Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses Haoyu Zhang1,2*, Thomas U. Ahearn1*, Julie Lecarpentier3, Daniel Barnes3, Jonathan Beesley4, Guanghao Qi2, Xia Jiang5, Tracy A. O’Mara4, Ni Zhao2, Manjeet K. Bolla6, Alison M. Dunning3, Joe Dennis6, Qin Wang6, Zumuruda Abu Ful7, Kristiina Aittomäki8, Irene L. Andrulis9, Hoda Anton-Culver10, Volker Arndt11, Kristan J. Aronson12, Banu K. Arun13, Paul L. Auer14,15, Jacopo Azzollini16, Daniel Barrowdale17, Heiko Becher18, Matthias W. Beckmann19, Sabine Behrens20, Javier Benitez21, Marina Bermisheva22, Katarzyna Bialkowska23, Ana Blanco24,25,26, Carl Blomqvist27,28, Natalia V. Bogdanova29,30,31, Stig E. Bojesen32,33,34,35, Bernardo Bonanni36, Davide Bondavalli36, Ake Borg37, Hiltrud Brauch38,39,40, Hermann Brenner11,40,41, Ignacio Briceno42, Annegien Broeks43, Sara Y. Brucker44, Thomas Brüning45, Barbara Burwinkel46,47, Saundra S. Buys48, Helen Byers49, Trinidad Caldés50, Maria A. Caligo51, Mariarosaria Calvello36, Daniele Campa20,52, Jose E. Castelao53, Jenny Chang-Claude20,54, Stephen J. Chanock1, Melissa Christiaens55, Hans Christiansen31, Wendy K. Chung56, Kathleen B.M. Claes57, Christine L. Clarke58, Sten Cornelissen43, Fergus J. Couch59, Angela Cox60, Simon S. Cross61, Kamila Czene62, Mary B. Daly63, Peter Devilee64, Orland Diez65, Susan M. Domchek66, Thilo Dörk30, Miriam Dwek67, Diana M. Eccles68, Arif B. Ekici69, D.Gareth Evans70,49, Peter A. Fasching71,19, Jonine Figueroa72, Lenka Foretova73, Florentia Fostira74, Eitan Friedman75, Debra Frost17, Manuela Gago- Dominguez76,77, Susan M. Gapstur78, Judy Garber79, José A. García-Sáenz50, Mia M. Gaudet78, Simon A. Gayther80, Graham G. Giles81,82,83, Andrew K. Godwin84, Mark S. Goldberg85,86,87, David E. Goldgar88, Anna González-Neira35, Mark H. Greene89, Jacek Gronwald23, Pascal Guénel90, Lothar Häberle91, Eric Hahnen92, Christopher A. Haiman93, Christopher R. Hake94, Per Hall62,95, Ute Hamann96, Elaine F. Harkness97,98, Bernadette A.M. Heemskerk-Gerritsen99, Peter Hillemanns30, Frans B.L. Hogervorst100, Bernd Holleczek101, Antoinette Hollestelle99, Maartje J. Hooning99, Robert N. Hoover1, John L. Hopper82, Anthony Howell102, Hanna Huebner19, Peter J. Hulick103, Evgeny N. Imyanitov104, kConFab Investigators105, ABCTB Investigators105, Claudine Isaacs106, Louise Izatt107, Agnes Jager99, Milena Jakimovska108, Anna Jakubowska23,109, Paul James110, Ramunas Janavicius111,112, Wolfgang Janni113, Esther M. John114, Michael E. Jones115, Audrey Jung20, Rudolf Kaaks20, Pooja Middha Kapoor20,116, Beth Y. Karlan117, Renske Keeman43, Sofia Khan118, Elza Khusnutdinova22,119, Cari M. Kitahara120, Yon- Dschun Ko121, Irene Konstantopoulou74, Linetta B. Koppert122, Stella Koutros1, Vessela N. Kristensen123,124, Anne-Vibeke Laenkholm125, Diether Lambrechts126,127, Susanna C. Larsson128,129, Pierre Laurent-Puig130, Conxi Lazaro131, Emilija Lazarova132, Flavio Lejbkowicz7, Goska Leslie6, Fabienne Lesueur133, Annika Lindblom134,135, Jolanta Lissowska136, Wing-Yee Lo38,137, Jennifer T. Loud89, Jan Lubinski23, Alicja Lukomska23, Robert J. MacInnis81,82, Arto Mannermaa138,139,140, Mehdi Manoochehri96, Siranoush Manoukian16, Sara Margolin95,141, Maria Elena Martinez77,142, Laura Matricardi143, Lesley McGuffog6, Catriona McLean144, Noura Mebirouk145, Alfons Meindl146, Usha Menon147, Austin Miller148, Elvira Mingazheva149, Marco Montagna143, Anna Marie Mulligan150,151, Claire Mulot130, Taru A. Muranen118, Katherine L. Nathanson66, Susan L. Neuhausen152, Heli Nevanlinna118, Patrick Neven55, William G. Newman49,70, Finn C. Nielsen153, Liene Nikitina-Zake154, Jesse Nodora155,156, Kenneth Offit157, Edith Olah158, Olufunmilayo I. Olopade159,160, Håkan Olsson161,162, Nick Orr163, Laura Papi164, Janos Papp158, Tjoung- Won Park-Simon30, Michael T. Parsons165, Bernard Peissel16, Ana Peixoto166, Beth Peshkin167, Paolo Peterlongo168, Julian Peto169,6, Kelly-Anne Phillips82,170,171, Marion Piedmonte148, Dijana Plaseska-Karanfilska108, Karolina Prajzendanc23, Ross Prentice14, Darya Prokofyeva119, Brigitte Rack113, Paolo Radice172, Susan J. Ramus173,174,175, Johanna Rantala176, Muhammad U. Rashid96,177, Gad Rennert7, Hedy S. Rennert7, Harvey A. Risch178, Atocha Romero179,180, Matti A. Rookus181, Matthias Rübner91, Thomas Rüdiger182, Emmanouil Saloustros183, Sarah Sampson184, Dale P. Sandler185, Elinor J. Sawyer186, Maren T. Scheuner187, Rita K. Schmutzler92, Andreas Schneeweiss47,188, Minouk J. Schoemaker115, Ben Schöttker11, Peter Schürmann30, Leigha Senter189, Priyanka Sharma190, Mark E. Sherman191, Xiao-Ou Shu192, Christian F. Singer193, Snezhana Smichkoska132, Penny Soucy194, Melissa C. Southey83, John J. Spinelli195,196, Jennifer Stone82,197, Dominique Stoppa-Lyonnet198, EMBRACE Study105, GEMO Study Collaborators105, Anthony J. Swerdlow115,199, Csilla I. Szabo200, Rulla M. Tamimi5,201,202, William J. Tapper203, Jack A. Taylor185,204, Manuel R. Teixeira166,180, MaryBeth Terry205, Mads Thomassen206, Darcy L. Thull207, Marc Tischkowitz208,209, Amanda E. Toland210, Rob A.E.M. Tollenaar211, Ian Tomlinson212,213, Diana Torres96,214, Melissa A. Troester215, Thérèse Truong90, Nadine Tung216, Michael Untch217, Celine M. Vachon218, Ans M.W. van den Ouweland219, Lizet E. van der Kolk100, Elke M. van Veen49,70, Elizabeth J. vanRensburg220, Ana Vega24,25,26, Barbara Wappenschmidt92, Clarice R. Weinberg221, Jeffrey N. Weitzel222, Hans Wildiers55, Robert Winqvist223,224,225,226, Alicja Wolk109,128,129, Xiaohong R. Yang1, Drakoulis Yannoukakos74, Wei Zheng192, Kristin K. Zorn227, Roger L. Milne81,82,83, Peter Kraft5,202, Jacques Simard194, Paul D.P. Pharoah3,6, Kyriaki Michailidou6,228,229, Antonis C. Antoniou6, Marjanka K. Schmidt43,230, Georgia Chenevix-Trench4, Douglas F. Easton3**, Nilanjan Chatterjee2,231**, Montserrat García-Closas1** 1Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA, 2Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA, 3Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK, 4Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia, 5Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA, 6Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, 7Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel, 8Department of Clinical Genetics, Helsinki University Hospital, University of Helsinki, Helsinki, Finland, 9Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, Canada, 10Department of Epidemiology, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA, USA, 11Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany, 12Department of Public Health Sciences, and Cancer Research Institute, Queen's University, Kingston, ON, Canada, 13Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA, 14Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA, 15Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA, 16Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy, 17Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK, 18Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, 19Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany, 20Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 21Centro de Investigación en Red de Enfermedades Raras (CIBERER), Valencia, Spain, 22Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia, 23Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland, 24Molecular Medicine Unit, Fundación Pública Galega de Medicina Xenómica, Santiago de Compostela, Spain, 25Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain, 26Centro de Investigación en Red de Enfermedades Raras (CIBERER), Santiago de Compostela, Spain, 27Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland, 28Department of Oncology, Örebro University Hospital, Örebro, Sweden, 29N.N. Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus, 30Gynaecology Research Unit, Hannover Medical School, Hannover, Germany, 31Department of Radiation Oncology, Hannover Medical School, Hannover, Germany, 32Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark, 33Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark, 34Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark, 35Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain, 36Division of Cancer Prevention and Genetics, IEO, European Institute of Oncology IRCCS, Milan, Italy, 37Department of Oncology, Lund University and Skåne University Hospital, Lund, Sweden, 38Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany, 39iFIT-Cluster of Excellence, University of Tübingen, Tübingen, Germany, 40German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany, 41Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany, 42Bioscience Department, Faculty of Medicine, Universidad de la Sabana, Chia, Colombia, 43Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands, 44Department of Women's Health, University of Tübingen, Tübingen, Germany, 45Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, Germany, 46Molecular Epidemiology Group, C080, German Cancer Research Center (DKFZ), Heidelberg, Germany, 47Molecular Biology of Breast Cancer, University Womens Clinic Heidelberg, University of Heidelberg, Heidelberg, Germany, 48Department of Medicine, Huntsman Cancer Institute, Salt Lake City, UT, USA, 49Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester NIHR Biomedical Research Centre, Manchester University Hospitals NHS, Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK, 50Medical Oncology Department, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Centro Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain, 51Section of Molecular Genetics, Dept. of Laboratory Medicine, University Hospital of Pisa, Pisa, Italy, 52Department of Biology, University of Pisa, Pisa, Italy, 53Oncology and Genetics Unit, Instituto de Investigacion Sanitaria Galicia Sur (IISGS), Xerencia de Xestion Integrada de Vigo-SERGAS, Vigo, Spain, 54Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany, 55Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium, 56Departments of Pediatrics and Medicine, Columbia University, New York, NY, USA, 57Centre for Medical Genetics, Ghent University, Gent, Belgium, 58Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia, 59Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA, 60Sheffield Institute for Nucleic Acids (SInFoNiA), Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK, 61Academic Unit of Pathology, Department of Neuroscience, University of Sheffield, Sheffield, UK, 62Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, 63Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA, 64Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands, 65Oncogenetics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain, 66Department of Medicine, Abramson Cancer Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA, 67Department of Biomedical Sciences, Faculty of Science and Technology, University of Westminster, London, UK, 68Cancer Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton, UK, 69Institute of Human Genetics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany, 70Division of Evolution and Genomic Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK, 71David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology, University of California at Los Angeles, Los Angeles, CA, USA, 72Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh Medical School, Edinburgh, UK, 73Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic, 74Molecular Diagnostics Laboratory, INRASTES, National Centre for Scientific Research "Demokritos", Athens, Greece, 75The Susanne Levy Gertner Oncogenetics Unit, Chaim