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Albujja MH, Vasudevan R, Alghamdi S, Pei CP, Bin Mohd Ghani KA, Ranneh Y, Ismail PB. A review of studies examining the association between genetic biomarkers (short tandem repeats and single-nucleotide polymorphisms) and risk of prostate cancer: the need for valid predictive biomarkers. Prostate Int 2020; 8:135-145. [PMID: 33425790 PMCID: PMC7767939 DOI: 10.1016/j.prnil.2019.11.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 10/22/2019] [Accepted: 11/08/2019] [Indexed: 01/22/2023] Open
Abstract
Prostate cancer (PCa) is a challenging polygenic disease because the genes that cause PCa remain largely elusive and are affected by several causal factors. Consequently, research continuously strives to identify a genetic marker which could be used as an indicator to predict the most vulnerable (i.e., predisposed) segments of the population to the disease or for the gene which may be directly responsible for PCa. To enhance the genetic etiology of PCa, this research sought to discover the key studies conducted in this field using data from the main journal publication search engines, as it was hoped that this could shed light on the main research findings from these studies, which in turn could assist in determining these genes or markers. From the research highlighted, the studies primarily used two kinds of markers: short tandem repeats or single-nucleotide polymorphisms. These markers were found to be quite prevalent in all the chromosomes within the research carried out. It also became apparent that the studies differed in both quantity and quality, as well as being conducted in a variety of societies. Links were also determined between the degree and strength of the relationship between these markers and the occurrence of the disease. From the studies identified, most recommended a larger and more diverse survey for the parameters which had not been studied before, as well as an increase in the size of the community (i.e., the population) being studied. This is an indication that work in this field is far from complete, and thus, current research remains committed toward finding genetic markers that can be used clinically for the diagnosis and screening of patients with PCa.
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Affiliation(s)
- Mohammed H. Albujja
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 Serdang, Selangor Malaysia
- Department of Forensic Sciences, Faculty of Criminal Justice, Naif Arab University of Security Sciences, Riyadh, Saudi Arabia
| | - Ramachandran Vasudevan
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 Serdang, Selangor Malaysia
- Malaysian Research Institute on Ageing (MYAGEING), Malaysia
| | - Saleh Alghamdi
- Research Center, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Chong P. Pei
- School of Biosciences, Faculty of Health & Medical Sciences, Taylors University, Malaysia
| | - Khairul A. Bin Mohd Ghani
- Department of Surgery, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 Serdang, Selangor Malaysia
| | - Yazan Ranneh
- Department of Nutrition and Dietetics, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 Serdang, Selangor Malaysia
| | - Patimah B. Ismail
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 Serdang, Selangor Malaysia
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2
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Prediction of inherited genomic susceptibility to 20 common cancer types by a supervised machine-learning method. Proc Natl Acad Sci U S A 2018; 115:1322-1327. [PMID: 29358382 PMCID: PMC5819441 DOI: 10.1073/pnas.1717960115] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Prevention and early intervention are the most effective ways of avoiding or minimizing psychological, physical, and financial suffering from cancer. However, such proactive action requires the ability to predict the individual's susceptibility to cancer with a measure of probability. Of the triad of cancer-causing factors (inherited genomic susceptibility, environmental factors, and lifestyle factors), the inherited genomic component may be derivable from the recent public availability of a large body of whole-genome variation data. However, genome-wide association studies have so far showed limited success in predicting the inherited susceptibility to common cancers. We present here a multiple classification approach for predicting individuals' inherited genomic susceptibility to acquire the most likely phenotype among a panel of 20 major common cancer types plus 1 "healthy" type by application of a supervised machine-learning method under competing conditions among the cohorts of the 21 types. This approach suggests that, depending on the phenotypes of 5,919 individuals of "white" ethnic population in this study, (i) the portion of the cohort of a cancer type who acquired the observed type due to mostly inherited genomic susceptibility factors ranges from about 33 to 88% (or its corollary: the portion due to mostly environmental and lifestyle factors ranges from 12 to 67%), and (ii) on an individual level, the method also predicts individuals' inherited genomic susceptibility to acquire the other types ranked with associated probabilities. These probabilities may provide practical information for individuals, heath professionals, and health policymakers related to prevention and/or early intervention of cancer.
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3
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Lindström S, Finucane H, Bulik-Sullivan B, Schumacher FR, Amos CI, Hung RJ, Rand K, Gruber SB, Conti D, Permuth JB, Lin HY, Goode EL, Sellers TA, Amundadottir LT, Stolzenberg-Solomon R, Klein A, Petersen G, Risch H, Wolpin B, Hsu L, Huyghe JR, Chang-Claude J, Chan A, Berndt S, Eeles R, Easton D, Haiman CA, Hunter DJ, Neale B, Price AL, Kraft P. Quantifying the Genetic Correlation between Multiple Cancer Types. Cancer Epidemiol Biomarkers Prev 2017; 26:1427-1435. [PMID: 28637796 PMCID: PMC5582139 DOI: 10.1158/1055-9965.epi-17-0211] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Revised: 05/03/2017] [Accepted: 06/06/2017] [Indexed: 01/01/2023] Open
Abstract
Background: Many cancers share specific genetic risk factors, including both rare high-penetrance mutations and common SNPs identified through genome-wide association studies (GWAS). However, little is known about the overall shared heritability across cancers. Quantifying the extent to which two distinct cancers share genetic origin will give insights to shared biological mechanisms underlying cancer and inform design for future genetic association studies.Methods: In this study, we estimated the pair-wise genetic correlation between six cancer types (breast, colorectal, lung, ovarian, pancreatic, and prostate) using cancer-specific GWAS summary statistics data based on 66,958 case and 70,665 control subjects of European ancestry. We also estimated genetic correlations between cancers and 14 noncancer diseases and traits.Results: After adjusting for 15 pair-wise genetic correlation tests between cancers, we found significant (P < 0.003) genetic correlations between pancreatic and colorectal cancer (rg = 0.55, P = 0.003), lung and colorectal cancer (rg = 0.31, P = 0.001). We also found suggestive genetic correlations between lung and breast cancer (rg = 0.27, P = 0.009), and colorectal and breast cancer (rg = 0.22, P = 0.01). In contrast, we found no evidence that prostate cancer shared an appreciable proportion of heritability with other cancers. After adjusting for 84 tests studying genetic correlations between cancer types and other traits (Bonferroni-corrected P value: 0.0006), only the genetic correlation between lung cancer and smoking remained significant (rg = 0.41, P = 1.03 × 10-6). We also observed nominally significant genetic correlations between body mass index and all cancers except ovarian cancer.Conclusions: Our results highlight novel genetic correlations and lend support to previous observational studies that have observed links between cancers and risk factors.Impact: This study demonstrates modest genetic correlations between cancers; in particular, breast, colorectal, and lung cancer share some degree of genetic basis. Cancer Epidemiol Biomarkers Prev; 26(9); 1427-35. ©2017 AACR.
