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Is the gene-environment interaction paradigm relevant to genome-wide studies? The case of education and body mass index. Demography 2014; 51:119-39. [PMID: 24281739 DOI: 10.1007/s13524-013-0259-4] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
This study uses data from the Framingham Heart Study to examine the relevance of the gene-environment interaction paradigm for genome-wide association studies (GWAS). We use completed college education as our environmental measure and estimate the interactive effect of genotype and education on body mass index (BMI) using 260,402 single-nucleotide polymorphisms (SNPs). Our results highlight the sensitivity of parameter estimates obtained from GWAS models and the difficulty of framing genome-wide results using the existing gene-environment interaction typology. We argue that SNP-environment interactions across the human genome are not likely to provide consistent evidence regarding genetic influences on health that differ by environment. Nevertheless, genome-wide data contain rich information about individual respondents, and we demonstrate the utility of this type of data. We highlight the fact that GWAS is just one use of genome-wide data, and we encourage demographers to develop methods that incorporate this vast amount of information from respondents into their analyses.
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Abstract
BACKGROUND Genomewide association studies (GWASs) on antidepressant efficacy have yielded modest results. A possible reason is that response is influenced by other factors, which possibly interact with genetic variation. We used a GWAS model to predict antidepressant response, by including predictors previously known to affect response, such as quality of life (QoL). We also evaluated the association between genes, previously implicated in gene-environment (G × E) interactions, and response using an enrichment analysis. METHOD We examined a sample of 1426 depressed patients from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial: 774 responders, 652 non-responders and 418,865 single nucleotide polymorphisms (SNPs) were analysed. First, in a GWAS model, we investigated whether genetic variations interact with patients' levels of QoL to predict response, after controlling for demographic characteristics, severity and population stratification. Second, we conducted an enrichment analysis exploring whether candidate genes that have emerged from prior G × E interaction studies on depression are associated with treatment response. RESULTS The GWAS model, with QoL as a moderator, yielded one SNP (rs520210) associated with response in the NEDD4L gene (p = 3.64 × 10⁻⁸). In the Caucasian sample only, we observed a drop in significance for this SNP. The enrichment analysis showed that SNPs within serotonergic genes contained more significant markers that predicted response, compared with a random set of genes in the genome. CONCLUSIONS Our findings point to possible target genes, which are proposed for further independent replication. Our enrichment analysis provides further support, in a genomewide context, of the role of serotonergic genes in influencing antidepressant response.
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Affiliation(s)
- N Antypa
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
| | - A Drago
- IRCCS Centro S. Giovanni di Dio, Fatebenefratelli, Brescia, Italy
| | - A Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
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Véron A, Blein S, Cox DG. Genome-wide association studies and the clinic: a focus on breast cancer. Biomark Med 2014; 8:287-96. [DOI: 10.2217/bmm.13.121] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Breast cancer is the most frequently diagnosed cancer among women worldwide, and has long been considered to be a genetic disease. A wide range of genetic variants, both rare mutations and more common variants, have been shown to influence breast cancer risk. In particular, recent studies have identified a number of common genetic variants, or single nucleotide polymorphisms, that are associated with breast cancer risk. In this review, we will briefly present the genetic epidemiology of breast cancer, genome-wide association study technology and how this technology may influence breast cancer screening in the clinic.
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Affiliation(s)
- Amélie Véron
- Université de Lyon, F-69000 Lyon, France
- Université Lyon 1, ISPB, Lyon, F-69622, France
- INSERM U1052, Centre de Recherche en Cancérologie de Lyon, F-69000 Lyon, France
- CNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, F-69000 Lyon, France
- Centre Léon Bérard, F-69008 Lyon, France
| | - Sophie Blein
- Université de Lyon, F-69000 Lyon, France
- Université Lyon 1, ISPB, Lyon, F-69622, France
- INSERM U1052, Centre de Recherche en Cancérologie de Lyon, F-69000 Lyon, France
- CNRS UMR5286, Centre de Recherche en Cancérologie de Lyon, F-69000 Lyon, France
- Centre Léon Bérard, F-69008 Lyon, France
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Schoeps A, Rudolph A, Seibold P, Dunning AM, Milne RL, Bojesen SE, Swerdlow A, Andrulis I, Brenner H, Behrens S, Orr N, Jones M, Ashworth A, Li J, Cramp H, Connley D, Czene K, Darabi H, Chanock SJ, Lissowska J, Figueroa JD, Knight J, Glendon G, Mulligan AM, Dumont M, Severi G, Baglietto L, Olson J, Vachon C, Purrington K, Moisse M, Neven P, Wildiers H, Spurdle A, Kosma VM, Kataja V, Hartikainen JM, Hamann U, Ko YD, Dieffenbach AK, Arndt V, Stegmaier C, Malats N, Arias Perez J, Benítez J, Flyger H, Nordestgaard BG, Truong T, Cordina-Duverger E, Menegaux F, Silva IDS, Fletcher O, Johnson N, Häberle L, Beckmann MW, Ekici AB, Braaf L, Atsma F, van den Broek AJ, Makalic E, Schmidt DF, Southey MC, Cox A, Simard J, Giles GG, Lambrechts D, Mannermaa A, Brauch H, Guénel P, Peto J, Fasching PA, Hopper J, Flesch-Janys D, Couch F, Chenevix-Trench G, Pharoah PDP, Garcia-Closas M, Schmidt MK, Hall P, Easton DF, Chang-Claude J. Identification of new genetic susceptibility loci for breast cancer through consideration of gene-environment interactions. Genet Epidemiol 2014; 38:84-93. [PMID: 24248812 PMCID: PMC3995140 DOI: 10.1002/gepi.21771] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2013] [Revised: 10/15/2013] [Accepted: 10/15/2013] [Indexed: 11/06/2022]
Abstract
Genes that alter disease risk only in combination with certain environmental exposures may not be detected in genetic association analysis. By using methods accounting for gene-environment (G × E) interaction, we aimed to identify novel genetic loci associated with breast cancer risk. Up to 34,475 cases and 34,786 controls of European ancestry from up to 23 studies in the Breast Cancer Association Consortium were included. Overall, 71,527 single nucleotide polymorphisms (SNPs), enriched for association with breast cancer, were tested for interaction with 10 environmental risk factors using three recently proposed hybrid methods and a joint test of association and interaction. Analyses were adjusted for age, study, population stratification, and confounding factors as applicable. Three SNPs in two independent loci showed statistically significant association: SNPs rs10483028 and rs2242714 in perfect linkage disequilibrium on chromosome 21 and rs12197388 in ARID1B on chromosome 6. While rs12197388 was identified using the joint test with parity and with age at menarche (P-values = 3 × 10(-07)), the variants on chromosome 21 q22.12, which showed interaction with adult body mass index (BMI) in 8,891 postmenopausal women, were identified by all methods applied. SNP rs10483028 was associated with breast cancer in women with a BMI below 25 kg/m(2) (OR = 1.26, 95% CI 1.15-1.38) but not in women with a BMI of 30 kg/m(2) or higher (OR = 0.89, 95% CI 0.72-1.11, P for interaction = 3.2 × 10(-05)). Our findings confirm comparable power of the recent methods for detecting G × E interaction and the utility of using G × E interaction analyses to identify new susceptibility loci.
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Affiliation(s)
- Anja Schoeps
- Department of Cancer Epidemiology, German Cancer Research
Center (DKFZ), Heidelberg, Germany
- Institute of Public Health, University of Heidelberg,
Heidelberg, Germany
| | - Anja Rudolph
- Department of Cancer Epidemiology, German Cancer Research
Center (DKFZ), Heidelberg, Germany
| | - Petra Seibold
- Department of Cancer Epidemiology, German Cancer Research
Center (DKFZ), Heidelberg, Germany
| | - Alison M. Dunning
- Department of Oncology, University of Cambridge, Cambridge,
United Kingdom
| | - Roger L. Milne
- Genetic and Molecular Epidemiology Group, Spanish National
Cancer Research Centre (CNIO), Madrid, Spain
| | - Stig E. Bojesen
- Department of Clinical Biochemistry, Herlev Hospital,
Copenhagen University Hospital, Herlev, Denmark
- Copenhagen General Population Study, Herlev Hospital,
Copenhagen University Hospital, Herlev, Denmark
| | - Anthony Swerdlow
- Department of Genetics and Epidemiology, Institute of
Cancer Research, Sutton, United Kingdom
| | - Irene Andrulis
- Department of Molecular Genetics, Lunenfeld-Tanenbaum
Research Institute of Mount Sinai Hospital, Toronto, Canada
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research,
German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium, Heidelberg, Germany
| | - Sabine Behrens
- Department of Cancer Epidemiology, German Cancer Research
Center (DKFZ), Heidelberg, Germany
| | - Nicholas Orr
- Department of Breast Cancer Research, Institute of Cancer
Research, London, United Kingdom
| | - Michael Jones
- Copenhagen General Population Study, Herlev Hospital,
Copenhagen University Hospital, Herlev, Denmark
| | - Alan Ashworth
- Department of Human Genetics, Genome Institute of
Singapore, Singapore, Singapore
| | - Jingmei Li
- Department of Human Genetics, Genome Institute of
Singapore, Singapore, Singapore
| | - Helen Cramp
- Department of Oncology, Institute for Cancer Studies,
University of Sheffield, Sheffield, United Kingdom
| | - Dan Connley
- Department of Oncology, Institute for Cancer Studies,
University of Sheffield, Sheffield, United Kingdom
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics,
Karolinska Institutet, Stockholm, Sweden
| | - Hatef Darabi
- Department of Medical Epidemiology and Biostatistics,
Karolinska Institutet, Stockholm, Sweden
| | - Stephen J. Chanock
- Division of Cancer Epidemiology and Genetics, National
Cancer Institute, Rockville, Maryland, United States of America
| | - Jolanta Lissowska
- Department of Cancer Epidemiology and Prevention, M.
Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw,
Poland
| | - Jonine D. Figueroa
- Division of Cancer Epidemiology and Genetics, National
Cancer Institute, Rockville, Maryland, United States of America
| | - Julia Knight
- Lunenfeld-Tanenbaum Research Institute of Mount Sinai
Hospital, Toronto, Canada
- Prosserman Centre for Health Research, Toronto,
Canada
| | - Gord Glendon
- Prosserman Centre for Health Research, Toronto,
Canada
| | - Anna M. Mulligan
- Laboratory Medicine Program, University Health Network,
Toronto, Canada
| | - Martine Dumont
- Cancer Genomics Laboratory, Centre Hospitalier
Universitaire de Québec Research Center, Laval University, Québec,
Canada
- Department of Molecular Medicine, Faculty of Medicine,
Quebec, Canada
| | - Gianluca Severi
- Cancer Epidemiology Centre, Cancer Council Victoria,
Melbourne, Australia
| | - Laura Baglietto
- Cancer Epidemiology Centre, Cancer Council Victoria,
Melbourne, Australia
- Centre for Molecular, Environmental, Genetic, and
Analytic Epidemiology, University of Melbourne, Melbourne, Australia
| | - Janet Olson
- Department of Health Sciences Research, Mayo Clinic,
Minnesota, United States of America
| | - Celine Vachon
- Department of Health Sciences Research, Mayo Clinic,
Minnesota, United States of America
| | - Kristen Purrington
- Department of Health Sciences Research, Mayo Clinic,
Minnesota, United States of America
| | - Matthieu Moisse
- Vesalius Research Center (VRC), VIB, Flanders,
Belgium
- Laboratory of Translational Genetics, Department of
Oncology, University of Leuven, Leuven, Belgium
| | - Patrick Neven
- Department of Multidisciplinary Breast Cancer, University
Hospital Gasthuisberg, Leuven, Belgium
| | - Hans Wildiers
- Department of Multidisciplinary Breast Cancer, University
Hospital Gasthuisberg, Leuven, Belgium
| | - Amanda Spurdle
- Department of Molecular Cancer Epidemiology, Queensland
Institute of Medical Research, Brisbane Australia
| | | | - Vesa Kataja
- Pathology Department, University of Kuopio, Kuopio,
Finland
| | | | - Ute Hamann
- Division of Molecular Genetics of Breast Cancer, German
Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Yon-Dschun Ko
- Department of Internal Medicine, Evangelische Kliniken
Bonn GmbH, Johanniter Krankenhaus, Bonn, Germany
| | - Aida K. Dieffenbach
- Division of Clinical Epidemiology and Aging Research,
German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium, Heidelberg, Germany
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research,
German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Núria Malats
- Genetic and Molecular Epidemiology Group, Spanish National
Cancer Research Centre (CNIO), Madrid, Spain
| | - JoséI. Arias Perez
- Servicio de Cirugía General y Especialidades,
Hospital Monte Naranco, Oviedo, Spain
| | - Javier Benítez
- Human Genetics Group, Spanish National Cancer Reserach
Centre (CNIO), Madrid, Spain
| | - Henrik Flyger
- Department of Breast Surgery, Herlev Hospital, Copenhagen
University Hospital, Herlev, Denmark
| | - Børge G. Nordestgaard
- Department of Clinical Biochemistry, Herlev Hospital,
Copenhagen University Hospital, Herlev, Denmark
- Copenhagen General Population Study, Herlev Hospital,
Copenhagen University Hospital, Herlev, Denmark
| | - Théresè Truong
- Unité Mixte de Recherche Scientifique (UMRS) 1018,
University Paris-Sud, Villejuif, France
- INSERM (National Institute of Health and Medical
Research), CESP (Center for Research in Epidemiology and Population Health), U1018,
Environmental Epidemiology of Cancer, Villejuif, France
| | - Emilie Cordina-Duverger
- Unité Mixte de Recherche Scientifique (UMRS) 1018,
University Paris-Sud, Villejuif, France
- INSERM (National Institute of Health and Medical
Research), CESP (Center for Research in Epidemiology and Population Health), U1018,
Environmental Epidemiology of Cancer, Villejuif, France
| | - Florence Menegaux
- Unité Mixte de Recherche Scientifique (UMRS) 1018,
University Paris-Sud, Villejuif, France
- INSERM (National Institute of Health and Medical
Research), CESP (Center for Research in Epidemiology and Population Health), U1018,
Environmental Epidemiology of Cancer, Villejuif, France
| | - Isabel dos Santos Silva
- Department of Non-Communicable Disease Epidemiology,
London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Olivia Fletcher
- Breakthrough Breast Cancer Research Centre, Institute of
Cancer Research, London, United Kingdom
| | - Nichola Johnson
- Breakthrough Breast Cancer Research Centre, Institute of
Cancer Research, London, United Kingdom
| | - Lothar Häberle
- Department of Gynecology and Obstetrics, University
Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen,
Germany
| | - Matthias W. Beckmann
- Department of Gynecology and Obstetrics, University
Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen,
Germany
| | - Arif B. Ekici
- Institute of Human Genetics, Friedrich-Alexander
University Erlangen-Nuremberg, Erlangen, Germany
| | - Linde Braaf
- Division of Molecular Pathology, Netherlands Cancer
Institute, Amsterdam, The Netherlands
- Division of Psychosocial Research and Epidemiology,
Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Femke Atsma
- Department of Donor Studies, Sanquin Nijmegen, Nijmegen,
The Netherlands
| | - Alexandra J. van den Broek
- Division of Psychosocial Research and Epidemiology,
Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Enes Makalic
- Department of Analytic Epidemiology, University of
Melbourne, Melbourne, Australia
| | - Daniel F. Schmidt
- Centre for Molecular, Environmental, Genetic, and
Analytic Epidemiology, University of Melbourne, Melbourne, Australia
| | | | - Angela Cox
- Department of Oncology, Institute for Cancer Studies,
University of Sheffield, Sheffield, United Kingdom
| | - Jacques Simard
- Cancer Genomics Laboratory, Centre Hospitalier
Universitaire de Québec Research Center, Laval University, Québec,
Canada
- Department of Molecular Medicine, Faculty of Medicine,
Quebec, Canada
| | - Graham G. Giles
- Cancer Epidemiology Centre, Cancer Council Victoria,
Melbourne, Australia
- Centre for Molecular, Environmental, Genetic, and
Analytic Epidemiology, University of Melbourne, Melbourne, Australia
| | - Diether Lambrechts
- Vesalius Research Center (VRC), VIB, Flanders,
Belgium
- Laboratory of Translational Genetics, Department of
Oncology, University of Leuven, Leuven, Belgium
| | - Arto Mannermaa
- Department of Pathology and Forensic Medicine, Kuopio
University Hospital, University of Kuopio, Kuopio, Finland
| | - Hiltrud Brauch
- Dr. Margarete Fischer-Bosch Institute of Clinical
Pharamcology, Stuttgart, Germany
| | - Pascal Guénel
- Unité Mixte de Recherche Scientifique (UMRS) 1018,
University Paris-Sud, Villejuif, France
- INSERM (National Institute of Health and Medical
Research), CESP (Center for Research in Epidemiology and Population Health), U1018,
Environmental Epidemiology of Cancer, Villejuif, France
| | - Julian Peto
- Department of Non-Communicable Disease Epidemiology,
London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Peter A. Fasching
- Department of Gynecology and Obstetrics, University
Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen,
Germany
- Department of Medicine, David Geffen School of Medicine,
University of California, Los Angeles, United States
| | - John Hopper
- Centre for Molecular, Environmental, Genetic, and
Analytic Epidemiology, University of Melbourne, Victoria, Australia
| | - Dieter Flesch-Janys
- Department of Cancer Epidemiology/Clinical Cancer
Registry, University Clinic Hamburg-Eppendorf, Hamburg, Germany
- Institute for Medical Biometrics and Epidemiology,
University Clinic Hamburg-Eppendorf, Hamburg, Germany
| | - Fergus Couch
- Department of Experimental Pathology, Mayo Clinic,
Rochester, Minnesota, United States of America
| | - Georgia Chenevix-Trench
- Department of Molecular Cancer Epidemiology, Queensland
Institute of Medical Research, Brisbane Australia
| | - Paul D. P. Pharoah
- Department of Oncology and Public Health and Primary
Care, University of Cambridge, Cambridge, United Kingdom
| | - Montserrat Garcia-Closas
- Division of Genetics and Epidemiology, Breakthrough
Breast Cancer Research Centre, Institute of Cancer Research, London, United
Kingdom
| | - Marjanka K. Schmidt
- Division of Molecular Pathology, Netherlands Cancer
Institute, Amsterdam, The Netherlands
- Division of Psychosocial Research and Epidemiology,
Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics,
Karolinska Institutet, Stockholm, Sweden
| | - Douglas F. Easton
- Department of Public Health and Primary Care, University
of Cambridge, Cambridge, United Kingdom
| | - Jenny Chang-Claude
- Correspondence to: Jenny Chang-Claude, Department
of Cancer Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer
Feld 581, 69120 Heidelberg, Germany.
