postoperative narcotic prescriptions after ambulatory procedures performed by 3 separate services (hand, sports, and foot and ankle services) were evaluated over 4 months prior to and after the mandatory opioid education and subsequent release of service-specific guidelines. RESULTS Overall, there was a significant decrease in pills and total oral morphine equivalents prescribed after dissemination of guidelines compared with the pre-intervention cohort procedures (p < 0.001) performed by the sports and hand services. With regard to the sports medicine service, the mean difference in pills prescribed was 6.47 pills (95% confidence interval [CI], 5.4 to 7.5 pills) for knee arthroscopy, 5.6 pills (95% CI, 2.5 to 8.7 pills) for shoulder arthroscopy, and 16.3 pills (95% CI, 13.6 to 19.1 pills) for hip arthroscopy. With regard to the hand service, the mean difference in pills prescribed was 13.0 pills (95% CI, 10.2 to 15.8 pills) for level-1 procedures, 12.4 pills (95% CI, 9.9 to 15.0 pills) for carpal tunnel release, and 21.7 pills (95% CI, 18.0 to 25.3 pills) for distal radial fractures. The decrease in pills prescribed in the post-intervention cohort amounts to almost 30,000 fewer opioid pills prescribed per year after these 6 procedures alone. There was no significant change (p > 0.05) in either the number of pills or the oral morphine equivalents prescribed after any of the 3 procedures performed by the foot and ankle service (ankle arthroscopy, bunion surgery, and Achilles tendon repair). CONCLUSIONS We developed a prescriber education program and followed up with consensus-based guidelines for postoperative opioid prescriptions. These interventions caused a significant decrease in excessive opioid-prescribing practices after ambulatory orthopaedic surgery at our hospital. We urge initiatives by national orthopaedic organizations to develop and promote education programs and procedure and disease-specific opioid-prescribing guidelines.
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Affiliation(s)
- Jeffrey G Stepan
- Department of Orthopedic Surgery, Hospital for Special Surgery, New York, NY
| | - Francis C Lovecchio
- Department of Orthopedic Surgery, Hospital for Special Surgery, New York, NY
| | - Ajay Premkumar
- Department of Orthopedic Surgery, Hospital for Special Surgery, New York, NY
| | | | - Todd J Albert
- Department of Orthopedic Surgery, Hospital for Special Surgery, New York, NY
| | - James W Baurley
- BioRealm, Culver City, California.,Bina Nusantara University, Jakarta, Indonesia
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Saccone NL, Baurley JW, Bergen AW, David SP, Elliott HR, Foreman MG, Kaprio J, Piasecki TM, Relton CL, Zawertailo L, Bierut LJ, Tyndale RF, Chen LS. The Value of Biosamples in Smoking Cessation Trials: A Review of Genetic, Metabolomic, and Epigenetic Findings. Nicotine Tob Res 2018; 20:403-413. [PMID: 28472521 PMCID: PMC5896536 DOI: 10.1093/ntr/ntx096] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Accepted: 05/01/2017] [Indexed: 02/03/2023]
Abstract
Introduction Human genetic research has succeeded in definitively identifying multiple genetic variants associated with risk for nicotine dependence and heavy smoking. To build on these advances, and to aid in reducing the prevalence of smoking and its consequent health harms, the next frontier is to identify genetic predictors of successful smoking cessation and also of the efficacy of smoking cessation treatments ("pharmacogenomics"). More broadly, additional biomarkers that can be quantified from biosamples also promise to aid "Precision Medicine" and the personalization of treatment, both pharmacological and behavioral. Aims and Methods To motivate ongoing and future efforts, here we review several compelling genetic and biomarker findings related to smoking cessation and treatment. Results These Key results involve genetic variants in the nicotinic receptor subunit gene CHRNA5, variants in the nicotine metabolism gene CYP2A6, and the nicotine metabolite ratio. We also summarize reports of epigenetic changes related to smoking behavior. Conclusions The results to date demonstrate the value and utility of data generated from biosamples in clinical treatment trial settings. This article cross-references a companion paper in this issue that provides practical guidance on how to incorporate biosample collection into a planned clinical trial and discusses avenues for harmonizing data and fostering consortium-based, collaborative research on the pharmacogenomics of smoking cessation. Implications Evidence is emerging that certain genotypes and biomarkers are associated with smoking cessation success and efficacy of smoking cessation treatments. We review key findings that open potential avenues for personalizing smoking cessation treatment according to an individual's genetic or metabolic profile. These results provide important incentive for smoking cessation researchers to collect biosamples and perform genotyping in research studies and clinical trials.