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Affiliation(s)
- Sara Lindström
- Department of Epidemiology, University of Washington, Seattle, Washington.
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Hilary Finucane
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Brendan Bulik-Sullivan
- The Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts
| | - Fredrick R Schumacher
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio
- Seidman Cancer Center, University Hospitals, Cleveland, Ohio
| | - Christopher I Amos
- Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Kristin Rand
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Stephen B Gruber
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - David Conti
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Jennifer B Permuth
- Department of Cancer Epidemiology, Moffitt Cancer Center and Research Institute, Tampa, Florida
- Department of Gastrointestinal Oncology, Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Hui-Yi Lin
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida
| | - Ellen L Goode
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Thomas A Sellers
- Department of Cancer Epidemiology, Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Laufey T Amundadottir
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, U.S. Department of Health and Human Services, Bethesda, Maryland
| | - Rachael Stolzenberg-Solomon
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, U.S. Department of Health and Human Services, Bethesda, Maryland
| | - Alison Klein
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Gloria Petersen
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Harvey Risch
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut
| | - Brian Wolpin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Li Hsu
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Jeroen R Huyghe
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Andrew Chan
- Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts
| | - Sonja Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, U.S. Department of Health and Human Services, Bethesda, Maryland
| | - Rosalind Eeles
- Division of Genetics and Epidemiology, The Institute of Cancer Research, and Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Douglas Easton
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - David J Hunter
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Benjamin Neale
- The Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts
| | - Alkes L Price
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
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4
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Mcbride W, Mcintyre G, Carroll K, Mossey P. Subphenotyping and Classification of Orofacial Clefts: Need for Orofacial Cleft Subphenotyping Calls for Revised Classification. Cleft Palate Craniofac J 2016; 53:539-49. [DOI: 10.1597/15-029] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Nonsyndromic orofacial clefting (OFC) describes a range of phenotypes that represent the most common craniofacial birth defects in humans, with an overall birth prevalence of 1:700 live births. Because of the lifelong negative implications on health and well-being associated with OFC and the numbers of people affected, quality research into its etiology, diagnosis, treatment outcomes, and preventative strategies is essential. A range of different methods is used for recording and classifying OFC subphenotypes, one of which is the International Classification of Diseases (ICD) system. However, there is a general perception that research is being hampered by a lack of sensitivity and specificity in grouping those with OFC into subphenotypes, with potential heterogeneity and confounding in epidemiologic, genetic, and genotype-phenotype correlation studies. This article provides a background to the necessity of OFC research, discusses current controversies within cleft subphenotyping, and provides a brief overview of current OFC classifications as well as their limitations. The LAHSHAL classification is described in the context of a potentially useful tool for OFC that could complement the ICD-10/ICD-11 Beta coding systems to become a simply understood, universally accepted, clinically friendly, and research-sensitive instrument. Empowering registries, clinicians, and researchers to use a common classification system would have significant implications for OFC research across the world at a time when accurate subphenotyping is crucial and health care research is becoming increasingly tailored toward the individual.
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Affiliation(s)
- W.A. Mcbride
- Queen's University Dental School, Belfast, Northern Ireland
| | - G.T. Mcintyre
- Consultant of Orthodontics, Dundee Dental Hospital and School, Dundee, Scotland
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Carrick DM, Black A, Gohagan JK, Khan A, Pettit K, Williams C, Yu K, Yurgalevitch S, Huang WY, Zhu C. The PLCO Biorepository: Creating, Maintaining, and Administering a Unique Biospecimen Resource. Rev Recent Clin Trials 2016; 10:212-22. [PMID: 26238117 DOI: 10.2174/1574887110666150730121429] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Revised: 07/14/2015] [Accepted: 07/16/2015] [Indexed: 12/12/2022]
Abstract
Inclusion of biospecimens in population-based studies is an integral part of understanding disease etiology, identifying biomarkers and developing prevention and treatment strategies. The Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial collected, processed and stored biospecimens from participants to create a biorepository of specimens which serves as a useful resource for a broad research community. PLCO collected blood samples from consented screening arm participants at six screening rounds and a buccal sample from consented control arm participants. In addition, formalin-fixed paraffin embedded tumor tissue specimens were collected for participants in both arms for selected cancer sites. Collection of biospecimens at multiple timepoints (i.e. serial samples) and prior to cancer diagnosis, paired with rich epidemiologic and screening data, makes the PLCO collection of biospecimens a uniquely valuable resource. As such, access to the PLCO biorepository is granted to investigators by a rigorous scientific review process and guided by a steering committee which is responsible for developing and implementing the biospecimen use policies. Here, we describe the procedures for biospecimen collection, processing, storage, requisition, and distribution, as well as data management employed in PLCO. We also provide examples of how the biospecimens have been used to advance cancer research and describe relevant lessons learned to help inform cohorts wishing to add or modify biospecimen collection.
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Affiliation(s)
- Danielle M Carrick
- Epidemiology and Genomics Research Program DCCPS, NCI, NIH, 9609 Medical Center Drive 4E224 Rockville, MD 20850, USA.
