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Imputation and subset-based association analysis
across different cancer types identifies multiple
independent risk loci in the TERT-CLPTM1L region
on chromosome 5p15.33
Zhaoming Wang1,5, Bin Zhu1, Mingfeng Zhang1, Hemang Parikh1, Jinping Jia1, Charles C.
Chung1,5, Joshua N. Sampson1, Jason W. Hoskins1, Amy Hutchinson1, 5, Laurie Burdette1,5,
Abdisamad Ibrahim1, Christopher Hautman1,5, Preethi S. Raj1, Christian C. Abnet1, Andrew A.
Adjei6,7, Anders Ahlbom8, Demetrius Albanes1, Naomi E. Allen11, Christine B. Ambrosone12,
Melinda Aldrich13,14, Pilar Amiano15,16, Christopher Amos17, Ulrika Andersson18, Gerald
Andriole Jr23, Irene L. Andrulis24, Cecilia Arici25, Alan A. Arslan26,27,28, Melissa A. Austin29, Dalsu
Baris1, Donald A. Barkauskas30, Bryan A. Bassig1,32, Laura E. Beane Freeman1, Christine D. Berg2,
Sonja I. Berndt1, Pier Alberto Bertazzi33,34, Richard B. Biritwum6,7, Amanda Black1, William
Blot13,14,35, Heiner Boeing36, Paolo Boffetta37, Kelly Bolton1, 38, Marie-Christine Boutron-Ruault39,
Paige M. Bracci40, Paul Brennan41, Louise A. Brinton1, Michelle Brotzman42, H. Bas
Bueno-de-Mesquita43,44, Julie E. Buring45, Mary Ann Butler46, Qiuyin Cai13,14, Geraldine
Cancel-Tassin47,49, Federico Canzian50, Guangwen Cao51, Neil E. Caporaso1, Alfredo Carrato52,
Tania Carreon46, Angela Carta24, Gee-Chen Chang53,54, I-Shou Chang55, Jenny Chang-Claude50,
Xu Che58, Chien-Jen Chen60,61, Chih-Yi Chen62, Chung-Hsing Chen55, Constance Chen63, Kuan-Yu
Chen66, Yuh-Min Chen67,69,70, Anand P. Chokkalingam71, Lisa W. Chu72, Francoise
Clavel-Chapelon73, Graham A. Colditz74, Joanne S. Colt1, David Conti30, Michael B. Cook1, Victoria
K. Cortessis30, E. David Crawford75, Olivier Cussenot47,48,49, Faith G. Davis76, Immaculata De
Vivo63,77,78, Xiang Deng1,5, Ti Ding79, Colin P. Dinney80, Anna Luisa Di Stefano85, W. Ryan Diver86,
Eric J. Duell87, Joanne W. Elena88, Jin-Hu Fan89, Heather Spencer Feigelson90, Maria Feychting8,
Jonine D. Figueroa1, Adrienne M. Flanagan91,92, Joseph F. Fraumeni Jr1, Neal D. Freedman1,
Brooke L. Fridley93, Charles S. Fuchs94,95, Manuela Gago-Dominguez98, Steven Gallinger99,
Yu-Tang Gao101, Susan M. Gapstur86, Montserrat Garcia-Closas1,102, Reina Garcia-Closas103, Julie
M. Gastier-Foster104, J. Michael Gaziano96,97,105, Daniela S. Gerhard4, Carol A. Giffen106, Graham G.
Giles107, Elizabeth M. Gillanders108, Edward L. Giovannucci63,64, Michael Goggins110,111, 112, Nalan
Gokgoz100, Alisa M. Goldstein1, Carlos Gonzalez113, Richard Gorlick114, Mark H. Greene1, Myron
Gross115, H. Barton Grossman80, Robert Grubb III116, Jian Gu81, Peng Guan117, Christopher A.
Haiman31, Goran Hallmans19, Susan E. Hankinson95, Curtis C. Harris109, Patricia Hartge1, Claudia
Hattinger118, Richard B. Hayes1,119,120, Qincheng He117, Lee Helman3, Brian E. Henderson31,
Published by Oxford University Press 2014. This work is written by (a) US Governm ent employee(s) and is in the public domain in the
US.
∗
To whom correspondence should be addressed at: Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer
Institute, Advanced Technology Center, 8717 Grovemont Circle, Bethesda, MD 20892-4605, USA. Email: amundadottirl@mail.nih.gov
†
Present address: Center for Pediatrics and Adolescent Medicine, Department of Pediatric Hematology and Oncology, Hannover Medical School,
Hannover, Germany
Human Molecular Genetics, 2014 1–18
doi:10.1093/hmg/ddu363
HMG Advance Access published August 11, 2014
at University of Hong Kong Libraries on August 20, 2014http://hmg.oxfordjournals.org/Downloaded from
Roger Henriksson18, Judith Hoffman-Bolton121, Chancellor Hohensee122, Elizabeth A. Holly40,
Yun-Chul Hong123,124, Robert N. Hoover1, H. Dean Hosgood III126, Chin-Fu Hsiao56,57, Ann W.
Hsing72,127, Chao Agnes Hsiung56,NanHu
1,WeiHu
1, Zhibin Hu128, Ming-Shyan Huang66, David J.
Hunter63,77,78,129, Peter D. Inskip1, Hidemi Ito130, Eric J. Jacobs86, Kevin B. Jacobs4,5,131, Mazda
Jenab41, Bu-Tian Ji1, Christoffer Johansen132,133, Mattias Johansson41,20, Alison Johnson134,
Rudolf Kaaks50, Ashish M. Kamat80, Aruna Kamineni135, Margaret Karagas17, Chand Khanna3,
Kay-Tee Khaw137, Christopher Kim1, In-Sam Kim138,139, Yeul Hong Kim140,141, 142, Young-Chul
Kim143, Young Tae Kim125, Chang Hyun Kang125, Yoo Jin Jung125, Cari M. Kitahara1, Alison P.
Klein110,111,112,144, Robert Klein145, Manolis Kogevinas147,148,149,150, Woon-Puay Koh151,68, Takashi
Kohno152, Laurence N. Kolonel153, Charles Kooperberg122, Christian P. Kratz1,
{
, Vittorio Krogh154,
Hideo Kunitoh152,155, Robert C. Kurtz145, Nilgun Kurucu156, Qing Lan1, Mark Lathrop157,158, Ching C.
Lau159, Fernando Lecanda162, Kyoung-Mu Lee124,222, Maxwell P. Lee3, Loic Le Marchand153, Seth P.
Lerner160, Donghui Li82, Linda M. Liao1, Wei-Yen Lim68, Dongxin Lin59, Jie Lin81, Sara Lindstrom63,
Martha S. Linet1, Jolanta Lissowska163, Jianjun Liu164,165,Bo
¨rje Ljungberg21, Josep Lloreta149,
Daru Lu166,167, Jing Ma77,78, Nuria Malats168, Satu Mannisto169, Neyssa Marina170, Giuseppe
Mastrangelo171, Keitaro Matsuo130,172, Katherine A. McGlynn1, Roberta McKean-Cowdin81, Lorna
H. McNeill83, Robert R. McWilliams173, Beatrice S. Melin18, Paul S. Meltzer3, James E. Mensah6,7,
Xiaoping Miao174, Dominique S. Michaud175, Alison M. Mondul1, Lee E. Moore1, Kenneth Muir176,
Shelley Niwa42, Sara H. Olson146, Nick Orr177, Salvatore Panico179, Jae Yong Park138,139,180, Alpa V.
Patel86, Ana Patino-Garcia162, Sofia Pavanello171, Petra H. M. Peeters181,182, Beata Peplonska184,
Ulrike Peters122, Gloria M. Petersen173, Piero Picci118, Malcolm C. Pike31,146, Stefano Porru25,
Jennifer Prescott63,77,78, Xia Pu81, Mark P. Purdue1, You-Lin Qiao185, Preetha Rajaraman1, Elio
Riboli182, Harvey A. Risch186, Rebecca J. Rodabough122, Nathaniel Rothman1, Avima M. Ruder46,
Jeong-Seon Ryu187, Marc Sanson85, Alan Schned17, Fredrick R. Schumacher31, Ann G.
Schwartz188, Kendra L. Schwartz189, Molly Schwenn190, Katia Scotlandi118, Adeline Seow68, Consol
Serra191,192, Massimo Serra118, Howard D. Sesso45, Gianluca Severi107, Hongbing Shen128,Min
Shen1, Sanjay Shete193, Kouya Shiraishi152, Xiao-Ou Shu13,14, Afshan Siddiq183, Luis
Sierrasesumaga162, Sabina Sierri194, Alan Dart Loon Sihoe195, Debra T. Silverman1, Matthias
Simon196, Melissa C. Southey197, Logan Spector198, Margaret Spitz161, Meir Stampfer77,78, Par
Stattin21, Mariana C. Stern31, Victoria L. Stevens86, Rachael Z. Stolzenberg-Solomon1, Daniel O.
Stram31, Sara S. Strom84, Wu-Chou Su199, Malin Sund22, Sook Whan Sung136, Anthony
Swerdlow102,178, Wen Tan59, Hideo Tanaka130, Wei Tang1, Ze-Zhang Tang79, Adonina Tardon200,
Evelyn Tay6,7, Philip R. Taylor1, Yao Tettey6,7, David M. Thomas201, Roberto Tirabosco92, Anne
Tjonneland202, Geoffrey S. Tobias1, Jorge R. Toro1, Ruth C. Travis11, Dimitrios Trichopoulos65,
Rebecca Troisi1, Ann Truelove42, Ying-Huang Tsai203, Margaret A. Tucker1, Rosario Tumino204,
David Van Den Berg31, Stephen K. Van Den Eeden205, Roel Vermeulen206, Paolo Vineis207,208, Kala
Visvanathan121, Ulla Vogel209,210, Chaoyu Wang1, Chengfeng Wang58, Junwen Wang1,5, 211, Sophia
S. Wang214, Elisabete Weiderpass215,216,9, 217, Stephanie J. Weinstein1, Nicolas Wentzensen1,
William Wheeler106, Emily White122, John K. Wiencke218, Alicja Wolk10, Brian M. Wolpin94,95, Maria
Pik Wong212, Margaret Wrensch218,ChenWu
59, Tangchun Wu171,XifengWu
81, Yi-Long Wu219,JayS.