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Affiliation(s)
- Nancy L Saccone
- Department of Genetics and Division of Biostatistics, Washington University School of Medicine, St. Louis, MO
| | | | | | - Sean P David
- Department of Medicine, Stanford University, Stanford, CA
| | - Hannah R Elliott
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Marilyn G Foreman
- Pulmonary and Critical Care Medicine, Morehouse School of Medicine, Atlanta, GA
| | - Jaakko Kaprio
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
| | - Thomas M Piasecki
- Department of Psychological Sciences, University of Missouri, Columbia, MO
| | - Caroline L Relton
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Laurie Zawertailo
- Nicotine Dependence Service, Centre for Addiction and Mental Health, and Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada
| | - Laura J Bierut
- Siteman Cancer Center, Institute of Public Health, and Department of Psychiatry, Washington University School of Medicine, St. Louis, MO
| | - Rachel F Tyndale
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, and Departments of Pharmacology & Toxicology and Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Li-Shiun Chen
- Siteman Cancer Center, Institute of Public Health, and Department of Psychiatry, Washington University School of Medicine, St. Louis, MO
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Chen LS, Zawertailo L, Piasecki TM, Kaprio J, Foreman M, Elliott HR, David SP, Bergen AW, Baurley JW, Tyndale RF, Baker TB, Bierut LJ, Saccone NL. Leveraging Genomic Data in Smoking Cessation Trials in the Era of Precision Medicine: Why and How. Nicotine Tob Res 2018; 20:414-424. [PMID: 28498934 PMCID: PMC5896450 DOI: 10.1093/ntr/ntx097] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Accepted: 05/09/2017] [Indexed: 01/11/2023]
Abstract
Implications This article outlines a framework for the consistent integration of biological data/samples into smoking cessation pharmacotherapy trials, aligned with the objectives of the recently unveiled Precision Medicine Initiative. Our goal is to encourage and provide support for treatment researchers to consider biosample collection and genotyping their existing samples as well as integrating genetic analyses into their study design in order to realize precision medicine in treatment of nicotine dependence.
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Affiliation(s)
- Li-Shiun Chen
- Siteman Cancer Center, Institute of Public Health, and Department of Psychiatry, Washington University School of Medicine, St. Louis, MI
| | - Laurie Zawertailo
- Nicotine Dependence Service, Centre for Addiction and Mental Health, and Dept. of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada
| | - Thomas M Piasecki
- Department of Psychological Sciences, University of Missouri, Columbia, MI
| | - Jaakko Kaprio
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
| | - Marilyn Foreman
- Pulmonary and Critical Care Medicine, Morehouse School of Medicine, Atlanta, GA
| | - Hannah R Elliott
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Sean P David
- Department of Medicine, Stanford University, Stanford, CA
| | | | | | - Rachel F Tyndale
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, and Departments of Pharmacology & Toxicology and Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Timothy B Baker
- Tobacco Research and Intervention, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Laura J Bierut
- Siteman Cancer Center, Institute of Public Health, and Department of Psychiatry, Washington University School of Medicine, St. Louis, MI
| | - Nancy L Saccone
- Department of Genetics, Washington University School of Medicine, St. Louis, MI
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Baurley JW, McMahan CS, Ervin CM, Pardamean B, Bergen AW. Biosignature Discovery for Substance Use Disorders Using Statistical Learning. Trends Mol Med 2018; 24:221-235. [PMID: 29409736 PMCID: PMC5836808 DOI: 10.1016/j.molmed.2017.12.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Revised: 12/14/2017] [Accepted: 12/14/2017] [Indexed: 12/19/2022]
Abstract
There are limited biomarkers for substance use disorders (SUDs). Traditional statistical approaches are identifying simple biomarkers in large samples, but clinical use cases are still being established. High-throughput clinical, imaging, and 'omic' technologies are generating data from SUD studies and may lead to more sophisticated and clinically useful models. However, analytic strategies suited for high-dimensional data are not regularly used. We review strategies for identifying biomarkers and biosignatures from high-dimensional data types. Focusing on penalized regression and Bayesian approaches, we address how to leverage evidence from existing studies and knowledge bases, using nicotine metabolism as an example. We posit that big data and machine learning approaches will considerably advance SUD biomarker discovery. However, translation to clinical practice, will require integrated scientific efforts.
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Affiliation(s)
- James W Baurley
- BioRealm, Culver City, CA, USA; Bina Nusantara University, Jakarta, Indonesia.
| | | | | | - Bens Pardamean
- BioRealm, Culver City, CA, USA; Bina Nusantara University, Jakarta, Indonesia
| | - Andrew W Bergen
- BioRealm, Culver City, CA, USA; Oregon Research Institute, Eugene, OR, USA
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Affiliation(s)
- Bens Pardamean
- Bina Nusantara University, Jl. Kebon Jeruk Raya No. 27, Jakarta, 11530, Indonesia.