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6
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Lu Y, Cuellar-Partida G, Painter JN, Nyholt DR, Morris AP, Fasching PA, Hein A, Burghaus S, Beckmann MW, Lambrechts D, Van Nieuwenhuysen E, Vergote I, Vanderstichele A, Doherty JA, Rossing MA, Wicklund KG, Chang-Claude J, Eilber U, Rudolph A, Wang-Gohrke S, Goodman MT, Bogdanova N, Dörk T, Dürst M, Hillemanns P, Runnebaum IB, Antonenkova N, Butzow R, Leminen A, Nevanlinna H, Pelttari LM, Edwards RP, Kelley JL, Modugno F, Moysich KB, Ness RB, Cannioto R, Høgdall E, Jensen A, Giles GG, Bruinsma F, Kjaer SK, Hildebrandt MAT, Liang D, Lu KH, Wu X, Bisogna M, Dao F, Levine DA, Cramer DW, Terry KL, Tworoger SS, Missmer S, Bjorge L, Salvesen HB, Kopperud RK, Bischof K, Aben KKH, Kiemeney LA, Massuger LFAG, Brooks-Wilson A, Olson SH, McGuire V, Rothstein JH, Sieh W, Whittemore AS, Cook LS, Le ND, Gilks CB, Gronwald J, Jakubowska A, Lubiński J, Gawełko J, Song H, Tyrer JP, Wentzensen N, Brinton L, Trabert B, Lissowska J, Mclaughlin JR, Narod SA, Phelan C, Anton-Culver H, Ziogas A, Eccles D, Gayther SA, Gentry-Maharaj A, Menon U, Ramus SJ, Wu AH, Dansonka-Mieszkowska A, Kupryjanczyk J, Timorek A, Szafron L, Cunningham JM, Fridley BL, Winham SJ, Bandera EV, Poole EM, Morgan TK, Risch HA, Goode EL, Schildkraut JM, Webb PM, Pearce CL, Berchuck A, Pharoah PDP, Montgomery GW, Zondervan KT, Chenevix-Trench G, MacGregor S. Shared genetics underlying epidemiological association between endometriosis and ovarian cancer. Hum Mol Genet 2015; 24:5955-64. [PMID: 26231222 PMCID: PMC4581608 DOI: 10.1093/hmg/ddv306] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2015] [Revised: 07/16/2015] [Accepted: 07/24/2015] [Indexed: 12/13/2022] Open
Abstract
Epidemiological studies have demonstrated associations between endometriosis and certain histotypes of ovarian cancer, including clear cell, low-grade serous and endometrioid carcinomas. We aimed to determine whether the observed associations might be due to shared genetic aetiology. To address this, we used two endometriosis datasets genotyped on common arrays with full-genome coverage (3194 cases and 7060 controls) and a large ovarian cancer dataset genotyped on the customized Illumina Infinium iSelect (iCOGS) arrays (10 065 cases and 21 663 controls). Previous work has suggested that a large number of genetic variants contribute to endometriosis and ovarian cancer (all histotypes combined) susceptibility. Here, using the iCOGS data, we confirmed polygenic architecture for most histotypes of ovarian cancer. This led us to evaluate if the polygenic effects are shared across diseases. We found evidence for shared genetic risks between endometriosis and all histotypes of ovarian cancer, except for the intestinal mucinous type. Clear cell carcinoma showed the strongest genetic correlation with endometriosis (0.51, 95% CI = 0.18-0.84). Endometrioid and low-grade serous carcinomas had similar correlation coefficients (0.48, 95% CI = 0.07-0.89 and 0.40, 95% CI = 0.05-0.75, respectively). High-grade serous carcinoma, which often arises from the fallopian tubes, showed a weaker genetic correlation with endometriosis (0.25, 95% CI = 0.11-0.39), despite the absence of a known epidemiological association. These results suggest that the epidemiological association between endometriosis and ovarian adenocarcinoma may be attributable to shared genetic susceptibility loci.
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Affiliation(s)
| | | | | | - Dale R Nyholt
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia
| | - Andrew P Morris
- Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Genetics and Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Peter A Fasching
- Division of Hematology and Oncology, Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA, Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen Nuremberg, Universitaetsstrasse 21-23, 91054 Erlangen, Germany
| | - Alexander Hein
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen Nuremberg, Universitaetsstrasse 21-23, 91054 Erlangen, Germany
| | - Stefanie Burghaus
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen Nuremberg, Universitaetsstrasse 21-23, 91054 Erlangen, Germany
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen Nuremberg, Universitaetsstrasse 21-23, 91054 Erlangen, Germany
| | - Diether Lambrechts
- Laboratory for Translational Genetics, Department of Oncology, University of Leuven, Leuven, Belgium, Vesalius Research Center, VIB, Leuven, Belgium
| | - Els Van Nieuwenhuysen
- Division of Gynecologic Oncology, Department of Obstetrics and Gynaecology and Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | - Ignace Vergote
- Division of Gynecologic Oncology, Department of Obstetrics and Gynaecology and Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | - Adriaan Vanderstichele
- Division of Gynecologic Oncology, Department of Obstetrics and Gynaecology and Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | - Jennifer Anne Doherty
- Department of Community and Family Medicine, Section of Biostatistics & Epidemiology, Geisel School of Medicine, Dartmouth College, Hanover, NH, USA
| | - Mary Anne Rossing
- Department of Epidemiology, University of Washington, Seattle, WA, USA, Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Kristine G Wicklund
- Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
| | - Ursula Eilber
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
| | - Anja Rudolph
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
| | - Shan Wang-Gohrke
- Department of Obstetrics and Gynecology, University of Ulm, Ulm, Germany
| | - Marc T Goodman
- Cancer Prevention and Control, Samuel Oschin Comprehensive Cancer Institute and Department of Biomedical Sciences, Community and Population Health Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | | | | | - Matthias Dürst
- Department of Gynecology, Jena-University Hospital-Friedrich Schiller University, Jena, Germany
| | - Peter Hillemanns
- Clinics of Obstetrics and Gynaecology, Hannover Medical School, Hannover, Germany
| | - Ingo B Runnebaum
- Department of Gynecology, Jena-University Hospital-Friedrich Schiller University, Jena, Germany
| | | | | | - Arto Leminen
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Liisa M Pelttari
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Robert P Edwards
- Division of Gynecologic Oncology, Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA, Womens Cancer Research Program, Magee-Womens Research Institute and University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
| | - Joseph L Kelley
- Division of Gynecologic Oncology, Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Francesmary Modugno
- Division of Gynecologic Oncology, Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA, Womens Cancer Research Program, Magee-Womens Research Institute and University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA, Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA
| | - Kirsten B Moysich
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY, USA
| | - Roberta B Ness
- The University of Texas School of Public Health, Houston, TX, USA
| | - Rikki Cannioto
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY, USA
| | - Estrid Høgdall
- Department of Virus, Lifestyle and Genes, Danish Cancer Society Research Center, Copenhagen, Denmark, Molecular Unit, Department of Pathology, Herlev Hospital, Herlev, Denmark
| | - Allan Jensen
- Department of Virus, Lifestyle and Genes, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Graham G Giles
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia, Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia
| | - Fiona Bruinsma
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia
| | - Susanne K Kjaer
- Department of Virus, Lifestyle and Genes, Danish Cancer Society Research Center, Copenhagen, Denmark, Department of Gynaecology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | | | - Dong Liang
- College of Pharmacy and Health Sciences, Texas Southern University, Houston, TX, USA