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Hutter CM, Mechanic LE, Chatterjee N, Kraft P, Gillanders EM. Gene-environment interactions in cancer epidemiology: a National Cancer Institute Think Tank report. Genet Epidemiol 2013; 37:643-57. [PMID: 24123198 PMCID: PMC4143122 DOI: 10.1002/gepi.21756] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2013] [Revised: 08/06/2013] [Accepted: 08/14/2013] [Indexed: 01/04/2023]
Abstract
Cancer risk is determined by a complex interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified hundreds of common (minor allele frequency [MAF] > 0.05) and less common (0.01 < MAF < 0.05) genetic variants associated with cancer. The marginal effects of most of these variants have been small (odds ratios: 1.1-1.4). There remain unanswered questions on how best to incorporate the joint effects of genes and environment, including gene-environment (G × E) interactions, into epidemiologic studies of cancer. To help address these questions, and to better inform research priorities and allocation of resources, the National Cancer Institute sponsored a "Gene-Environment Think Tank" on January 10-11, 2012. The objective of the Think Tank was to facilitate discussions on (1) the state of the science, (2) the goals of G × E interaction studies in cancer epidemiology, and (3) opportunities for developing novel study designs and analysis tools. This report summarizes the Think Tank discussion, with a focus on contemporary approaches to the analysis of G × E interactions. Selecting the appropriate methods requires first identifying the relevant scientific question and rationale, with an important distinction made between analyses aiming to characterize the joint effects of putative or established genetic and environmental factors and analyses aiming to discover novel risk factors or novel interaction effects. Other discussion items include measurement error, statistical power, significance, and replication. Additional designs, exposure assessments, and analytical approaches need to be considered as we move from the current small number of success stories to a fuller understanding of the interplay of genetic and environmental factors.
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Affiliation(s)
- Carolyn M Hutter
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
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Tang H, Wei P, Duell EJ, Risch HA, Olson SH, Bueno-de-Mesquita HB, Gallinger S, Holly EA, Petersen GM, Bracci PM, McWilliams RR, Jenab M, Riboli E, Tjønneland A, Boutron-Ruault MC, Kaaks R, Trichopoulos D, Panico S, Sund M, Peeters PHM, Khaw KT, Amos CI, Li D. Genes-environment interactions in obesity- and diabetes-associated pancreatic cancer: a GWAS data analysis. Cancer Epidemiol Biomarkers Prev 2013; 23:98-106. [PMID: 24136929 DOI: 10.1158/1055-9965.epi-13-0437-t] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Obesity and diabetes are potentially alterable risk factors for pancreatic cancer. Genetic factors that modify the associations of obesity and diabetes with pancreatic cancer have previously not been examined at the genome-wide level. METHODS Using genome-wide association studies (GWAS) genotype and risk factor data from the Pancreatic Cancer Case Control Consortium, we conducted a discovery study of 2,028 cases and 2,109 controls to examine gene-obesity and gene-diabetes interactions in relation to pancreatic cancer risk by using the likelihood-ratio test nested in logistic regression models and Ingenuity Pathway Analysis (IPA). RESULTS After adjusting for multiple comparisons, a significant interaction of the chemokine signaling pathway with obesity (P = 3.29 × 10(-6)) and a near significant interaction of calcium signaling pathway with diabetes (P = 1.57 × 10(-4)) in modifying the risk of pancreatic cancer were observed. These findings were supported by results from IPA analysis of the top genes with nominal interactions. The major contributing genes to the two top pathways include GNGT2, RELA, TIAM1, and GNAS. None of the individual genes or single-nucleotide polymorphism (SNP) except one SNP remained significant after adjusting for multiple testing. Notably, SNP rs10818684 of the PTGS1 gene showed an interaction with diabetes (P = 7.91 × 10(-7)) at a false discovery rate of 6%. CONCLUSIONS Genetic variations in inflammatory response and insulin resistance may affect the risk of obesity- and diabetes-related pancreatic cancer. These observations should be replicated in additional large datasets. IMPACT A gene-environment interaction analysis may provide new insights into the genetic susceptibility and molecular mechanisms of obesity- and diabetes-related pancreatic cancer.
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Affiliation(s)
- Hongwei Tang
- Authors' Affiliations: Departments of Gastrointestinal Medical Oncology and Epidemiology, The University of Texas MD Anderson Cancer Center; Division of Biostatistics and Human Genetics Center, School of Public Health, University of Texas Health Science Center, Houston, Texas; Catalan Institute of Oncology (ICO-IDIBELL), Barcelona, Spain; Yale University School of Public Health, New Haven, Connecticut; Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York; National Institute for Public Health and the Environment (RIVM), Bilthoven and Department of Gastroenterology and Hepatology, University Medical Center Utrecht, Utrecht; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands; Samuel Lunenfeld Research Institute, Toronto General Hospital, University of Toronto, Toronto, Canada; Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, California; Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota; International Agency for Research on Cancer, Lyon; Institut national de la santé et de la recherche medicale (INSERM), Centre for research in Epidemiology and Population Health (CESP), U1018, Nutrition, Hormones and Women's Health team; Univ. Paris Sud, UMRS 1018; IGR, F-94805, Villejuif, France; Division of Epidemiology, Public Health, and Primary Care, Imperial College London, London; School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom; Institute of Cancer Epidemiology, Danish Cancer Society, Copenhagen, Denmark; Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany; Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts; Bureau of Epidemiologic Research, Academy of Athens; Hellenic Health Foundation, Athens, Greece; Dipartimento di Medicina Clinica e Chirurgia, Federico II University, Naples, Italy; and Department
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Winham SJ, Biernacka JM. Gene-environment interactions in genome-wide association studies: current approaches and new directions. J Child Psychol Psychiatry 2013; 54:1120-34. [PMID: 23808649 PMCID: PMC3829379 DOI: 10.1111/jcpp.12114] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/03/2013] [Indexed: 01/20/2023]
Abstract
BACKGROUND Complex psychiatric traits have long been thought to be the result of a combination of genetic and environmental factors, and gene-environment interactions are thought to play a crucial role in behavioral phenotypes and the susceptibility and progression of psychiatric disorders. Candidate gene studies to investigate hypothesized gene-environment interactions are now fairly common in human genetic research, and with the shift toward genome-wide association studies, genome-wide gene-environment interaction studies are beginning to emerge. METHODS We summarize the basic ideas behind gene-environment interaction, and provide an overview of possible study designs and traditional analysis methods in the context of genome-wide analysis. We then discuss novel approaches beyond the traditional strategy of analyzing the interaction between the environmental factor and each polymorphism individually. RESULTS Two-step filtering approaches that reduce the number of polymorphisms tested for interactions can substantially increase the power of genome-wide gene-environment studies. New analytical methods including data-mining approaches, and gene-level and pathway-level analyses, also have the capacity to improve our understanding of how complex genetic and environmental factors interact to influence psychologic and psychiatric traits. Such methods, however, have not yet been utilized much in behavioral and mental health research. CONCLUSIONS Although methods to investigate gene-environment interactions are available, there is a need for further development and extension of these methods to identify gene-environment interactions in the context of genome-wide association studies. These novel approaches need to be applied in studies of psychology and psychiatry.