Sheba Medical Center, Ramat Gan, Israel, 76Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain, 77Moores Cancer Center, University of California San Diego, La Jolla, CA, USA, 78Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA, 79Cancer Risk and Prevention Clinic, Dana-Farber Cancer Institute, Boston, MA, USA, 80Center for Bioinformatics and Functional Genomics and the Cedars Sinai Genomics Core, Cedars-Sinai Medical Center, Los Angeles, CA, USA, 81Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia, 82Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia, 83Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia, 84Department of Pathology and Laboratory Medicine, Kansas University Medical Center, Kansas City, KS, USA, 85Department of Medicine, McGill University, Montréal, QC, Canada, 86Division of Clinical Epidemiology, Royal Victoria Hospital, McGill University, Montréal, QC, Canada, 87Breast Cancer Research Unit, Cancer Research Institute, University Malaya Medical Centre, Kuala Lumpur, Malaysia, 88Department of Dermatology, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT, USA, 89Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA, 90Cancer & Environment Group, Center for Research in Epidemiology and Population Health (CESP), INSERM, University Paris-Sud, University Paris-Saclay, Villejuif, France, 91Department of Gynaecology and Obstetrics, University Hospital Erlangen, Friedrich- Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen- EMN, Erlangen, Germany, 92Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany, 93Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA, 94Waukesha Memorial Hospital-Pro Health Care, Waukesha, WI, USA, 95Department of Oncology, Södersjukhuset, Stockholm, Sweden, 96Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany, 97Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK, 98Nightingale Breast Screening Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, UK, 99Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands, 100Family Cancer Clinic, The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Amsterdam, The Netherlands, 101Saarland Cancer Registry, Saarbrücken, Germany, 102Division of Cancer Sciences, University of Manchester, Manchester, UK, 103Center for Medical Genetics, NorthShore University HealthSystem, Evanston, IL, USA, 104N.N. Petrov Institute of Oncology, St. Petersburg, Russia, 105A full list of authors can be found in the Supplementary Note, 106Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA, 107Clinical Genetics, Guy’s and St. Thomas’ NHS Foundation Trust, London, UK, 108Research Centre for Genetic Engineering and Biotechnology "Georgi D. Efremov", Macedonian Academy of Sciences and Arts, Skopje, Republic of Macedonia, 109Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland, 110Parkville Familial Cancer Centre, Peter MacCallum Cancer Center, Melbourne, Victoria, Australia, 111Hematology, oncology and transfusion medicine center, Dept. of Molecular and Regenerative Medicine, Vilnius University Hospital Santariskiu Clinics, Vilnius, Lithuania, 1124/30/01, Vilnius, Lithuania, 113Department of Gynaecology and Obstetrics, University Hospital Ulm, Ulm, Germany, 114Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA, 115Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK, 116Faculty of Medicine University of Heidelberg, Heidelberg, Germany, 117David Geffen School of Medicine, Department of Obstetrics and Gynecology, University of California at Los Angeles, Los Angeles, CA, USA, 118Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland, 119Department of Genetics and Fundamental Medicine, Bashkir State Medical University, Ufa, Russia, 120Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA, 121Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany, 122Department of Surgical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands, 123Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway, 124Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway, 125Depastment of Surgical Pathology, Zealand University Hospital, Slagelse, Denmark, 126VIB Center for Cancer Biology, VIB, Leuven, Belgium, 127Laboratory for Translational Genetics, Department of Human Genetics, University of Leuven, Leuven, Belgium, 128Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden, 129Department of Surgical Sciences, Uppsala University, Uppsala, Sweden, 130Université Paris Sorbonne Cité, INSERM UMR-S1147, Paris, France, 131Molecular Diagnostic Unit, Hereditary Cancer Program, ICO-IDIBELL (Bellvitge Biomedical Research Institute, Catalan Institute of Oncology), CIBERONC, Barcelona, Spain, 132Ss. Cyril and Methodius University in Skopje, Medical Faculty, University Clinic of Radiotherapy and Oncology, Skopje, Republic of North Macedonia, 133Genetic Epidemiology of Cancer team, Inserm U900, Paris, France, 134Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden, 135Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden, 136Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie Cancer Center, Oncology Institute, Warsaw, Poland, 137University of Tübingen, Tübingen, Germany, 138Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland, 139Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland, 140Imaging Center, Department of Clinical Pathology, Kuopio University Hospital, Kuopio, Finland, 141Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden, 142Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA, USA, 143Immunology and Molecular Oncology Unit, Veneto Institute of Oncology IOV - IRCCS, Padua, Italy, 144Department of Anatomical Pathology, The Alfred Hospital, Prahran, Victoria, Australia, 145Genetic Epidemiology of Cancer team, Inserm U900, Institut Curie, PSL University, Mines ParisTech, Paris, France, 146Department of Gynecology and Obstetrics, Ludwig Maximilian University of Munich, Munich, Germany, 147MRC Clinical Trials Unit at UCL, Institute of Clinical Trials & Methodology, University College London, London, UK, 148NRG Oncology, Statistics and Data Management Center, Roswell Park Cancer Institute, Buffalo, NY, USA, 149Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa, Russia, 150Laboratory Medicine Program, University Health Network, Toronto, ON, Canada, 151Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada, 152Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, CA, USA, 153Center for Genomic Medicine, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark, 154Latvian Biomedical Research and Study Centre, Riga, Latvia, 155Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA, 156Department of Family Medicine and Public Health, School of Medicine, University of California, San Diego, La Jolla, CA, USA, 157Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA, 158Department of Molecular Genetics, National Institute of Oncology, Budapest, Hungary, 159Center for Clinical Cancer Genetics, The University of Chicago, Chicago, IL, USA, 160Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia, 161Department of Cancer Epidemiology, Clinical Sciences, Lund University, Lund, Sweden, 162Clinical Genetics Service, Department of Medicine, Memorial Sloan- Kettering Cancer Center, New York, NY, USA, 163Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Ireland, UK, 164Unit of Medical Genetics, Department of Biomedical, Experimental and Clinical Sciences,, University of Florence, Florence, Italy, 165Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia, 166Department of Genetics, Portuguese Oncology Institute, Porto, Portugal, 167Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA, 168Genome Diagnostics Program, IFOM, The FIRC Institute of Molecular Oncology, Milan, Italy, 169Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK, 170Peter MacCallum Cancer Center, Melbourne, Victoria, Australia, 171Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia, 172Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy, 173Adult Cancer Program, Lowy Cancer Research Centre, University of NSW Sydney, Sydney, New South Wales, Australia, 174School of Women's and Children's Health, Faculty of Medicine, University of NSW Sydney, Sydney, New South Wales, Australia, 175The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Sydney, New South Wales, Australia, 176Clinical Genetics, Karolinska Institutet, Stockholm, Sweden, 177Department of Basic Sciences, Shaukat Khanum Memorial Cancer Hospital and Research Centre (SKMCH & RC), Lahore, Pakistan, 178Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA, 179Medical Oncology Department, Hospital Universitario Puerta de Hierro, Madrid, Spain, 180Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal, 181Department of Epidemiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands, 182Institute of Pathology, Staedtisches Klinikum Karlsruhe, Karlsruhe, Germany, 183Department of Oncology, University Hospital of Larissa, Larissa, Greece, 184Prevent Breast Cancer Centre and Nightingale Breast Screening Centre, Manchester University NHS Foundation Trust, Manchester, UK, 185Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA, 186Research Oncology, Guy’s Hospital, King's College London, London, UK, 187Cancer Genetics and Prevention Program, University of California San Francisco, San Francisco, CA, USA, 188National Center for Tumor Diseases, University Hospital and German Cancer Research Center, Heidelberg, Germany, 189Clinical Cancer Genetics Program, Division of Human Genetics, Department of Internal Medicine, The Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA, 190Department of Internal Medicine, Division of Oncology, University of Kansas Medical Center, Westwood, KS, USA, 191Department of Health Sciences Research, Mayo Clinic College of Medicine, Jacksonville, FL, USA, 192Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 193Dept of OB/GYN and Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria, 194Genomics Center, Centre Hospitalier Universitaire de Québec – Université Laval, Research Center, Québec City, QC, Canada, 195Population Oncology, BC Cancer, Vancouver, BC, Canada, 196School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada, 197The Curtin UWA Centre for Genetic Origins of Health and Disease, Curtin University and University of Western Australia, Perth, Western Australia, Australia, 198Department of Genetics, Inserm U830, Institut Curie, Paris Descartes Sorbonne-Paris-Cité University, Paris, France, 199Division of Breast Cancer Research, The Institute of Cancer Research, London, UK, 200National Human Genome Research Institute, National Cancer Institute, Bethesda, MD, USA, 201Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA, 202Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA, 203Faculty of Medicine, University of Southampton, Southampton, UK, 204Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA, 205Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA, 206Department of Clinical Genetics, Odense University Hospital, Odence C, Denmark, 207Department of Medicine, Magee-Womens Hospital, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA, 208Program in Cancer Genetics, Departments of Human Genetics and Oncology, McGill University, Montréal, QC, Canada, 209Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK, 210Department of Cancer Biology and Genetics, The Ohio State University, Columbus, OH, USA, 211Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands, 212Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK, 213Wellcome Trust Centre for Human Genetics and Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK, 214Institute of Human Genetics, Pontificia Universidad Javeriana, Bogota, Colombia, 215Department of Epidemiology, Gilliungs School of Global Public Health and UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, 216Department of Medical Oncology, Beth Israel Deaconess Medical Center, Boston, MA, USA, 217Department of Gynecology and Obstetrics, Helios Clinics Berlin- Buch, Berlin, Germany, 218Department of Health Science Research, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA, 219Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands, 220Department of Genetics, University of Pretoria, Arcadia, South Africa, 221Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA, 222Clinical Cancer Genomics, City of Hope, Duarte, CA, USA, 223Laboratory of Cancer Genetics and Tumor Biology, Cancer and Translational Medicine Research Unit, Biocenter Oulu, University of Oulu, Oulu, Finland, 224Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre Oulu, Oulu, Finland, 225Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada, 226Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands, 227Magee-Womens Hospital, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA, 228Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus, 229Cyprus School of Molecular Medicine, Nicosia, Cyprus, 230Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Amsterdam, The Netherlands, 231Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA *Contributed equally **Jointly supervised this work Conflicts of interest: None to report Corresponding Author Nilanjan Chatterjee 615 N. Wolfe Street Room E3612 Baltimore, Maryland 21205 nchatte2@jhu.edu Breast cancer susceptibility variants frequently show heterogeneity in associations by tumor subtype1-3. To identify novel loci, we performed a genome-wide association study (GWAS) including 133,384 breast cancer cases and 113,789 controls, plus 18,908 BRCA1 mutation carriers (9,414 with breast cancer) of European ancestry, using both standard and novel methodologies that account for underlying tumor heterogeneity by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status and tumor grade. We identified 32 novel susceptibility loci (P < 5.0 × 10-8), 15 of which showed evidence for associations with at least one tumor feature (false discovery rate (FDR) <0.05). Five loci showed associations (P<0.05) in opposite directions between luminal- and non-luminal subtypes. In-silico analyses showed these five loci contained cell-specific enhancers that differed between normal luminal and basal mammary cells. The genetic correlations between five intrinsic-like subtypes ranged from 0.35 to 0.80. The proportion of genome-wide chip heritability explained by all known susceptibility loci was 37.6% for triple-negative and 54.2% for luminal A-like disease. The odds ratios of polygenic risk scores (PRSs), which included 330 variants, for the highest 1% quantiles compared to middle quantiles were 5.63 and 3.02 for luminal A-like and triple-negative disease, respectively. These findings provide an improved understanding of genetic predisposition to breast cancer subtypes and will inform the development of subtype-specific polygenic risk scores. Based on the largest GWAS to date from the Breast Cancer Association Consortium (BCAC), over 170 independent breast cancer susceptibility variants have been identified. Many of these variants show differential associations by tumor subtypes, particularly ER-positive versus ER-negative or triple-negative disease1-3. However, prior GWAS have not simultaneously accounted for the high correlations between multiple, correlated tumor markers, such as ER, PR, HER2 and grade, to identify specific source(s) of etiologic heterogeneity. We performed a breast cancer GWAS using both standard analyses and a novel two-stage polytomous regression method that efficiently characterizes etiologic heterogeneity while accounting for tumor marker correlations and missing data4. The study populations and genotyping are described elsewhere1,2,5,6 and in the Online Methods. Briefly, we analyzed data from 118,474 cases and 96,201 controls of European ancestry participating in 82 studies from the BCAC and 9,414 affected and 9,494 unaffected BRCA1 mutation carriers from 60 studies from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) with genotyping data from one of two Illumina genome-wide custom arrays. In analyses of overall breast cancer, we also included summary level data from 11 other breast cancer GWAS (14,910 cases and 17,588 controls) without subtype information. Our study expands upon previous BCAC GWAS1 with additional data on 10,407 cases and 7,815 controls, an approximate increase of 10% and 9%, respecitvely. (Supplementary Tables 1-4). The statistical methods are further described in the Online Methods and in Extended Data Figure 1. To identify variants for overall breast cancer (invasive, in situ or unknown invasiveness) in BCAC, we used standard logistic regression to estimate odds ratios (OR) and 95% confidence-intervals (CI) adjusting for country and principal components (PCs). iCOGS and OncoArray data were evaluated separately and the results were combined with those from the 11 other GWAS using fixed-effects meta- analysis. To identify breast cancer susceptibility variants displaying evidence of heterogeneity, we used a novel score-test based on a two-stage polytomous model4 that allows flexible, yet parsimonious, modelling of associations in the presence of underlying heterogeneity by ER, PR, HER2 and/or grade (Online Methods, Supplementary Note). The model handles missing tumor characteristic data by implementing an efficient Expectation-Maximization algorithm4,7. These analyses were restricted to BCAC controls and invasive cases (Online Methods). We fit an additional two-stage model to estimate case-control ORs and 95% CI between the variants and intrinsic-like subtypes defined by combinations of ER, PR, HER2 and grade8 (Online Methods): (1) luminal A-like, (2) luminal B/HER2-negative-like, (3) luminal B-like, (4) HER2-enriched-like and (5) triple-negative or basal-like. We analyzed iCOGS and OncoArray data separately, adjusting for PCs and age, and meta-analyzed the results using a fixed-effects model. We evaluated the effect of country using a leave-one-out sensitivity analysis (Online Methods). Among BRCA1 mutation carriers who are prone to develop triple-negative disease9, we estimated per-allele hazard ratios (HRs) within a retrospective cohort analysis framework. We assumed estimated ORs for BCAC triple-negative cases and estimated HRs from CIMBA BRCA1 carriers approximated the same underlying relative risk9, and we used a fixed-effect meta-analysis to combine these results (Online Methods). Among all novel variants, we used the two-stage polytomous model to test for heterogeneity in associations across subtypes, globally and by tumor-specific markers (Online Methods). Overall, we identified 32 novel independent susceptibility loci marked by variants with P < 5.0 × 10-8 (Figure 1, Supplementary Table 5-7, Supplementary Figure 1-5): 22 variants using standard logistic regression, 16 variants using the two-stage polytomous model (eight of which were detected by standard logistic regression) and three variants in the CIMBA/BCAC-triple-negative meta-analysis (rs78378222 was also detected by the two-stage polytomous model in BCAC). Fourteen additional variants (P < 5.0 × 10-8) were excluded, 13 because they lacked evidence of association independent of known susceptibility variants in conditional analyses (P ≥ 1.0 × 10-6; Supplementary Table 8-10), and one (chr22:40042814) for showing a high-degree of sensitivity in the leave-one-out country analysis following exclusion of studies from the USA (Supplementary Figure 6). Supplementary Figures 7-8 and Supplementary Table 11 show associations between all 32 variants and the intrinsic-like subtypes. Fifteen of the 32 variants showed heterogeneity evidence (FDR < 0.05) according to the global heterogeneity test (Figure 2, Supplementary Table 12). ER (7 variants) and grade (7 variants) most often contributed to observed heterogeneity (marker-specific P < 0.05), followed by HER2 (4 variants) and PR (2 variants). rs17215231, identified in the CIMBA/BCAC-triple-negative meta-analysis, was the only variant found exclusively associated with triple-negative disease (OR=0.85, 95%CI=0.81-0.89). rs2464195, also identified in the CIMBA/BCAC-triple-negative meta- analysis, was associated with both triple-negative (OR=0.93, 95%CI=0.91-0.96) and luminal B-like subtypes (OR=0.96, 95%CI=0.92-0.99; Supplementary Table 11) and is in linkage disequilibrium (LD; r2=0.62) with rs7953249, which is differentially associated with risk of ovarian cancer subtypes10. Five variants showed associations with luminal and non-luminal subtypes in opposite directions (Figure 3). Four variants were associated in opposite directions with luminal A-like and triple-negative subtypes (respectively, for rs78378222 OR=1.13, 95%CI=1.05-1.20 vs OR=0.67, 95%CI=0.57- 0.80; for rs206435 OR=1.03, 95%CI=1.01-1.05 vs OR=0.95, 95%CI=0.92-0.98; for rs141526427 OR=0.96, 95%CI=0.94-0.98 vs OR=1.04, 95%CI=1.01-1.08; and for rs6065254 OR=0.96, 95%CI=0.94-0.97 vs OR=1.04, 95%CI=1.01-1.07). The tumor- marker heterogeneity test showed associations for rs78378222 with ER (PER = 7.0 × 10- 6) and HER2 (PHER2 = 2.07 × 10-4), rs206435 with ER (PER = 2.8 × 10-3) and grade (Pgrade = 2.8 × 10-4) and rs141526427 (PER = 1.3 × 10-3) and rs6065254 (PER = 4.3 × 10-3) with ER. rs7924772 showed opposite case-control associations between HER2-negative and HER2-positive subtypes and, consistent with these findings, was exclusively associated with HER2 (PHER2 = 1.4 × 10-6; Figure 3). rs78378222, located in the 3’ UTR of TP53, also showed opposite associations with high-grade serous cancers (OR=0.75, P = 3.7 × 10-4) and low-grade serous cancers (OR=1.58, P = 1.5 × 10-4; -). Prior analyses11 did not find rs78378222 associated with breast cancer risk, likely due to its opposite effects between subtypes. Candidate causal variants were defined (CCVs; Online Methods) for each novel locus and we investigated the CCVs in relation to previously-annotated enhancers in primary breast cells12. Based on combinations of H3K4me1 and H3K27ac histone modification ChIP-seq signals, putative enhancers in basal cells (BC), luminal progenitor cells (LP) and mature luminal cells (LM) were characterized as “OFF,” “PRIMED”, and “ACTIVE” (Online Methods). We defined “ANYSWITCH” enhancers as those exhibiting different characterizations between cell types. Among the five loci identified with associations in opposite directions between subtypes, at least one CCV per locus overlapped an “ANYSWITCH” enhancer (Figure 4). For example, rs78378222 overlapped an ACTIVE enhancer in basal cells, PRIMED in luminal progenitor cells and OFF in mature luminal cells. In comparison, 63% of the loci with consistent direction of associations across subtypes overlapped with an “ANYSWITCH” enhancer (Supplementary Table 13-14). These results suggest that some variants may modulate enhancer activity in a cell-type specific manner, thus, differentially influencing risk of tumor subtypes. We used INQUIST to intersect CCVs with functional annotation data from public databases to identify potential target genes1 (Supplementary Note, Supplementary Table 15). We predicted 179 unique target genes for 26 of the 32 independent