| | - Karen H Lu
- Department of Gynecologic Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | | | - Fanny Dao
- Gynecology Service, Department of Surgery and
| | | | - Daniel W Cramer
- Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital, Boston, MA, USA
| | - Kathryn L Terry
- Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital, Boston, MA, USA, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Shelley S Tworoger
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA, Channing Division of Network Medicine, Department of Medicine and
| | - Stacey Missmer
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA, Channing Division of Network Medicine, Department of Medicine and Department of Obstetrics and Gynecology, Gynecology and Reproductive Biology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Line Bjorge
- Department of Gynecology and Obstetrics, Haukeland University Hospital, Bergen, Norway, Department of Clinical Science, Centre for Cancer Biomarkers, University of Bergen, Bergen, Norway
| | - Helga B Salvesen
- Department of Gynecology and Obstetrics, Haukeland University Hospital, Bergen, Norway, Department of Clinical Science, Centre for Cancer Biomarkers, University of Bergen, Bergen, Norway
| | - Reidun K Kopperud
- Department of Gynecology and Obstetrics, Haukeland University Hospital, Bergen, Norway, Department of Clinical Science, Centre for Cancer Biomarkers, University of Bergen, Bergen, Norway
| | - Katharina Bischof
- Department of Gynecology and Obstetrics, Haukeland University Hospital, Bergen, Norway, Department of Clinical Science, Centre for Cancer Biomarkers, University of Bergen, Bergen, Norway
| | - Katja K H Aben
- Radboud University Medical Centre, Radboud Institute for Health Sciences, Nijmegen, The Netherlands, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands
| | - Lambertus A Kiemeney
- Radboud University Medical Centre, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
| | - Leon F A G Massuger
- Department of Obstetrics and Gynaecology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Angela Brooks-Wilson
- Canada's Michael Smith Genome Sciences Centre and Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada
| | - Sara H Olson
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Valerie McGuire
- Department of Health Research and Policy-Epidemiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Joseph H Rothstein
- Department of Health Research and Policy-Epidemiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Weiva Sieh
- Department of Health Research and Policy-Epidemiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Alice S Whittemore
- Department of Health Research and Policy-Epidemiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Linda S Cook
- Division of Epidemiology and Biostatistics, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Nhu D Le
- Cancer Control Research, BC Cancer Agency, Vancouver, BC, Canada
| | - C Blake Gilks
- Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Jacek Gronwald
- Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland
| | - Anna Jakubowska
- Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland
| | - Jan Lubiński
- Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland
| | - Jan Gawełko
- Medical Faculty, Institute of Nursing and Health Sciences, University of Rzeszów, Rzeszów, Poland
| | | | | | - Nicolas Wentzensen
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Louise Brinton
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Britton Trabert
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | | | | | - Steven A Narod
- Women's College Research Institute, University of Toronto, Toronto, ON, Canada
| | - Catherine Phelan
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Hoda Anton-Culver
- Department of Epidemiology and Center for Cancer Genetics Research & Prevention, School of Medicine, University of California Irvine, Irvine, CA, USA
| | | | - Diana Eccles
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - Simon A Gayther
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | | | - Usha Menon
- Women's Cancer, Institute for Women's Health, University College London, London, UK
| | - Susan J Ramus
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Anna H Wu
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Agnieszka Dansonka-Mieszkowska
- Department of Pathology and Laboratory Diagnostics, The Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland
| | - Jolanta Kupryjanczyk
- Department of Pathology and Laboratory Diagnostics, The Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland
| | - Agnieszka Timorek
- Department of Obstetrics, Gynecology and Oncology, IInd Faculty of Medicine, Warsaw Medical University and Brodnowski Hospital, Warsaw, Poland
| | - Lukasz Szafron
- Department of Pathology and Laboratory Diagnostics, The Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland
| | - Julie M Cunningham
- Division of Experimental Pathology, Department of Laboratory Medicine and Pathology
| | - Brooke L Fridley
- Department of Biostatistics, University of Kansas, Kansas City, KS, USA
| | - Stacey J Winham
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research and
| | - Elisa V Bandera
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Elizabeth M Poole
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA, Channing Division of Network Medicine, Department of Medicine and
| | - Terry K Morgan
- Department of Pathology and Department of Obstetrics and Gynecology, OHSU, Portland, OR, USA
| | - Harvey A Risch
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA
| | - Ellen L Goode
- Division of Epidemiology, Department of Health Science Research, Mayo Clinic, Rochester, MN, USA
| | - Joellen M Schildkraut
- Department of Community and Family Medicine and Cancer Control and Population Sciences, Duke Cancer Institute, Durham, NC, USA
| | | | - Celeste L Pearce
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, CA, USA, Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA and
| | - Andrew Berchuck
- Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA
| | - Paul D P Pharoah
- Department of Oncology and Department of Public Health and Primary Care, The Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | | | - Krina T Zondervan
- Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Genetics and Endometriosis CaRe Centre, Nuffield Dept of Obstetrics & Gynaecology, University of Oxford, Oxford, UK
| | - Georgia Chenevix-Trench
- Genetics and Computational Biology Department, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia
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7
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Szulkin R, Whitington T, Eklund M, Aly M, Eeles RA, Easton D, Kote-Jarai ZS, Olama AAA, Benlloch S, Muir K, Giles GG, Southey MC, Fitzgerald LM, Henderson BE, Schumacher F, Haiman CA, Schleutker J, Wahlfors T, Tammela TLJ, Nordestgaard BG, Key TJ, Travis RC, Neal DE, Donovan JL, Hamdy FC, Pharoah P, Pashayan N, Khaw KT, Stanford JL, Thibodeau SN, McDonnell SK, Schaid DJ, Maier C, Vogel W, Luedeke M, Herkommer K, Kibel AS, Cybulski C, Lubiński J, Kluźniak W, Cannon-Albright L, Brenner H, Butterbach K, Stegmaier C, Park JY, Sellers T, Lim HY, Slavov C, Kaneva R, Mitev V, Batra J, Clements JA, Spurdle A, Teixeira MR, Paulo P, Maia S, Pandha H, Michael A, Kierzek A, Gronberg H, Wiklund F. Prediction of individual genetic risk to prostate cancer using a polygenic score. Prostate 2015; 75:1467-74. [PMID: 26177737 PMCID: PMC7745998 DOI: 10.1002/pros.23037] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2015] [Accepted: 05/22/2015] [Indexed: 12/24/2022]
Abstract
BACKGROUND Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction. METHODS We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls. RESULTS The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P = 0.0012) and the net reclassification index with 0.21 (P = 8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk. CONCLUSIONS Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction.