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Affiliation(s)
- Stacey J Winham
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester MN 55905
| | - Joanna M. Biernacka
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester MN 55905,Department of Psychiatry and Psychology, Mayo Clinic, Rochester MN 55905
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58
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Comparisons of power of statistical methods for gene-environment interaction analyses. Eur J Epidemiol 2013; 28:785-97. [PMID: 24005774 DOI: 10.1007/s10654-013-9837-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2013] [Accepted: 07/22/2013] [Indexed: 10/26/2022]
Abstract
Any genome-wide analysis is hampered by reduced statistical power due to multiple comparisons. This is particularly true for interaction analyses, which have lower statistical power than analyses of associations. To assess gene-environment interactions in population settings we have recently proposed a statistical method based on a modified two-step approach, where first genetic loci are selected by their associations with disease and environment, respectively, and subsequently tested for interactions. We have simulated various data sets resembling real world scenarios and compared single-step and two-step approaches with respect to true positive rate (TPR) in 486 scenarios and (study-wide) false positive rate (FPR) in 252 scenarios. Our simulations confirmed that in all two-step methods the two steps are not correlated. In terms of TPR, two-step approaches combining information on gene-disease association and gene-environment association in the first step were superior to all other methods, while preserving a low FPR in over 250 million simulations under the null hypothesis. Our weighted modification yielded the highest power across various degrees of gene-environment association in the controls. An optimal threshold for step 1 depended on the interacting allele frequency and the disease prevalence. In all scenarios, the least powerful method was to proceed directly to an unbiased full interaction model, applying conventional genome-wide significance thresholds. This simulation study confirms the practical advantage of two-step approaches to interaction testing over more conventional one-step designs, at least in the context of dichotomous disease outcomes and other parameters that might apply in real-world settings.
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59
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Lin X, Lee S, Christiani DC, Lin X. Test for interactions between a genetic marker set and environment in generalized linear models. Biostatistics 2013; 14:667-81. [PMID: 23462021 PMCID: PMC3769996 DOI: 10.1093/biostatistics/kxt006] [Citation(s) in RCA: 76] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2012] [Revised: 01/21/2013] [Accepted: 01/28/2013] [Indexed: 11/13/2022] Open
Abstract
We consider in this paper testing for interactions between a genetic marker set and an environmental variable. A common practice in studying gene-environment (GE) interactions is to analyze one single-nucleotide polymorphism (SNP) at a time. It is of significant interest to analyze SNPs in a biologically defined set simultaneously, e.g. gene or pathway. In this paper, we first show that if the main effects of multiple SNPs in a set are associated with a disease/trait, the classical single SNP-GE interaction analysis can be biased. We derive the asymptotic bias and study the conditions under which the classical single SNP-GE interaction analysis is unbiased. We further show that, the simple minimum p-value-based SNP-set GE analysis, can be biased and have an inflated Type 1 error rate. To overcome these difficulties, we propose a computationally efficient and powerful gene-environment set association test (GESAT) in generalized linear models. Our method tests for SNP-set by environment interactions using a variance component test, and estimates the main SNP effects under the null hypothesis using ridge regression. We evaluate the performance of GESAT using simulation studies, and apply GESAT to data from the Harvard lung cancer genetic study to investigate GE interactions between the SNPs in the 15q24-25.1 region and smoking on lung cancer risk.
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Affiliation(s)
- Xinyi Lin
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
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60
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Gauderman WJ, Zhang P, Morrison JL, Lewinger JP. Finding novel genes by testing G × E interactions in a genome-wide association study. Genet Epidemiol 2013; 37:603-13. [PMID: 23873611 PMCID: PMC4348012 DOI: 10.1002/gepi.21748] [Citation(s) in RCA: 83] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2013] [Revised: 05/30/2013] [Accepted: 06/13/2013] [Indexed: 11/06/2022]
Abstract
In a genome-wide association study (GWAS), investigators typically focus their primary analysis on the direct (marginal) associations of each single nucleotide polymorphism (SNP) with the trait. Some SNPs that are truly associated with the trait may not be identified in this scan if they have a weak marginal effect and thus low power to be detected. However, these SNPs may be quite important in subgroups of the population defined by an environmental or personal factor, and may be detectable if such a factor is carefully considered in a gene-environment (G × E) interaction analysis. We address the question "Using a genome wide interaction scan (GWIS), can we find new genes that were not found in the primary GWAS scan?" We review commonly used approaches for conducting a GWIS in case-control studies, and propose a new two-step screening and testing method (EDG×E) that is optimized to find genes with a weak marginal effect. We simulate several scenarios in which our two-step method provides 70-80% power to detect a disease locus while a marginal scan provides less than 5% power. We also provide simulations demonstrating that the EDG×E method outperforms other GWIS approaches (including case only and previously proposed two-step methods) for finding genes with a weak marginal effect. Application of this method to a G × Sex scan for childhood asthma reveals two potentially interesting SNPs that were not identified in the marginal-association scan. We distribute a new software program (G×Escan, available at http://biostats.usc.edu/software) that implements this new method as well as several other GWIS approaches.
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Affiliation(s)
- W James Gauderman
- Department of Preventive Medicine, University of Southern California, Los Angeles, California 90089, USA.
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61
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Sohns M, Viktorova E, Amos CI, Brennan P, Fehringer G, Gaborieau V, Han Y, Heinrich J, Chang-Claude J, Hung RJ, Müller-Nurasyid M, Risch A, Thomas D, Bickeböller H. Empirical hierarchical bayes approach to gene-environment interactions: development and application to genome-wide association studies of lung cancer in TRICL. Genet Epidemiol 2013; 37:551-559. [PMID: 23893921 DOI: 10.1002/gepi.21741] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2012] [Revised: 04/15/2013] [Accepted: 05/09/2013] [Indexed: 11/06/2022]
Abstract
The analysis of gene-environment (G × E) interactions remains one of the greatest challenges in the postgenome-wide association studies (GWASs) era. Recent methods constitute a compromise between the robust but underpowered case-control and powerful case-only methods. Inferences of the latter are biased when the assumption of gene-environment (G-E) independence in controls fails. We propose a novel empirical hierarchical Bayes approach to G × E interaction (EHB-GE), which benefits from greater rank power while accounting for population-based G-E correlation. Building on Lewinger et al.'s ([2007] Genet Epidemiol 31:871-882) hierarchical Bayes prioritization approach, the method first obtains posterior G-E correlation estimates in controls for each marker, borrowing strength from G-E information across the genome. These posterior estimates are then subtracted from the corresponding case-only G × E estimates. We compared EHB-GE with rival methods using simulation. EHB-GE has similar or greater rank power to detect G × E interactions in the presence of large numbers of G-E correlations with weak to strong effects or only a low number of such correlations with large effect. When there are no or only a few weak G-E correlations, Murcray et al.'s method ([2009] Am J Epidemiol 169:219-226) identifies markers with low G × E interaction effects better. We applied EHB-GE and competing methods to four lung cancer case-control GWAS from the Interdisciplinary Research in Cancer of the Lung/International Lung Cancer Consortium with smoking as environmental factor. A number of genes worth investigating were identified by the EHB-GE approach.
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Affiliation(s)
- Melanie Sohns
- Department of Genetic Epidemiology, University Medical Center, Georg-August University of Goettingen, Goettingen, Germany
| | - Elena Viktorova
- Department of Genetic Epidemiology, University Medical Center, Georg-August University of Goettingen, Goettingen, Germany
| | - Christopher I Amos
- Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Paul Brennan
- International Agency for Research on Cancer (IARC), Lyon, France
| | - Gord Fehringer
- Prosserman Centre for Health Research, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | | | - Younghun Han
- Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Joachim Heinrich
- Institute of Epidemiology I, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jenny Chang-Claude
- Unit of Genetic Epidemiology, Division of Cancer Epidemiology, Deutsches Krebsforschungszentrum, Heidelberg, Germany
| | - Rayjean J Hung
- Prosserman Centre for Health Research, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Martina Müller-Nurasyid
- Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology and Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany.,Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Angela Risch
- Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center, Heidelberg, Germany
| | - Duncan Thomas
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Heike Bickeböller
- Department of Genetic Epidemiology, University Medical Center, Georg-August University of Goettingen, Goettingen, Germany
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62
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Jiao S, Hsu L, Bézieau S, Brenner H, Chan AT, Chang-Claude J, Le Marchand L, Lemire M, Newcomb PA, Slattery ML, Peters U. SBERIA: set-based gene-environment interaction test for rare and common variants in complex diseases. Genet Epidemiol 2013; 37:452-64. [PMID: 23720162 PMCID: PMC3713231 DOI: 10.1002/gepi.21735] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2013] [Revised: 04/04/2013] [Accepted: 04/30/2013] [Indexed: 01/28/2023]
Abstract
Identification of gene-environment interaction (G × E) is important in understanding the etiology of complex diseases. However, partially due to the lack of power, there have been very few replicated G × E findings compared to the success in marginal association studies. The existing G × E testing methods mainly focus on improving the power for individual markers. In this paper, we took a different strategy and proposed a set-based gene-environment interaction test (SBERIA), which can improve the power by reducing the multiple testing burdens and aggregating signals within a set. The major challenge of the signal aggregation within a set is how to tell signals from noise and how to determine the direction of the signals. SBERIA takes advantage of the established correlation screening for G × E to guide the aggregation of genotypes within a marker set. The correlation screening has been shown to be an efficient way of selecting potential G × E candidate SNPs in case-control studies for complex diseases. Importantly, the correlation screening in case-control combined samples is independent of the interaction test. With this desirable feature, SBERIA maintains the correct type I error level and can be easily implemented in a regular logistic regression setting. We showed that SBERIA had higher power than benchmark methods in various simulation scenarios, both for common and rare variants. We also applied SBERIA to real genome-wide association studies (GWAS) data of 10,729 colorectal cancer cases and 13,328 controls and found evidence of interaction between the set of known colorectal cancer susceptibility loci and smoking.