Wunder23, Yong-Bing Xiang101,JunXu
213, Hannah P. Yang1, Pan-Chyr Yang66, Yasushi Yatabe220,
2Human Molecular Genetics, 2014
at University of Hong Kong Libraries on August 20, 2014http://hmg.oxfordjournals.org/Downloaded from
Yuanqing Ye81, Edward D. Yeboah6,7, Zhihua Yin117, Chen Ying68, Chong-Jen Yu199, Kai Yu1,
Jian-Min Yuan221, Krista A. Zanetti88, Anne Zeleniuch-Jacquotte27,28, Wei Zheng13,14, Baosen
Zhou117, Lisa Mirabello1, Sharon A. Savage1, Peter Kraft63,65, Stephen J. Chanock1, 5, Meredith
Yeager1,5, Maria Terese Landi1, Jianxin Shi1, Nilanjan Chatterjee1and Laufey T. Amundadottir1,∗
1
Division of Cancer Epidemiology and Genetics,
2
Division of Cancer Prevention,
3
Center for Cancer Research and
4
Office
of Cancer Genomics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health,
Bethesda, MD, USA,
5
Cancer Genomics Research Laboratory, National Cancer Institute, Division of Cancer
Epidemiology and Genetics, SAIC-Frederick, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD,
USA,
6
Korle Bu Teaching Hospital, PO BOX 77, Accra, Ghana,
7
University of Ghana Medical School, PO Box 4236, Accra,
Ghana,
8
Unit of Epidemiology, Institute of Environmental Medicine,
9
Department of Medical Epidemiology and
Biostatistics and
10
Unit of Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm,
Sweden,
11
Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Oxford, UK,
12
Department of
Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY, USA,
13
Division of Epidemiology, Department
of Medicine, Vanderbilt Epidemiology Center,
14
Vanderbilt-Ingram Cancer Center, Vanderbilt University School of
Medicine, Nashville, TN, USA,
15
Public Health Division of Gipuzkoa, Basque Regional Health Department, San
Sebastian, Spain,
16
CIBERESP, CIBER Epidemiologia y Salud Publica, Madrid, Spain,
17
Geisel School of Medicine at
Dartmouth, Hanover, NH, USA,
18
Department of Radiation Sciences, Oncology,
19
Department of Public Health and
Clinical Medicine/Nutritional Research,
20
Department of Public Health and Clinical Medicine,
21
Department of Surgical
and Perioperative Sciences, Urology and Andrology and
22
Department of Surgical and Perioperative Sciences/Surgery,
Umea
˚University, Umea
˚, Sweden,
23
Division of Urologic Surgery, Washington University School of Medicine, St Louis,
MO, USA,
24
Litwin Centre for Cancer Genetics, Samuel Lunenfeld Research Institute, Mt Sinai Hospital, University of
Toronto, Toronto, ON, Canada,
25
Department of Medical and Surgical Specialties, Radiological Sciences and Public
Health, University of Brescia, Italy,
26
Department of Obstetrics and Gynecology and
27
Department of Population Health,
New York University School of Medicine, New York, NY, USA,
28
New York University Cancer Institute, New York, NY,
USA,
29
Department of Epidemiology, University of Washington, Seattle, WA, USA,
30
Department of Preventive Medicine,
Biostatistics Division, Keck School of Medicine and
31
Department of Preventive Medicine, Keck School of Medicine,
University of Southern California, Los Angeles, CA, USA,
32
Division of Environmental Health Sciences, Yale School of
Public Health, New Haven, Connecticut, USA,
33
Department of Clinical Sciences and Community Health, University of
Milan,
34
Department of Preventive Medicine, Fondazione IRCCS Ca’ Granda Policlinico Hospital, Milan, Italy,
35
International Epidemiology Institute, Rockville, MD, USA,
36
Department of Epidemiology, German Institute of Human
Nutrition, Potsdam-Rehbruecke, Germany,
37
Institute for Translational Epidemiology, Hematology and Medical
Oncology, Mount Sinai Hospital School of Medicine, New York, NY, USA,
38
Department of Oncology, University of
Cambridge, Cambridge CB2 2RE, UK,
39
Institut National de la Sante et de la Recherche Medicale (INSERM) and Institut
Gustave Roussy, Villejuif, France,
40
Department of Epidemiology and Biostatistics, University of California
San Francisco, San Francisco, CA, USA,
41
International Agency for Research on Cancer (IARC-WHO), Lyon, France,
42
Westat, Rockville, MD, USA,
43
National Institute for Public Health and the Environment (RIVM), Bilthoven, The
Netherlands,
44
Department of Gastroenterology and Hepatology, University Medical Centre Utrecht, Utrecht, The
Netherlands,
45
Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, USA,
46
Centers for Disease
Control and Prevention, National Institute for Occupational Safety and Health, Cincinnati, OH, USA,
47
CeRePP, Paris,
France,
48
AP-HP, Department of Urology, Tenon Hospital, GHU-Est, Paris, France,
49
UPMC Univ Paris 06, GRC n85,
ONCOTYPE-URO, Paris, France,
50
Genomic Epidemiology Group, German Cancer Research Center (DKFZ),
Heidelberg, Germany,
51
Department of Epidemiology, Second Military Medical University, Shanghai, China,
52
Medical
Oncology Department, Hospital Ramo
´n y Cajal, Madrid, Spain,
53
Faculty of Medicine, School of Medicine, National
Yang-Ming University, Taipei, Taiwan,
54
Division of Chest Medicine, Department of Internal Medicine, Taichung Veterans
General Hospital, Taichung, Taiwan,
55
National Institute of Cancer Research,
56
Division of Biostatistics and
Bioinformatics, Institute of Population Health Sciences and
57
Taiwan Lung Cancer Tissue/Specimen Information
Resource Center, National Health Research Institutes, Zhunan, Taiwan,
58
Department of Abdominal Surgery and
59
State
Key Laboratory of Molecular Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking
Union Medical College, Beijing, China,
60
Genomics Research Center, Academia Sinica, Taipei, Taiwan,
61
Graduate
Human Molecular Genetics, 2014 3
at University of Hong Kong Libraries on August 20, 2014http://hmg.oxfordjournals.org/Downloaded from
Institute of Epidemiology, College of Public Health, National Taiwan University, Taipei, Taiwan,
62
Cancer Center, China
Medical University Hospital, Taipei, Taiwan,
63
Program in Molecular and Genetic Epidemiology,
64
Department of Nutrition
and
65
Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA,
66
Department of Internal
Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan,
67
Department of Epidemiology and Public Health, Yong Loo Lin School of Medicine and
68
Saw Swee Hock School of
Public Health, National University of Singapore, Singapore,
69
Chest Department, Taipei Veterans General Hospital,
Taipei, Taiwan,
70
College of Medical Science and Technology, Taipei Medical University, Taiwan,
71
School of Public
Health, University of California, Berkeley, CA, USA,
72
Cancer Prevention Institute of California, Fremont, CA, USA,
73
Inserm, Centre for Research in Epidemiology and Population Health (CESP), Villejuif, France,
74
Washington University
School of Medicine, St. Louis, MO, USA,
75
Urologic Oncology, University of Colorado, Aurora, CO, USA,
76
Department of
Public Health Sciences, School of Public Health, University of Albe rta, Edmonton, AB, Canada T6G 2R3,
77
Department of
Medicine, Channing Division of Network Medicine and
78
Brigham and Women’s Hospital and Harvard Medical School,
Boston, MA, USA,
79
Shanxi Cancer Hospital, Taiyuan, Shanxi, People’s Republic of China,
80
Department of Urology,
81
Department of Epidemiology,
82
Department of Gastrointestinal Medical Oncology,
83
Department of Health Disparities
Research, Division of OVP, Cancer Prevention and Population Sciences, and Center for Community-Engaged
Translational Research, Duncan Family Institute and
84
Department of Epidemiology, Division of Cancer Prevention and
Population Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, USA,
85
Service de Neurologie
Mazarin, GH Pitie-Salpetriere, APHP, and UMR 975 INSERM-UPMC, CRICM, Paris, France,
86
Epidemiology Research
Program, American Cancer Society, Atlanta, GA, USA,
87
Unit of Nutrition, Environment and Cancer, Cancer
Epidemiology Research Program, Bellvitge Biomedical Research Institute, Catalan Institute of Oncology (ICO-IDIBELL),
Barcelona, Spain,
88
Epidemiology and Genomics Research Program, Division of Cancer Control and Population
Sciences, Bethesda, MD, USA,
89
Shanghai Cancer Institute, Shanghai, People’s Republic of China,
90
Institute for Health
Research, Kaiser Permanente, Denver, CO, USA,
91
UCL Cancer Institute, Huntley Street, London WC1E 6BT, UK,
92
Royal National Orthopaedic Hospital NHS Trust, Stanmore, Middlesex HA7 4LP, UK,
93
Department of Biostatistics,
University of Kansas Medical Center, Kansas City, KS, USA,
94
Department of Medical Oncology, Dana-Farber Cancer
Institute, Boston, MA, USA,
95
Channing Laboratory, Department of Medicine,
96
Division of Preventive Medicine,
Department of Medicine and
97
Division of Aging, Department of Medicine, Brigham and Women’s Hospital and Harvard
Medical School, Boston, MA, USA,
98
Genomic Medicine Group, Galician Foundation of Genomic Medicine, Complejo
Hospitalario Universitario de Santiago, Servicio Galego de Saude (SERGAS), Instituto de Investigacio
´n Sanitaria de
Santiago (IDIS), Santiago de Compostela, Spain,
99
Samuel Lunenfeld Research Institute and
100
Lunenfeld-Tanenbaum
Research Institute, Mount Sinai Hospital, Toronto, ON, Canada,
101
Department of Epidemiology, Shanghai Cancer
Institute, Renji Hospital, Shanghai Jiaotaong University School of Medicine, Shanghai, China,
102
Division of Genetics and
Epidemiology, Institute of Cancer Research, Sutton, UK,
103
Unidad de Investigacio
´n, Hospital Universitario de Canarias,
La Laguna, Spain,
104
Nationwide Children’s Hospital, and The Ohio State University Department of Pathology and
Pediatrics, Columbus, OH, USA,
105
Massachusetts Veteran’s Epidemiology, Research and Information Center, Geriatric
Research Education and Clinical Center, Veterans Affairs Boston Healthcare System, Boston, MA, USA,
106
Information
Management Services Inc., Calverton, MD, USA,
107
Cancer Epidemiology Centre, The Cancer Council Victoria & Centre
for Molecular, Environmental, Genetic, and Analytic Epidemiology, The University of Melbourne, Victoria, Australia,
108
Division of Cancer Control and Population Sciences and
109
Laboratory of Human Carcinogenesis, Center for Cancer
Research, National Cancer Institute, Bethesda, MD, USA,
110
Department of Oncology,
111
Department of Pathology and
112
Department of Medicine, The Sol Goldman Pancreatic Research Center, The Johns Hopkins University School of
Medicine, Baltimore, MD, USA,
113
Unit of Nutrition, Environment and Cancer, Cancer Epidemiology Research
Programme, Catalan Institute of Oncology (ICO), Barcelona, Spain,
114
Albert Einstein College of Medicine, The
Children’s Hospital at Montefiore, Bronx, NY, USA,
115
Department of Laboratory Medicine and Pathology, School of
Medicine, University of Minnesota, Minneapolis, MN, USA,
116
Department of Urology, Washington University School of
Medicine, St. Louis, MO, USA,
117
Department of Epidemiology, School of Public Health, China Medical University,
Shenyang, China,
118
Laboratory of Experimental Oncology, Orthopaedic Rizzoli Institute, Bologna, Italy,
119
Department
of Population Health, New York University Langone Medical Center and
120
Department of Environmental Medicine,
New York University Langone Medical Center, New York University Cancer Institute, New York, NY, USA,
121
Johns
Hopkins Bloomberg School of Public Health, Baltimore, MD, USA,
122
Division of Public Health Sciences, Fred Hutchinson
Cancer Research Center, Seattle, WA, USA,
123
Institute of Environmental Medicine, Seoul National University Medical
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at University of Hong Kong Libraries on August 20, 2014http://hmg.oxfordjournals.org/Downloaded from
Research Center, Seoul, Republic of Korea,
124
Department of Preventive Medicine and
125
Cancer Research Institute,
Seoul National University College of Medicine, Seoul, Republic of Korea,
126
Department of Epidemiology and Population
Health, Albert Einstein College of Medicine, Bronx, NY, USA,
127
Stanford Cancer Institute, Stanford University, Stanford,
CA, USA,
128
Department of Epidemiology and Biostatistics, Cancer Center, Nanjing Medical University, Nanjing, China,
129
Broad Institute of Harvard and MIT, Cambridge, MA, USA,
130
Division of Epidemiology and Prevention, Aichi Cancer
Center Research Institute, Nagoya, Japan,
131
Bioinformed, LLC, Gaithersburg, MD, USA,
132
Department of Oncology,
Finsen Center, Rigshospitalet, Copenhagen, Denmark,
133
Unit of Survivorship, Danish Cancer Society Research Center,
Copenhagen, Denmark,
134
Vermont Cancer Registry, Burlington, VT, USA,
135
Group Health Research Institute, Seattle,
WA, USA,
136
Department of Thoracic and Cardiovascular Surgery, Seoul St Mary’s Hospital, Seoul, South Korea,
137
School of Clinical Medicine, University of Cambridge, UK,
138
Department of Biochemistry and
139
Department of Cell
Biology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea,
140
Genomic Research Center for
Lung and Breast/Ovarian Cancers, Korea University Anam Hospital, Seoul, Republic of Korea,
141
Department of Internal
Medicine and Division of Brain and
142
Division of Oncology/Hematology, Department of Internal Medicine, Korea
University College of Medicine, Seoul, Republic of Korea,
143
Lung and Esophageal Cancer Clinic, Chonnam National
University Hwasun Hospital, Hwasun-eup, Republic of Korea,
144
Department of Epidemiology, Johns Hopkins Bloomberg
School of Public Health, Baltimore, MD, USA,
145
Department of Medicine and
146
Department of Epidemiology and
Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA,
147
Centre for Research in Environmental
Epidemiology (CREAL), Barcelona, Spain,
148
IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain,
149
CIBER Epidemiologı
´a y Salud Pu
´blica (CIBERESP), Barcelona, Spain,
150
National School of Public Health, Athens,
Greece,
151
Duke-NUS Graduate Medical School, Singapore, Singapore,
152
Division of Genome Biology, National Cancer
Center Research Institute, Tokyo, Japan,
153
Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI,
USA,
154
Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy,
155
Department of Respiratory Medicine, Mitsui
Memorial Hospital, Tokyo, Japan,
156
Department of Pediatric Oncology, A.Y. Ankara Oncology Training and Research
Hospital, Yenimahalle- Ankara, Turkey,
157
Centre National de Genotypage, IG/CEA, Evry Cedex, France,
158
Centre
d’E
´tude du Polymorphism Humain (CEPH), Paris, France,
159
Texas Children’s Cancer and Hematology Centers,
160
Scott
Department of Urology and
161
Dan L. Duncan Center, Baylor College of Medicine, Houston, TX, USA,
162
Department of
Pediatrics, University Clinic of Navarra, Universidad de Navarra, Pamplona, Spain,
163
Department of Cancer
Epidemiology and Prevention, Maria Sklodowska-Curie Cancer Center and Institute of Oncology, Warsaw, Poland,
164
Human Genetics Division, Genome Institute of Singapore, Singapore,
165
School of Life Sciences, Anhui Medical
University, Hefei, China,
166
Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences,
Fudan University, Shanghai, China,
167
State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan
University, Shanghai, China,
168
Centro Nacional de Investigaciones Oncologicas, Melchor Ferna
´ndez Almagro, 3,
Madrid E-28029, Spain,
169
National Institute for Health and Welfare, Helsinki, Finland,
170
Lucile Packard Children’s
Hospital, Stanford University, Palo Alto, CA, USA,
171
Department of Cardiac, Thoracic and Vascular Sciences, University
of Padova, Padua, Italy,
172
Department of Preventive Medicine, Kyushu University Faculty of Medical Scicence, Fukuoka,
Japan,
173
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA,
174
Key Laboratory for
Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Sciences and
Technology, Wuhan, China,
175
Department of Epidemiology, Division of Biology and Medicine, Brown University,
Providence, RI, USA,
176
Health Sciences Research Institute, University of Warwick, Coventry, UK,
177
Complex Traits
Genetics Team and
178
Division of Breast Cancer Research, Institute of Cancer Research, London, UK,
179
Dipartimento di
Medicina Clinica e Chirurgia, Federico II University, Naples, Italy,
180
Lung Cancer Center, Kyungpook National University
Medical Center, Daegu, Republic of Korea,
181
Julius Center for Health Sciences and Primary Care, University Medical
Center, Utrecht, Utrecht, The Netherlands,
182
Department of Epidemiology and Biostatistics, School of Public Health,
Imperial College London, London, UK,
183
Department of Genomics of Common Disease, School of Public Health, Imperial
College London, London, UK,
184
Nofer Institute of Occupational Medicine, Lodz, Poland,
185
Department of Epidemiology,
Cancer Institute (Hospital), Chinese Academy of Medical Sciences, Beijing, People’s Republ ic of China,
186
Yale School of
Public Health, New Haven, CT, USA,
187
Department of Internal Medicine, Inha University College of Medicine, Incheon,
Korea,
188
Karmanos Cancer Institute and Department of Oncology and
189
Karmanos Cancer Institute and Department of
Family Medicine and Public Health Sciences, Wayne State University School of Medicine, Detroit, MI, USA,
190
Maine
Cancer Registry, Augusta, ME, USA,
191
Centre for Research in Occupational Health, Universitat Pompeu Fabra,
Barcelona, Spain,
192
CIBER of Epidemiology and Public Health (CIBERESP),
193
Department of Biostatistics, MD
Human Molecular Genetics, 2014 5
at University of Hong Kong Libraries on August 20, 2014http://hmg.oxfordjournals.org/Downloaded from
Anderson Cancer Center, Houston, TX, USA,
194
Nutritional Epidemiology Unit, Fondazione IRCCS Istituto Nazionale dei
Tumori, Milan, Italy,
195
Department of Surgery, Division of Cardiothoracic Surgery, Queen Mary Hospital, Hong Kong,
China,
196
Department of Neurosurgery, University of Bonn Medical Center, Bonn, Germany,
197
Department of Pathology,
The University of Melbourne, Melbourne, VIC, Australia,
198
University of Minnesota, Minneapolis, MN, USA,
199
Department of Internal Medicine, National Cheng Kung University Hospital and College of Medicine, Tainan, Taiwan,
200
Instituto Universitario de Oncologı
´a, Universidad de Oviedo, Oviedo, Spain,
201
Sir Peter MacCallum Department of
Oncology, University of Melbourne, St Andrew’s Place, East Melbourne, VIC, Australia,
202
Danish Cancer Society
Research Center, Copenhagen, Denmark,
203
Department of Pulmonary Medicine, Chang Gung Memorial Hospital,
Chiayi, Taiwan,
204
Cancer Registry Associazione Iblea Ricerca Epidemiologica, Onlus and Asp Ragusa, Ragusa Italy,
205
Kaiser Permanente Northern California, Oakland, CA, USA,
206
Division of Environmental Epidemiology, Institute for
Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands,
207
Imperial College, London, UK,
208
Human Genetics Foundation (HuGeF), Torino Italy,
209
National Research Centre for the Working Environment,
Copenhagen, Denmark,
210
National Food Institute, Technical University of Denmark, Soborg, Denmark,
211
Department
of Biochemistry and Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
SAR, China,
212
Department of Pathology and
213
School of Public Health, Li Ka Shing (LKS) Faculty of Medicine, The
University of Hong Kong, Hong Kong, China,
214
Division of Cancer Etiology, Department of Population Sciences, City of
Hope and the Beckman Research Institute, Duarte, CA, USA,
215
Department of Community Medicine, Faculty of Health
Sciences, University of Tromsø, The Arctic University of Norway, Tromsø, Norway,
216
Department of Research, Cancer
Registry of Norway, Oslo, Norway,
217
Samfundet Folkha
¨lsan, Helsinki, Finland,
218
University of California San Francisco,
San Francisco, CA, USA,
219
Guangdong Lung Cancer Institute, Medical Research Center and Cancer Center of
Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China,
220
Department of Pathology
and Molecular Diagnostics, Aichi Cancer Center Hospital and
221
University of Pittsburgh Cancer Institute, Pittsburgh, PA,
USA and
222
Department of Environmental Health, Korea National Open University, Seoul, Republic of Korea
Received April 22, 2014; Revised June 30, 2014; Accepted July 8, 2014
Genome-wide association studies (GWAS) have mapped risk alleles for at least 10 distinct cancers to a small
region of 63 000 bp on chromosome 5p15.33. This region harbors the TERT and CLPTM1L genes; the former
encodes the catalytic subunit of telomerase reverse transcriptase and the latter may play a role in apoptosis.