| | - James W Baurley
- Bina Nusantara University, Jl. Kebon Jeruk Raya No. 27, Jakarta, 11530, Indonesia
| | - Carissa I Pardamean
- Bina Nusantara University, Jl. Kebon Jeruk Raya No. 27, Jakarta, 11530, Indonesia
| | - Jane C Figueiredo
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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Baurley JW, Edlund CK, Pardamean CI, Conti DV, Krasnow R, Javitz HS, Hops H, Swan GE, Benowitz NL, Bergen AW. Genome-Wide Association of the Laboratory-Based Nicotine Metabolite Ratio in Three Ancestries. Nicotine Tob Res 2016; 18:1837-1844. [PMID: 27113016 PMCID: PMC4978985 DOI: 10.1093/ntr/ntw117] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Accepted: 04/12/2016] [Indexed: 12/29/2022]
Abstract
Introduction: Metabolic enzyme variation and other patient and environmental characteristics influence smoking behaviors, treatment success, and risk of related disease. Population-specific variation in metabolic genes contributes to challenges in developing and optimizing pharmacogenetic interventions. We applied a custom genome-wide genotyping array for addiction research (Smokescreen), to three laboratory-based studies of nicotine metabolism with oral or venous administration of labeled nicotine and cotinine, to model nicotine metabolism in multiple populations. The trans-3′-hydroxycotinine/cotinine ratio, the nicotine metabolite ratio (NMR), was the nicotine metabolism measure analyzed. Methods: Three hundred twelve individuals of self-identified European, African, and Asian American ancestry were genotyped and included in ancestry-specific genome-wide association scans (GWAS) and a meta-GWAS analysis of the NMR. We modeled natural-log transformed NMR with covariates: principal components of genetic ancestry, age, sex, body mass index, and smoking status. Results: African and Asian American NMRs were statistically significantly (P values ≤ 5E-5) lower than European American NMRs. Meta-GWAS analysis identified 36 genome-wide significant variants over a 43 kilobase pair region at CYP2A6 with minimum P = 2.46E-18 at rs12459249, proximal to CYP2A6. Additional minima were located in intron 4 (rs56113850, P = 6.61E-18) and in the CYP2A6-CYP2A7 intergenic region (rs34226463, P = 1.45E-12). Most (34/36) genome-wide significant variants suggested reduced CYP2A6 activity; functional mechanisms were identified and tested in knowledge-bases. Conditional analysis resulted in intergenic variants of possible interest (P values < 5E-5). Conclusions: This meta-GWAS of the NMR identifies CYP2A6 variants, replicates the top-ranked single nucleotide polymorphism from a recent Finnish meta-GWAS of the NMR, identifies functional mechanisms, and provides pan-continental population biomarkers for nicotine metabolism. Implications: This multiple ancestry meta-GWAS of the laboratory study-based NMR provides novel evidence and replication for genome-wide association of CYP2A6 single nucleotide and insertion–deletion polymorphisms. We identify three regions of genome-wide significance: proximal, intronic, and distal to CYP2A6. We replicate the top-ranking single nucleotide polymorphism from a recent GWAS of the NMR in Finnish smokers, identify a functional mechanism for this intronic variant from in silico analyses of RNA-seq data that is consistent with CYP2A6 expression measured in postmortem lung and liver, and provide additional support for the intergenic region between CYP2A6 and CYP2A7.
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Affiliation(s)
| | | | | | | | | | | | | | - Gary E Swan
- Stanford University School of Medicine , Stanford , CA
| | - Neal L Benowitz
- University of California, San Francisco School of Medicine , San Francisco , CA
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Abstract
BACKGROUND Addictive disorders are a class of chronic, relapsing mental disorders that are responsible for increased risk of mental and medical disorders and represent the largest, potentially modifiable cause of death. Tobacco dependence is associated with increased risk of disease and premature death. While tobacco control efforts and therapeutic interventions have made good progress in reducing smoking prevalence, challenges remain in optimizing their effectiveness based on patient characteristics, including genetic variation. In order to maximize collaborative efforts to advance addiction research, we have developed a genotyping array called Smokescreen. This custom array builds upon previous work in the analyses of human genetic variation, the genetics of addiction, drug metabolism, and response to therapy, with an emphasis on smoking and nicotine addiction. RESULTS The Smokescreen genotyping array includes 646,247 markers in 23 categories. The array design covers genome-wide common variation (65.67, 82.37, and 90.72% in African (YRI), East Asian (ASN), and European (EUR) respectively); most of the variation with a minor allele frequency ≥ 0.01 in 1014 addiction genes (85.16, 89.51, and 90.49% for YRI, ASN, and EUR respectively); and nearly all variation from the 1000 Genomes Project Phase 1, NHLBI GO Exome Sequencing Project and HapMap databases in the regions related to smoking behavior and nicotine metabolism: CHRNA5-CHRNA3-CHRNB4 and CYP2A6-CYP2B6. Of the 636 pilot DNA samples derived from blood or cell line biospecimens that were genotyped on the array, 622 (97.80%) passed quality control. In passing samples, 90.08% of markers passed quality control. The genotype reproducibility in 25 replicate pairs was 99.94%. For 137 samples that overlapped with HapMap2 release 24, the genotype concordance was 99.76%. In a genome-wide association analysis of the nicotine metabolite ratio in 315 individuals participating in nicotine metabolism laboratory studies, we identified genome-wide significant variants in the CYP2A6 region (min p = 9.10E-15). CONCLUSIONS We developed a comprehensive genotyping array for addiction research and demonstrated its analytic validity and utility through pilot genotyping of HapMap and study samples. This array allows researchers to perform genome-wide, candidate gene, and pathway-based association analyses of addiction, tobacco-use, treatment response, comorbidities, and associated diseases in a standardized, high-throughput platform.
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Affiliation(s)
- James W Baurley
- BioRealm LLC, 6101 W. Centinela Ave., Suite 270, Culver City, CA, 90230-6359, USA.
| | - Christopher K Edlund
- BioRealm LLC, 6101 W. Centinela Ave., Suite 270, Culver City, CA, 90230-6359, USA.
| | - Carissa I Pardamean
- BioRealm LLC, 6101 W. Centinela Ave., Suite 270, Culver City, CA, 90230-6359, USA.
| | - David V Conti
- BioRealm LLC, 6101 W. Centinela Ave., Suite 270, Culver City, CA, 90230-6359, USA.
| | - Andrew W Bergen
- BioRealm LLC, 6101 W. Centinela Ave., Suite 270, Culver City, CA, 90230-6359, USA.