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Affiliation(s)
- Robert Szulkin
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Thomas Whitington
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Martin Eklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Markus Aly
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
- Department of Clinical Sciences at Danderyds Hospital, Stockholm, Sweden
| | - Rosalind A. Eeles
- The Institute of Cancer Research, London, UK
- Royal Marsden National Health Service (NHS) Foundation Trust, London and Sutton, UK
| | - Douglas Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge, UK
| | | | - Ali Amin Al Olama
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge, UK
| | - Sara Benlloch
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge, UK
| | - Kenneth Muir
- Institute of Population Health, University of Manchester, Manchester, UK
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Graham G. Giles
- Cancer Epidemiology Centre, The Cancer Council Victoria, Melbourne Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
| | - Melissa C. Southey
- Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Victoria, Australia
| | - Liesel M. Fitzgerald
- Cancer Epidemiology Centre, The Cancer Council Victoria, Melbourne Victoria, Australia
| | - Brian E. Henderson
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California
| | - Fredrick Schumacher
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California
| | - Christopher A. Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California
| | - Johanna Schleutker
- Department of Medical Biochemistry and Genetics Institute of Biomedicine Kiinamyllynkatu 10, University of Turku, Finland
- BioMediTech, University of Tampere and FimLab Laboratories, Tampere, Finland
| | - Tiina Wahlfors
- BioMediTech, University of Tampere and FimLab Laboratories, Tampere, Finland
| | - Teuvo LJ Tammela
- Department of Urology, Tampere University Hospital and Medical School, University of Tampere, Finland
| | - Børge G. Nordestgaard
- Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Herlev, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen
| | - Tim J. Key
- Cancer Epidemiology, Nuffield Department of Population Health University of Oxford, Oxford, UK
| | - Ruth C. Travis
- Cancer Epidemiology, Nuffield Department of Population Health University of Oxford, Oxford, UK
| | - David E. Neal
- University of Cambridge, Department of Oncology, Addenbrooke’s Hospital, Cambridge
- Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, UK
| | - Jenny L. Donovan
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Freddie C. Hamdy
- Nuffield Department of Surgical Sciences, University of Oxford, Faculty of Medical Science, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Paul Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Nora Pashayan
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
- University College London, Department of Applied Health Research, London, UK
| | - Kay-Tee Khaw
- Clinical Gerontology Unit, University of Cambridge, Cambridge, UK
| | - Janet L. Stanford
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
| | | | | | | | | | - Walther Vogel
- Institute of Human Genetics, University Hospital Ulm, Germany
| | | | - Kathleen Herkommer
- Department of Urology, Klinikum rechts der Isar der Technischen Universitaet Muenchen, Munich, Germany
| | - Adam S. Kibel
- Division of Urologic Surgery, Brigham and Womens Hospital, Dana-Farber Cancer Institute, Boston, Massachesetts
| | - Cezary Cybulski
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Jan Lubiński
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Wojciech Kluźniak
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Lisa Cannon-Albright
- Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, Utah
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, Utah
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Katja Butterbach
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Jong Y. Park
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida
| | - Thomas Sellers
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida
| | - Hui-Yi Lim
- Biostatistics Program, Moffitt Cancer Center, Tampa, Florida
| | - Chavdar Slavov
- Department of Urology and Alexandrovska University Hospital, Medical University, Sofia, Bulgaria
| | - Radka Kaneva
- Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University, Sofia, Bulgaria
| | - Vanio Mitev
- Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University, Sofia, Bulgaria
| | - Jyotsna Batra
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, Australia
| | - Judith A. Clements
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, Australia
| | - The Australian Prostate Cancer BioResource
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, Australia
- Australian Prostate Cancer BioResource, Brisbane, Queensland, Australia
| | - Amanda Spurdle
- Molecular Cancer Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia
| | - Manuel R. Teixeira
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
- Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal
| | - Paula Paulo
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
| | - Sofia Maia
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
| | | | | | | | | | - Henrik Gronberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
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8
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Lu Y, Ek WE, Whiteman D, Vaughan TL, Spurdle AB, Easton DF, Pharoah PD, Thompson DJ, Dunning AM, Hayward NK, Chenevix-Trench G, Macgregor S. Most common 'sporadic' cancers have a significant germline genetic component. Hum Mol Genet 2014; 23:6112-8. [PMID: 24943595 PMCID: PMC4271103 DOI: 10.1093/hmg/ddu312] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2014] [Revised: 05/22/2014] [Accepted: 06/16/2014] [Indexed: 12/20/2022] Open
Abstract
Common cancers have been demarcated into 'hereditary' or 'sporadic' ('non-hereditary') types historically. Such distinctions initially arose from work identifying rare, highly penetrant germline mutations causing 'hereditary' cancer. While rare mutations are important in particular families, most cases in the general population are 'sporadic'. Twin studies have suggested that many 'sporadic' cancers show little or no heritability. To quantify the role of germline mutations in cancer susceptibility, we applied a method for estimating the importance of common genetic variants (array heritability, h(2)g) to twelve cancer types. The following cancers showed a significant (P < 0.05) array heritability: melanoma USA set h(2)g = 0.19 (95% CI = 0.01-0.37) and Australian set h(2)g = 0.30 (0.10-0.50); pancreatic h(2)g = 0.18 (0.06-0.30); prostate h(2)g = 0.81 (0.32-1); kidney h(2)g = 0.18 (0.04-0.32); ovarian h(2)g = 0.30 (0.18-0.42); esophageal adenocarcinoma h(2)g = 0.24 (0.14-0.34); esophageal squamous cell carcinoma h(2)g = 0.19 (0.07-0.31); endometrial UK set h(2)g = 0.23 (0.01-0.45) and Australian set h(2)g = 0.39 (0.02-0.76). Three cancers showed a positive but non-significant effect: breast h(2) g = 0.13 (0-0.56); gastric h(2)g = 0.11 (0-0.27); lung h(2)g = 0.10 (0-0.24). One cancer showed a small effect: bladder h(2)g = 0.01 (0-0.11). Among these cancers, previous twin studies were only able to show heritability for prostate and breast cancer, but we can now make much stronger statements for several common cancers which emphasize the important role of genetic variants in cancer susceptibility. We have demonstrated that several 'sporadic' cancers have a significant inherited component. Larger genome-wide association studies in these cancers will continue to find more loci, which explain part of the remaining polygenic component.