signals. Notably, rs78378222 has been reported associated with TP53 mRNA levels in blood and adipose tissue11, which we did not replicate in breast tissue. However, our findings of rs78378222 overlapping a cell type-specific regulatory element in breast basal epithelial cells, implicates enhancer function as another potential TP53 transcriptional control mechanism. Twenty-three target genes in 14 regions were predicted with high confidence (designated “Level 1”), of which 22 target genes in 13 regions were predicted to be distally regulated. Four target genes were previously predicted by INQUISIT13,14, POLR3C, RNF115, SOX4 and TBX3 – a known somatic breast cancer driver gene15 – and genes implicated by transcriptome-wide association studies (LINC0088616 and YBEY17). We used LD-regression to investigate genetic correlations18,19 between subtypes and compare enrichment of genomic features20 between luminal A-like and triple- negative subtypes (Online Methods). All subtypes were moderately- to highly correlated, with luminal A-like and triple-negative having a correlation of 0.46 (SE=0.05). The correlation in breast cancer of BRCA1 carriers and triple-negative was 0.83 (SE=0.08), suggesting a high-degree of similarity in the genetic basis between these subtypes (Figure 5; Supplementary Table 16). To compare genomic enrichment, we first evaluated 53 annotations and found triple-negative tumors were most enriched for “super-enhancers, extend500bp” (3.04-fold, P = 3.3 × 10-6), and “digital genomic footprint, extend500bp” (from DNase hypersensitive sites) (2.2-fold, P = 4.0 × 10-4); however, no annotations significantly differed between luminal A-like and triple-negative tumors (Supplementary Table 17, Supplementary Figure 9). Investigating cell- specific enrichment of histone markers H3K4me1, H3K3me3, H3K9ac and H3K27ac (Supplementary Note) found both luminal-A and triple-negative subtypes enriched for gastrointestinal cell types and suppression of central nervous system cell types (Supplementary Figure 10). The proportion of genome-wide chip heritability explained by the 32 novel variants, plus 178 previously identified variants1,2,21, was 54.2%, 37.6% and 26.9% for luminal A-like, triple-negative and BRCA1 carriers, respectively (Table 1, Supplementary Table 18). These 210 variants explained approximately 18.3% of the two-fold familial relative risk for invasive breast cancer, while all reliably imputable variants on the OncoArray explained 37.1% (Online Methods). The per-standard deviation ORs between PRSs for luminal-A like and triple-negative subtypes (Online Methods), that included 313 published variants22 and 17 novel variants that were independent of the 313 variants (Supplementary Table 19), was 1.83 (95% CI=1.78- 1.88) and 1.65 (1.57-1.73), with corresponding area under receiver-operator curves of 66.09 and 63.58, respectively (Extended Data Figure 2-6). These analyses demonstrate the benefit of combining standard GWAS methods with methods accounting for underlying tumor heterogeneity. Moreover, these methods and results may help clarify mechanisms predisposing to specific molecular subtypes, and provide precise risk estimates for subtypes to inform development of subtype- specific PRSs22. However, to expand the generalizability of our findings, these analyses should be replicated and expanded in multi-ancestry populations. Acknowledgments and Funding We thank all the individuals who took part in these studies and all the researchers, clinicians, technicians and administrative staff who have enabled this work to be carried out. Genotyping for the OncoArray was funded by the government of Canada through Genome Canada and the Canadian Institutes of Health Research (GPH-129344), the Ministère de l'Économie, de la Science et de l'Innovation du Québec through Génome Québec, the Quebec Breast Cancer Foundation for the PERSPECTIVE project, the US National Institutes of Health (NIH) (1 U19 CA 148065 for the Discovery, Biology and Risk of Inherited Variants in Breast Cancer (DRIVE) project and X01HG007492 to the Center for Inherited Disease Research (CIDR) under contract HHSN268201200008I), Cancer Research UK (C1287/A16563), the Odense University Hospital Research Foundation (Denmark), the National R&D Program for Cancer Control–Ministry of Health and Welfare (Republic of Korea) (1420190), the Italian Association for Cancer Research (AIRC; IG16933), the Breast Cancer Research Foundation, the National Health and Medical Research Council (Australia) and German Cancer Aid (110837). Genotyping for the iCOGS array was funded by the European Union (HEALTH-F2- 2009-223175), Cancer Research UK (C1287/A10710, C1287/A10118 and C12292/A11174]), NIH grants (CA128978, CA116167 and CA176785) and the Post- Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 (GAME-ON initiative)), an NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, the Ministère de l'Économie, Innovation et Exportation du Québec (PSR-SIIRI-701), the Komen Foundation for the Cure, the Breast Cancer Research Foundation and the Ovarian Cancer Research Fund. Combination of the GWAS data was supported in part by the NIH Cancer Post-Cancer GWAS initiative (1 U19 CA 148065) (DRIVE, part of the GAME-ON initiative). LD score regression analysis was supported by grant CA194393. BCAC was funded by Cancer Research UK (C1287/A16563) and by the European Union via its Seventh Framework Programme (HEALTH-F2-2009-223175, COGS) and the Horizon 2020 Research and Innovation Programme (633784, B-CAST; 634935, BRIDGES). CIMBA was funded by Cancer Research UK (C12292/A20861 and C12292/A11174). Dr. Nilanjan Chatterjee's was funded by NHGRI (1R01 HG010480-01). For a full description of funding and acknowledgments, see the Supplementary Note. References 1. Michailidou, K. et al. Association analysis identifies 65 new breast cancer risk loci. Nature 551, 92-94 (2017). 2. Milne, R.L. et al. Identification of ten variants associated with risk of estrogen- receptor-negative breast cancer. Nat Genet 49, 1767-1778 (2017). 3. Garcia-Closas, M. et al. Genome-wide association studies identify four ER negative-specific breast cancer risk loci. Nat Genet 45, 392-8, 398e1-2 (2013). 4. Zhang, H. et al. A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics. Biostatistics, Doi: 10.1093/biostatistics/kxz065 (2020). 5. Michailidou, K. et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet 45, 353-61, 361e1-2 (2013). 6. Michailidou, K. et al. Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer. Nat Genet 47, 373-80 (2015). 7. Dempster, A.P., Laird, N.M. & Rubin, D.B. Maximum Likelihood from Incomplete Data Via Em Algorithm. Journal of the Royal Statistical Society Series B-Methodological 39, 1-38 (1977). 8. Curigliano, G. et al. De-escalating and escalating treatments for early-stage breast cancer: the St. Gallen International Expert Consensus Conference on the Primary Therapy of Early Breast Cancer 2017. Ann Oncol 28, 1700-1712 (2017). 9. Spurdle, A.B. et al. Refined histopathological predictors of BRCA1 and BRCA2 mutation status: a large-scale analysis of breast cancer characteristics from the BCAC, CIMBA, and ENIGMA consortia. Breast Cancer Res 16, 3419 (2014). 10. Phelan, C.M. et al. Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer. Nat Genet 49, 680-691 (2017). 11. Stacey, S.N. et al. A germline variant in the TP53 polyadenylation signal confers cancer susceptibility. Nat Genet 43, 1098-103 (2011). 12. Pellacani, D. et al. Analysis of Normal Human Mammary Epigenomes Reveals Cell-Specific Active Enhancer States and Associated Transcription Factor Networks. Cell Rep 17, 2060-2074 (2016). 13. Beesley, J. et al. Chromatin interactome mapping at 139 independent breast cancer risk signals. bioRxiv, 520916 (2019). 14. Fachal, L. et al. Fine-mapping of 150 breast cancer risk regions identifies 178 high confidence target genes. Nat Genet 52, 56-73 (2020). 15. Nik-Zainal, S. et al. Landscape of somatic mutations in 560 breast cancer whole- genome sequences. Nature 534, 47-54 (2016). 16. Ferreira, M.A. et al. Genome-wide association and transcriptome studies identify target genes and risk loci for breast cancer. Nat Commun 10, 1741 (2019). 17. Wu, L. et al. A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer. Nat Genet 50, 968-978 (2018). 18. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat Genet 47, 1236-41 (2015). 19. Bulik-Sullivan, B.K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47, 291-5 (2015). 20. Finucane, H.K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet 47, 1228-35 (2015). 21. Ahearn, T.U. et al. Common breast cancer risk loci predispose to distinct tumor subtypes. bioRxiv, 733402 (2019). 22. Mavaddat, N. et al. Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes. Am J Hum Genet 104, 21-34 (2019). Figure Legends for main text Figure 1. Ideogram of all the independent genome-wide significant breast cancer susceptibility variants in overall, subtypes, BCAC triple-negative (TN) and CIMBA BRCA1 carriers meta- analysis. The 32 novel variants are labeled with arrows. The other significant variants are within +-500 or LD > 0.3 with previously reported variants. Figure 2. Heatmap and clustering of p-values from marker specific heterogeneity test for 32 breast cancer susceptibility loci (n = 106,278 invasive cases, n = 91,477 controls). P-values are for associations between the most significant variants marking each loci and estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) or grade, adjusting for top ten principal components and age. P-values are raw p-values from two-tailed z-test statistics. Fifteen variants in red color were significant according to the global heterogeneity tests (FDR <0.05), of which 14 were identified by methods accounting for tumor heterogeneity. TN, triple negative. Figure 3. Susceptibility variants with associations in opposite direction across subtypes. The case-control odds ratios (OR) and 95% confidence intervals (95% CI)1 (left panel) are for associations of each of the five variants and risk for breast cancer intrinsic-like subtypes2 estimated from the first-stage of the two-stage polytomous regression fixed-effects model (n = 106,278 invasive cases, n = 91,477 controls). The case-case ORs 95%CI (right panel) are estimated from the second stage parameters of a fixed effect two-stage polytomous models testing for heterogeneity between the five variants and estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and grade, where ER, PR, HER2, and grade are mutually adjusted for each other. MAF, minor allele frequency. Figure 4. Heatmap of candidate causal variants (CCVs) overlapping with enhancer states in primary breast subpopulations for five variants with associations in opposite direction across subtypes. Three different breast subpopulations were considered: basal cells (BC), luminal progenitor (LP) and luminal cells mature (LM). Based on a combination of H3K4me1 and H3K27ac histone modification ChiP-seq signals, putative enhancers in BC, LP, and LM were characterized as “OFF”, “PRIMED” and “ACTIVE” (Online Methods). The CCVs overlapping with enhancers were colored as red, otherwise were white. Figure 5. Genetic correlation between the five intrinsic-like breast cancer subtypes and BRCA1 mutation carriers estimated through LD score regression. See Supplementary Table 16 for further details. Both the color and size of the circles reflect the strength of the genetic correlations. Table 1. Genetic variance of invasive breast cancer explained by identified susceptibility variants and all reliably genome-wide imputable variants1 Phenotype Genetic variance for 210 identified susceptibility variants2 Genetic variance for 32 newly identified variants2 Genetic variance for all GWAS variants3 Proportion of genetic variance explained by identified susceptibility loci4 Invasive breast cancer5 0.253 0.016 0.515 45.51% Luminal A-like 0.336 0.022 0.620 54.22% Luminal B/HER2-negative-like 0.233 0.018 0.597 38.95% Luminal B-like 0.270 0.020 0.740 36.46% HER2-enriched-like 0.200 0.011 0.689 29.05% Triple negative 0.185 0.025 0.492 37.63% CIMBA BRCA1 carriers 0.083 0.016 0.309 26.86% 1 Genetic variance corresponds to heritability on the frailty-scale, which assumes the polygenetic log-additive model as the underlying model. 