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Affiliation(s)
- Shuo Jiao
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA.
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63
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Lewinger JP, Morrison JL, Thomas DC, Murcray CE, Conti DV, Li D, Gauderman WJ. Efficient two-step testing of gene-gene interactions in genome-wide association studies. Genet Epidemiol 2013; 37:440-51. [PMID: 23633124 DOI: 10.1002/gepi.21720] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2012] [Revised: 12/12/2012] [Accepted: 02/06/2013] [Indexed: 11/06/2022]
Abstract
Exhaustive testing of all possible SNP pairs in a genome-wide association study (GWAS) generally yields low power to detect gene-gene (G × G) interactions because of small effect sizes and stringent requirements for multiple-testing correction. We introduce a new two-step procedure for testing G × G interactions in case-control GWAS to detect interacting single nucleotide polymorphisms (SNPs) regardless of their marginal effects. In an initial screening step, all SNP pairs are tested for gene-gene association in the combined sample of cases and controls. In the second step, the pairs that pass the screening are followed up with a traditional test for G × G interaction. We show that the two-step method is substantially more powerful to detect G × G interactions than the exhaustive testing approach. For example, with 2,000 cases and 2,000 controls, the two-step method can have more than 90% power to detect an interaction odds ratio of 2.0 compared to less than 50% power for the exhaustive testing approach. Moreover, we show that a hybrid two-step approach that combines our newly proposed two-step test and the two-step test that screens for marginal effects retains the best power properties of both. The two-step procedures we introduce have the potential to uncover genetic signals that have not been previously identified in an initial single-SNP GWAS. We demonstrate the computational feasibility of the two-step G × G procedure by performing a G × G scan in the asthma GWAS of the University of Southern California Children's Health Study.
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Affiliation(s)
- Juan Pablo Lewinger
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90032, USA.
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64
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Wu C, Chang J, Ma B, Miao X, Zhou Y, Liu Y, Li Y, Wu T, Hu Z, Shen H, Jia W, Zeng Y, Lin D, Kraft P. The case-only test for gene-environment interaction is not uniformly powerful: an empirical example. Genet Epidemiol 2013; 37:402-7. [PMID: 23595356 DOI: 10.1002/gepi.21713] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2012] [Revised: 01/14/2013] [Accepted: 02/05/2013] [Indexed: 11/06/2022]
Abstract
The case-only test has been proposed as a more powerful approach to detect gene-environment (G × E) interactions. This approach assumes that the genetic and environmental factors are independent. Although it is well known that Type I error rate will increase if this assumption is violated, it is less widely appreciated that G × E correlation can also lead to power loss. We illustrate this phenomenon by comparing the performance of the case-only test to other approaches to detect G × E interactions in a genome-wide association study (GWAS) of esophageal squamous-cell carcinoma (ESCC) in Chinese populations. Some of these approaches do not use information on the correlation between exposure and genotype (standard logistic regression), whereas others seek to use this information in a robust fashion to boost power without increasing Type I error (two-step, empirical Bayes, and cocktail methods). G × E interactions were identified involving drinking status and two regions containing genes in the alcohol metabolism pathway, 4q23 and 12q24. Although the case-only test yielded the most significant tests of G × E interaction in the 4q23 region, the case-only test failed to identify significant interactions in the 12q24 region which were readily identified using other approaches. The low power of the case-only test in the 12q24 region is likely due to the strong inverse association between the single nucleotide polymorphism (SNPs) in this region and drinking status. This example underscores the need to consider multiple approaches to detect G × E interactions, as different tests are more or less sensitive to different alternative hypotheses and violations of the G × E independence assumption.
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Affiliation(s)
- Chen Wu
- Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, MA, USA
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65
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Pang H, Jung SH. Sample size considerations of prediction-validation methods in high-dimensional data for survival outcomes. Genet Epidemiol 2013; 37:276-82. [PMID: 23471879 DOI: 10.1002/gepi.21721] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2012] [Revised: 01/21/2013] [Accepted: 02/09/2013] [Indexed: 11/09/2022]
Abstract
A variety of prediction methods are used to relate high-dimensional genome data with a clinical outcome using a prediction model. Once a prediction model is developed from a data set, it should be validated using a resampling method or an independent data set. Although the existing prediction methods have been intensively evaluated by many investigators, there has not been a comprehensive study investigating the performance of the validation methods, especially with a survival clinical outcome. Understanding the properties of the various validation methods can allow researchers to perform more powerful validations while controlling for type I error. In addition, sample size calculation strategy based on these validation methods is lacking. We conduct extensive simulations to examine the statistical properties of these validation strategies. In both simulations and a real data example, we have found that 10-fold cross-validation with permutation gave the best power while controlling type I error close to the nominal level. Based on this, we have also developed a sample size calculation method that will be used to design a validation study with a user-chosen combination of prediction. Microarray and genome-wide association studies data are used as illustrations. The power calculation method in this presentation can be used for the design of any biomedical studies involving high-dimensional data and survival outcomes.
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Affiliation(s)
- Herbert Pang
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA
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66
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Kauffmann F, Demenais F. Gene-environment interactions in asthma and allergic diseases: challenges and perspectives. J Allergy Clin Immunol 2013. [PMID: 23195523 DOI: 10.1016/j.jaci.2012.10.038] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The concept of gene-environment (GxE) interactions has dramatically evolved in the last century and has now become a central theme in studies that assess the causes of human disease. Despite the numerous efforts to discover genes associated in asthma and allergy through various approaches, including the recent genome-wide association studies, investigation of GxE interactions has been mainly limited to candidate genes, candidate environmental exposures, or both. This review discusses the various strategies from hypothesis-driven strategies to the full agnostic search of GxE interactions with an illustration from recently published articles. Challenges raised by each piece of the puzzle (ie, phenotype, environment, gene, and analysis of GxE interaction) are put forward, and tentative solutions are proposed. New perspectives to integrate various types of data generated by new sequencing technologies and to progress toward a systems biology approach of disease are outlined. The future of a molecular network-based approach of disease to which GxE interactions are related requires space for innovative and multidisciplinary research. Assembling the various parts of a puzzle in a complex system could well occur in a way that might not necessarily follow the rules of logic.
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Affiliation(s)
- Francine Kauffmann
- INSERM, CESP Centre for research in Epidemiology and Population Health, U1018, Respiratory and Environmental Epidemiology Team, Villejuif, France
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67
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Hancock DB, Artigas MS, Gharib SA, Henry A, Manichaikul A, Ramasamy A, Loth DW, Imboden M, Koch B, McArdle WL, Smith AV, Smolonska J, Sood A, Tang W, Wilk JB, Zhai G, Zhao JH, Aschard H, Burkart KM, Curjuric I, Eijgelsheim M, Elliott P, Gu X, Harris TB, Janson C, Homuth G, Hysi PG, Liu JZ, Loehr LR, Lohman K, Loos RJF, Manning AK, Marciante KD, Obeidat M, Postma DS, Aldrich MC, Brusselle GG, Chen TH, Eiriksdottir G, Franceschini N, Heinrich J, Rotter JI, Wijmenga C, Williams OD, Bentley AR, Hofman A, Laurie CC, Lumley T, Morrison AC, Joubert BR, Rivadeneira F, Couper DJ, Kritchevsky SB, Liu Y, Wjst M, Wain LV, Vonk JM, Uitterlinden AG, Rochat T, Rich SS, Psaty BM, O'Connor GT, North KE, Mirel DB, Meibohm B, Launer LJ, Khaw KT, Hartikainen AL, Hammond CJ, Gläser S, Marchini J, Kraft P, Wareham NJ, Völzke H, Stricker BHC, Spector TD, Probst-Hensch NM, Jarvis D, Jarvelin MR, Heckbert SR, Gudnason V, Boezen HM, Barr RG, Cassano PA, Strachan DP, Fornage M, Hall IP, Dupuis J, Tobin MD, London SJ. Genome-wide joint meta-analysis of SNP and SNP-by-smoking interaction identifies novel loci for pulmonary function. PLoS Genet 2012; 8:e1003098. [PMID: 23284291 PMCID: PMC3527213 DOI: 10.1371/journal.pgen.1003098] [Citation(s) in RCA: 116] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2012] [Accepted: 10/01/2012] [Indexed: 01/20/2023] Open
Abstract
Genome-wide association studies have identified numerous genetic loci for spirometic measures of pulmonary function, forced expiratory volume in one second (FEV(1)), and its ratio to forced vital capacity (FEV(1)/FVC). Given that cigarette smoking adversely affects pulmonary function, we conducted genome-wide joint meta-analyses (JMA) of single nucleotide polymorphism (SNP) and SNP-by-smoking (ever-smoking or pack-years) associations on FEV(1) and FEV(1)/FVC across 19 studies (total N = 50,047). We identified three novel loci not previously associated with pulmonary function. SNPs in or near DNER (smallest P(JMA = )5.00×10(-11)), HLA-DQB1 and HLA-DQA2 (smallest P(JMA = )4.35×10(-9)), and KCNJ2 and SOX9 (smallest P(JMA = )1.28×10(-8)) were associated with FEV(1)/FVC or FEV(1) in meta-analysis models including SNP main effects, smoking main effects, and SNP-by-smoking (ever-smoking or pack-years) interaction. The HLA region has been widely implicated for autoimmune and lung phenotypes, unlike the other novel loci, which have not been widely implicated. We evaluated DNER, KCNJ2, and SOX9 and found them to be expressed in human lung tissue. DNER and SOX9 further showed evidence of differential expression in human airway epithelium in smokers compared to non-smokers. Our findings demonstrated that joint testing of SNP and SNP-by-environment interaction identified novel loci associated with complex traits that are missed when considering only the genetic main effects.