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Affiliation(s)
| | | | | | - Thomas L Vaughan
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - Douglas F Easton
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, and Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Paul D Pharoah
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, and Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Deborah J Thompson
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, and
| | - Alison M Dunning
- Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
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9
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Pu X, Wang L, Chang JY, Hildebrandt MAT, Ye Y, Lu C, Skinner HD, Niu N, Jenkins GD, Komaki R, Minna JD, Roth JA, Weinshilboum RM, Wu X. Inflammation-related genetic variants predict toxicity following definitive radiotherapy for lung cancer. Clin Pharmacol Ther 2014; 96:609-15. [PMID: 25054431 PMCID: PMC4206576 DOI: 10.1038/clpt.2014.154] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2014] [Accepted: 07/16/2014] [Indexed: 12/25/2022]
Abstract
Definitive radiotherapy improves locoregional control and survival in inoperable non-small cell lung cancer (NSCLC) patients. However, radiation-induced toxicities (pneumonitis/esophagitis) are common dose-limiting inflammatory conditions. We therefore conducted a pathway-based analysis to identify inflammation-related SNPs associated with radiation-induced pneumonitis or esophagitis. 11,930 SNPs were genotyped in 201 stage I-III NSCLC patients treated with definitive radiotherapy. Validation was performed in an additional 220 NSCLC cases. After validation, 19 SNPs remained significant. A polygenic risk score (PRS) was generated to summarize the effect from validated SNPs. Significant improvements in discriminative ability were observed by adding the PRS into the clinical/epidemiological variable-based model. We then used 277 lymphoblastoid cell-lines to assess radiation sensitivity and eQTL relationships of the identified SNPs. Three genes (PRKCE,DDX58 and TNFSF7) were associated with radiation sensitivity. We concluded that inflammation-related genetic variants could contribute to the development of radiation-induced toxicities. These loci could assist in predicting those unfavorable events.
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Affiliation(s)
- X Pu
- Department of Epidemiology, The University of Texas, MD Anderson Cancer Center, Houston, Texas, USA
| | - L Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - J Y Chang
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas, USA
| | - M A T Hildebrandt
- Department of Epidemiology, The University of Texas, MD Anderson Cancer Center, Houston, Texas, USA
| | - Y Ye
- Department of Epidemiology, The University of Texas, MD Anderson Cancer Center, Houston, Texas, USA
| | - C Lu
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas, USA
| | - H D Skinner
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas, USA
| | - N Niu
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - G D Jenkins
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - R Komaki
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas, USA
| | - J D Minna
- Hamon Center for Therapeutic Oncology Research, The University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - J A Roth
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - R M Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - X Wu
- Department of Epidemiology, The University of Texas, MD Anderson Cancer Center, Houston, Texas, USA
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10
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Benke KS, Wu Y, Fallin DM, Maher B, Palmer LJ. Strategy to control type I error increases power to identify genetic variation using the full biological trajectory. Genet Epidemiol 2013; 37:419-30. [PMID: 23633177 DOI: 10.1002/gepi.21733] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2013] [Revised: 03/21/2013] [Accepted: 04/02/2013] [Indexed: 01/18/2023]
Abstract
Genome-wide association studies have been successful in identifying loci that underlie continuous traits measured at a single time point. To additionally consider continuous traits longitudinally, it is desirable to look at SNP effects at baseline and over time using linear-mixed effects models. Estimation and interpretation of two coefficients in the same model raises concern regarding the optimal control of type I error. To investigate this issue, we calculate type I error and power under an alternative for joint tests, including the two degree of freedom likelihood ratio test, and compare this to single degree of freedom tests for each effect separately at varying alpha levels. We show which joint tests are the optimal way to control the type I error and also illustrate that information can be gained by joint testing in situations where either or both SNP effects are underpowered. We also show that closed form power calculations can approximate simulated power for the case of balanced data, provide reasonable approximations for imbalanced data, but overestimate power for complicated residual error structures. We conclude that a two degree of freedom test is an attractive strategy in a hypothesis-free genome-wide setting and recommend its use for genome-wide studies employing linear-mixed effects models.
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Affiliation(s)
- K S Benke
- Johns Hopkins Bloomberg School of Public Health, Mental Health Department, Baltimore, Maryland 21205, USA.
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11
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Abstract
Polygenic scores have recently been used to summarise genetic effects among an ensemble of markers that do not individually achieve significance in a large-scale association study. Markers are selected using an initial training sample and used to construct a score in an independent replication sample by forming the weighted sum of associated alleles within each subject. Association between a trait and this composite score implies that a genetic signal is present among the selected markers, and the score can then be used for prediction of individual trait values. This approach has been used to obtain evidence of a genetic effect when no single markers are significant, to establish a common genetic basis for related disorders, and to construct risk prediction models. In some cases, however, the desired association or prediction has not been achieved. Here, the power and predictive accuracy of a polygenic score are derived from a quantitative genetics model as a function of the sizes of the two samples, explained genetic variance, selection thresholds for including a marker in the score, and methods for weighting effect sizes in the score. Expressions are derived for quantitative and discrete traits, the latter allowing for case/control sampling. A novel approach to estimating the variance explained by a marker panel is also proposed. It is shown that published studies with significant association of polygenic scores have been well powered, whereas those with negative results can be explained by low sample size. It is also shown that useful levels of prediction may only be approached when predictors are estimated from very large samples, up to an order of magnitude greater than currently available. Therefore, polygenic scores currently have more utility for association testing than predicting complex traits, but prediction will become more feasible as sample sizes continue to grow. Recently there has been much interest in combining multiple genetic markers into a single score for predicting disease risk. Even if many of the individual markers have no detected effect, the combined score could be a strong predictor of disease. This has allowed researchers to demonstrate that some diseases have a strong genetic basis, even if few actual genes have been identified, and it has also revealed a common genetic basis for distinct diseases. These analyses have so far been performed opportunistically, with mixed results. Here I derive formulae based on the heritability of disease and size of the study, allowing researchers to plan their analyses from a more informed position. I show that discouraging results in some previous studies were due to the low number of subjects studied, but a modest increase in study size would allow more successful analysis. However, I also show that, for genetics to become useful for predicting individual risk of disease, hundreds of thousands of subjects may be needed to estimate the gene effects. This is larger than most existing studies, but will become more common in the near future, so that gene scores will become more useful for predicting disease than has appeared to date.