2 Susceptibil ity variants included 178 variants previously identified or replicated1,2 and 32 newly identified variants in this paper. 3 Genetic variance of all reliably genome-wide imputable variants was estimated through LD-score regression described in Nat Genet 47, 291-5 (2015). and Nat Genet 47, 1236-41 (2015). Under the frailty-scale, the genetic variance for all GWAS variants is characterized by population variance of the underlying true polygenic risk score as 𝜎𝐺𝑊𝐴𝑆 2 = 𝑉𝑎𝑟(∑ 𝛽𝑚𝐺𝑚 𝑀 𝑚=1 ), where 𝐺𝑚 is the standardized genotype for the 𝑚th variant, 𝛽𝑚 is the true log odds ratio for the 𝑚th variant and 𝑀 are the total number of causal variants among the GWAS variants. (Online Methods). 4 Proportion of genetic variance explained by 210 identified GWAS significant variants over the genetic variance explained by all GWAS variants. 5 Invasive breast cancer summary level statistics were generated from 106,278 invasive cases and 91,477 controls, which were th e same samples used in subtypes analyses (Supplementary Table 2). Online Methods Study populations The overall breast cancer analyses included women of European ancestry from 82 BCAC studies from over 20 countries, with genotyping data derived from two Illumina genome-wide custom arrays, the iCOGS and OncoArray (Supplementary Table 1). Most of the studies were case-control studies in the general population, or hospital setting, or nested within population-based cohorts, but a subset of studies oversampled cases with a family history of the disease. We included controls and cases of invasive breast cancer, carcinoma in-situ, and cases of unknown invasiveness. Information on clinicopathologic characteristics were collected by the individual studies and combined in a central database after quality control checks. We used BCAC database version ‘freeze’ 10 for these analyses. Among a subset of participants (n=16,766) that were genotyped on both the iCOGS and OncoArray arrays, we kept only the OncoArray data. One study (LMBC) contributing to the iCOGS dataset was excluded due to inflation of the test statistics that was not corrected by adjustment for the first ten PCs. We also excluded OncoArray data from Norway (the Norwegian Breast Cancer Study) because there were no controls available from Norway with OncoArray data. All participating studies were approved by their appropriate ethics or institutional review board and all participants provided informed consent. The total sample size for this analysis, including iCOGS, OncoArray and other GWAS data, comprised 133,384 cases and 113,789 controls. In the GWAS analyses accounting for underlying heterogeneity according to ER, PR, HER2 and grade, we included genotyping data from 81 BCAC studies. These analyses were restricted to controls and cases of invasive breast cancer. We excluded cases of carcinoma in-situ and cases with missing information on invasiveness, as ~96% of in-situ cases were missing some or all of the tumor markers and in-situ cases potentially have different tumor correlations compared to invasive cases, which could potentially bias the estimates from Expectation-Maximization algorithm (Supplementary Table 2). We also excluded all studies from a specific country if there were no controls for that country, or if the tumor marker data were missing on two or more of the tumor marker subtypes (see footnote of Supplementary Table 2 for further explanation of excluded studies). We did not include the summary results from the 14,910 cases and 17,588 controls from the 11 other GWAS in subtype analyses because these studies did not provide data on tumor characteristics. We also excluded invasive cases (n=293) and controls (n=4,285) with missing data on age at diagnosis or age at enrollment, information required by the Expectation-Maximization algorithm to impute missing tumor characteristics. In total, the final sample for the two-stage polytomous logistic regression comprised 106,278 invasive cases and 91,477 controls. Participants included from CIMBA were women of European ancestry, aged 18 years or older with a pathogenic BRCA1 variant. Most participants were sampled through cancer genetics clinics. In some instances, multiple members of the same family were enrolled. OncoArray genotype data was available from 58 studies from 24 countries. Following quality control and removal of participants that overlapped with the BCAC OncoArray study, data were available on 15,566 BRCA1 mutation carriers, of whom 7,784 were affected with breast cancer (Supplementary Table 3). We also obtained iCOGS genotype data on 3,342 BRCA1 mutation carriers (1,630 with breast cancer) from 54 studies through CIMBA. All BRCA1 mutation carriers provided written informed consent and participated under ethically approved protocols. Genotyping, quality control, and imputation Details on genotype calling, quality control and imputation for the OncoArray, iCOGS, and GWAS are described elsewhere1,2,5,6. Genotyped or imputed variants (including bi-allelic and multi-allelic single nucleotide polymorphisms (SNPs) and small indels) marking each of the loci were determined using the iCOGS and the OncoArray genotyping arrays and imputation to the 1000 Genomes Project (Phase 3) reference panel. We included variants, from each component GWAS with an imputation quality score of >0.3. We restricted analysis to variants with a minor allele frequency >0.005 in the overall breast cancer analysis and >0.01 in the subtype analysis. Known breast cancer susceptibility variants Prior studies identified susceptibility variants from genome-wide analyses at a significance level P < 5.0 × 10−8 for all breast cancer types, ER-negative or ER-positive breast cancer, in BRCA1 or BRCA2 mutation carriers, or in meta-analyses of these1-3. We defined known breast cancer susceptibility variants as those variants that were identified or replicated in prior BCAC analyses1,2. To help ensure that novel, independent susceptibility variants were identified, we excluded from these analyses variants within 500 kb of a previously published variant. These excluded regions have been subject to a separate, fine-mapping conditional analyses that are focused on identifying additional independent susceptibility variants in these regions14. Standard analysis of BCAC data Logistic regression analyses were conducted separately for the iCOGS and OncoArray datasets, adjusting for country and the array-specific first 10 PCs for ancestry informative variants. The methods for estimating PCs have been described elsewhere1,2. For the remaining GWAS, adjustment for inflation was done by adjusting for up to three PCs and using genomic control adjustment, as previously described1. We evaluated the associations between approximately 10.8 million variants with imputation quality scores (r2) ≥0.3 and minor allele frequency (MAF) >0.005. We excluded variants located within ±500 kb of, or in LD (r2≥0.1) with known susceptibility variants21. The association effect size estimates from these, and the previously derived estimates from the 11 other GWAS, were then combined using a fixed effects meta-analysis. Since individual level genotyping data were not available for some previous GWAS, we conservatively approximated the potential overlap between the GWAS and iCOGS and OncoArray datasets, based on the populations contributing to each GWAS (iCOGS/GWAS: 626 controls and 923 cases; OncoArray/GWAS: 20 controls and 990 cases). We then used these adjusted data to estimate the correlation in the effect size estimates, and incorporated these into the meta-analysis using the method of Lin and Sullivan23. Subtypes analysis of BCAC data We described the two-stage polytomous logistic regression in more detail elsewhere4,24 (Supplementary Note). In brief, this method allows for efficient testing of a variant-disease association in the presence of tumor subtype heterogeneity defined by multiple tumor characteristics, while accounting for multiple testing and missing data on tumor characteristics. In the first stage, the model uses a polytomous logistic regression to model case-control ORs between the variants and all possible subtypes that could be of interest, defined by the combination of the tumor markers. For example, in a model fit to evaluate heterogeneity according to ER, PR and HER2 positive/negative status, and grade of differentiation (low, intermediate and high grade), the first stage incorporates case-control ORs for 24 subtypes defined by the cross-classification of these factors. The second stage restructures the first-stage subtype-specific case-control ORs parameters into second-stage parameters through a decomposition procedure resulting in a second-stage baseline parameter that represents a case-control OR of a baseline cancer subtype, and case-case ORs parameters for each individual tumor characteristic. The second-stage case-case parameters can be used to perform heterogeneity tests with respect to each specific tumor marker while adjusting for the other tumor markers in the model. The two-stage model efficiently handles missing data by implementing an Expectation-Maximization algorithm4,7 that essentially performs iterative “imputation” of the missing tumor characteristics conditional on available tumor characteristics and baseline covariates based on an underlying two-stage polytomous model. In the two-stage model, the frequency of different tumor subtypes corresponding to different combinations of the tumor characteristics are allowed to vary freely through the model-free specification of the intercepts of the first-stage polytomous model (αm, see Supplementary Note for details), in other words, the intercepts are kept saturated. As these parameters are estimated from the data itself, the methodology accounts for the correlation among the tumor markers in a robust manner that does not require strong modelling assumptions. To identify novel susceptibility loci, we used both a fixed-effect two-stage polytomous model and a mixed-effect two-stage polytomous model. The score-test we developed based on the mixed-effect model allows coefficients associated with individual tumor characteristics to enter as either fixed- or random-effect terms. Our previous analyses have shown that incorporation of random effect terms can improve power of the score-test by essentially reducing the effective degrees-of-freedom associated with fixed effects related to exploratory markers (i.e., markers for which there is little prior evidence to suggest that they are a source of heterogeneity)4. On the other hand, incorporation of fixed-effect terms can preserve distinct associations of known important tumor characteristics, such as ER. In the mixed-effect two-stage polytomous model, we therefore kept ER as a fixed