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Affiliation(s)
- Dana B. Hancock
- Behavioral Health Epidemiology Program, Research Triangle Institute International, Research Triangle Park, North Carolina, United States of America
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, U.S. Department of Health and Human Services, Research Triangle Park, North Carolina, United States of America
| | - María Soler Artigas
- Departments of Health Sciences and Genetics, University of Leicester, Leicester, United Kingdom
| | - Sina A. Gharib
- Center for Lung Biology, University of Washington, Seattle, Washington, United States of America
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - Amanda Henry
- Division of Therapeutics and Molecular Medicine, University of Nottingham, Queen's Medical Centre, Nottingham, United Kingdom
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Public Health Sciences, Division of Biostatistics and Epidemiology, University of Virginia, Charlottesville, Virginia, United States of America
| | - Adaikalavan Ramasamy
- Respiratory Epidemiology and Public Health, Imperial College London, London, United Kingdom
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Department of Medical and Molecular Genetics, King's College London, Guy's Hospital, London, United Kingdom
| | - Daan W. Loth
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Inspectorate of Healthcare, The Hague, The Netherlands
| | - Medea Imboden
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Beate Koch
- Department of Internal Medicine B, University Hospital Greifswald, Greifswald, Germany
| | - Wendy L. McArdle
- ALSPAC, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - Albert V. Smith
- Icelandic Heart Association, Kopavogur, Iceland
- University of Iceland, Reykjavik, Iceland
| | - Joanna Smolonska
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Akshay Sood
- Department of Medicine, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Wenbo Tang
- Division of Nutritional Sciences, Cornell University, Ithaca, New York, United States of America
| | - Jemma B. Wilk
- Division of Aging, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, United States of America
| | - Guangju Zhai
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
- Discipline of Genetics, Faculty of Medicine, Memorial University of Newfoundland, St. John's, Newfoundland, Canada
| | - Jing Hua Zhao
- MRC Epidemiology Unit, Institute of Metabolic Science, Cambridge, United Kingdom
| | - Hugues Aschard
- Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Kristin M. Burkart
- Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York, United States of America
| | - Ivan Curjuric
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Mark Eijgelsheim
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC-HPA Centre for Environment and Health, Imperial College London, London, United Kingdom
| | - Xiangjun Gu
- Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Tamara B. Harris
- National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Christer Janson
- Department of Medical Sciences, Respiratory Medicine, Uppsala University, Uppsala, Sweden
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Pirro G. Hysi
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Jason Z. Liu
- Department of Statistics, University of Oxford, United Kingdom
| | - Laura R. Loehr
- Gillings School of Global Public Health, Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Kurt Lohman
- Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Ruth J. F. Loos
- MRC Epidemiology Unit, Institute of Metabolic Science, Cambridge, United Kingdom
| | - Alisa K. Manning
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
- Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Kristin D. Marciante
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - Ma'en Obeidat
- Division of Therapeutics and Molecular Medicine, University of Nottingham, Queen's Medical Centre, Nottingham, United Kingdom
| | - Dirkje S. Postma
- Department of Pulmonology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- GRIAC Research Institute, University Medical Center Groningen, Groningen, The Netherlands
| | - Melinda C. Aldrich
- Department of Thoracic Surgery and Division of Epidemiology, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Guy G. Brusselle
- Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
| | - Ting-hsu Chen
- Section of Pulmonary and Critical Care Medicine, Department of Medicine, Veterans Administration Boston Healthcare System, Boston, Massachusetts, United States of America
- The Pulmonary Center, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, United States of America
| | | | - Nora Franceschini
- Gillings School of Global Public Health, Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Joachim Heinrich
- Institute of Epidemiology I, Helmholtz Zentrum München, Munich, Germany
| | - Jerome I. Rotter
- Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
| | - Cisca Wijmenga
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - O. Dale Williams
- Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, Florida, United States of America
| | - Amy R. Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Cathy C. Laurie
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Thomas Lumley
- Department of Statistics, University of Auckland, Auckland, New Zealand
| | - Alanna C. Morrison
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Bonnie R. Joubert
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, U.S. Department of Health and Human Services, Research Triangle Park, North Carolina, United States of America
| | - Fernando Rivadeneira
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
- Netherlands Genomics Initiative (NGI)–sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, The Netherlands
| | - David J. Couper
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Stephen B. Kritchevsky
- Sticht Center on Aging, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Matthias Wjst
- Institute of Lung Biology and Disease, Comprehensive Pneumology Center, Helmholtz Zentrum München, Neuherberg, Germany
- Institute for Medical Statistics and Epidemiology (IMSE), Technical University Munich, Munich, Germany
| | - Louise V. Wain
- Departments of Health Sciences and Genetics, University of Leicester, Leicester, United Kingdom
| | - Judith M. Vonk
- GRIAC Research Institute, University Medical Center Groningen, Groningen, The Netherlands
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- The LifeLines Cohort Study, Groningen, The Netherlands
| | - André G. Uitterlinden
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
- Netherlands Genomics Initiative (NGI)–sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, The Netherlands
| | - Thierry Rochat
- Division of Pulmonary Medicine, University Hospitals of Geneva, Geneva, Switzerland
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
- Departments of Medicine and Health Services, University of Washington, Seattle, United States of America
- Group Health Research Institute, Group Health Cooperative, Seattle, Washington
| | - George T. O'Connor
- The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, United States of America
- The Pulmonary Center, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, United States of America
| | - Kari E. North
- Gillings School of Global Public Health, Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Daniel B. Mirel
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Bernd Meibohm
- College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Lenore J. Launer
- National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Kay-Tee Khaw
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Anna-Liisa Hartikainen
- Department of Obstetrics and Gynecology, Institute of Clinical Medicine, University of Oulu, Oulu, Finland
| | - Christopher J. Hammond
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Sven Gläser
- Department of Internal Medicine B, University Hospital Greifswald, Greifswald, Germany
| | | | - Peter Kraft
- Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Nicholas J. Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, Cambridge, United Kingdom
| | - Henry Völzke
- Institute for Community Medicine, Study of Health In Pomerania (SHIP)/Clinical Epidemiological Research, University of Greifswald, Greifswald, Germany
| | - Bruno H. C. Stricker
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Inspectorate of Healthcare, The Hague, The Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Timothy D. Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Nicole M. Probst-Hensch
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Deborah Jarvis
- Respiratory Epidemiology and Public Health, Imperial College London, London, United Kingdom
- MRC-HPA Centre for Environment and Health, Imperial College London, London, United Kingdom
| | - Marjo-Riitta Jarvelin
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC-HPA Centre for Environment and Health, Imperial College London, London, United Kingdom
- Department of Children, Young People, and Families, National Institute for Health and Welfare, Oulu, Finland
- Institute of Health Sciences, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Susan R. Heckbert
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
- Group Health Research Institute, Group Health Cooperative, Seattle, Washington
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- University of Iceland, Reykjavik, Iceland
| | - H. Marike Boezen
- GRIAC Research Institute, University Medical Center Groningen, Groningen, The Netherlands
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- The LifeLines Cohort Study, Groningen, The Netherlands
| | - R. Graham Barr
- Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York, United States of America
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Patricia A. Cassano
- Division of Nutritional Sciences, Cornell University, Ithaca, New York, United States of America
- Department of Public Health, Weill Cornell Medical College, New York, New York, United States of America
| | - David P. Strachan
- Division of Population Health Sciences and Education, St. George's University of London, London, United Kingdom
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Ian P. Hall
- Division of Therapeutics and Molecular Medicine, University of Nottingham, Queen's Medical Centre, Nottingham, United Kingdom
| | - Josée Dupuis
- The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, United States of America
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Martin D. Tobin
- Departments of Health Sciences and Genetics, University of Leicester, Leicester, United Kingdom