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Bergen AW, Krasnow R, Javitz HS, Swan GE, Li MD, Baurley JW, Chen X, Murrelle L, Zedler B. Total Exposure Study Analysis consortium: a cross-sectional study of tobacco exposures. BMC Public Health 2015; 15:866. [PMID: 26346437 PMCID: PMC4561475 DOI: 10.1186/s12889-015-2212-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2014] [Accepted: 09/02/2015] [Indexed: 12/03/2022] Open
Abstract
Background The Total Exposure Study was a stratified, multi-center, cross-sectional study designed to estimate levels of biomarkers of tobacco-specific and non-specific exposure and of potential harm in U.S. adult current cigarette smokers (≥one manufactured cigarette per day over the last year) and tobacco product non-users (no smoking or use of any nicotine containing products over the last 5 years). The study was designed and sponsored by a tobacco company and implemented by contract research organizations in 2002–2003. Multiple analyses of smoking behavior, demographics, and biomarkers were performed. Study data and banked biospecimens were transferred from the sponsor to the Virginia Tobacco and Health Research Repository in 2010, and then to SRI International in 2012, for independent analysis and dissemination. Methods We analyzed biomarker distributions overall, and by biospecimen availability, for comparison with existing studies, and to evaluate generalizability to the entire sample. We calculated genome-wide statistical power for a priori hypotheses. We performed clinical chemistries, nucleic acid extractions and genotyping, and report correlation and quality control metrics. Results Vital signs, clinical chemistries, and laboratory measures of tobacco specific and non-specific toxicants are available from 3585 current cigarette smokers, and 1077 non-users. Peripheral blood mononuclear cells, red blood cells, plasma and 24-h urine biospecimens are available from 3073 participants (2355 smokers and 719 non-users). In multivariate analysis, participants with banked biospecimens were significantly more likely to self-identify as White, to be older, to have increased total nicotine equivalents per cigarette, decreased serum cotinine, and increased forced vital capacity, compared to participants without. Effect sizes were small (Cohen’s d-values ≤ 0.11). Power for a priori hypotheses was 57 % in non-Hispanic Black (N = 340), and 96 % in non-Hispanic White (N = 1840), smokers. All DNA samples had genotype completion rates ≥97.5 %; 68 % of RNA samples yielded RIN scores ≥6.0. Conclusions Total Exposure Study clinical and laboratory assessments and biospecimens comprise a unique resource for cigarette smoke health effects research. The Total Exposure Study Analysis Consortium seeks to perform molecular studies in multiple domains and will share data and analytic results in public repositories and the peer-reviewed literature. Data and banked biospecimens are available for independent or collaborative research. Electronic supplementary material The online version of this article (doi:10.1186/s12889-015-2212-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Andrew W Bergen
- Center for Health Sciences, SRI International, 333 Ravenswood Avenue, Menlo Park, CA, 94025, USA.
| | - Ruth Krasnow
- Center for Health Sciences, SRI International, 333 Ravenswood Avenue, Menlo Park, CA, 94025, USA.
| | - Harold S Javitz
- Center for Health Sciences, SRI International, 333 Ravenswood Avenue, Menlo Park, CA, 94025, USA.
| | - Gary E Swan
- Stanford Prevention Research Center, Stanford University School of Medicine, Palo Alto, CA, 94305, USA.
| | - Ming D Li
- Department of Psychiatry and Neurobehavioral Sciences, University of Virginia, Charlottesville, VA, 22911, USA.
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Figueiredo JC, Ly S, Magee KS, Ihenacho U, Baurley JW, Sanchez-Lara PA, Brindopke F, Nguyen THD, Nguyen V, Tangco MI, Giron M, Abrahams T, Jang G, Vu A, Zolfaghari E, Yao CA, Foong A, DeClerk YA, Samet JM, Magee W. Parental risk factors for oral clefts among Central Africans, Southeast Asians, and Central Americans. ACTA ACUST UNITED AC 2015; 103:863-79. [PMID: 26466527 PMCID: PMC5049483 DOI: 10.1002/bdra.23417] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2014] [Revised: 07/11/2015] [Accepted: 07/15/2015] [Indexed: 12/20/2022]
Abstract
Background Several lifestyle and environmental exposures have been suspected as risk factors for oral clefts, although few have been convincingly demonstrated. Studies across global diverse populations could offer additional insight given varying types and levels of exposures. Methods We performed an international case–control study in the Democratic Republic of the Congo (133 cases, 301 controls), Vietnam (75 cases, 158 controls), the Philippines (102 cases, 152 controls), and Honduras (120 cases, 143 controls). Mothers were recruited from hospitals and their exposures were collected from interviewer‐administered questionnaires. We used logistic regression modeling to estimate odds ratios (OR) and 95% confidence intervals (CI). Results Family history of clefts was strongly associated with increased risk (maternal: OR = 4.7; 95% CI, 3.0–7.2; paternal: OR = 10.5; 95% CI, 5.9–18.8; siblings: OR = 5.3; 95% CI, 1.4–19.9). Advanced maternal age (5 year OR = 1.2; 95% CI, 1.0–1.3), pregestational hypertension (OR = 2.6; 95% CI, 1.3–5.1), and gestational seizures (OR = 2.9; 95% CI, 1.1–7.4) were statistically significant risk factors. Lower maternal (secondary school OR = 1.6; 95% CI, 1.2–2.2; primary school OR = 2.4, 95% CI, 1.6–2.8) and paternal education (OR = 1.9; 95% CI, 1.4–2.5; and OR = 1.8; 95% CI, 1.1–2.9, respectively) and paternal tobacco smoking (OR = 1.5, 95% CI, 1.1–1.9) were associated with an increased risk. No other significant associations between maternal and paternal factors were found; some environmental factors including rural residency, indoor cooking with wood, chemicals and water source appeared to be associated with an increased risk in adjusted models. Conclusion Our study represents one of the first international studies investigating risk factors for clefts among multiethnic underserved populations. Our findings suggest a multifactorial etiology including both maternal and paternal factors. Birth Defects Research (Part A) 103:863–879, 2015. © 2015 The Authors Birth Defects Research Part A: Clinical and Molecular Teratology Published by Wiley Periodicals, Inc.