effect, but modeled PR, HER2 and grade as random effects. We evaluated variants with MAF >0.01 (~10.0 million) and r2≥0.3, and excluded variants within ±500 kb of, or in LD (r2≥0.1) with known susceptibility variants. A MAF >0.01 was chosen to ensure an adequate sample size to generate stable estimates. We reported variants that passed the p-value threshold of P < 5.0 × 10-8 in either the fixed- or mixed-effect models. Both fixed/mixed-effect models adjusted for top ten PCs and age. As age is correlated with the tumor characteristics25, we added age as a covariate to improve the statistical power of Expectation-Maximization (EM) algorithm. Country was not adjusted for in the subtype analyses, since doing so required adequate sample size of each subtype in each country to allow for convergence of the two-stage polytomous model. Instead, we assessed the influence of country on signals identified by the two-stage models by performing a ‘leave one out’ sensitivity analyses in which we reevaluated novel signals after excluding data from each individual country. Data from the OncoArray and iCOGS arrays were analyzed separately and then meta-analyzed using fixed-effects meta-analysis. Statistical analysis of CIMBA data We tested for associations between variants and breast cancer risk for BRCA1 mutation carriers using a score test statistic based on the retrospective likelihood of observing the variant genotypes conditional on breast cancer phenotypes (breast cancer status and censoring time)26. Analyses were performed separately for iCOGS and OncoArray data. To allow for non-independence among related individuals, a kinship-adjusted test was used that accounted for familial correlations27. We stratified analyses by country of residence and, for countries where the strata were sufficiently large (United States and Canada), by Ashkenazi Jewish ancestry. The results from the iCOGS and OncoArray data were then pooled using fixed-effects meta-analysis. Meta-analysis of BCAC and CIMBA As the great majority of BRCA1 related breast cancers are triple-negative28, we performed a meta-analysis with the BCAC triple-negative results to increase the power to detect associations for the triple-negative subtype. We performed a fixed-effects meta-analysis of the results from BCAC triple-negative cases and CIMBA BRCA1 mutation carriers, using an inverse-variance fixed-effects approach implemented in METAL29. The estimates of association used were the logarithm of the per-allele hazard ratio estimate for association with breast cancer risk for BRCA1 mutation carriers from CIMBA and the logarithm of the per-allele odds ratio estimate for association with risk of triple-negative breast cancer based on BCAC data. Conditional analyses We performed two sets of conditional analyses. First, we investigated for evidence of multiple independent signals in identified loci by performing forward selection logistic regression, in which we adjusted the lead variant and analyzed association for all remaining variants within ±500 kb of the lead variants, irrespective of LD. Second, we confirmed the independence of 20 variants that were located within ±2 MB of a known susceptibility region by conditioning the identified signals on the nearby known signal. Since these 20 variants are already genome-wide significant in the original GWAS scan and the conditional analyses restricted to local regions, we therefore used a significance threshold of P < 1 × 10-6 to control for type-one error30. Heterogeneity analysis of new association signals We evaluated all novel signals for evidence of heterogeneity using the two-stage polytomous model. We first performed a global test for heterogeneity under the mixed- effect model test to identify variants showing evidence of heterogeneity with respect to any of the underlying tumor markers, ER, PR, HER2 and/or grade. We accounted for multiple testing of the global heterogeneity test using a FDR <0.05 under the Benjamini - Hochberg procedure31. Among the variants with observed heterogeneity, we then further used a fixed-effect two-stage model to evaluate influence of specific tumor characteristic(s) driving observed heterogeneity, adjusted for the other markers in the model. We also fit a separate fixed-effect two-stage models to estimate case-control ORs and 95% confidence intervals (CI) for five surrogate intrinsic-like subtypes defined by combinations of ER, PR, HER2 and grade8: (1) luminal A-like (ER+ and/or PR+, HER2-, grade 1 & 2); (2) luminal B/HER2-negative-like (ER+ and/or PR+, HER2-, grade 3); (3) luminal B-like (ER+ and/or PR+, HER2+); (4) HER2-enriched-like (ER- and PR-, HER2+), and (5) triple-negative (ER-, PR-, HER2-). Further, we conducted sensitivity analysis by fitting a standard polytomous model among cases with complete data on the five-intrinsic-like subtypes for the 32 novel variants and compared these results with the results from two-stage polytomous model accounting for missing tumor data. Candidate causal variants We defined credible sets of candidate causal variants (CCVs) as variants located within ±500 kb of the lead variants in each novel region and with P values within 100- fold of magnitude of the lead variants. This is approximately equivalent to selecting variants whose posterior probability of causality is within two orders of magnitude of the most significant variant32,33. This approach was applied for detecting a set of potentially causal variants for all 32 identified variants. For the novel variants located within ±2 Mb of the known signals, we used the conditional P values to adjust for the known signals’ associations. Enhancer states analysis in breast sub-populations We obtained enhancer maps for three enriched primary breast sub-populations (basal, luminal progenitor, and mature luminal) from Pellacani et al.12. Enhancer annotations were defined as ACTIVE, PRIMED, or OFF based on a combination of H3K27ac and H3K4me1 histone modification ChIP-seq signals using FPKM thresholds as previously described12. Briefly, genomic regions containing high H3K4me1 signal observed in any cell type were used to define the superset of breast regulatory elements. Sub-population cell type-specific H3K27ac signal (which is characteristic of active elements) within these elements was used as a measure of overall regulatory activity, where "ACTIVE" sites were characterized by H3K4me1-high, H3K27ac-high; "PRIMED" by H3K4me1-high, H3K27ac-low; and "OFF" by H3K4me1-low, H3K27ac- low. This enabled annotation of each enhancer element as either “OFF”, “PRIMED” or “ACTIVE” in all cell types. We then defined enhancers which exhibit differing states between at least one cell type as "ANYSWITCH" enhancers. Genetic correlation analyses We used LD score regression18-20 to estimate the genetic correlation between five intrinsic-like breast cancer subtypes. The analysis used the summary statistics based on the meta-analysis of the OncoArray, and iCOGS, and CIMBA meta-analysis. The genetic correlation18 analysis was restricted to the ~1 million variants included in HapMap 3 with MAF > 1% and imputation quality score R2>0.3 in the OncoArray data. Since two-stage polytomous models integrated an imputation algorithm for missing tumor characteristic data, we modified the LD score regression to generate the effective sample size for each variant (Supplementary Note). Genetic variance explained by identified susceptibility variants and all genome- wide imputable variants Genetic variance corresponds to heritability on the frailty-scale, which assumes a polygenetic log-additive model as the underlying model. Under the log-additive model, the frailty-scale heritability explained by the identified variants can be estimated by: ∑ 2𝑝𝑖(1 − 𝑝𝑖)(?̂?𝑖 2 − 𝜏𝑖 2) 𝑛 𝑖=1 , where 𝑛 is the total number of identified variants, 𝑝𝑖 is the MAF for 𝑖th variant, 𝛽i is the log odds ratio estimate for the 𝑖th variant, and 𝜏𝑖 is the standard error of 𝛽i. To obtain the frailty scale heritability for invasive breast cancer explained by all of the GWAS variants, we used LD score regression to estimate heritability (σ𝐺𝑊𝐴𝑆 2 ) using the full set of summary statistics from either standard logistic regression for overall invasive breast cancer, the two-stage polytomous regression for the intrinsic-like subtypes, or the CIMBA BRCA1 analysis for BRCA1 carriers. σ𝐺𝑊𝐴𝑆 2 is characterized by population variance of the underlying true polygenetic risk scores as σ𝐺𝑊𝐴𝑆 2 = 𝑉𝑎𝑟(∑ 𝛽𝑚𝐺𝑚 𝑀 𝑚=1 ), where 𝐺𝑚 is the standardized genotype for the 𝑚th variant, 𝛽𝑚 is the true log odds ratio for the 𝑚th variant and 𝑀 are the total number of causal variants among the GWAS variants. Thus, the proportion of heritability explained by identified variants relative to all imputable variants is: ∑ 2𝑝𝑖(1 − 𝑝𝑖)(?̂?i 2 − 𝜏𝑖 2)𝑛𝑖=1 /σ𝐺𝑊𝐴𝑆 2 . To estimate the proportion of the familial risk of invasive breast cancer that is explained by susceptibility variants, we defined the familial relative risk, λ, as the familial relative risk assuming a polygenic log-additive model that explains all the familial aggregation of the disease34. Under the frailty scale, we define the broad sense heritability35 as σ2. The relationship between λ and σ2 was shown to be σ2 = 2 ∗ log (𝜆) 34. We assumed λ = 2 as the overall familial relative risk of invasive breast cancer34, thus σ2 = 2log (2) and the proportion of the familial relative risk explained by identified susceptibility variants is ∑ 𝑝𝑖(1 − 𝑝𝑖)(?̂?𝑖 2 − 𝜏𝑖 2)𝑛𝑖=1 log (2)⁄ , and the proportion of the familial relative risk explained by GWAS variants is σ𝐺𝑊𝐴𝑆 2 [2 ∗ log(2)]⁄ . Analyses of heritability and the proportion of explained familial risk were restricted to 106,278 invasive cases and 91,477 controls (Supplementary Table 2). In addition, we compared estimates of GWAS chip hereditability across five-intrinsic subtypes using LD-score regression where the summary statistics were derived using either standard polytomous model applied to complete cases or the novel two-stage method that incorporates cases with missing tumor characteristics. PRSs for five intrinsic-like subtypes We constructed PRSs for the intrinsic-like subtypes, incorporating the newly identified variants and 313 variants previously reported in the development of PRSs for overall and ER-specific breast cancer22. The 313 SNPs include SNPs that didn’t reach genome-wide significance. After excluding variants within 500 kb of the 313 SNPs or LD>=0.1, 17 out of the 32 novel variants were independent with the 313 SNPs. The BCAC data were split into the training dataset and test dataset with a proportion of 80% and 20%, respectively. Half of the test dataset were five studies nested within prospective cohorts including KARMA, MMHS, PLCO, SISTER, UKBGS (Supplementary Table 2) and the other half was randomly selected among the subjects in OncoArray, excluding studies of bilateral breast cancer, studies or sub studies with oversampling for family history, cases with ambiguous diagnosis, and cases with missing tumor characteristics. We obtained the overall and ER-specific log odds ratios for 313 SNPs by respectively fitting standard and ER-specific logistic regression on the training dataset. We obtained the log odds ratio for 330 SNPs by fitting the fixed-effect two-stage polytomous model for five intrinsic-like subtypes on the training dataset (Supplementary Table 19). Reporting Summary Further information on research design is available in the Nature Research Reporting Summary linked to this article. Data Availability Statement Summary level statistics are available from http://bcac.ccge.medschl.cam.ac.uk/bcacdata/ and http://cimba.ccge.medschl.cam.ac.uk/projects/. Requests for data can be made to the corresponding author or the Data Access Coordination Committees (DACCs) of BCAC (see above URL) and CIMBA (see above URL). BCAC DACC approval is required to access data from the ABCFS, ABCS, ABCTB, BBCC, BBCS, BCEES, BCFR-NY, BCFR-PA, BCFR-UT, BCINIS, BSUCH, CBCS, CECILE, CGPS, CTS, DIETCOMPLYF, ESTHER, GC-HBOC, GENICA, GEPARSIXTO, GESBC, HABCS, HCSC, HEBCS, HMBCS, HUBCS, KARBAC, KBCP, LMBC, MABCS, MARIE, MBCSG, MCBCS, MISS, MMHS, MTLGEBCS, NC-BCFR, OFBCR, ORIGO, pKARMA, POSH, PREFACE, RBCS, SKKDKFZS, SUCCESSB, SUCCESSC, SZBCS, TNBCC, UCIBCS, UKBGS and UKOPS studies (Supplementary Table 1). CIMBA DACC approval is required to access data from the BCFR-ON, CONSIT TEAM, DKFZ, EMBRACE, FPGMX, GC- HBOC, GEMO, G-FAST, HEBCS, HEBON, IHCC, INHERIT, IOVHBOCS, IPOBCS, MCGILL, MODSQUAD, NAROD, OCGN, OUH and UKGRFOCR studies (Supplementary Table 3). Code Availability statement The data analysis code of this paper is available at https://github.com/andrewhaoyu/breast_cancer_data_analysis. The implementation of this two-stage polytomous regression method is available in a R package called TOP (https://github.com/andrewhaoyu/TOP) with a detailed tutorial available at https://github.com/andrewhaoyu/TOP/blob/master/inst/TOP.pdf. Methods-only references 23. Lin, D.Y. & Sullivan, P.F. Meta-analysis of genome-wide association studies with overlapping subjects. Am J Hum Genet 85, 862-72 (2009). 