To investigate further the genetic architecture of common susceptibility alleles in this region, we conducted
an agnostic subset-based meta-analysis (association analysis based on subsets) across six distinct cancers
in 34 248 cases and 45 036 controls. Based on sequential conditional analysis, we identified as many as six in-
dependent risk loci marked by common single-nucleotide polymorphisms: five in the TERT gene (Region 1:
rs7726159, P52.10 310
239
; Region 3: rs2853677, P53.30 310
236
and P
Conditional
52.36 310
28
;Region4:
rs2736098, P53.87 310
212
and P
Conditional
55.19 310
26
, Region 5: rs13172201, P50.041 and P
Conditional
5
2.04 310
26
; and Region 6: rs10069690, P57.49 310
215
and P
Conditional
55.35 310
27
) and one in the neighbor-
ing CLPTM1L gene (Region 2: rs451360; P51.90 310
218
and P
Conditional
57.06 310
216
). Between three and five
cancers mapped to each independent locus with both risk-enhancing and protective effects. Allele-specific
effects on DNA methylation were seen for a subset of risk loci, indicating that methylation and subsequent
effects on gene expression may contribute to the biology of risk variants on 5p15.33. Our results provide
strong support for extensive pleiotropy across this region of 5p15.33, to an extent not previously observed in
other cancer susceptibility loci.
INTRODUCTION
Genome-wide association studies (GWAS) have identified inde-
pendent susceptibility loci in a region on chromosome 5p15.33
that are associated with at least 10 distinct cancers. The pub-
lished findings include bladder (1), estrogen-negative breast
(2), glioma (3), lung (4–7), ovary (8), melanoma (9), non-
melanoma skin (10,11), pancreas (12), prostate (13) and testicu-
lar germ cell cancer (14). This degree of pleiotropy for common
susceptibility alleles suggests that the region harbors an import-
ant set of elements that could influence multiple cancers. It has
been observed previously that one allele may be protective for
one cancer while conferring susceptibility to another (15).
These independent loci map to 63,000 bp of 5p15.33 that
harbors two plausible candidate genes: TERT, which encodes
the catalytic subunit of telomerase reverse transcriptase (16)
and CLPTM1L, which encodes the cleft lip and palate-associated
transmembrane 1 like protein (also called cisplatin resistance
6Human Molecular Genetics, 2014
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related protein, CRR9). CLPTM1L appears to play a role in
apoptosis and cytokinesis, is overexpressed in both lung and
pancreatic cancer and is required for KRAS driven lung cancer
(17–21). Germline mutations in TERT can cause dyskeratosis
congenita (DC), a cancer-prone inherited bone marrow failure
syndrome caused by aberrant telomere biology (22). Clinically
related telomere biology disorders, including idiopathic pul-
monary fibrosis and acquired aplastic anemia, can also be
caused by germline TERT mutations (reviewed in 23).
To investigate the genetic architecture of common suscepti-
bility alleles across this region of 5p15.33 in multiple cancer
sites, we utilized a recently developed method called association
analysis based on subsets (ASSET) that combines association
signals for an SNP across multiple traits by exploring subsets
of studies for true association signals in the same, or the opposite
direction, while accounting for the multiple testing required
(24). The method has been shown to be more powerful than
the standard meta-analysis in the presence of heterogeneity,
where the effect of a specific SNP might be restricted to only a
subset of traits or/and may have different directions of associa-
tions for different traits (24).
RESULTS
In this study, we conducted a cross-cancer fine-mapping anal ysis
of a region on chromosome 5p15.33 known to be associated with
multiple cancer sites. We imputed each dataset across a 2 Mb
window (chr5: 250 000–2 250 000; hg19) using the 1000
Genomes (1000G) and DCEG reference datasets (25,26) and
applied a subset-based meta-analysis method (ASSET) (24)to
combine results across six cancers (11 studies) (see Materials
and Methods for details). This method has been shown to
improve power and interpretation when compared with other
traditional methods for the analysis of heterogeneous traits (24).
In the first analysis, we focused on six distinct cancer sites in
which 5p15.33 had previously been reported and had a nominal
P-value in our dataset (‘Tier-I studies’ scans, see Materials and
Methods). We performed the analysis across all studies (77%
European, 7% African American and 16% Asian ancestry,
ALL scans), and, because the majority of studies and subjects
were of European ancestry, we conducted parallel analyses in
this group only (EUR scans). Bonferroni correction was used
to assess significance, using the threshold at 1.3 ×10
25
, based
on the number of single-nucleotide polymorphisms (SNPs) ana-
lyzed across the region (n¼1924) and the two analyses per-
formed (ALL or EUR scans) (see Materials and Methods). In
the second analysis, we examined the regions identified above
in eight cancers in which 5p15.33 had not been reported in the
literature (NHGRI Catalog of Published GWAS studies: http://
www.genome.gov/gwastudies/), or did not show a nominal
P-value in our dataset (‘Tier-II studies’).
Application of ASSET by sequential conditioning of asso-
ciated SNPs revealed up to six independent loci on 5p15.33,
each influencing risk of multiple cancers (Fig. 1, Table 1; Sup-
plementary Material, Table S1). In the primary analysis of all
subjects, we performed the ASSET meta-analysis based on un-
conditional association results from each of the six cancer
scans (11 studies). This identified rs7726159 with the lowest
P-value (P¼2.10 ×10
239
), thus marking Region 1. The next
four SNPs, ranked by P-values, were highly correlated with
the index SNP based on 1000G CEU data: rs7725218 (P¼
2.98 ×10
239
, pair-wise r
2
¼0.90), rs4449583 (P¼3.37 ×
10
239
, pair-wise r
2
¼1.0), rs7705526 (P¼1.00 ×10
236
, pair-
wise r
2
¼0.74) and rs4975538 (P¼4.11 ×10
232
, pair-wise
r
2
¼0.76). These five SNPs reside in the second and third
intron of the TERT gene and are common, with effect allele fre-
quencies ranging between 0.18 and 0.43 in African (AFR),
0.35–0.37 in Asian (ASN) and 0.32 – 0.38 in European (EUR)
populations, each estimated in the 1000G project (Supplemen-
tary Material, Table S2). A search for surrogates using an r
2
threshold of 0.7 across a 1 Mb window centered on the index
SNP did not identify additional highly correlated SNPs. The
effect allele (A) of rs7726159 was positively associated with
glioma (Glioma Scan) and lung cancer (Asian Lung) (P¼
4.38 ×10
236
,OR
Combined
¼1.47; 95% CI ¼1.38– 1.56), but
negatively associated with testicular cancer (TGCT NCI), pros-
tate cancer (Pegasus and AdvPrCa) and pancreatic cancer
(ChinaPC) (P¼5.07 ×10
26
,OR
Combined
¼0.85; 95% CI ¼
0.80–0.91) (Fig. 2A).
The most significant SNP after conditioning on rs7726159 was
rs451360 (P¼1.90 ×10
218
;P
Conditional
¼7.06 ×10
216
), res-
iding in intron 13 of CLPTM1L and marking Region 2 (Fig. 1,
Table 1). Six SNPs were correlated with rs451360 with an r
2
.
0.7, all located within 500 kb of this SNP and spanning the
entire length of CLPTM1L: rs380145, rs13170453, rs37004,
rs36115365, rs35953391 and rs7446461. This effect allele
(rs451360-A) was positively associated with pancreatic cancer
(PanScan) and testicular cancer (TGCT NCI) (P¼4.38 ×
10
213
,OR
Combined
¼1.34; 95% CI ¼1.24 – 1.45), but negative-
ly associated with lung cancer (AA Lung, Asian Lung and Eur
Lung) (P¼9.50 ×10
28
,OR
Combined
¼0.85; 95% CI ¼0.80–
0.90) (Fig. 2B). Although large differences were seen in the
effect allele frequencies across the 1000G continental popula-
tions, 0.02– 0.03 in AFR, 0.12 in ASN and 0.17 – 0.24 in EUR
(Supplementary Material, Table S2), the signal was still suffi-
ciently strong to be detected, particularly in African and Asian
lung studies, suggesting its importance in lung cancer etiology.
In our sequential conditional analysis, rs2853677 (located in
the first intron of TERT) was the most significant SNP after con-
ditioning on both rs7726159 and rs451360, thus marking Region
3(P¼3.30 ×10
236
;P
Conditional
¼2.36 ×10
28
) (Fig. 1,
Table 1). No additional SNPs with an r
2
.0.7 were located
within 500 kb of this SNP, which has relatively low LD with
both rs7726159 (r
2
¼0.13) and rs451360 (r
2
¼0.12) in
1000G CEU data. Region 3 (rs2853677-A) was positively asso-
ciated with testicular cancer (TGCT NCI) and pancreatic cancer
(PanScan and ChinaPC) (P¼1.36 ×10
27
,OR
Combined
¼1.22;
95% CI ¼1.13 – 1.31), but negatively associated with lung
cancer (Asian Lung and AA Lung) and glioma (Glioma scan)
(P¼2.79 ×10
231
,OR
Combined
¼0.73; 95% CI ¼0.70 – 0.77)
(Fig. 2C). The effect allele frequency for rs2853677 was consist-
ent across the three continental 1000G populations correspond-
ing to the studies included in this analysis: 0.60 in EUR, 0.67 in
ASN and 0.71 in AFR (Supplementary Material, Table S2).
A conditional analysis based on the three SNPs above
(rs7726159, rs451360 and rs2853677) yielded Region 4,
marked by rs2736098 (P¼3.87 ×10
212
;P
Conditional
¼
5.19 ×10
26
), a synonymous variant (A305A) in the second
exon of TERT (Fig. 1, Table 1). Three additional SNPs with an
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Figure 1. Sequential conditional analyses and ASSET meta-analyses identified up to six independent signals for the TERT-CLPTM1L region on chromosome 5p15.33.