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Affiliation(s)
- Frank Dudbridge
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.
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12
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Hiraki LT, Major JM, Chen C, Cornelis MC, Hunter DJ, Rimm EB, Simon KC, Weinstein SJ, Purdue MP, Yu K, Albanes D, Kraft P. Exploring the genetic architecture of circulating 25-hydroxyvitamin D. Genet Epidemiol 2013; 37:92-8. [PMID: 23135809 PMCID: PMC3524394 DOI: 10.1002/gepi.21694] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2012] [Revised: 09/25/2012] [Accepted: 09/28/2012] [Indexed: 01/08/2023]
Abstract
The primary circulating form of vitamin D is 25-hydroxy vitamin D (25(OH)D), a modifiable trait linked with a growing number of chronic diseases. In addition to environmental determinants of 25(OH)D, including dietary sources and skin ultraviolet B (UVB) exposure, twin- and family-based studies suggest that genetics contribute substantially to vitamin D variability with heritability estimates ranging from 43% to 80%. Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) located in four gene regions associated with 25(OH)D. These SNPs collectively explain only a fraction of the heritability in 25(OH)D estimated by twin- and family-based studies. Using 25(OH)D concentrations and GWAS data on 5,575 subjects drawn from five cohorts, we hypothesized that genome-wide data, in the form of (1) a polygenic score comprised of hundreds or thousands of SNPs that do not individually reach GWAS significance, or (2) a linear mixed model for genome-wide complex trait analysis, would explain variance in measured circulating 25(OH)D beyond that explained by known genome-wide significant 25(OH)D-associated SNPs. GWAS identified SNPs explained 5.2% of the variation in circulating 25(OH)D in these samples and there was little evidence additional markers significantly improved predictive ability. On average, a polygenic score comprised of GWAS-identified SNPs explained a larger proportion of variation in circulating 25(OH)D than scores comprised of thousands of SNPs that were on average, nonsignificant. Employing a linear mixed model for genome-wide complex trait analysis explained little additional variability (range 0-22%). The absence of a significant polygenic effect in this relatively large sample suggests an oligogenetic architecture for 25(OH)D.
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Affiliation(s)
- Linda T Hiraki
- Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, Massachusetts 02215, USA.
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13
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Abstract
Aromatase inhibitors (AIs) are an important class of endocrine drugs used in the treatment of early and advanced breast cancer in postmenopausal women. A number of studies have taken candidate approaches to assess the role of variants in genes encoding enzymes important in AI metabolism, notably CYP19A1 (aromatase), in AI response. These studies have shown conflicting, but interesting, results suggesting that CYP19A1 variants may be important in both the efficacy and toxicity of AIs. A recent genome-wide association study has identified a variant, creating an estrogen response element in TCL1A, which is associated with an increased risk of the musculoskeletal side effects associated with AI use. As breast cancer incidence increases, predictive biomarkers of response to AIs will become more important to ensure the most effective use of endocrine treatments.
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Affiliation(s)
- Kristen D Hadfield
- Genetic Medicine, Manchester Academic Health Sciences Centre (MAHSC), University of Manchester & Central Manchester University Hospitals NHS Foundation Trust, M13 9WL, UK
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14
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Moorman PG. Genetic markers for ovarian cancer risk: are we close to seeing a clinical impact? Per Med 2012; 9:565-567. [PMID: 29768790 DOI: 10.2217/pme.12.58] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Affiliation(s)
- Patricia G Moorman
- Department of Community & Family Medicine, Cancer Prevention, Detection & Control Research Program, Box 2715, Duke University Medical Center, Durham, NC 27710, USA.
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Pierce BL, Tong L, Kraft P, Ahsan H. Unidentified genetic variants influence pancreatic cancer risk: an analysis of polygenic susceptibility in the PanScan study. Genet Epidemiol 2012; 36:517-24. [PMID: 22644738 PMCID: PMC10967700 DOI: 10.1002/gepi.21644] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2012] [Revised: 04/11/2012] [Accepted: 04/25/2012] [Indexed: 12/20/2022]
Abstract
Genome-wide association (GWA) studies have identified several pancreatic cancer (PanCa) susceptibility loci. Methods for assessment of polygenic susceptibility can be employed to detect the collective effect of additional association signals for PanCa. Using data on 492,651 autosomal single nucleotide polymorphisms (SNPs) from the PanScan GWA study (2,857 cases, 2,967 controls), we employed polygenic risk score (PRS) cross-validation (CV) methods to (a) confirm the existence of unidentified association signals, (b) assess the predictive value of PRSs, and (c) assess evidence for polygenic effects in specific genomic locations (genic vs. intergenic). After excluding SNPs in known PanCa susceptibility regions, we constructed PRS models using a training GWA dataset and then tested the model in an independent testing dataset using fourfold CV. We also employed a "power-replication" approach, where power to detect SNP associations was calculated using a training dataset, and power was tested for association with "replication status" in a testing dataset. PRS scores constructed using ≥ 10% of genome-wide SNPs showed significant association with PanCa (P< 0.05) across the majority of CV analyses. Associations were stronger for PRSs restricted to genic SNPs compared to intergenic PRSs. The power-replications approach produced weaker associations that were not significant when restricting to SNPs with low pairwise linkage disequilibrium, whereas PRS results were robust to such restrictions. Although the PRS approach will not dramatically improve PanCa prediction, it provides strong evidence for unidentified association signals for PanCa. Our results suggest that focusing association studies on genic regions and conducting larger GWA studies can reveal additional PanCa susceptibility loci.