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Affiliation(s)
- Jane C Figueiredo
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Stephanie Ly
- Division of Plastic & Maxillofacial Surgery, Children's Hospital Los Angeles, Los Angeles, California.,Department of Community Health Sciences and California Center for Population Research, UCLA Fielding School of Public Health, Los Angeles, California
| | | | - Ugonna Ihenacho
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - James W Baurley
- BioRealm LLC, Los Angeles, California.,Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Pedro A Sanchez-Lara
- Departments of Pediatrics and Pathology & Laboratory Medicine, Keck School of Medicine, University of Southern California, Children's Hospital Los Angeles, Los Angeles, California
| | - Frederick Brindopke
- Division of Plastic & Maxillofacial Surgery, Children's Hospital Los Angeles, Los Angeles, California
| | | | | | - Maria Irene Tangco
- Operation Smile Philippines, Manila, Philippines.,Department of Surgery, Faculty of Medicine and Surgery, University of Santo Tomas, Manila, Philippines
| | | | | | - Grace Jang
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Annie Vu
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Emily Zolfaghari
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Caroline A Yao
- Division of Plastic & Maxillofacial Surgery, Children's Hospital Los Angeles, Los Angeles, California
| | - Athena Foong
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Yves A DeClerk
- Departments of Pediatrics and Biochemistry and Molecular Biology, Keck School of Medicine, University of Southern California and Children's Hospital Los Angeles, Los Angeles, California
| | - Jonathan M Samet
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - William Magee
- Division of Plastic & Maxillofacial Surgery, Children's Hospital Los Angeles, Los Angeles, California
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27
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Bergen AW, Michel M, Nishita D, Krasnow R, Javitz HS, Conneely KN, Lessov-Schlaggar CN, Hops H, Zhu AZX, Baurley JW, McClure JB, Hall SM, Baker TB, Conti DV, Benowitz NL, Lerman C, Tyndale RF, Swan GE. Drug Metabolizing Enzyme and Transporter Gene Variation, Nicotine Metabolism, Prospective Abstinence, and Cigarette Consumption. PLoS One 2015; 10:e0126113. [PMID: 26132489 PMCID: PMC4488893 DOI: 10.1371/journal.pone.0126113] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2014] [Accepted: 03/29/2015] [Indexed: 11/28/2022] Open
Abstract
The Nicotine Metabolite Ratio (NMR, ratio of trans-3’-hydroxycotinine and cotinine), has previously been associated with CYP2A6 activity, response to smoking cessation treatments, and cigarette consumption. We searched for drug metabolizing enzyme and transporter (DMET) gene variation associated with the NMR and prospective abstinence in 2,946 participants of laboratory studies of nicotine metabolism and of clinical trials of smoking cessation therapies. Stage I was a meta-analysis of the association of 507 common single nucleotide polymorphisms (SNPs) at 173 DMET genes with the NMR in 449 participants of two laboratory studies. Nominally significant associations were identified in ten genes after adjustment for intragenic SNPs; CYP2A6 and two CYP2A6 SNPs attained experiment-wide significance adjusted for correlated SNPs (CYP2A6 PACT=4.1E-7, rs4803381 PACT=4.5E-5, rs1137115, PACT=1.2E-3). Stage II was mega-regression analyses of 10 DMET SNPs with pretreatment NMR and prospective abstinence in up to 2,497 participants from eight trials. rs4803381 and rs1137115 SNPs were associated with pretreatment NMR at genome-wide significance. In post-hoc analyses of CYP2A6 SNPs, we observed nominally significant association with: abstinence in one pharmacotherapy arm; cigarette consumption among all trial participants; and lung cancer in four case:control studies. CYP2A6 minor alleles were associated with reduced NMR, CPD, and lung cancer risk. We confirmed the major role that CYP2A6 plays in nicotine metabolism, and made novel findings with respect to genome-wide significance and associations with CPD, abstinence and lung cancer risk. Additional multivariate analyses with patient variables and genetic modeling will improve prediction of nicotine metabolism, disease risk and smoking cessation treatment prognosis.