SNPs marking each region are plotted in the upper panel with two P-values (solid diamonds correspond to an unconditional test and open diamonds correspond to a
conditional test) on a negative log scale (left y-axis) against genomic coordinates (x-axis, hg19). Cancers from different GWAS scans (acronyms detailed in box in top
panel) that are associated within each region in the subset meta-analysis are listed (red, positively associated; green, negatively associated) from the unconditional
ASSET meta-analysis. Effect alleles are shown next to SNP identifiers. Recombination hotspots (curved lines, top panel) were inferred from three populations
from the DCEG Imputation Reference Set version 1 (red, CEU; green, ASN; blue, YRI) as the likelihood ratio statistics (right y-axis). Also shown are the gene struc-
tures for TERT,MIR4457 and CLPTM1L (middle panel), and LD heat map based on r
2
using the 1000 Genomes CEU population (lower panel). Results are shown for
the ALL analysis except the region marked by rs10069690 (top panel) and labeled with a ‘∗’ that was identified in the European ancestry-only analysis (EUR).
8Human Molecular Genetics, 2014
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Table 1. Association results for SNPs on chromosome 5p15.33 with the risk of cancer
SNP Gene Region Position Unconditional OR (95% CI) Unconditional
P-value
Significant phenotype clusters Conditional OR (95% CI) Conditional
P-valuePositively
associated
Negatively
associated
Positively
associated
Negatively
associated
Positively
associated
Negatively
associated
ALL
rs7726159 TERT 1 1282319 1.47 (1.38– 1.56) 0.85 (0.80– 0.91) 2.10 ×10
239
AsianLung, Glioma Scan TGCT NCI, Pegasus,
AdvPrCa, ChinaPC
rs451360 CLPTM1L 2 1319680 1.34 (1.24– 1.45) 0.85 (0.80– 0.90) 1.90 ×10
218
PanScan, TGCT NCI EurLung, AfrAmLung,
AsianLung
1.33 (1.23– 1.44) 0.86 (0.81– 0.92) 7.06 ×10
216
rs2853677 TERT 3 1287194 1.22 (1.13– 1.31) 0.73 (0.70– 0.77) 3.30 ×10
236
TGCT NCI, PanScan,
ChinaPC
AsianLung
a
,
Glioma Scan,
AfrAmLung
1.11 (0.94– 1.30) 0.80 (0.74– 0.86) 2.36 ×10
28
rs2736098 TERT 4 1294086 1.15 (1.10– 1.21) 0.81 (0.74– 0.89) 3.87 ×10
212
AfrAmLung, Pegasus,
EurLung
a
, Bladder NCI
PanScan, TGCT NCI
a
1.18 (1.10– 1.25) 0.94 (0.67– 1.31) 5.19 ×10
26
rs13172201 TERT 5 1271661 1.06 (0.80– 1.41) 0.84 (0.73– 0.96) 5.00 ×10
22
EurLung, Pegasus
a
,
PanScan, AfrAmLung
a
TGCT NCI, Glioma Scan 1.13 (1.03 –1.23) 0.81 (0.70– 0.92) 1.31 ×10
24
EUR
rs4449583 TERT 1 1284135 1.50 (1.35– 1.68) 0.89 (0.83– 0.94) 1.02 ×10
215
Glioma Scan TGCT NCI, Pegasus,
AdvPrCa, PanScan
rs13170453 CLPTM1L 2 1317481 1.34 (1.24– 1.45) 0.87 (0.80– 0.95) 6.69 ×10
215
PanScan, TGCT NCI EurLung 1.33 (1.22–1.44) 0.86 (0.80 –0.93) 6.67 ×10
214
rs10069690 TERT 6 1279790 1.48 (1.31– 1.67) 0.87 (0.83– 0.92) 7.49 ×10
215
Glioma Scan
a
AdvPrCa, TGCT NCI
a
,
PanScan
a
, Bladder
NCI, Pegasus
a
NA 0.77 (0.69–0.85) 5.35 ×10
27
rs13172201 TERT 5 1271661 1.07 (0.88– 1.29) 0.84 (0.73– 0.96) 4.08 ×10
22
EurLung, Pegasus
a
,
PanScan
TGCT NCI, Glioma Scan 1.13 (1.04 –1.22) 0.82 (0.75– 0.90) 2.04 ×10
26
rs2736098 TERT 4 1294086 1.14 (1.08– 1.20) 0.81 (0.74– 0.89) 5.73 ×10
210
Pegasus, EurLung
a
, Bladder
NCI
a
PanScan, TGCT NCI 1.23 (1.11 –1.35) 0.88 (0.75 –1.02) 6.31 ×10
25
The results from the imputation and subset-based ASSET meta-analysis is shown for the ‘ALL’ scans that include 11 GWAS scans performed in subjects of European, Asian and African American ancestry; and for the ‘EUR’ scans that include
eight scans performed in subjects of European ancestry. Scan acronyms are detailed in Materials and Methods. Listed are SNPs that mark each of the regions identified, gene, genomic location, unconditional and conditional P-values and
GWAS scans that were positively or negatively associated with the minor allele for each SNP/region. Note that different highly correlated SNPs may mark the same region in the ‘ALL’ vs. the ‘EUR’ analysis(Regions 1 and 2). NA indicates
that no scan was associated with a particular region.
a
Cancer sites that were no longer significant in the conditional analysis.
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Figure 2. (A–F) Forest plots for individual risk loci on chr5p15.33 for the unconditional ASSET meta-analysis. For each cancer/GWAS scan, OR and 95% CI were
listed and plotted along each line as per the unconditional association analysis. A vertical line of OR ¼1 indicates the null. Two summary lines list ORs for the posi-
tively or negatively associated subsets as estimated by the ASSET program. (A) rs7726159, (B) rs451360, (C) rs2853677, (D) rs2736098, (E) rs13172201 and (F)
rs10069690 in the analysis of European-ancestry studies only. Forest plots for the conditional analyses are shown in Supplementary Material, Figure S1A– E.
10 Human Molecular Genetics, 2014
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r
2
.0.7 were located within 500 kb of this SNP: rs2853669,
rs2736108 and rs2736107, all in the promoter of TERT,
from 200 to 2700 bp upstream of the transcriptional start
site. This region (rs2736098-T) was positively associated with
lung cancer (Eur Lung and AA Lung), prostate cancer
(Pegasus) and bladder cancer (Bladder NCI) (P¼2.58 ×
10
28
,OR
Combined
¼1.15; 95% CI ¼1.10 –1.21), and negative-
ly associated with testicular cancer (TGCT NCI) and pancreatic
cancer (PanScan) (P¼4.89 ×10
26
,OR
Combined
¼0.81; 95%
CI ¼0.74–0.89) (Fig. 2D). The effect allele frequencies dis-
played a wide range across the three continental populations in
1000G, interestingly with the lowest frequency in the most
ancient population, 0.06–0.08 (AFR), whereas the other two
populations were comparably high: 0.23 – 0.29 (EUR) and
0.22–0.33 (ASN) (Supplementary Material, Table S2).
An additional suggestive region (Region 5) marked by
rs13172201 (P¼0.05; P
Conditional
¼1.31 ×10
24
) was deter-
mined by our sequential conditional analyses (Fig. 1, Table 1),
unmasked mainly due to conditioning on rs7726159 (Region 1).
The risk alleles for rs13172201 and rs7726159 were negatively
correlated (r¼20.27, based on 1000G CEU data) and, in an ex-
ploratory analysis of rs13172201 in the Eur Lung scan, this SNP
appeared to have a stronger association in rs7726159 CC carriers
(P¼7.0 ×10
24
,OR ¼1.21 95% CI ¼1.08 –1.35) when com-
pared with rs7726159 AC/AA carriers (P¼0.10, OR ¼1.12
95% CI ¼0.98 – 1.27).
Region 5 (rs13172201-C) was positively associated with lung
cancer (Eur Lung and AA Lung), prostate cancer (Pegasus) and
pancreatic cancer (PanScan) and negatively associated with tes-
ticular cancer (TGCT NCI) and glioma (Glioma scan) (Fig. 2E).
The effect allele for rs13172201, the sentinel SNP in Region 5,
was the minor allele in European (0.26 in EUR) and African
(0.39 in AFR) populations, while it has become the major
allele in Asians (0.85 in ASN).
In an analysis restricted to studies of European ancestry (EUR
scans), we noted strong associations for Regions 1, 2, 4 and 5
(Table 1) but not Region 3 (marked by rs2853677). The condi-
tional P-value for Region 5, suggestive in the analysis based
on all ethnic groups, improved in this subset and surpassed the
threshold of 1.3 ×10
25
(rs13172201: P¼0.041; P
Conditional
¼
2.04 ×10
26
). An additional region, Region 6, marked by
rs10069690 (P¼7.49 ×10
215
;P
Conditional
¼5.35 ×10
27
)in
intron 4 of TERT was identified in the European ancestry-only
analysis (Fig. 1, Table 1). The significance for this region did
not reach our Bonferroni-corrected P-value threshold in the ana-
lysis of all studies (P¼5.4 ×10
24
after conditioning on
rs7726159, rs451360, rs2853677 and rs2736098). As Regions
3 and 6 were located between the same two recombination hot-
spots (Fig. 1), we assessed correlation in 1000G CEU subjects
and noted virtually no LD (rs10069690, rs2853677, r
2
¼
0.0052), thus supporting the notion that they are independent
signals. Low LD existed for these two SNPs in the 1000G YRI
(r
2
¼0.098) and CHB/JPT (r
2
¼0.048) populations (Supple-
mentary Material, Table S3). Region 6 (rs10069690-T) was
positively associated with glioma (Glioma scan) (P¼4.07 ×
10
210
,OR
Combined
¼1.48; 95% CI ¼1.31 –1.67) and negative-
ly associated with testicular (TGCT NCI), prostate (Pegasus and
AdvPrCa), bladder (Bladder NCI) and pancreatic cancer
(PanScan) (P¼4.95 ×10
27
,OR
Combined
¼0.87; 95% CI ¼
0.83–0.92) (Fig. 2F). Highly correlated SNPs (r
2
.0.7) were
not observed within 500 kb of rs10069690. Notably, the
P-value for rs10069690 in the Advanced Prostate cancer scan
improved from 1.64 ×10
25
to 2.03 ×10
210
after conditioning
on Region 1. The correlation between rs10069690 and
rs7726159 (Region 1) is r
2
¼0.13 in the 1000G CEU, r
2
¼
0.30 in YRI and r
2
¼0.42 in CHB/JPT populations (Supplemen-
tary Material, Table S3). SNP rs10069690 was nominally
significant in the other two prostate cancer scans with uncondi-
tional P-values of 0.003 (Pegasus) and 0.02 (CGEMS PrCa)
but was not significant after conditioning on the first region in
these scans (P¼0.36 in Pegasus, P¼0.078 in CGEMS PrCa).
For the six signals noted, Regions 1, 3 and 6 are flanked by two
recombination hotspots that separate them from Region 5 on the
telomeric side and from Region 4 on the centromeric side. Re-
combination hotspots also separate Regions 2 and 4 (Fig. 1).
The LD between SNPs in loci 1, 3 and 6 was low to moderate
(r
2
¼0.0052, 0.131 and 0.449 in 1000G CEU, r
2
¼0.0981,
0.298 and 0.0765 in YRI and r
2
¼0.0484, 0.415 and 0.341 in
CHB/JPT); however, the conditional analyses supported the
presence of three signals bounded by strong recombination hot-
spots on either side. Region 5 is the most telomeric one and sepa-
rated from the rest by a strong recombination hotspot.
Supplementary Material, Table S1 shows P-values for the six
regions along each step of the sequential conditional analysis
to reflect the change in significance in the analysis.
We also assessed the associations for each of the regions in the
‘Tier-II studies’ comprising nine GWAS datasets across eight
cancers, including 11 385 cases and 18 322 controls. None of
the regions showed significant association (data not shown).
In addition to characterizing independent signals in the
TERT-CLPTM1L region, we have fine-mapped previously
reported signals. For pancreatic cancer, the reported GWAS
SNP rs401681 had a P-value of 3.7 ×10
27
and an OR of 1.19
(12). After imputation, an improved P-value was seen for
rs451360 (marking Region 2) (P¼2.0 ×10
210
;OR¼1.29).
After conditioning on rs451360, the P-value for rs401681 was
no longer significant (P¼0.1). The LD between these two
SNPs is moderate (r
2
¼0.35). For glioma, the GWAS SNP
rs2736100 had a P-value of 8.49 ×10
29
and OR of 1.08 in the
Glioma scan (27). The best imputed SNP rs449583 (r
2
¼1
with rs7726159, marking Region 1) showed a much improved
P-value of 4.1 ×10
214
with an OR of 1.50, and the P-value of
rs2736100 was no longer significant after conditioning on
rs449583 (P¼0.64). The LD between these two SNPs was mod-
erate (r
2
¼0.39).
Bioinformatic analyses using public data bases (ENCODE
and TCGA) were performed to investigate the possible function
of SNPs that mark each of the six regions as regulators of expres-
sion of TERT,orCLPTM1L, as well as other genes. Based on
ENCODE data, the strongest evidence for putative regulatory
functions was seen for SNPs in Regions 1 (rs7725218 and
rs4975538), 2 (rs36115365 and rs380145), 4 (rs2736108 and
rs2853669) and 5 (rs13172201) with evidence of an open chro-
matin conformation, regulatory histone modification marks
and transcription factor binding in multiple cell types such as
prostate, pancreas, breast, lung and brain (Supplementary Mater-
ial, Table S2).