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Affiliation(s)
- Brandon L Pierce
- Department of Health Studies, University of Chicago, Chicago, Illinois 60637, USA.
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Lee SH, Goddard ME, Wray NR, Visscher PM. A Better Coefficient of Determination for Genetic Profile Analysis. Genet Epidemiol 2012; 36:214-24. [DOI: 10.1002/gepi.21614] [Citation(s) in RCA: 189] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Varghese JS, Thompson DJ, Michailidou K, Lindström S, Turnbull C, Brown J, Leyland J, Warren RML, Luben RN, Loos RJ, Wareham NJ, Rommens J, Paterson AD, Martin LJ, Vachon CM, Scott CG, Atkinson EJ, Couch FJ, Apicella C, Southey MC, Stone J, Li J, Eriksson L, Czene K, Boyd NF, Hall P, Hopper JL, Tamimi RM, Rahman N, Easton DF. Mammographic breast density and breast cancer: evidence of a shared genetic basis. Cancer Res 2012; 72:1478-84. [PMID: 22266113 PMCID: PMC3378688 DOI: 10.1158/0008-5472.can-11-3295] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Percent mammographic breast density (PMD) is a strong heritable risk factor for breast cancer. However, the pathways through which this risk is mediated are still unclear. To explore whether PMD and breast cancer have a shared genetic basis, we identified genetic variants most strongly associated with PMD in a published meta-analysis of five genome-wide association studies (GWAS) and used these to construct risk scores for 3,628 breast cancer cases and 5,190 controls from the UK2 GWAS of breast cancer. The signed per-allele effect estimates of single-nucleotide polymorphisms (SNP) were multiplied with the respective allele counts in the individual and summed over all SNPs to derive the risk score for an individual. These scores were included as the exposure variable in a logistic regression model with breast cancer case-control status as the outcome. This analysis was repeated using 10 different cutoff points for the most significant density SNPs (1%-10% representing 5,222-50,899 SNPs). Permutation analysis was also conducted across all 10 cutoff points. The association between risk score and breast cancer was significant for all cutoff points from 3% to 10% of top density SNPs, being most significant for the 6% (2-sided P = 0.002) to 10% (P = 0.001) cutoff points (overall permutation P = 0.003). Women in the top 10% of the risk score distribution had a 31% increased risk of breast cancer [OR = 1.31; 95% confidence interval (CI), 1.08-1.59] compared with women in the bottom 10%. Together, our results show that PMD and breast cancer have a shared genetic basis that is mediated through a large number of common variants.
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Affiliation(s)
- Jajini S Varghese
- Centre for Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Deborah J Thompson
- Centre for Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Kyriaki Michailidou
- Centre for Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Sara Lindström
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
| | - Clare Turnbull
- Section of Cancer Genetics, The Institute of Cancer Research, Sutton, Surrey, UK
| | - Judith Brown
- Centre for Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Jean Leyland
- Centre for Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Ruth ML Warren
- Department of Radiology, University of Cambridge, Addenbrooke’s NHS Foundation Trust Cambridge, UK
| | - Robert N Luben
- Centre for Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Ruth J Loos
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
| | - Johanna Rommens
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Andrew D Paterson
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Lisa J Martin
- Campbell Family Institute for Breast Cancer Research, Ontario Cancer Institute, Toronto, Ontario, Canada
| | - Celine M Vachon
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | | | - Fergus J Couch
- Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Carmel Apicella
- Centre for M.E.G.A. Epidemiology, Melbourne School of Population Health, The University of Melbourne, Melbourne, Australia
| | - Melissa C Southey
- Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Melbourne, Australia
| | - Jennifer Stone
- Centre for M.E.G.A. Epidemiology, Melbourne School of Population Health, The University of Melbourne, Melbourne, Australia
| | - Jingmei Li
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Human Genetics, Genome Institute of Singapore, Singapore
| | - Louise Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Norman F Boyd
- Campbell Family Institute for Breast Cancer Research, Ontario Cancer Institute, Toronto, Ontario, Canada
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - John L Hopper
- Centre for M.E.G.A. Epidemiology, Melbourne School of Population Health, The University of Melbourne, Melbourne, Australia
| | - Rulla M Tamimi
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
- Channing Laboratory, Brigham and Women’s Hospital, Boston, MA, USA
| | - Nazneen Rahman
- Section of Cancer Genetics, The Institute of Cancer Research, Sutton, Surrey, UK
| | - Douglas F Easton
- Centre for Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
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Machiela MJ, Chen CY, Chen C, Chanock SJ, Hunter DJ, Kraft P. Evaluation of polygenic risk scores for predicting breast and prostate cancer risk. Genet Epidemiol 2011; 35:506-514. [PMID: 21618606 DOI: 10.1002/gepi.20600] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2010] [Revised: 04/08/2011] [Accepted: 04/27/2011] [Indexed: 11/08/2022]
Abstract
Recently, polygenic risk scores (PRS) have been shown to be associated with certain complex diseases. The approach has been based on the contribution of counting multiple alleles associated with disease across independent loci, without requiring compelling evidence that every locus had already achieved definitive genome-wide statistical significance. Whether PRS assist in the prediction of risk of common cancers is unknown. We built PRS from lists of genetic markers prioritized by their association with breast cancer (BCa) or prostate cancer (PCa) in a training data set and evaluated whether these scores could improve current genetic prediction of these specific cancers in independent test samples. We used genome-wide association data on 1,145 BCa cases and 1,142 controls from the Nurses' Health Study and 1,164 PCa cases and 1,113 controls from the Prostate Lung Colorectal and Ovarian Cancer Screening Trial. Ten-fold cross validation was used to build and evaluate PRS with 10 to 60,000 independent single nucleotide polymorphisms (SNPs). For both BCa and PCa, the models that included only published risk alleles maximized the cross-validation estimate of the area under the ROC curve (0.53 for breast and 0.57 for prostate). We found no significant evidence that PRS using common variants improved risk prediction for BCa and PCa over replicated SNP scores.
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Affiliation(s)
- Mitchell J Machiela
- Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - Chia-Yen Chen
- Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - Constance Chen
- Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - Stephen J Chanock
- Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD
| | - David J Hunter
- Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - Peter Kraft
- Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
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