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Affiliation(s)
- Andrew W. Bergen
- Center for Health Sciences, SRI International, Menlo Park, California, United States of America
- * E-mail:
| | - Martha Michel
- Academic Research Systems, University of California San Francisco, San Francisco, California, United States of America
| | - Denise Nishita
- Center for Health Sciences, SRI International, Menlo Park, California, United States of America
| | - Ruth Krasnow
- Center for Health Sciences, SRI International, Menlo Park, California, United States of America
| | - Harold S. Javitz
- Center for Health Sciences, SRI International, Menlo Park, California, United States of America
| | - Karen N. Conneely
- Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | | | - Hyman Hops
- Oregon Research Institute, Eugene, Oregon, United States of America
| | - Andy Z. X. Zhu
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada
| | | | - Jennifer B. McClure
- Group Health Research Institute, Seattle, Washington, United States of America
| | - Sharon M. Hall
- Department of Psychiatry, University of California San Francisco, San Francisco, California, United States of America
| | - Timothy B. Baker
- Center for Tobacco Research and Intervention, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States of America
| | - David V. Conti
- Department of Preventive Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Neal L. Benowitz
- Departments of Medicine and of Bioengineering & Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
| | - Caryn Lerman
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Rachel F. Tyndale
- Cambell Family Mental Health Research Institute, Centre for Addiction and Mental Health, and Departments of Psychiatry, and of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada
| | - Gary E. Swan
- Oregon Research Institute, Eugene, Oregon, United States of America
- Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Palo Alto, California, United States of America
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28
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Figueiredo JC, Ly S, Raimondi H, Magee K, Baurley JW, Sanchez-Lara PA, Ihenacho U, Yao C, Edlund CK, van den Berg D, Casey G, DeClerk YA, Samet JM, Magee W. Genetic risk factors for orofacial clefts in Central Africans and Southeast Asians. Am J Med Genet A 2014; 164A:2572-80. [DOI: 10.1002/ajmg.a.36693] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2013] [Accepted: 06/27/2014] [Indexed: 12/18/2022]
Affiliation(s)
- Jane C. Figueiredo
- Department of Preventive Medicine; Keck School of Medicine; University of Southern California; Los Angeles California
| | - Stephanie Ly
- Division of Plastic & Maxillofacial Surgery; Children's Hospital Los Angeles; Los Angeles California
| | | | | | - James W. Baurley
- BioRealm LLC; Los Angeles California
- Bioinformatics Research Group; Bina Nusantara University; Jakarta Indonesia
| | - Pedro A. Sanchez-Lara
- Departments of Pediatrics and Pathology & Laboratory Medicine; Keck School of Medicine; University of Southern California; Children's Hospital Los Angeles; Los Angeles California
| | - Ugonna Ihenacho
- Department of Preventive Medicine; Keck School of Medicine; University of Southern California; Los Angeles California
| | - Caroline Yao
- Division of Plastic & Maxillofacial Surgery; Children's Hospital Los Angeles; Los Angeles California
| | - Christopher K. Edlund
- Department of Preventive Medicine; Keck School of Medicine; University of Southern California; Los Angeles California
- BioRealm LLC; Los Angeles California
| | - David van den Berg
- Department of Preventive Medicine; Keck School of Medicine; University of Southern California; Los Angeles California
| | - Graham Casey
- Department of Preventive Medicine; Keck School of Medicine; University of Southern California; Los Angeles California
| | - Yves A. DeClerk
- Departments of Pediatrics and Biochemistry and Molecular Biology; Keck School of Medicine; University of Southern California and Children's Hospital Los Angeles; Los Angeles California
| | - Jonathan M. Samet
- Department of Preventive Medicine; Keck School of Medicine; University of Southern California; Los Angeles California
| | - William Magee
- Division of Plastic & Maxillofacial Surgery; Children's Hospital Los Angeles; Los Angeles California
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29
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Abstract
BACKGROUND Testing for marginal associations between numerous genetic variants and disease may miss complex relationships among variables (e.g., gene-gene interactions). Bayesian approaches can model multiple variables together and offer advantages over conventional model building strategies, including using existing biological evidence as modeling priors and acknowledging that many models may fit the data well. With many candidate variables, Bayesian approaches to variable selection rely on algorithms to approximate the posterior distribution of models, such as Markov-Chain Monte Carlo (MCMC). Unfortunately, MCMC is difficult to parallelize and requires many iterations to adequately sample the posterior. We introduce a scalable algorithm called PEAK that improves the efficiency of MCMC by dividing a large set of variables into related groups using a rooted graph that resembles a mountain peak. Our algorithm takes advantage of parallel computing and existing biological databases when available. RESULTS By using graphs to manage a model space with more than 500,000 candidate variables, we were able to improve MCMC efficiency and uncover the true simulated causal variables, including a gene-gene interaction. We applied PEAK to a case-control study of childhood asthma with 2,521 genetic variants. We used an informative graph for oxidative stress derived from Gene Ontology and identified several variants in ERBB4, OXR1, and BCL2 with strong evidence for associations with childhood asthma. CONCLUSIONS We introduced an extremely flexible analysis framework capable of efficiently performing Bayesian variable selection on many candidate variables. The PEAK algorithm can be provided with an informative graph, which can be advantageous when considering gene-gene interactions, or a symmetric graph, which simply divides the model space into manageable regions. The PEAK framework is compatible with various model forms, allowing for the algorithm to be configured for different study designs and applications, such as pathway or rare-variant analyses, by simple modifications to the model likelihood and proposal functions.