We next examined the TCGA datasets for expression (eQTL)
and methylation (meQTL) quantitative trait loci for lung adeno-
carcinoma (LUAD), prostate adenocarcinoma (PRAD) and
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glioblastoma multiforme (GBM). We did not observe signifi-
cant eQTLs (P.0.41, data not shown) but noted multiple
meQTLs in LUAD and PRAD tumor samples (Supplementary
Material, Tables S5 and S6). Methylation at a subset of CpG
probes with meQTLs correlated with expression of TERT
and/or CLPTM1L, including two for Region 4 in TCGA
LUAD samples (cg26209169:
b
¼20.47, P¼1.18 ×10
25
;
cg11624060:
b
¼20.36, P¼0.001). These CpGs are located
1800 bp downstream of CLPTM1L (227 bp apart), overlap
with key transcription factor binding sites (e.g. TCF3, TCF4,
HNF3A, MAX, RUNX3/AML2, ATF-2 and USF1/USF2) and
active histone modification marks from ENCODE, and are nega-
tively correlated with expression of TERT and CLPTM1L (Sup-
plementary Material, Table S5 and Fig. S2). Replication was
seen in normal lung samples (cg26209169 and Region 4,
b
¼20.650, P¼5.17 ×10
25
; cg11624060 and Region 4,
b
¼20.493, P¼0.0027) from EAGLE (28). The most signifi-
cant meQTLs in TCGA PRAD samples were seen for Region 1
(cg03935379:
b
¼21.06, P¼8.47 ×10
215
; cg06531176:
b
¼21.18, P¼2.61 ×10
215
). These replicated in EAGLE
(P¼5.93 ×10
28
and P¼0.002, respectively), did not correl-
ate with expression of TERT or CLPTM1L, and were both
located within exon 3 of TERT (Supplementary Material,
Table S6).
Analysis of TCGA data also revealed increased expression of
TERT and CLPTM1L in tumors compared with normal tissues
for lung and prostate cancer (on average 1.29- to 2.02-fold
change for paired samples). Copy number differences were
more evident in lung tumors (average number of copies was
2.02 in normal and 2.54 in tumors for 51 paired samples, P¼
1.10 ×10
27
) (Supplementary Material, Fig. S3).
DISCUSSION
Chr5p15.33 harbors a unique cancer susceptibility region that
contains at least two plausible candidate genes: TERT and
CLTPM1L. Through a subset-based meta-analysis of GWAS
data drawn from six different cancers from three continental
populations, we have characterized up to six independent,
common, susceptibility alleles, all with evidence of both
risk-enhancing and protective effects, differing by cancer type.
TERT encodes the catalytic subunit of the telomerase reverse
transcriptase, which, in combination with an RNA template
(TERC), adds nucleotide repeats to chromosome ends (29).
Although telomerase is active in germ cells and in early develop-
ment, it remains repressed in most adult tissues. Telomeres
shorten with each cell division and when they reach a critically
short length, cellular senescence or apoptosis is triggered.
Cancer cells can continue to divide despite critically short telo-
meres, by upregulating telomerase or by alternative lengthening
of telomeres (16,30,31). While studies investigating the relation-
ship between surrogate tissue (i.e. buccal or blood cell DNA)
telomere length and cancer risk have been contradictory,
larger prospective studies have not reported an association for
risk but only survivorship (32 –35). Heritability estimates of
telomere length in twin studies suggest a significant genetic con-
tribution, between 36 and 78% (36,37). GWAS SNPs on 5p15.33
have been associated with telomere length implying that TERT
may indeed be the gene targeted by at least some risk variants
in this region (38 –40). In addition, germline TERT promoter
mutations have been identified in familial melanoma as well as
somatic mutations in multiple cancers (41,42).
The most commonly reported SNP in the TERT gene,
rs2736100, was first reported in several GWAS: glioma (3,43),
lung cancer in European and Asians (7,44 –46) and testicular
cancer (14). We have fine-mapped this locus (Region 1) to a
set of five correlated SNPs in the second and third intron of
TERT (marked by rs7726159). In addition to the cancers listed
above, we noted novel contributions to this locus by prostate
and pancreatic cancer. Fine-mapping efforts in lung (47) and
ovarian cancer (48) have reported the same SNP. Region 3
(rs2853677), located in the first intron of TERT, has been asso-
ciated with glioma in Chinese subjects (49) and lung cancer in
Japanese subjects (50), in agreement with the strong contribution
to this region seen in our analysis by scans performed in indivi-
duals of Asian ancestry. In addition to lung cancer and glioma,
we noted novel associations for Region 3 with pancreatic and tes-
ticular cancer. Region 4 was marked by a synonymous SNP
(rs2736098) located in the second exon of TERT, with three add-
itional highly correlated SNPs in the promoter region. This
region has been reported via fine-mapping in lung, bladder, pros-
tate, ovarian and breast cancer, and shown to influence TERT
promoter activity (8). Novel contributions to Region 4 were
noted for pancreatic and testicular cancer.
In our analysis, we uncovered a new susceptibility locus,
Region 5 (marked by rs13172201, Fig. 1), which surpassed the
Bonferroni threshold in European studies. We found evidence
for a negative correlation between this SNP and rs7726159
(Region 1), indicating a possible interaction. This locus is not
significant at a GWAS threshold and requires confirmation in
independent samples. Region 6 (marked by rs10069690) has
previously been associated with estrogen- and progesterone
receptor-negative breast cancer in populations of European
and African ancestry (2,51); our analysis adds five cancers to
this list: glioma, prostate, testicular germ cell, pancreas and
urinary bladder.
The gene adjacent to TERT, namely CLPTM1L, encodes a
protein that is overexpressed in lung and pancreatic cancer, pro-
motes growth and survival, and is required for KRAS driven lung
cancer, indicating that it is a plausible candidate gene in this
region (17–21). The locus in CLPTM1L (Region 2) has previ-
ously been associated with risk of cancer in multiple GWAS,
marked by rs401681 or rs402710 in pancreatic, lung and
bladder cancer as well as in melanoma (1,4,5,12,52). Our subset-
based approach has fine-mapped this signal to a set of seven cor-
related SNPs that span the entire length of CLPTM1L.
Two recent papers from the Collaborative Oncology
Gene-Environment Study (COGs) fine-mapped 5p15.33 in pros-
tate, breast and ovarian cancer and identified four of the six loci
noted in the current study (53,54). In prostate cancer, COGs iden-
tified three regions that corresponded to our Region 1 (COGs
Region 1, rs7725218), Region 3 (COGs Region 2, rs2853676,
r
2
¼0.32 with rs2853677) and Region 4 (COGs Region 3,
rs2853669) (54). Interestingly, COGs reported protective
alleles in Region 1 associated with increased TERT expression
in benign prostate tissue samples. The fourth COGs prostate
cancer locus, marked by rs13190087, was not significant in our
study (P¼0.089), possibly due to a more specific effect for
prostate cancer for this locus where our study had less power.
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In breast and ovarian cancer, COGs identifiedthree regions corre-
sponding toour Region 1 (COGs Region 2, rs7705526, associated
with risk ofovarian cancer with lowmalignant potential,telomere
length and promoter activity), Region 4 (COGs Region 1,
rs2736108, associated with risk of ER-negative and BRCA1 mu-
tation carrier breast cancer, telomere length and altered promoter
activity) and Region 6 (COGs Region 3, rs10069690, associated
with risk of ER-negative breast cancer, breast cancer in BRCA1
carriers and invasive ovarian cancer) (53). Regions 2 (in
CLPTM1L)and5(inTERT) were not observed in the COGs
reports, perhaps due to the choice of SNPs by COGs for fine-
mapping as well as the more comprehensive reference set for
1000 Genomes used to conduct our imputation, or because of
cancer-specific effects for these loci.
It is becoming increasingly clear that DNA methylation is
under genetic control. Regions of variable methylation exist
across tissues and individuals, tend to be located in intergenic
regions, overlapping known regulatory elements. Notably,
these are enriched for disease-associated SNPs (28,55,56). Ana-
lysis of TCGA data, while not uncovering significant eQTLs,
indicated that DNA methylation could play a role in the under-
lying biology at 5p15.33. Methylation in a small region down-
stream of CLPTM1L, with features supporting an active
regulatory function, was consistent with lower methylation
levels in carriers of risk alleles for lung cancer (Region 4) and
higher expression of TERT and CLPTM1L. Increased expression
of both genes is consistent with a pro-tumorigenic role in lung
cancer (19,21,31). For prostate cancer, the most notable
meQTLs were located within exon 3 of TERT with increased
rates of methylation for carriers of risk alleles in Regions 1 and
6. Although gene-body methylation has been observed to posi-
tively correlate with gene expression (57), we did not see evi-
dence to support this for this particular set of CpGs. As a large
fraction of meQTLs does not overlap with eQTLs (55), they
may influence molecular phenotypes other than gene expression
such as alternative promoter usage, splicing and even mutations
(58–60). It is intriguing that methylation QTLs observed in
TCGA data differ to some degree between lung and prostate
cancer, and that none were observed in glioblastoma. This indi-
cates that the TERT-CLPTM1L region may harbor multiple ele-
ments that have the capacity to influence molecular phenotypes
that in turn impact cancer development. However, only a subset
of these elements may be active in each organ, thus leading to dif-
ferent mechanistic avenues for risk modulation in different
tissues. It is possible that the interplay between risk variants,
multiple biological mechanisms and attributed genes, in addition
to environmental and lifestyle factors that differentially influ-
ence various cancers may eventually come to explain how the
same alleles at this complex locus can mediate opposing
cancer risk in different organs.
In summary, we report up to six independent loci on
chr5p15.33, each influencing the risk of multiple cancers. We
observed pleiotropy for common susceptibility alleles in this
region, defined as the phenomenon wherein a single genetic
locus affects multiple phenotypes (61). These alleles could influ-
ence multiple cancers distinctly, perhaps in response to environ-
mental factors or in interactions with other genes. Our cardinal
observations underscore the complexity of the alleles and
suggest the importance of tissue-specific factors that contribute
to cancer susceptibility. Further laboratory analysis is needed to
validate our findings using TCGA data, and investigate the
optimal functional variants in each of the six independent loci
in order to provide a clearer understanding of each of the loci
in this multi-cancer susceptibility region.
MATERIALS AND METHODS
Study participants
Participants were drawn from a total of 20 previous GWAS scans
of 13 distinct cancer types: bladder, breast, endometrial, esopha-
geal squamous, gastric, glioma, lung, osteosarcoma, ovarian,
pancreatic, prostate, renal cancer and testicular germ cell
tumors. We first assessed a set of 11 GWAS representing six dis-
tinct cancers (‘Tier-I studies’) in which 5p15.33 had previously
been implicated (NHGRI Catalog of Published GWAS studies:
http://www.genome.gov/gwastudies/). The GWAS scans and
their acronyms were: Asian lung cancer scan (AsianLung),
European lung cancer scan (EurLung), African American lung
(AA Lung), PanScan, China pancreatic cancer scan
(ChinaPC), Testicular germ cell tumor (TGCT NCI) scan,
glioma scan, Bladder NCI scan, Pegasus prostate cancer scan
(Pegasus), CGEMS prostate cancer scan (CGEMS PrCa) and
Advanced prostate cancer scan (Adv PrCa) (see case and
control counts in Supplementary Material, Tables S4A – D). In
a second analysis, we separately assessed a set of nine GWAS
scans representing eight cancers (‘Tier-II studies’) in which
5p15.33 had not been previously reported in the literature
(NHGRI Catalog of Published GWAS studies: http://www.
genome.gov/gwastudies/). These studies were: Asian esopha-
geal scan (Asian EsoCa), Asian gastric cancer scan (Asian
GastCa), CGEMS Breast cancer scan (CGEMS Breast), Endo-
metrial cancer scan (EndomCa), ER negative breast cancer
scan (ERneg BPC3 BrCa), Ghana prostate cancer scan (Ghana
PrCa), Osteosarcoma scan (OS), Ovarian cancer scan (OvCa)
and Renal cancer scan (Renal US) (see case and control counts
in Supplementary Material, Tables S4E – H). Studies were con-
ducted in individuals of European background (EUR scans)
but we did include studies in populations of Asian ancestry
(i.e., esophageal squamous, gastric, non-smoking lung and pan-
creatic cancers) and African ancestry (i.e. lung and prostate
cancer) (ALL scans). Study characteristics, genotyping and
quality control have been previously published for all studies
listed by cancer type and GWAS scan acronym: bladder
cancer/Bladder NCI (1,62), breast cancer/CGEMS BrCa (63),
breast cancer/ERneg BPC3 BrCa (64), endometrial cancer/
EnCa (65), gastric cancer and esophageal squamous cell carcin-
oma/Asian UpperGI (66), glioma/Glioma scan (27), lung cancer
in Europeans/EurLung (7), lung cancer in African Americans/
AALung (67), lung cancer in non-smoking women from Asia/
AsianLung (68,69), osteosarcoma/OS (70), ovarian cancer/
OvCa (71), pancreatic cancer/PanScan (12,72), pancreatic
cancer in Asians/ChinaPC (73), prostate cancer/Pegasus (un-
published data), prostate cancer/CGEMS PrCa (74), advanced
prostate cancer/AdvPrCa (75), prostate cancer in Africans/Gha-
naPrCa (unpublished data), renal cancer/Renal US (76) and tes-
ticular germ cell tumors/TGCT NCI (77).
Each participating study obtained informed consent from
study participants and approval from its Institutional Review
Board (IRB) including IRB certification permitting data
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sharing in accordance with the National Institutes of Health
(NIH) Policy for Sharing of Data Obtained in NIH Supported
or Conducted GWAS.