24. Chatterjee, N. A Two-Stage Regression Model for Epidemiological Studies with Multivariate Disease Classification Data. Journal of the American Statistical Association 99, 127-138 (2004). 25. Anderson, W.F., Rosenberg, P.S., Prat, A., Perou, C.M. & Sherman, M.E. How many etiological subtypes of breast cancer: two, three, four, or more? J Natl Cancer Inst 106(2014). 26. Barnes, D.R. et al. Evaluation of association methods for analysing modifiers of disease risk in carriers of high-risk mutations. Genet Epidemiol 36, 274-91 (2012). 27. Antoniou, A.C. et al. A locus on 19p13 modifies risk of breast cancer in BRCA1 mutation carriers and is associated with hormone receptor-negative breast cancer in the general population. Nat Genet 42, 885-92 (2010). 28. Mavaddat, N. et al. Pathology of breast and ovarian cancers among BRCA1 and BRCA2 mutation carriers: results from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). Cancer Epidemiol Biomarkers Prev 21, 134-47 (2012). 29. Willer, C.J., Li, Y. & Abecasis, G.R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190-1 (2010). 30. Hendricks, A.E., Dupuis, J., Logue, M.W., Myers, R.H. & Lunetta, K.L. Correction for multiple testing in a gene region. Eur J Hum Genet 22, 414-8 (2014). 31. Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological) 57, 289-300 (1995). 32. Udler, M.S., Tyrer, J. & Easton, D.F. Evaluating the power to discriminate between highly correlated SNPs in genetic association studies. Genet Epidemiol 34, 463-8 (2010). 33. Wellcome Trust Case Control, C. et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat Genet 44, 1294-301 (2012). 34. Pharoah, P.D. et al. Polygenic susceptibility to breast cancer and implications for prevention. Nat Genet 31, 33-6 (2002). 35. Visscher, P.M., Hill, W.G. & Wray, N.R. Heritability in the genomics era--concepts and misconceptions. Nat Rev Genet 9, 255-66 (2008). Known variants Significant variants in overall analysis Overall analysis identified 22 novel variants Subtypes analysis identified 8 novel variants Significant variants in subtypes analysis Significant variants in BCAC TN and CIMBA BRCA1 carriers meta-analysis BCAC TN and CIMBA BRCA1 carriers meta-analysis identified 2 novel variants GradeHER2PRER Significant in two-stage polytomous regression Significant in BCAC TN and CIMBA BRCA1 meta analysis Significant in Standard logistic regression Significant in both two-stage polytomous regression and BCAC TN and CIMBA BRCA1 meta analysis Significant in both standard logistic regression and two-stage polytomous regression rs78378222 rs9712235 rs141526427 rs6065254 rs7760611 rs7924772 rs206435 rs17215231 rs4742903 chr1:145126177 rs79518236 rs138044103 rs12962334 rs188092014 rs1375631 rs13039563 rs1061657 rs495367 rs2464195 rs5776993 chr12:29140260 rs13277568 rs9808759 rs2886671 rs17743054 rs4602255 rs142890050 rs11652463 rs10838267 rs13256025 rs11065822 rs34052812 P-value legend 1.0x10-6 1.0x10-4 1.0x10-3 0.05 0.20 Case-control OR and 95% CIVariant chromosome Position Case-case OR and 95% CIMAF 1 Per-minor allele odds ratio and 95% confidence limits 2 luminal A-like (ER+ and/or PR+, HER2-, grade 1 & 2); luminal B/HER2-negative-like (ER+ and/or PR+, HER2-, grade 3); luminal B-like (ER+ and/or PR+, HER2+); (4) HER2-enriched-like (ER- and PR-, HER2+), and triple-negative (ER-, PR-, HER2-) Luminal A-like Luminal B/HER2-negative-like Luminal B-like HER2-enriched-like Triple-negative BRCA1 mutation carriers ER PR HER2 grade Candidate causal variants (CCVs) En ha nc er st at e En ha nc er st at e En ha nc er st at e En ha nc er st at e OFF PRIMED ACTIVE OFF PRIMED ACTIVE OFF PRIMED ACTIVE OFF PRIMED ACTIVE OFF PRIMED ACTIVE OFF PRIMED ACTIVE OFF PRIMED ACTIVE OFF PRIMED ACTIVE OFF PRIMED ACTIVE a) Lead SNP: rs78378222 CHR: 17 Position: 7,571,752 b) Lead SNP: rs141526427 CHR: 20 Position: 11,502,618 c) Lead SNP: rs6065254 CHR: 20 Position: 39,248,265 OFF PRIMED ACTIVE OFF PRIMED ACTIVE OFF PRIMED ACTIVE d) Lead SNP: rs7924772 CHR: 11 Position: 120,233,626 En ha nc er st at e OFF PRIMED ACTIVE OFF PRIMED ACTIVE OFF PRIMED ACTIVE e) Lead SNP: rs206435 CHR: 18 Position: 10,354,649 En ha nc er st at e Candidate causal variants (CCVs) Luminal B-like Luminal A-like Luminal B/HER2-negative-like HER2-enriched-like Triple-negative CIMBA BRCA1 Lum inal B-lik e Lum inal A-li ke HER 2-en rich ed-l ike Trip le-n ega tive CIM BA BRC A1 Lum inal B/H ER2 -neg ativ e-lik e 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1 Supplementary figure 1. Variants associations with overall breast cancer risk identified using standard logistic regression (n = 133,384 cases, n = 113,789 controls). a) Manhattan plot showing -log10P values for variant associations with breast cancer risk. b) Manhattan plot after excluding previous known regions (Online Methods) c) Quantile-Quantile (Q-Q) plot of observed P-values versus expected P-values for all variants. d) QQ plot1 after excluding previous known regions. P-values are raw p-values from two-tailed z-test statistics. Bonferroni correction was used to account for multiple testing (cut off P-value = 5x 10-8). 1) 𝜆1000 scale the genomic inflation factor 𝜆 to a study with sample size of 1000 cases and 1000 controls using the formula 𝜆1000 = 1 + 500 ∗ (𝜆 − 1)/( 1 𝑛𝑐𝑎𝑠𝑒𝑠 + 1 𝑛𝑐𝑜𝑛𝑡𝑟𝑜𝑙 ) Formatted: Superscript Supplementary figure 2. Variant associations with breast cancer risk using a mixed-effect two-stage model (Oline Methods) accounting for tumor heterogeneity according to the ER, PR, HER2, and grade (n = 106,278 invasive cases, n = 91,477 controls). a) Manhattan plot showing -log10P values for variant associations with breast cancer risk. b) Manhattan plot showing -log10P values for variant associations with breast cancer risk after excluding previously known regions (Online Methods) and 22 loci identified through standard logistic regression analysis (Supplementary Figure 2). c) QQ plot1 of observed P-values versus expected P-values for all variants. d) QQ plot of observed P-values versus expected P-values for remaining variants after excluding previously known regions and 22 loci identified through standard logistic regression analysis. P-values are raw p-values from two-tailed z-test statistics. Bonferroni correction was used to account for multiple testing (cut off P-value = 5 x 10-8). 1) 𝜆1000 scale the genomic inflation factor 𝜆 to a study with sample size of 1000 cases and 1000 controls using the formula 𝜆1000 = 1 + 500 ∗ (𝜆 − 1)/( 1 𝑛𝑐𝑎𝑠𝑒𝑠 + 1 𝑛𝑐𝑜𝑛𝑡𝑟𝑜𝑙 ) Supplementary figure 3. Variant associations with breast cancer risk using a fixed-effect two-stage model (Oline Methods) accounting for tumor heterogeneity according to the ER, PR, HER2, and grade (n = 106,278 invasive cases, n = 91,477 controls). a) Manhattan plot showing -log10P values for variant associations with breast cancer risk. b) Manhattan plot showing -log10P values for variant associations with breast cancer risk after excluding previously known regions (Online Methods) and 22 loci identified through standard logistic regression analysis (Supplementary Figure 2). c) QQ plot1 of observed P-values versus expected P-values for all variants. d) QQ plot of observed P-values versus expected P-values for remaining variants after excluding previously known regions and 22 loci identified through standard analysis. P-values are raw p-values from two-tailed z-test statistics. Bonferroni correction was used to account for multiple testing (cut off P-value = 5x 10-8). 1) 𝜆1000 scale the genomic inflation factor 𝜆 to a study with sample size of 1000 cases and 1000 controls using the formula 𝜆1000 = 1 + 500 ∗ (𝜆 − 1)/( 1 𝑛𝑐𝑎𝑠𝑒𝑠 + 1 𝑛𝑐𝑜𝑛𝑡𝑟𝑜𝑙 ) Supplementary figure 4. Variant association with triple-negative triple negative (TN) breast cancer risk using a fixed-effect meta-analysis of results between BCAC TN and CIMBA BRCA1 carriers (BCAC: n = 8,602 effective triple-negative cases, n = 91,477 controls; CIMBA BRCA1 carriers: n = 9,414 cases, n = 9,494 controls). a) Manhattan plot showing -log10P values for variant associations with triple-negative TN breast cancer risk. b) Manhattan plot showing -log10P values for variant associations with triple-negative TN breast cancer risk after excluding previously known regions (Online Methods). c) QQ plot1 of observed P-values versus expected P-values for all variants d) QQ plot of observed P-values versus expected P- values for remaining variants after excluding previously known regions. P-values are raw p-values from two-tailed z-test statistics. Bonferroni correction was used to account for multiple testing (cut off P-value = 5x 10-8). 