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Affiliation(s)
- James W Baurley
- Bioinformatics Research Group, Bina Nusantara University, Jakarta, Indonesia
- BioRealm LLC, Monument, USA
| | - David V Conti
- Department of Preventive Medicine, University of Southern California, Los Angeles, USA
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30
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Torgerson DG, Ampleford EJ, Chiu GY, Gauderman WJ, Gignoux CR, Graves PE, Himes BE, Levin AM, Mathias RA, Hancock DB, Baurley JW, Eng C, Stern DA, Celedón JC, Rafaels N, Capurso D, Conti DV, Roth LA, Soto-Quiros M, Togias A, Li X, Myers RA, Romieu I, Van Den Berg DJ, Hu D, Hansel NN, Hernandez RD, Israel E, Salam MT, Galanter J, Avila PC, Avila L, Rodriquez-Santana JR, Chapela R, Rodriguez-Cintron W, Diette GB, Adkinson NF, Abel RA, Ross KD, Shi M, Faruque MU, Dunston GM, Watson HR, Mantese VJ, Ezurum SC, Liang L, Ruczinski I, Ford JG, Huntsman S, Chung KF, Vora H, Li X, Calhoun WJ, Castro M, Sienra-Monge JJ, del Rio-Navarro B, Deichmann KA, Heinzmann A, Wenzel SE, Busse WW, Gern JE, Lemanske RF, Beaty TH, Bleecker ER, Raby BA, Meyers DA, London SJ, Gilliland FD, Burchard EG, Martinez FD, Weiss ST, Williams LK, Barnes KC, Ober C, Nicolae DL. Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat Genet 2011; 43:887-92. [PMID: 21804549 PMCID: PMC3445408 DOI: 10.1038/ng.888] [Citation(s) in RCA: 637] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2010] [Accepted: 06/16/2011] [Indexed: 11/09/2022]
Abstract
Asthma is a common disease with a complex risk architecture including both genetic and environmental factors. We performed a meta-analysis of North American genome-wide association studies of asthma in 5,416 individuals with asthma (cases) including individuals of European American, African American or African Caribbean, and Latino ancestry, with replication in an additional 12,649 individuals from the same ethnic groups. We identified five susceptibility loci. Four were at previously reported loci on 17q21, near IL1RL1, TSLP and IL33, but we report for the first time, to our knowledge, that these loci are associated with asthma risk in three ethnic groups. In addition, we identified a new asthma susceptibility locus at PYHIN1, with the association being specific to individuals of African descent (P = 3.9 × 10(-9)). These results suggest that some asthma susceptibility loci are robust to differences in ancestry when sufficiently large samples sizes are investigated, and that ancestry-specific associations also contribute to the complex genetic architecture of asthma.
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Affiliation(s)
- Dara G Torgerson
- Department of Human Genetics, University of Chicago, Chicago, Illinois, USA
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31
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Abstract
Unraveling complex interactions has been a challenge in epidemiologic research. We introduce a pathway modeling framework that discovers plausible pathways from observational data, and allows estimation of both the net effect of the pathway and the types of interactions occurring among genetic or environmental risk factors. Each discovered pathway structure links combinations of observed variables through intermediate latent nodes to a final node, the outcome. Biologic knowledge can be readily applied in this framework as a prior on pathway structure to give preference to more biologically plausible models, thereby providing more precise estimation of Bayes factors for pathways of greatest interest by Markov Chain Monte Carlo (MCMC) methods.Data were simulated for binary inputs of which only a subset was involved in different pathway topologies. Our algorithm was then used to recover the pathway from the simulated data. The posterior distributions of inputs, pairwise and higher-order interactions, and topologies were obtained by MCMC methods. The evidence in favor of a particular pathway or interaction was summarized using Bayes factors. Our method can correctly identify the risk factors and interactions involved in the simulated pathway. We apply our framework to an asthma case-control data set with polymorphisms in 12 genes.
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Affiliation(s)
- James W Baurley
- Department of Preventive Medicine, University of Southern California, Los Angeles, USA
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32
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Hunninghake GM, Soto-Quirós ME, Avila L, Kim HP, Lasky-Su J, Rafaels N, Ruczinski I, Beaty TH, Mathias RA, Barnes KC, Wilk JB, O'Connor GT, Gauderman WJ, Vora H, Baurley JW, Gilliland F, Liang C, Sylvia JS, Klanderman BJ, Sharma SS, Himes BE, Bossley CJ, Israel E, Raby BA, Bush A, Choi AM, Weiss ST, Celedón JC. TSLP polymorphisms are associated with asthma in a sex-specific fashion. Allergy 2010; 65:1566-75. [PMID: 20560908 DOI: 10.1111/j.1398-9995.2010.02415.x] [Citation(s) in RCA: 95] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
BACKGROUND Single nucleotide polymorphisms (SNPs) in thymic stromal lymphopoietin (TSLP) have been associated with IgE (in girls) and asthma (in general). We sought to determine whether TSLP SNPs are associated with asthma in a sex-specific fashion. METHODS We conducted regular and sex-stratified analyses of association between SNPs in TSLP and asthma in families of children with asthma in Costa Rica. Significant findings were replicated in whites and African-American participants in the Childhood Asthma Management Program, in African-Americans in the Genomic Research on Asthma in the African Diaspora study, in whites and Hispanics in the Children's Health Study, and in whites in the Framingham Heart Study (FHS). MAIN RESULTS Two SNPs in TSLP (rs1837253 and rs2289276) were significantly associated with a reduced risk of asthma in combined analyses of all cohorts (P values of 2 × 10(-5) and 1 × 10(-5) , respectively). In a sex-stratified analysis, the T allele of rs1837253 was significantly associated with a reduced risk of asthma in males only (P = 3 × 10(-6) ). Alternately, the T allele of rs2289276 was significantly associated with a reduced risk of asthma in females only (P = 2 × 10(-4) ). Findings for rs2289276 were consistent in all cohorts except the FHS. CONCLUSIONS TSLP variants are associated with asthma in a sex-specific fashion.