Genotyping
Arrays used for scanning included the Illumina HumanHap
series (317 +240S, 550, 610 K, 660 W and 1 M), as well as
the Illumina Omni series (OmniExpress, Omni1M, Omni2.5
and Omni5M). The majority of the studies were genotyped at
the Cancer Genomics Research Laboratory (formerly Core
Genotyping Facility) of the National Cancer Institute (NCI) of
the NIH. The ChinaPC GWAS (Affymetrix 6.0) was genotyped
at CapitalBio in Beijing, China. This necessitated imputation
before the cross-cancer subset-based meta-analysis. We used a
combination of public resources, 1000 Genomes (1000G) (25)
and DCEG (26) reference datasets, to impute existing GWAS
datasets (78) using IMPUTE2 (79).
In addition to the standard QC procedures previously applied
in the primary GWAS publications, we further filtered SNPs as
follows: (i) completion rate per locus ,90%, (ii) MAF ,
0.01, (iii) Hardy– Weinberg proportion P-value ,1×10
26
,
(iv) exclusion of A/T or G/C SNPs.
Lift over the genomic coordinates to NCBI genome build 37
or hg19
Because the March 2012 release of the 1000 Genomes Project
data is based on NCBI genome build 37 (hg19), we utilized the
LiftOver tool (http://hgdownload.cse.ucsc.edu/) to convert
genomic coordinates for scan data from build 36 to build 37.
The tool re-maps only coordinates, but not SNP identifiers. We
prepared the inference.bed file and then performed the lift over
as follows:
/tools/liftover/liftOver inference.bed /tools/liftover/hg18
ToHg19.over.chain.gz output.bed unlifted.bed
A small number of SNPs that failed LiftOver, mostly because
they could not be unambiguously mapped to the genome by
NCBI, were dropped from each imputation inference set.
Strand alignment with 1000 Genomes reference data set
Since A/T or G/C SNPs were excluded, strand alignment for the
scan data required checking allele matches between the infer-
ence set and reference set locus by locus. If they did not match,
alleles were complemented and checked again for matching.
SNPs that failed both approaches were excluded from the infer-
ence data. Locus identifiers were normalized to those used in the
1000 Genomes data based on genomic coordinates, although the
IMPUTE2 program uses only the chromosome/location to align
each locus overlapping between the imputation inference and
reference set.
Conversion of genotype files into WTCCC format
After LiftOver to genome build 37 and ensuring that alleles were
reported on the forward strand, we converted the genotype data
into IMPUTE2 format using GLU. We split the genotype file into
one per chromosome and sorted SNPs in order of genomic loca-
tion using the GLU transform module.
Imputation of a 2Mb window on chr5p15.33
We used both the 1000G data (March 2012 release) (25) and the
DCEG imputation reference set (26) as reference datasets to
improve overall imputation accuracy. The IMPUTE2 program
(79) was used to impute a 2 Mb window on chr5p15.33 from
250 000 to 2 250 000 (hg19) with a 250 kb buffer on either
side as well as other recommended default settings. For the asso-
ciation analysis, we focused on a smaller region from chr5:
1 250 000–1 450 000 delineated by recombination hotspots
(discussed below).
Post-imputation filtering and association analysis
We excluded imputed loci with INFO ,0.5 from subsequent
analyses. SNPTEST (79) was used for the association analysis
with covariate adjustment and score test of the log additive
genetic effect. The same adjustments as used originally in each
individual scan were used. Note that the per SNP imputation ac-
curacy score (IMPUTE’s INFO field) is calculated by both
IMPUTE2 and SNPTEST. The two INFO metrics calculated
during imputation by IMPUTE2 and during association testing
by SNPTEST are strongly correlated, especially when the addi-
tive model is fitted (78). We chose the INFO metric calculated by
SNPTEST for post-imputation SNP filtering.
Subset and conditional analyses
Association outputs from SNPTEST were reformatted and sub-
sequently analyzed using the ASSET program, an R package
(http://www.bioconductor.org/packages/devel/bioc/html/ASSET.
html; https://r-forge.r-project.org/scm/viewvc.php/∗checkout∗/p
kg/inst/doc/vignette.Rnw?root=asset) for subset-based meta-
analyses (24). ASSET is a suite of statistical tools specifically
designed to be powerful for pooling association signals across mul-
tiple studies when true effects may exist only in a subset of the
studies and could be in opposite directions across studies. The
method explores all possible subset (or a restricted set if user spe-
cifies so) of studies and evaluates fixed-effect meta-analysis-type
test-statistics for each subset. The final test-statistics is obtained
by maximizing the subset-specific test-statistics over all possible
subsets and then evaluating its significant after efficient adjustment
for multiple testing, taking into account the correlation between
test-statistics across different subsets due to overlapping subjects.
The method not only returns a P-value for significance for the
overall evidence of association of an SNP across studies, but also
outputs the ‘best subset’ containing the studies that contributed to
the overall association signal. For detection of SNP association
signals with effects in opposite directions, ASSET allows subset
search separately for positively and negatively associated studies
and then combines association signals from two directions using
a chi-square test-statistics. The method can take into account cor-
relation due to overlapping subject across studies (e.g. share con-
trols). More details about these and other features of the method
can be found elsewhere [22].
For our current study, the matrices of the overlapping counts
for cases–controls across datasets, which are utilized by
ASSET to adjust for possible correlation across studies, were
constructed and passed into the ASSET program (Supplemen-
tary Tables S4A– H). We used a two-sided test P-value, which
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can combine association signals in opposite directions, to assess
the overall significance of whether an SNP was associated with
the cancers under study. For detection of independent suscepti-
bility SNPs, we performed sequential conditional analysis in
which in each step the ASSET analysis is repeated by condition-
ing on SNPs that have been detected to be most significant in pre-
vious steps. The process was repeated until the P-value for the
most significant SNP for a step remained ,1.3 ×10
25
, a con-
servative threshold that corresponds to Bonferroni adjustment
for the 1924 SNPs used in the analysis for an alpha level of
0.05 and the two analyses performed (for the ALL vs. the EUR
scans).
In the primary analysis, we included all GWAS scans in which
one or more susceptibility alleles on 5p15.33 had been previous-
ly noted at genome-wide significant threshold (‘Tier-I studies’).
We further required a nominal signal in our data (P,0.05). This
yielded 11 GWAS across six distinct cancer sites and includes
34 248 cases and 45 036 cancer-free controls (Supplementary
Material, Tables S4A – D). In a secondary analysis, we assessed
the associations for each of the six regions in scans in which
5p15.33 had not been previously reported in the literature (http://
www.genome.gov/gwastudies/), or did not show a nominal
P-value in the GWAS datasets used in the current study
(‘Tier-II studies’). This yielded nine GWAS datasets across
eight cancers, including a total of 11 385 cases and 18 322 con-
trols (Supplementary Material, Tables S4E – H).
Recombination hotspot estimation
Recombination hotspots were identified in the region of 5p15.33
harboring TERT and CLPTM1L (1 264 068–1 360 487) using
SequenceLDhot (80), a program that uses the approximate mar-
ginal likelihood method (81) and calculates likelihood ratio sta-
tistics at a set of possible hotspots. We tested three sample sets
from East Asians (n¼88), CEU (n¼116) and YRI (n¼59)
from the DCEG Imputation Reference Set. The PHASE v2.1
program was used to calculate background recombination
rates (82,83).
Validation of imputation accuracy
Imputation accuracy was assessed bydirect TaqMan genotyping.
TaqMan genotyping assays (ABI, Foster City, CA, USA) were
optimized for six SNPs (rs7726159, rs451360, rs2853677,
rs2736098, rs10069690 and rs13172201) in the independent
regions. In an analysis of 2327 samples from the Glioma brain
tumor study (Glioma BTS, 330 samples) (27), testicular germ
cell tumor (TGCT STEED study, 865 samples) (77) and
Pegasus (PLCO, 1132 samples) (unpublished data), the allelic
R
2
(84) measured between imputed and assayed genotypes
were 0.88, 0.98, 0.86, 0.85, 0.81 and 0.61 for the six SNPs
listed in the same order as above.
Bioinformatic analysis of functional potential
HaploReg v2 (http://www.broadinstitute.org/mammals/haploreg/
haploreg.php) was used to annotate functional and regulatory po-
tential of highly significant and highly correlated SNPs that mark
each of the regions identified (using ENCODE data) (85). Regulo-
meDB (http://regulome.stanford.edu/) was used to assess and
score regulatory potential of SNPs in each locus (86). eQTL
effects were assessed using the Multiple Tissue Human Expres-
sion Resource database (http://www.sanger.ac.uk/resources/
software/genevar/) but significant findings at a P,1×10
23
threshold were not noted (data not shown) (87). Predicted
effects of SNPs on splicing were assessed using NetGene2 (http://
www.cbs.dtu.dk/services/NetGene2/) (88) but no effect were seen
for any of the SNPs in the six regions (data not shown).
We carried out eQTL and methylation quantitative trait locus
(meQTL) analyses to assess potential functional consequences
of SNPs in the six regions identified in normal and tumor
derived tissue samples from TCGA: LUAD (52/403 normal/
tumor samples for eQTL analysis: 26/354 normal/tumor
samples for meQTL analysis), PRAD (31/133 normal/tumor
for eQTL; 39/158 normal/tumor for meQTL) and GBM (109
tumor for eQTL; 83 tumor for meQTL; normal GBM samples
were not available). Transcriptome (Illumina HiSeq 2000,
level 3), methylation (Illumina Infinium Human DNA Methyla-
tion 450 platform, level 3), genotype data (Affymetrix Genome-
Wide Human SNP Array 6.0 platform, level 2) and phenotypes
were downloaded from the TCGA data portal (https://tcga-
data.nci.nih.gov/tcga/). Methylation probes located on X/Y
chromosomes, annotated in repetitive genomic regions (GEO
GPL16304), with SNPs (Illumina dbSNP137.snpupdate.ta-
ble.v2) with MAF .1% in the respective TCGA samples,
with missing rate .5%, as well as 65 quality control probes on
the 450 K array. We excluded transcripts on X/Y chromosomes
and those with missing rate .5%. A principle component ana-
lysis was conducted on a genome-wide level in R using gene ex-
pression and methylation data (separately in normal and tumor
tissues, and after excluding transcripts with variance ,10
28
and methylation probes with variance ,0.001). Genotype im-
putation was performed as described above for the 2 Mb
window centered on TERT and CLPTM1L. For eQTL analysis,
normalized transcript counts for CLPTM1L and TERT were
normal quantile transformed and regressed against the imputed
dosage of minor allele for each risk locus (six loci, 19 SNPs).
The regression model included age, gender (not for PRAD),
stage (only for tumor samples), copy number, top five principle
components (PCs) of imputed genotype dosage and top five PCs
of transcript counts to account for possible measured or unmeas-
ured confounders and to increase detection power. The meQTL
analysis was conducted in a similar manner in TCGA LUAD,
PRAD GBM samples; beta-values of methylation at 169 CpG
probes in the region encompassing TERT and CLPTM1L were
normal quantile transformed and regressed as described above
with the exception of inclusion of the top five PCs of methy lation
instead of expression values. We report the estimate of regres-
sion coefficient of imputed dosage, its standard error and
P-values, adjusted by the Benjamini – Hochberg procedure for
controlling false discovery rate (89). Spearman’s rank-order cor-
relation was calculated to assess the relationship between the
methylation and gene expression for TCGA LUAD (n¼486),
PRAD (n¼186) and GBM (n¼126) tumor samples.
P-values were adjusted by the Benjamini – Hochberg procedure
as described above. For the purpose of visualizing meQTLs, the
most likely genotype was selected from the imputed genotype
dosages.
Methylation QTLs were assessed in EAGLE normal lung
tissue samples (n¼215) as previously described with the
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addition of imputation of the 19 SNPs in the 6 regions under
study here (28).
AUTHORS’ CONTRIBUTIONS
Conceived and designed the experiments: Z.W., N.C.,
L.T.A. Performed the experiments: Z.W., B.Z., M.Z., H.P., J.J.,
C.C.C., J.N.S., J.W.H., A.H., L.B., A.I., C.H., L.T.A. Analyzed
the data: Z.W., B.Z., M.Z., H.P., J.J., C.C.C., J.N.S., J.W.H.,
M.Y., N.C., L.T.A. Contributed reagents/materials/analysis
tools: all authors. Wrote the paper: ZW and LTA. Contributed
to the writing of the paper: all authors.
SUPPLEMENTARY MATERIAL
Supplementary Material is available at HMG online.
ACKNOWLEDGEMENTS
The authors acknowledge the contribution of the staff of the
Cancer Genomics Research Laboratory for their invaluable
help throughout the project.
Conflict of Interest statement: None declared.
FUNDING
This work was supported by the Intramural Research Program
and by contract number HHSN261200800001E of the US
National Institutes of Health (NIH), National Cancer Institute.
The content of this publication does not necessarily reflect the
views or policies of the Department of Health and Human Ser-
vices nor does mention of trade names, commercial products
or organizations imply endorsement by the U.S. Government.
Additional funding acknowledgements are listed in Supplemen-
tary Material. The funders had no role in study design, data col-
lection and analysis, decision to publish or preparation of the
manuscript.
REFERENCES
1. Rothman, N., Garcia-Closas, M., Chatterjee, N., Malats, N., Wu, X.,
Figueroa, J.D., Real, F.X., Van Den Berg, D., Matullo, G., Baris, D. et al.
(2010) A multi-stage genome-wide association study of bladder cancer
identifies multiple susceptibility loci. Nat. Genet.,42, 978– 984.