| | - Stephanie J. London
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, U.S. Department of Health and Human Services, Research Triangle Park, North Carolina, United States of America
- Laboratory of Respiratory Biology, National Institute of Environmental Health Sciences, National Institutes of Health, U.S. Department of Health and Human Services, Research Triangle Park, North Carolina, United States of America
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Pleil JD, Williams MA, Sobus JR. Chemical Safety for Sustainability (CSS): Human in vivo biomonitoring data for complementing results from in vitro toxicology—A commentary. Toxicol Lett 2012; 215:201-7. [DOI: 10.1016/j.toxlet.2012.10.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2012] [Revised: 10/14/2012] [Accepted: 10/15/2012] [Indexed: 01/12/2023]
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The TNF-α -308 Promoter Gene Polymorphism and Chronic HBV Infection. HEPATITIS RESEARCH AND TREATMENT 2012; 2012:493219. [PMID: 23133749 PMCID: PMC3485862 DOI: 10.1155/2012/493219] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/18/2012] [Revised: 09/13/2012] [Accepted: 10/01/2012] [Indexed: 01/30/2023]
Abstract
Background and Aims. TNF-α -308 allele promoter polymorphism has been known to be a potential prognostic factor in patients with chronic HBV infection. We tried to determine how TNF-α -308 allele promoter polymorphism would affect the prognosis in patients with chronic HBV infection. Methods. We searched MEDLINE, EMBASE, and reference lists of relevant review articles related to the association between “TNF-α G-308A promoter polymorphism” with “chronic HBV infection”. We only focused on searching -308 locus in published studies. We reviewed 21 original articles about TNF-α -308 allele polymorphism and its effect on prognosis in patients with chronic HBV infection and discussed the results. Results. conflicting results were observed. The results were divided into 3 groups including neutral, negative, and positive associations between TNF-α -308 allele polymorphism and prognosis in patients with chronic HBV infection. We summarized the primary data as a table. Conclusions. Authors concluded that although there is an upward trend in evidence to claim that there is a positive relation between TNF-α G-308A promoter polymorphisms and resolution of chronic HBV infection, due to many biases and limitations observed in reviewed studies, an organized well-designed study is needed for clarifying the real association.
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Hsu L, Jiao S, Dai JY, Hutter C, Peters U, Kooperberg C. Powerful cocktail methods for detecting genome-wide gene-environment interaction. Genet Epidemiol 2012; 36:183-94. [PMID: 22714933 DOI: 10.1002/gepi.21610] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Identifying gene and environment interaction (G × E) can provide insights into biological networks of complex diseases, identify novel genes that act synergistically with environmental factors, and inform risk prediction. However, despite the fact that hundreds of novel disease-associated loci have been identified from genome-wide association studies (GWAS), few G × Es have been discovered. One reason is that most studies are underpowered for detecting these interactions. Several new methods have been proposed to improve power for G × E analysis, but performance varies with scenario. In this article, we present a module-based approach to integrating various methods that exploits each method's most appealing aspects. There are three modules in our approach: (1) a screening module for prioritizing Single Nucleotide Polymorphisms (SNPs); (2) a multiple comparison module for testing G × E; and (3) a G × E testing module. We combine all three of these modules and develop two novel "cocktail" methods. We demonstrate that the proposed cocktail methods maintain the type I error, and that the power tracks well with the best existing methods, despite that the best methods may be different under various scenarios and interaction models. For GWAS, where the true interaction models are unknown, methods like our "cocktail" methods that are powerful under a wide range of situations are particularly appealing. Broadly speaking, the modular approach is conceptually straightforward and computationally simple. It builds on common test statistics and is easily implemented without additional computational efforts. It also allows for an easy incorporation of new methods as they are developed. Our work provides a comprehensive and powerful tool for devising effective strategies for genome-wide detection of gene-environment interactions.
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Affiliation(s)
- Li Hsu
- Biostatistics and Biomathematics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
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71
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Dai JY, Kooperberg C, Leblanc M, Prentice RL. Two-stage testing procedures with independent filtering for genome-wide gene-environment interaction. Biometrika 2012; 99:929-944. [PMID: 23843674 DOI: 10.1093/biomet/ass044] [Citation(s) in RCA: 76] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Several two-stage multiple testing procedures have been proposed to detect gene-environment interaction in genome-wide association studies. In this article, we elucidate general conditions that are required for validity and power of these procedures, and we propose extensions of two-stage procedures using the case-only estimator of gene-treatment interaction in randomized clinical trials. We develop a unified estimating equation approach to proving asymptotic independence between a filtering statistic and an interaction test statistic in a range of situations, including marginal association and interaction in a generalized linear model with a canonical link. We assess the performance of various two-stage procedures in simulations and in genetic studies from Women's Health Initiative clinical trials.
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Affiliation(s)
- James Y Dai
- Public Health Science Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, U.S.A. ,
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72
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Agopian AJ, Eastcott LM, Mitchell LE. Age of onset and effect size in genome-wide association studies. ACTA ACUST UNITED AC 2012; 94:908-11. [PMID: 22933422 DOI: 10.1002/bdra.23066] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2012] [Revised: 06/22/2012] [Accepted: 07/08/2012] [Indexed: 11/10/2022]
Abstract
BACKGROUND Genome-wide association studies (GWAS) have identified many susceptibility loci for complex traits, but have not identified the majority of the genetic contribution to common diseases. We explored whether the magnitude of associations detected in GWAS and, therefore, the likelihood of detecting a significant association for a given sample size, is generally greater for childhood-onset traits (e.g., birth defects) than for traits with onset in adulthood. METHODS Data were obtained from the National Human Genome Research Institute Catalog of Published GWAS. Traits were categorized as having an average age of onset in childhood (<18 years, n = 15 traits), early adulthood (18-54 years, n = 32 traits), or late adulthood (≥55 years, n = 31 traits). The relationship between age of onset category and the magnitude of significant associations detected by GWAS was assessed using logistic regression. RESULTS Associations characterized by an odds ratio (OR) ≥ 1.5 were significantly more common for GWAS of childhood traits than for late adulthood-onset traits after adjustment for several covariates (adjusted OR, 2.55; 95% confidence interval, 1.37-4.73). Results in subgroup analyses using more stringent inclusion criteria (based on sample size, effect size, p value threshold for inclusion, and novel variant-trait associations) were similar. CONCLUSIONS These findings suggest that, on average, marker-trait associations detected in GWAS for traits with young onset may have a larger magnitude of effect than those for traits with adult onset. Therefore, GWAS for young-onset traits, such as birth defects, may be more likely than those for adult-onset traits to identify major genetic risk factors. Birth Defects Research (Part A) 2012. © 2012 Wiley Periodicals, Inc.
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Affiliation(s)
- A J Agopian
- Human Genetics Center, Division of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, TX 77030, USA
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73
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Holloway JW, Savarimuthu Francis S, Fong KM, Yang IA. Genomics and the respiratory effects of air pollution exposure. Respirology 2012; 17:590-600. [PMID: 22404320 DOI: 10.1111/j.1440-1843.2012.02164.x] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Adverse health effects from air pollutants remain important, despite improvement in air quality in the past few decades. The exact mechanisms of lung injury from exposure to air pollutants are not yet fully understood. Studying the genome (e.g. single-nucleotide polymorphisms (SNP) ), epigenome (e.g. methylation of genes), transcriptome (mRNA expression) and microRNAome (microRNA expression) has the potential to improve our understanding of the adverse effects of air pollutants. Genome-wide association studies of SNP have detected SNP associated with respiratory phenotypes; however, to date, only candidate gene studies of air pollution exposure have been performed. Changes in epigenetic processes, such DNA methylation that leads to gene silencing without altering the DNA sequence, occur with air pollutant exposure, especially global and gene-specific methylation changes. Respiratory cell line and animal models demonstrate distinct gene expression signatures in the transcriptome, arising from exposure to particulate matter or ozone. Particulate matter and other environmental toxins alter expression of microRNA, which are short non-coding RNA that regulate gene expression. While it is clearly important to contain rising levels of air pollution, strategies also need to be developed to minimize the damaging effects of air pollutant exposure on the lung, especially for patients with chronic lung disease and for people at risk of future lung disease. Careful study of genomic responses will improve our understanding of mechanisms of lung injury from air pollution and enable future clinical testing of interventions against the toxic effects of air pollutants.