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Affiliation(s)
- G M Hunninghake
- Channing Laboratory, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
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33
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DeWan AT, Triche EW, Xu X, Hsu LI, Zhao C, Belanger K, Hellenbrand K, Willis-Owen SAG, Moffatt M, Cookson WOC, Himes BE, Weiss ST, Gauderman WJ, Baurley JW, Gilliland F, Wilk JB, O'Connor GT, Strachan DP, Hoh J, Bracken MB. PDE11A associations with asthma: results of a genome-wide association scan. J Allergy Clin Immunol 2010; 126:871-873.e9. [PMID: 20920776 DOI: 10.1016/j.jaci.2010.06.051] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2009] [Revised: 06/13/2010] [Accepted: 06/29/2010] [Indexed: 10/19/2022]
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34
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Engelman CD, Baurley JW, Chiu YF, Joubert BR, Lewinger JP, Maenner MJ, Murcray CE, Shi G, Gauderman WJ. Detecting gene-environment interactions in genome-wide association data. Genet Epidemiol 2010; 33 Suppl 1:S68-73. [PMID: 19924704 DOI: 10.1002/gepi.20475] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Despite the importance of gene-environment (GxE) interactions in the etiology of common diseases, little work has been done to develop methods for detecting these types of interactions in genome-wide association study data. This was the focus of Genetic Analysis Workshop 16 Group 10 contributions, which introduced a variety of new methods for the detection of GxE interactions in both case-control and family-based data using both cross-sectional and longitudinal study designs. Many of these contributions detected significant GxE interactions. Although these interactions have not yet been confirmed, the results suggest the importance of testing for interactions. Issues of sample size, quantifying the environmental exposure, longitudinal data analysis, family-based analysis, selection of the most powerful analysis method, population stratification, and computational expense with respect to testing GxE interactions are discussed.
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Affiliation(s)
- Corinne D Engelman
- Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin 53726-2397, USA.
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35
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Abstract
Genetic epidemiology is increasingly focused on complex diseases involving multiple genes and environmental factors, often interacting in complex ways. Although standard frequentist methods still have a role in hypothesis generation and testing for discovery of novel main effects and interactions, Bayesian methods are particularly well suited to modeling the relationships in an integrated "systems biology" manner. In this chapter, we provide an overview of the principles of Bayesian analysis and their advantages in this context and describe various approaches to applying them for both model building and discovery in a genome-wide setting. In particular, we highlight the ability of Bayesian methods to construct complex probability models via a hierarchical structure and to account for uncertainty in model specification by averaging over large spaces of alternative models.
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Affiliation(s)
- Melanie A Wilson
- Department of Preventive Medicine, University of Southern California, Los Angeles, California, USA
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36
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Thomas DC, Baurley JW, Brown EE, Figueiredo JC, Goldstein A, Hazra A, Wilson RT, Rothman N. Approaches to Complex Pathways in Molecular Epidemiology: Summary of a Special Conference of the American Association for Cancer Research. Cancer Res 2008; 68:10028-30. [DOI: 10.1158/0008-5472.can-08-1690] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Lewinger JP, Conti DV, Baurley JW, Triche TJ, Thomas DC. Hierarchical Bayes prioritization of marker associations from a genome-wide association scan for further investigation. Genet Epidemiol 2008; 31:871-82. [PMID: 17654612 DOI: 10.1002/gepi.20248] [Citation(s) in RCA: 71] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
We describe a hierarchical regression modeling approach to selection of a subset of markers from the first stage of a genomewide association scan to carry forward to subsequent stages for testing on an independent set of subjects. Rather than simply selecting a subset of most significant marker-disease associations at some cutoff chosen to maximize the cost efficiency of a multistage design, we propose a prior model for the true noncentrality parameters of these associations composed of a large mass at zero and a continuous distribution of nonzero values. The prior probability of nonzero values and their prior means can be functions of various covariates characterizing each marker, such as their location relative to genes or evolutionary conserved regions, or prior linkage or association data. We propose to take the top ranked posterior expectations of the noncentrality parameters for confirmation in later stages of a genomewide scan. The statistical performance of this approach is compared with the traditional p-value ranking by simulation studies. We show that the ranking by posterior expectations performs better at selecting the true positive association than a simple ranking of p-values if at least some of the prior covariates have predictive value.
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Affiliation(s)
- Juan Pablo Lewinger
- Department of Preventive Medicine, University of Southern California, Los Angeles, California 90089-9011, USA.
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