2. Haiman, C.A., Chen, G.K., Vachon, C.M., Canzian, F., Dunning, A.,
Millikan, R.C., Wang, X., Ademuyiwa, F., Ahmed, S., Ambrosone, C.B.
et al. (2011) A common variant at the TERT-CLPTM1L locus is associated
with estrogen receptor-negative breast cancer. Nat. Genet.,43, 1210 –1214.
3. Shete, S., Hosking, F.J., Robertson, L.B., Dobbins, S.E., Sanson, M.,
Malmer, B., Simon, M., Marie, Y., Boisselier, B., Delattre, J.Y. et al. (2009)
Genome-wide association study identifies five susceptibility loci for glioma.
Nat. Genet.,41, 899– 904.
4. Wang, Y., Broderick, P., Webb, E., Wu, X., Vijayakrishnan, J., Matakidou,
A., Qureshi, M., Dong, Q., Gu, X., Chen, W.V. et al. (2008) Common
5p15.33 and 6p21.33 variants influence lung cancer risk. Nat. Genet.,40,
1407– 1409.
5. McKay, J.D., Hung, R.J., Gaborieau, V., Boffetta, P., Chabrier, A., Byrnes,
G., Zaridze, D., Mukeria, A., Szeszenia-Dabrowska, N., Lissowska, J. et al.
(2008) Lung cancer susceptibility locus at 5p15.33. Nat. Genet.,40,
1404– 1406.
6. Broderick, P., Wang, Y., Vijayakrishnan, J., Matakidou, A., Spitz, M.R.,
Eisen, T., Amos, C.I. and Houlston, R.S. (2009) Deciphering the impact of
common genetic variation on lung cancer risk: a genome-wide association
study. Cancer Res.,69, 6633– 6641.
7. Landi, M.T., Chatterjee, N., Yu, K.,Goldin, L.R., Goldstein, A.M., Rotunno,
M., Mirabello, L., Jacobs, K., Wheeler, W., Yeager, M. et al. (2009) A
genome-wide association study of lung cancer identifies a region of
chromosome 5p15 associated with risk for adenocarcinoma. Am. J. Hum.
Genet.,85, 679– 691.
8. Beesley, J., Pickett, H.A., Johnatty, S.E., Dunning, A.M., Chen, X., Li, J.,
Michailidou, K., Lu, Y., Rider, D.N., Palmieri, R.T. et al. (2011) Functional
polymorphisms in the TERT promoter are associated with risk of serous
epithelial ovarian and breast cancers. PLoS ONE,6, e24987.
9. Rafnar, T., Sulem, P., Stacey, S.N., Geller, F., Gudmundsson, J., Sigurdsson,
A., Jakobsdottir, M., Helgadottir, H., Thorlacius, S., Aben, K.K. et al. (2009)
Sequence variants at the TERT-CLPTM1L locus associate with many cancer
types. Nat. Genet.,41, 221– 227.
10. Stacey, S.N., Sulem, P., Masson, G., Gudjonsson, S.A., Thorleifsson, G.,
Jakobsdottir, M., Sigurdsson, A., Gudbjartsson, D.F., Sigurgeirsson, B.,
Benediktsdottir, K.R. et al. (2009) New common variants affecting
susceptibility to basal cell carcinoma. Nat. Genet.,41, 909 –914.
11. Yang, X., Yang, B., Li, B. and Liu, Y. (2013) Association between
TERT-CLPTM1L rs401681[C] allele and NMSC cancer risk: a
meta-analysis including 45,184 subjects. Arch. Dermatol. Res.,1, 49– 52.
12. Petersen, G.M., Amundadottir, L., Fuchs, C.S., Kraft, P.,
Stolzenberg-Solomon, R.Z., Jacobs, K.B., Arslan, A.A.,
Bueno-de-Mesquita, H.B., Gallinger, S., Gross, M. et al. (2010) A
genome-wide association study identifies pancreatic cancer susceptibility
loci on chromosomes 13q22.1, 1q32.1 and 5p15.33. Nat. Genet.,42,
224–228.
13. Kote-Jarai, Z., Olama, A.A., Giles, G.G., Severi, G., Schleutker, J.,
Weischer, M., Campa, D., Riboli, E., Key, T., Gronberg, H. et al. (2011)
Seven prostate cancer susceptibility loci identified by a multi-stage
genome-wide association study. Nat. Genet.,43, 785– 791.
14. Turnbull, C., Rapley, E.A., Seal, S., Pernet, D., Renwick, A., Hughes, D.,
Ricketts, M., Linger, R., Nsengimana, J., Deloukas, P. et al. (2010) Variants
near DMRT1, TERT and ATF7IP are associated with testicular germ cell
cancer. Nat. Genet.,42, 604– 607.
15. Mocellin, S., Verdi, D., Pooley, K.A., Landi, M.T., Egan, K.M., Baird, D.M.,
Prescott, J., De Vivo, I. and Nitti, D. (2012) Telomerase reverse transcriptase
locus polymorphisms and cancer risk: a field synopsis and meta-analysis.
J. Natl. Cancer Inst.,104, 840– 854.
16. Kim, N.W., Piatyszek, M.A., Prowse, K.R., Harley, C.B., West, M.D., Ho,
P.L., Coviello, G.M., Wright, W.E., Weinrich, S.L. and Shay, J.W. (1994)
Specific association of human telomerase activity with immortal cells and
cancer. Science,266, 2011– 2015.
17. Yamamoto, K., Okamoto, A., Isonishi, S., Ochiai, K. and Ohtake, Y. (2001)
A novel gene, CRR9, which was up-regulated in CDDP-resistant ovarian
tumor cell line, was associated with apoptosis. Biochem. Biophys. Res.
Commun.,280, 1148– 1154.
18. James, M.A., Wen, W., Wang, Y., Byers, L.A., Heymach, J.V., Coombes,
K.R., Girard, L., Minna, J. and You, M. (2012) Functional characterization of
CLPTM1L as a lung cancer risk candidate gene in the 5p15.33 locus. PLoS
ONE,7, e36116.
19. Ni, Z., Tao, K., Chen, G., Chen, Q., Tang, J., Luo, X., Yin, P. and Wang, X.
(2012) CLPTM1L is overexpressed in lung cancer and associated with
apoptosis. PLoS ONE,7, e52598.
20. Jia, J., Bosley, A.D., Thompson, A., Hoskins, J.W., Cheuk, A., Collins, I.,
Parikh, H., Xiao, Z., Ylaya, K., Dzyadyk, M. et al. (2014) CLPTM1L
promotes growth and enhances aneuploidy in pancreatic cancer cells.
Cancer Res., 74, 2785– 2795.
21. James, M.A., Vikis, H.G., Tate, E., Rymaszewski, A.L. and You, M. (2014)
CRR9/CLPTM1L Regulates Cell Survival Signaling and Is Required for Ras
Transformation and Lung Tumorigenesis. Cancer Res.,74, 1116– 1127.
22. Ballew, B.J. and Savage, S.A. (2013) Updates on the biology and
management of dyskeratosis congenita and related telomere biology
disorders. Expert Rev. Hematol.,6, 327– 337.
23. Armanios, M. and Blackburn, E.H. (2012) The telomere syndromes. Nat.
Rev. Genet.,13, 693– 704.
24. Bhattacharjee, S., Rajaraman, P., Jacobs, K.B., Wheeler, W.A., Melin, B.S.,
Hartge, P., Yeager, M., Chung, C.C., Chanock, S.J. and Chatterjee, N. (2012)
A subset-based approach improves power and interpretation for the
combined analysis of genetic association studies of heterogeneous traits.
Am. J. Hum. Genet.,90, 821–835.
16 Human Molecular Genetics, 2014
at University of Hong Kong Libraries on August 20, 2014http://hmg.oxfordjournals.org/Downloaded from
25. Genomes. (2010) A map of human genome variation from population-scale
sequencing. Nature,467, 1061– 1073.
26. Wang, Z., Jacobs, K.B., Yeager, M., Hutchinson, A., Sampson, J.,
Chatterjee, N., Albanes, D., Berndt, S.I., Chung, C.C., Diver, W.R. et al.
(2012) Improved imputation of common and uncommon SNPs with a new
reference set. Nat. Genet.,44,6–7.
27. Rajaraman, P., Melin, B.S., Wang, Z., McKean-Cowdin, R., Michaud, D.S.,
Wang, S.S., Bondy, M., Houlston, R., Jenkins, R.B., Wrensch, M. et al.
(2012) Genome-wide association study of glioma and meta-analysis. Hum.
Genet.,131, 1877– 1888.
28. Shi, J., Marconett, C.N., Duan, J., Hyland, P.L., Li, P., Wang, Z., Wheeler,
W., Zhou, B., Campan, M., Lee, D.S. et al. (2014) Characterizing the genetic
basis of methylome diversity in histologically normal human lung tissue.
Nat. Commun.,5, 3365.
29. Cheung, A.L. and Deng, W. (2008) Telomere dysfunction, genome
instability and cancer. Front. Biosci.,13, 2075– 2090.
30. Cesare, A.J. and Reddel, R.R. (2010) Alternative lengthening of telomeres:
models, mechanisms and implications. Nat. Rev. Genet.,11, 319– 330.
31. Shay, J.W. and Bacchetti, S. (1997) A survey of telomerase activity in human
cancer. Eur. J. Cancer,33, 787– 791.
32. Hou, L., Zhang, X., Gawron, A.J. and Liu, J. (2012) Surrogate tissue telomere
length and cancer risk: shorter or longer? Cancer Lett.,319, 130– 135.
33. Pooley, K.A., Sandhu, M.S., Tyrer, J., Shah, M., Driver, K.E., Luben, R.N.,
Bingham, S.A., Ponder, B.A., Pharoah, P.D., Khaw, K.T. et al. (2010)
Telomere length in prospective and retrospective cancer case-control
studies. Cancer Res.,70, 3170– 3176.
34. De Vivo, I., Prescott, J., Wong, J.Y., Kraft, P., Hankinson, S.E. and Hunter,
D.J. (2009) A prospective study of relative telomere length and
postmenopausal breast cancer risk. Cancer Epidemiol. Biomarkers Prev.,
18, 1152– 1156.
35. Weischer, M., Nordestgaard, B.G., Cawthon, R.M., Freiberg, J.J.,
Tybjaerg-Hansen, A. and Bojesen, S.E. (2013) Short telomere length, cancer
survival, and cancer risk in 47102 individuals. J. Natl. Cancer Inst.,105,
459–468.
36. Slagboom, P.E., Droog, S. and Boomsma, D.I. (1994) Genetic determination
of telomere size in humans: a twin study of three age groups. Am. J. Hum.
Genet.,55, 876– 882.
37. Andrew, T., Aviv, A., Falchi, M., Surdulescu, G.L., Gardner, J.P., Lu, X.,
Kimura, M., Kato, B.S., Valdes, A.M. and Spector, T.D. (2006) Mapping
genetic loci that determine leukocyte telomere length in a large sample of
unselected female sibling pairs. Am. J. Hum. Genet.,78, 480 –486.
38. Nan, H., Qureshi, A.A., Prescott, J., De Vivo, I. and Han, J. (2011) Genetic
variants in telomere-maintaining genes and skin cancer risk. Hum. Genet.,
129, 247– 253.
39. Melin, B.S., Nordfjall, K., Andersson, U. and Roos, G. (2012) hTERT cancer
risk genotypes are associated with telomere length. Genet. Epidemiol.,36,
368–372.
40. Hsu, C.P., Hsu, N.Y., Lee, L.W. and Ko, J.L. (2006) Ets2 binding site single
nucleotide polymorphism at the hTERT gene promoter – effect on
telomerase expression and telomere length maintenance in non-small cell
lung cancer. Eur. J. Cancer,42, 1466–1474.
41. Horn, S., Figl, A., Rachakonda, P.S., Fischer, C., Sucker, A., Gast, A., Kadel,
S., Moll, I., Nagore, E., Hemminki, K. et al. (2013) TERT promoter
mutations in familial and sporadic melanoma. Science,339, 959–961.
42. Killela, P.J., Reitman, Z.J., Jiao, Y., Bettegowda, C., Agrawal, N., Diaz,
L.A. Jr, Friedman, A.H., Friedman, H., Gallia, G.L., Giovanella, B.C. et al.
(2013) TERT promoter mutations occur frequently in gliomas and a subset of
tumors derived from cells with low rates of self-renewal. Proc. Natl. Acad.
Sci. USA,110, 6021– 6026.
43. Simon, M., Hosking, F.J., Marie, Y., Gousias, K., Boisselier, B., Carpentier,
C., Schramm, J., Mokhtari, K., Hoang-Xuan, K., Idbaih, A. et al. (2010)
Genetic risk profiles identify different molecular etiologies for glioma. Clin.
Cancer Res.,16, 5252– 5259.
44. Yang, P., Li, Y., Jiang, R., Cunningham, J.M., Zhang, F. and de Andrade, M.
(2010) A rigorous and comprehensive validation: common genetic
variations and lung cancer. Cancer Epidemiol. Biomarkers Prev.,19,
240–244.
45. Hu, Z., Wu, C., Shi, Y., Guo, H., Zhao, X., Yin, Z., Yang, L., Dai, J., Hu, L.,
Tan, W. et al. (2011) A genome-wide association study identifies two new
lung cancer susceptibility loci at 13q12.12 and 22q12.2 in Han Chinese. Nat.
Genet.,43, 792– 796.
46. Miki, D., Kubo, M., Takahashi, A., Yoon, K.A., Kim, J., Lee, G.K., Zo, J.I.,
Lee, J.S., Hosono, N., Morizono, T. et al. (2010) Variation in TP63 is
associated with lung adenocarcinoma susceptibility in Japanese and Korean
populations. Nat. Genet.,42, 893– 896.