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Affiliation(s)
- John W Holloway
- Human Development and Health, University of Southampton, Southampton, UK.
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Abstract
Obesity and related complications are major health burdens. Almost 700 million adults are currently obese globally and the prevalence is predicted to rise towards 2030. The sudden change of lifestyle with physical inactivity and excessive calorie intake undoubtedly have a major part of the epidemic development; however, some individuals seem to be more prone to be affected by an unhealthy lifestyle than others. Hence, genetic predisposition also has an essential role in determining disease susceptibility and response to lifestyle factors. Since the introduction of genome-wide association studies (GWAS), the success of identifying obesity susceptibility variants have increased, and a total of 32 variants have been identified associating genome-wide significantly with body mass index (BMI) and 18 with measures of fat distribution during four overall obesity GWAS waves. However, the immediate success of the GWAS approach has eased off, but the proportion of explained variance for BMI by the identified obesity variants remains low. This review suggests and discusses new initiatives to take GWAS of obesity to the next level, including gene–environment interactions as modulating/masking factors, low-frequent or rare variants and ways to address such analyses, and finally reflections about the applicability of epigenetic modifications when elucidating the genetic background of obesity.
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Affiliation(s)
- C H Sandholt
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
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75
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Dai JY, Logsdon BA, Huang Y, Hsu L, Reiner AP, Prentice RL, Kooperberg C. Simultaneously testing for marginal genetic association and gene-environment interaction. Am J Epidemiol 2012; 176:164-73. [PMID: 22771729 DOI: 10.1093/aje/kwr521] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
In this article, the authors propose to simultaneously test for marginal genetic association and gene-environment interaction to discover single nucleotide polymorphisms that may be involved in gene-environment or gene-treatment interaction. The asymptotic independence of the marginal association estimator and various interaction estimators leads to a simple and flexible way of combining the 2 tests, allowing for exploitation of gene-environment independence in estimating gene-environment interaction. The proposed test differs from the 2-df test proposed by Kraft et al. (Hum Hered. 2007;63(2):111-119) in two respects. First, for the genetic association component, it tests for marginal association, which is often the primary objective in inference, rather than the main effect in a model with gene-environment interaction. Second, the gene-environment testing component can easily exploit putative gene-environment independence using either the case-only estimator or the empirical Bayes estimator, depending on whether the goal is gene-treatment interaction in a randomized trial or gene-environment interaction in an observational study. The use of the proposed joint test is illustrated through simulations and a genetic study (1993-2005) from the Women's Health Initiative.
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Affiliation(s)
- James Y Dai
- Public Health Science Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
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76
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Aschard H, Lutz S, Maus B, Duell EJ, Fingerlin TE, Chatterjee N, Kraft P, Van Steen K. Challenges and opportunities in genome-wide environmental interaction (GWEI) studies. Hum Genet 2012; 131:1591-613. [PMID: 22760307 DOI: 10.1007/s00439-012-1192-0] [Citation(s) in RCA: 110] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2012] [Accepted: 06/11/2012] [Indexed: 02/03/2023]
Abstract
The interest in performing gene-environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene-environment interaction studies and the technical challenges related to these studies-when the number of environmental or genetic risk factors is relatively small-has been described before. In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze genome-wide environmental interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for genome-wide association gene-gene interaction studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to "joining" two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes.
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Affiliation(s)
- Hugues Aschard
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA.
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Wason J, Dudbridge F. A general framework for two-stage analysis of genome-wide association studies and its application to case-control studies. Am J Hum Genet 2012; 90:760-73. [PMID: 22560088 DOI: 10.1016/j.ajhg.2012.03.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2012] [Revised: 02/17/2012] [Accepted: 03/09/2012] [Indexed: 02/03/2023] Open
Abstract
Two-stage analyses of genome-wide association studies have been proposed as a means to improving power for designs including family-based association and gene-environment interaction testing. In these analyses, all markers are first screened via a statistic that may not be robust to an underlying assumption, and the markers thus selected are then analyzed in a second stage with a test that is independent from the first stage and is robust to the assumption in question. We give a general formulation of two-stage designs and show how one can use this formulation both to derive existing methods and to improve upon them, opening up a range of possible further applications. We show how using simple regression models in conjunction with external data such as average trait values can improve the power of genome-wide association studies. We focus on case-control studies and show how it is possible to use allele frequencies derived from an external reference to derive a powerful two-stage analysis. An illustration involving the Wellcome Trust Case-Control Consortium data shows several genome-wide-significant associations, subsequently validated, that were not significant in the standard analysis. We give some analytic properties of the methods and discuss some underlying principles.
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Thomas DC, Lewinger JP, Murcray CE, Gauderman WJ. Invited commentary: GE-Whiz! Ratcheting gene-environment studies up to the whole genome and the whole exposome. Am J Epidemiol 2012; 175:203-7; discussion 208-9. [PMID: 22199029 DOI: 10.1093/aje/kwr365] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
One goal in the post-genome-wide association study era is characterizing gene-environment interactions, including scanning for interactions with all available polymorphisms, not just those showing significant main effects. In recent years, several approaches to such "gene-environment-wide interaction studies" have been proposed. Two contributions in this issue of the American Journal of Epidemiology provide systematic comparisons of the performance of these various approaches, one based on simulation and one based on application to 2 real genome-wide association study scans for type 2 diabetes. The authors discuss some of the broader issues raised by these contributions, including the plausibility of the gene-environment independence assumption that some of these approaches rely upon, the need for replication, and various generalizations of these approaches.
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Affiliation(s)
- Duncan C Thomas
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
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Cornelis MC, Tchetgen EJT, Liang L, Qi L, Chatterjee N, Hu FB, Kraft P. Gene-environment interactions in genome-wide association studies: a comparative study of tests applied to empirical studies of type 2 diabetes. Am J Epidemiol 2012; 175:191-202. [PMID: 22199026 PMCID: PMC3261439 DOI: 10.1093/aje/kwr368] [Citation(s) in RCA: 94] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2010] [Accepted: 06/08/2011] [Indexed: 12/28/2022] Open
Abstract
The question of which statistical approach is the most effective for investigating gene-environment (G-E) interactions in the context of genome-wide association studies (GWAS) remains unresolved. By using 2 case-control GWAS (the Nurses' Health Study, 1976-2006, and the Health Professionals Follow-up Study, 1986-2006) of type 2 diabetes, the authors compared 5 tests for interactions: standard logistic regression-based case-control; case-only; semiparametric maximum-likelihood estimation of an empirical-Bayes shrinkage estimator; and 2-stage tests. The authors also compared 2 joint tests of genetic main effects and G-E interaction. Elevated body mass index was the exposure of interest and was modeled as a binary trait to avoid an inflated type I error rate that the authors observed when the main effect of continuous body mass index was misspecified. Although both the case-only and the semiparametric maximum-likelihood estimation approaches assume that the tested markers are independent of exposure in the general population, the authors did not observe any evidence of inflated type I error for these tests in their studies with 2,199 cases and 3,044 controls. Both joint tests detected markers with known marginal effects. Loci with the most significant G-E interactions using the standard, empirical-Bayes, and 2-stage tests were strongly correlated with the exposure among controls. Study findings suggest that methods exploiting G-E independence can be efficient and valid options for investigating G-E interactions in GWAS.
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Affiliation(s)
- Marilyn C Cornelis
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA.
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Mukherjee B, Ahn J, Gruber SB, Chatterjee N. Testing gene-environment interaction in large-scale case-control association studies: possible choices and comparisons. Am J Epidemiol 2012; 175:177-90. [PMID: 22199027 DOI: 10.1093/aje/kwr367] [Citation(s) in RCA: 88] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Several methods for screening gene-environment interaction have recently been proposed that address the issue of using gene-environment independence in a data-adaptive way. In this report, the authors present a comparative simulation study of power and type I error properties of 3 classes of procedures: 1) the standard 1-step case-control method; 2) the case-only method that requires an assumption of gene-environment independence for the underlying population; and 3) a variety of hybrid methods, including empirical-Bayes, 2-step, and model averaging, that aim at gaining power by exploiting the assumption of gene-environment independence and yet can protect against false positives when the independence assumption is violated. These studies suggest that, although the case-only method generally has maximum power, it has the potential to create substantial false positives in large-scale studies even when a small fraction of markers are associated with the exposure under study in the underlying population. All the hybrid methods perform well in protecting against such false positives and yet can retain substantial power advantages over standard case-control tests. The authors conclude that, for future genome-wide scans for gene-environment interactions, major power gain is possible by using alternatives to standard case-control analysis. Whether a case-only type scan or one of the hybrid methods should be used depends on the strength and direction of gene-environment interaction and association, the level of tolerance for false positives, and the nature of replication strategies.
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Affiliation(s)
- Bhramar Mukherjee
- Department of Biostatistics, School of Public Health, the University of Michigan, Ann Arbor, USA
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