47. Pande, M., Spitz, M.R., Wu, X., Gorlov, I.P., Chen, W.V. and Amos, C.I.
(2011) Novel genetic variants in the chromosome 5p15.33 region associate
with lung cancer risk. Carcinogenesis,32, 1493– 1499.
48. Johnatty, S.E., Beesley, J., Chen, X., Macgregor, S., Duffy, D.L., Spurdle,
A.B., deFazio, A., Gava, N., Webb, P.M., Rossing, M.A. et al. (2010)
Evaluation of candidate stromal epithelial cross-talk genes identifies
association between risk of serous ovarian cancer and TERT, a cancer
susceptibility ‘hot-spot’. PLoS Genet.,6, e1001016.
49. Zhao, Y., Chen, G., Song, X., Chen, H., Mao, Y. and Lu, D. (2012)
Fine-mapping of a region of chromosome 5p15.33 (TERT-CLPTM1L)
suggests a novel locus in TERT and a CLPTM1L haplotype are associated
with glioma susceptibility in a Chinese population. Int. J. Cancer,131,
1569–1576.
50. Shiraishi, K., Kunitoh, H., Daigo, Y., Takahashi, A., Goto, K., Sakamoto, H.,
Ohnami, S., Shimada, Y., Ashikawa, K., Saito, A. et al. (2012) A
genome-wide association study identifies two new susceptibility loci for
lung adenocarcinoma in the Japanese population. Nat. Genet.,44, 900 –903.
51. Palmer, J.R., Ruiz-Narvaez, E.A., Rotimi, C.N., Cupples, L.A., Cozier, Y.C.,
Adams-Campbell, L.L. and Rosenberg, L. (2012) Genetic susceptibility loci
for subtypes of breast cancer in an African American population. Cancer
Epidemiol. Biomarkers Prev.,22, 127– 34.
52. Barrett, J.H., Iles, M.M., Harland, M., Taylor, J.C., Aitken, J.F., Andresen,
P.A., Akslen, L.A., Armstrong, B.K., Avril, M.F., Azizi, E. et al. (2011)
Genome-wide association study identifies three new melanoma
susceptibility loci. Nat. Genet.,43, 1108–1113.
53. Bojesen, S.E., Pooley, K.A., Johnatty, S.E., Beesley, J., Michailidou, K.,
Tyrer, J.P., Edwards, S.L., Pickett, H.A., Shen, H.C., Smart, C.E. et al.
(2013) Multiple independent variants at the TERT locus are associated with
telomere length and risks of breast and ovarian cancer. Nat. Genet.,45,
371–384.
54. Kote-Jarai, Z., Saunders, E.J., Leongamornlert, D.A., Tymrakiewicz, M.,
Dadaev, T., Jugurnauth-Little, S., Ross-Adams, H., Al Olama, A.A.,
Benlloch, S., Halim, S. et al. (2013) Fine-mapping identifies multiple
prostate cancer risk loci at 5p15, one of which associates with TERT
expression. Hum. Mol. Genet.,22, 2520– 2528.
55. Grundberg, E., Meduri, E., Sandling, J.K., Hedman, A.K., Keildson, S., Buil,
A., Busche, S., Yuan, W., Nisbet, J., Sekowska, M. et al. (2013) Global
analysis of DNA methylation variation in adipose tissue from twins reveals
links to disease-associated variants in distal regulatory elements.
Am. J. Hum. Genet.,93, 876–890.
56. Irizarry, R.A., Ladd-Acosta, C., Wen, B., Wu, Z., Montano, C., Onyango, P.,
Cui, H., Gabo, K., Rongione, M., Webster, M. et al. (2009) The human colon
cancer methylome shows similar hypo- and hypermethylation at conserved
tissue-specific CpG island shores. Nat. Genet.,41, 178 –186.
57. Jjingo, D., Conley, A.B., Yi, S.V., Lunyak, V.V. and Jordan, I.K. (2012) On
the presence and role of human gene-body DNA methylation. Oncotarget,3,
462–474.
58. Rideout, W.M. III, Coetzee, G.A., Olumi, A.F. and Jones, P.A. (1990)
5-Methylcytosine as an endogenous mutagen in the human LDL receptor and
p53 genes. Science,249, 1288–1290.
59. Maunakea, A.K., Nagarajan, R.P., Bilenky, M., Ballinger, T.J., D’Souza, C.,
Fouse, S.D., Johnson, B.E., Hong, C., Nielsen, C., Zhao, Y. et al. (2010)
Conserved role of intragenic DNA methylation in regulating alternative
promoters. Nature,466, 253– 257.
60. Shen, H. and Laird, P.W. (2013) Interplay between the cancer genome and
epigenome. Cell,153, 38– 55.
61. Solovieff, N., Cotsapas, C., Lee, P.H., Purcell, S.M. and Smoller, J.W.
(2013) Pleiotropy in complex traits: challenges and strategies. Nat. Rev.
Genet.,14, 483–495.
62. Figueroa JD, Y.Y., Siddiq, A., Garcia-Closas, M., Chatterjee, N. et al. (2014)
Genome-wide association study identifies multiple loci associated with
bladder cancer risk. Hum. Mol. Genet.,23, 1387–1398.
63. Hunter, D.J., Kraft, P., Jacobs, K.B., Cox, D.G., Yeager, M., Hankinson,
S.E., Wacholder, S., Wang, Z., Welch, R., Hutchinson, A. et al. (2007) A
genome-wide association study identifies alleles in FGFR2 associated with
risk of sporadic postmenopausal breast cancer. Nat. Genet.,39, 870–874.
64. Siddiq, A., Couch, F.J., Chen, G.K., Lindstrom, S., Eccles, D., Millikan,
R.C., Michailidou, K., Stram, D.O., Beckmann, L., Rhie, S.K. et al. (2012) A
meta-analysis of genome-wide association studies of breast cancer identifies
two novel susceptibility loci at 6q14 and 20q11. Hum. Mol. Genet.,21,
5373–5384.
Human Molecular Genetics, 2014 17
at University of Hong Kong Libraries on August 20, 2014http://hmg.oxfordjournals.org/Downloaded from
65. De Vivo, I., Prescott, J., Setiawan, V.W., Olson, S.H. and Wentzensen, N.,
Australian National Endometrial Cancer Study Group, Attia, J., Black, A.,
Brinton, L., Chen, C. et al. (2014) Genome-wide association study of
endometrial cancer in E2C2. Hum. Genet. 133, 211– 24.
66. Abnet, C.C., Freedman, N.D., Hu, N., Wang, Z., Yu, K., Shu, X.O., Yuan,
J.M., Zheng, W., Dawsey, S.M., Dong, L.M. et al. (2010) A shared
susceptibility locus in PLCE1 at 10q23 for gastric adenocarcinoma and
esophageal squamous cell carcinoma. Nat. Genet.,42, 764– 767.
67. Hinch, A.G., Tandon, A., Patterson, N., Song, Y., Rohland, N., Palmer, C.D.,
Chen, G.K., Wang, K., Buxbaum, S.G., Akylbekova, E.L. et al. (2011) The
landscape of recombination in African Americans. Nature,476, 170–175.
68. Hsiung, C.A., Lan, Q., Hong, Y.C., Chen, C.J., Hosgood, H.D., Chang, I.S.,
Chatterjee, N., Brennan, P., Wu, C., Zheng, W. et al. (2010) The 5p15.33
locus is associated with risk of lung adenocarcinoma in never-smoking
females in Asia. PLoS Genet.,6.
69. Lan, Q., Hsiung, C.A., Matsuo, K., Hong, Y.C., Seow, A., Wang, Z.,
Hosgood, H.D. 3rd, Chen, K., Wang, J.C., Chatterjee, N. et al. (2012)
Genome-wide association analysis identifies new lung cancer susceptibility
loci in never-smoking women in Asia. Nat. Genet.,44, 1330–1335.
70. Savage, S.A., Mirabello, L., Wang, Z., Gastier-Foster, J.M., Gorlick, R.,
Khanna, C., Flanagan, A.M., Tirabosco, R., Andrulis, I.L., Wunder, J.S. et al.
(2013) Genome-wide association study identifies two susceptibility loci for
osteosarcoma. Nat. Genet.,45, 799– 803.
71. Bolton, K.L., Tyrer, J., Song, H., Ramus, S.J., Notaridou, M., Jones, C., Sher,
T., Gentry-Maharaj, A., Wozniak, E., Tsai, Y.Y. et al. (2010) Common
variants at 19p13 are associated with susceptibility to ovarian cancer. Nat.
Genet.,42, 880– 884.
72. Amundadottir, L., Kraft, P., Stolzenberg-Solomon, R.Z., Fuchs, C.S.,
Petersen, G.M., Arslan, A.A., Bueno-de-Mesquita, H.B., Gross, M.,
Helzlsouer, K., Jacobs, E.J. et al. (2009) Genome-wide association study
identifies variants in the ABO locus associated with susceptibility to
pancreatic cancer. Nat. Genet.,41, 986–990.
73. Wu, C., Miao, X., Huang, L., Che, X., Jiang, G., Yu, D., Yang, X., Cao, G.,
Hu, Z., Zhou, Y. et al. (2012) Genome-wide association study identifies five
loci associated with susceptibility to pancreatic cancer in Chinese
populations. Nat. Genet.,44, 62– 66.
74. Thomas, G., Jacobs, K.B., Yeager, M., Kraft, P., Wacholder, S., Orr, N., Yu,
K., Chatterjee, N., Welch, R., Hutchinson, A. et al. (2008) Multiple loci
identified in a genome-wide association study of prostate cancer. Nat.
Genet.,40, 310– 315.
75. Schumacher, F.R., Berndt, S.I., Siddiq, A., Jacobs, K.B., Wang, Z.,
Lindstrom, S., Stevens, V.L., Chen, C., Mondul, A.M., Travis, R.C. et al.
(2011) Genome-wide association study identifies new prostate cancer
susceptibility loci. Hum. Mol. Genet.,20, 3867– 3875.
76. Purdue, M.P., Johansson, M., Zelenika, D., Toro, J.R., Scelo, G., Moore,
L.E., Prokhortchouk, E., Wu, X., Kiemeney, L.A., Gaborieau, V. et al.
(2011) Genome-wide association study of renal cell carcinoma identifies two
susceptibility loci on 2p21 and 11q13.3. Nat. Genet.,43, 60–65.
77. Schumacher, F.R., Wang, Z., Skotheim, R.I., Koster, R., Chung, C.C.,
Hildebrandt, M.A., Kratz, C.P., Bakken, A.C., Timothy Bishop, D., Cook,
M.B. et al. (2013) Testicular germ cell tumor susceptibility associated with
the UCK2 locus on chromosome 1q23. Hum. Mol. Genet.,22, 48– 53.
78. Marchini, J. and Howie, B. (2010) Genotype imputation for genome-wide
association studies. Nat. Rev. Genet.,11, 499– 511.
79. Marchini, J., Howie, B., Myers, S., McVean, G. and Donnelly, P. (2007) A
new multipoint method for genome-wide association studies by imputation
of genotypes. Nat. Genet.,39, 906– 913.
80. Fearnhead, P. (2006) SequenceLDhot: detecting recombination hotspots.
Bioinformatics,22, 3061– 3066.
81. Fearnhead, P. and Donnelly, P. (2002) Approximate likelihood methods for
estimating local recombination rates. J. R. Stat. Soc.,64, 657– 680.
82. Li, N. and Stephens, M. (2003) Modeling linkage disequilibrium and
identifying recombination hotspots using single-nucleotide polymorphism
data. Genetics,165, 2213– 2233.
83. Crawford, D.C., Bhangale, T., Li, N., Hellenthal, G., Rieder, M.J.,
Nickerson, D.A. and Stephens, M. (2004) Evidence for substantial fine-scale
variation in recombination rates across the human genome. Nat. Genet.,36,
700–706.
84. Browning, B.L. and Browning, S.R. (2009) A unified approach to genotype
imputation and haplotype-phase inference for large data sets of trios and
unrelated individuals. Am. J. Hum. Genet.,84, 210– 223.
85. Ward, L.D. and Kellis, M. (2012) HaploReg: a resource for exploring
chromatin states, conservation, and regulatory motif alterations within sets
of genetically linked variants. Nucleic Acids Res.,40, D930–934.
86. Boyle, A.P., Hong, E.L., Hariharan, M., Cheng, Y., Schaub, M.A.,
Kasowski, M., Karczewski, K.J., Park, J., Hitz, B.C., Weng, S. et al. (2012)
Annotation of functional variation in personal genomes using RegulomeDB.
Genome Res.,22, 1790– 1797.
87. Grundberg, E., Small, K.S., Hedman, A.K., Nica, A.C., Buil, A., Keildson,
S., Bell, J.T., Yang, T.P., Meduri, E., Barrett, A. et al. (2012) Mapping cis-
and trans-regulatory effects across multiple tissues in twins. Nat. Genet.,44,
1084– 1089.
88. Hebsgaard, S.M., Korning, P.G., Tolstrup, N., Engelbrecht, J., Rouze, P. and
Brunak, S. (1996) Splice site prediction in Arabidopsis thaliana pre-mRNA
by combining local and global sequence information. Nucleic Acids Res.,24,
3439– 3452.
89. Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery
rate – a practical and powerful approach to multiple testing. J. R. Stat. Soc. B,
57, 289– 300.
18 Human Molecular Genetics, 2014
at University of Hong Kong Libraries on August 20, 2014http://hmg.oxfordjournals.org/Downloaded from