1) 𝜆1000 scale the genomic inflation factor 𝜆 to a study with sample size of 1000 cases and 1000 controls using the formula 𝜆1000 = 1 + 500 ∗ (𝜆 − 1)/( 1 𝑛𝑐𝑎𝑠𝑒𝑠 + 1 𝑛𝑐𝑜𝑛𝑡𝑟𝑜𝑙 ) Supplementary figure 5. Regional plots of the 32 identified breast cancer variants. The first 22 variants were identified through standard logistic regression (n = 133,384 cases, n = 113,789 controls), the following eight variants were identified through two-stage polytomous regression (n = 106,278 invasive cases, n = 91,477 controls), the last two variants were identified through meta-analysis of BCAC triple-negative TN and CIMBA BRCA1 carriers (BCAC: n = 8,602 effective triple-negative cases, n = 91,477 controls; CIMBA BRCA1 carriers: n = 9,414 cases, n = 9,494 controls). Plotted area is showing ±500 KB region around the identified susceptibility variant. P-values are raw p-values from two-tailed z-test statistics. Bonferroni correction was used to account for multiple testing (cut off P-value = 5x 10-8). Supplementary figure 5 continued. Regional plots of the 32 identified breast cancer variants. The first 22 variants were identified through standard logistic regression (n = 133,384 cases, n = 113,789 controls), the following eight variants were identified through two-stage polytomous regression (n = 106,278 invasive cases, n = 91,477 controls), the last two variants were identified through meta-analysis of BCAC triple-negative and CIMBA BRCA1 carriers (BCAC: n = 8,602 effective triple-negative cases, n = 91,477 controls; CIMBA BRCA1 carriers: n = 9,414 cases, n = 9,494 controls). Plotted area is showing ±500 KB region around the identified susceptibility variant. P-values are raw p-values from two-tailed z-test statistics. Bonferroni correction was used to account for multiple testing (cut off P-value = 5x 10-8). Supplementary figure 5 continued. Regional plots of the 32 identified breast cancer variants. The first 22 variants were identified through standard logistic regression (n = 133,384 cases, n = 113,789 controls), the following eight variants were identified through two-stage polytomous regression (n = 106,278 invasive cases, n = 91,477 controls), the last two variants were identified through meta-analysis of BCAC triple-negative and CIMBA BRCA1 carriers (BCAC: n = 8,602 effective triple-negative cases, n = 91,477 controls; CIMBA BRCA1 carriers: n = 9,414 cases, n = 9,494 controls). Plotted area is showing ±500 KB region around the identified susceptibility variant. P-values are raw p-values from two-tailed z-test statistics. Bonferroni correction was used to account for multiple testing (cut off P-value = 5x 10-8). Known Susceptibility Region Supplementary figure 5 continued. Regional plots of the 32 identified breast cancer variants. The first 22 variants were identified through standard logistic regression (n = 133,384 cases, n = 113,789 controls), the following eight variants were identified through two-stage polytomous regression (n = 106,278 invasive cases, n = 91,477 controls), the last two variants were identified through meta-analysis of BCAC triple-negative and CIMBA BRCA1 carriers (BCAC: n = 8,602 effective triple-negative cases, n = 91,477 controls; CIMBA BRCA1 carriers: n = 9,414 cases, n = 9,494 controls). Plotted area is showing ±500 KB region around the identified susceptibility variant. P-values are raw p-values from two-tailed z-test statistics. Bonferroni correction was used to account for multiple testing (cut off P-value = 5x 10-8). Supplementary figure 5 continued. Regional plots of the 32 identified breast cancer variants. The first 22 variants were identified through standard logistic regression (n = 133,384 cases, n = 113,789 controls), the following eight variants were identified through two-stage polytomous regression (n = 106,278 invasive cases, n = 91,477 controls), the last two variants were identified through meta-analysis of BCAC triple-negative and CIMBA BRCA1 carriers (BCAC: n = 8,602 effective triple-negative cases, n = 91,477 controls; CIMBA BRCA1 carriers: n = 9,414 cases, n = 9,494 controls). Plotted area is showing ±500 KB region around the identified susceptibility variant. P-values are raw p-values from two-tailed z-test statistics. Bonferroni correction was used to account for multiple testing (cut off P-value = 5x 10-8). Known Susceptibility Region Supplementary figure 5 continued. Regional plots of the 32 identified breast cancer variants. The first 22 variants were identified through standard logistic regression (n = 133,384 cases, n = 113,789 controls), the following eight variants were identified through two-stage polytomous regression (n = 106,278 invasive cases, n = 91,477 controls), the last two variants were identified through meta-analysis of BCAC triple-negative and CIMBA BRCA1 carriers (BCAC: n = 8,602 effective triple-negative cases, n = 91,477 controls; CIMBA BRCA1 carriers: n = 9,414 cases, n = 9,494 controls). Plotted area is showing ±500 KB region around the identified susceptibility variant. P-values are raw p-values from two-tailed z-test statistics. Bonferroni correction was used to account for multiple testing (cut off P-value = 5x 10-8). P-values and Regional LD for chr1:145126177 in EUR Known Susceptibility Region Supplementary figure 5 continued. Regional plots of the 32 identified breast cancer variants. The first 22 variants were identified through standard logistic regression (n = 133,384 cases, n = 113,789 controls), the following eight variants were identified through two-stage polytomous regression (n = 106,278 invasive cases, n = 91,477 controls), the last two variants were identified through meta-analysis of BCAC triple-negative and CIMBA BRCA1 carriers (BCAC: n = 8,602 effective triple-negative cases, n = 91,477 controls; CIMBA BRCA1 carriers: n = 9,414 cases, n = 9,494 controls). Plotted area is showing ±500 KB region around the identified susceptibility variant. P-values are raw p-values from two-tailed z-test statistics. Bonferroni correction was used to account for multiple testing (cut off P-value = 5x 10-8). Supplementary figure 5 continued. Regional plots of the 32 identified breast cancer variants. The first 22 variants were identified through standard logistic regression (n = 133,384 cases, n = 113,789 controls), the following eight variants were identified through two-stage polytomous regression (n = 106,278 invasive cases, n = 91,477 controls), the last two variants were identified through meta-analysis of BCAC triple-negative and CIMBA BRCA1 carriers (BCAC: n = 8,602 effective triple-negative cases, n = 91,477 controls; CIMBA BRCA1 carriers: n = 9,414 cases, n = 9,494 controls). Plotted area is showing ±500 KB region around the identified susceptibility variant. P-values are raw p-values from two-tailed z-test statistics. Bonferroni correction was used to account for multiple testing (cut off P-value = 5x 10-8). Supplementary figure 6. Country Specific sensitivity analysis of eight novel genome-wide significant loci identified using the two-stage regression models (n = 106,278 invasive cases, n = 91,477 controls), and chr22:40042814 which was dropped since the signal was observed only in studies from the USA. Supplementary Figure 7. Associations1 between novel susceptibility variants identified using standard logistic regression with intrinsic-like breast cancer subtypes2 (right panel, n = 106,278 invasive cases, n = 91,477 controls) and the second-stage heterogeneity p-values from the two-stage polytomous logistic regression model (left panel, n = 106,278 invasive cases, n = 91,477 controls). 1 Per-minor allele odds ratio (95% confidence limits) 2. Luminal A-like (ER+ and/or PR+, HER2-, grade 1 & 2); luminal B/HER2-negative-like (ER+ and/or PR+, HER2-, grade 3); luminal B-like (ER+ and/or PR+, HER2+); HER2-enriched-like (ER- and PR-, HER2+); triple-negative (ER-, PR-, HER2-) 3. Based on a mixed-effect two-stage polytomous model testing for heterogeneity between susceptibility variants and ER, PR, HER2, and grade, where ER was entered into the model as a fixed-effect term and PR, HER2, and grade were entered into the model as random-effect terms. 4. Results from second stage case-case parameters from a fixed effect two-stage polytomous model testing for heterogeneity between susceptibility variants and ER, PR, HER2, and grade, where ER, PR, HER2, and grade are mutually adjusted for each other 5. Estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) Odds ratio and 95% CI Global etiologic heterogeneity P3 ER5 PR5 HER25 grade Tumor characteristic heterogeneity P4 Variant chromosome Position MAF Breast cancer risk by subtypes Luminal A-like Luminal B/HER2-negative-like Luminal B-like HER2-enriched-like Triple-negative BRCA1 mutation carriers Formatted: Font: 12 pt Formatted: Font: 12 pt Formatted: Font: 12 pt Formatted: Font: 12 pt Supplementary Figure 7 continued. Associations1 between novel susceptibility variants identified using standard logistic regression with intrinsic-like breast cancer subtypes2 (right panel, n = 106,278 invasive cases, n = 91,477 controls) and the second-stage heterogeneity p-values from the two-stage polytomous logistic regression model (left panel, n = 106,278 invasive cases, n = 91,477 controls). Associations1 between novel susceptibility variants identified using standard logistic regression with intrinsic-like breast cancer subtypes2 (right panel) and the second-stage heterogeneity p-values from the two-stage polytomous logistic regression model (left panel) Luminal A-like Luminal B/HER2-negative-like Luminal B-like HER2-enriched-like Triple-negative BRCA1 mutation carriers 1 Per-minor allele odds ratio (95% confidence limits) 2. Luminal A-like (ER+ and/or PR+, HER2-, grade 1 & 2); luminal B/HER2-negative-like (ER+ and/or PR+, HER2-, grade 3); luminal B-like (ER+ and/or PR+, HER2+); HER2-enriched-like (ER- and PR-, HER2+); triple-negative (ER-, PR-, HER2-) 3. Based on a mixed-effect two-stage polytomous model testing for heterogeneity between susceptibility variants and ER, PR, HER2, and grade, where ER was entered into the model as a fixed -effect term and PR, HER2, and grade were entered into the model as random-effect terms. 4. Results from second stage case-case parameters from a fixed effect two-stage polytomous model testing for heterogeneity between susceptibility variants and ER, PR, HER2, and grade, where ER, PR, HER2, and grade are mutually adjusted for each other 5. Estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) Tumor characteristic heterogeneity P4 chromosome Global etiologic heterogeneity P3 Variant ER 5 PR 5 HER2 5 grade MAF Position Breast cancer risk by subtypes Formatted: Font: Not Bold Supplementary Figure 8 Risk1 of breast cancer subtypes defined by intrinsic-like subtypes2 (n = 106,278 invasive cases, n = 91,477 controls) among loci identified using the two-stage polytomous logistic regression model and the CIMBA / BCAC triple-negative meta-analysis. 1 Per-minor allele odds ratio (95% confidence limits) 2. Luminal A-like (ER+ and/or PR+, HER2-, grade 1 & 2); luminal B/HER2-negative-like (ER+ and/or PR+, HER2-, grade 3); luminal B-like (ER+ and/or PR+, HER2+); HER2-enriched-like (ER- and PR-, HER2+); triple-negative (ER-, PR-, HER2-) 3. Based on a mixed-effect two-stage polytomous model testing for heterogeneity between susceptibility variants and ER, PR, HER2, and grade, where ER was entered into the model as a fixed -effect term and PR, HER2, and grade were entered into the model as random-effect terms. 4. Results from second stage case-case parameters from a fixed effect two-stage polytomous model testing for heterogeneity between susceptibility variants and ER, PR, HER2, and grade, where ER, PR, HER2, and grade are mutually adjusted for each other 5. Estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) Odds ratio and 95% CI Tumor characteristic heterogeneity P4 chromosome Global etiologic heterogeneity P3 Variant ER5 PR5 HER25 grade MAF Position Breast cancer risk by subtypes Luminal A-like Luminal B/HER2-negative-like Luminal B-like HER2-enriched-like Triple-negative BRCA1 mutation carriers Supplementary figure 9. a) Enrichment analysis1 results for 24 non-cell-type-specific, publicly available annotations for luminal A-like subtypes and triple-negative TN subtypes (n = 45,253 effective luminal A-like cases, n = 8,602 effective triple-negative cases, n = 91,477 controls). b) Enrichment analysis1 results for 24 main annotations with ±500 bp extension for luminal A-like subtypes and triple-negative TN subtypes. No significant differences were found between luminal A-like and triple-negative TN after adjusting for multiple testing. a) b) 1 Error bars represent Jackknife standard errors around the estimates of enrichment. Supplementary figure 10. Enrichment analysis results for 220 cell-type-specific annotations of four histone marks - H3K4me1, H3K4me3, H3K9ac and H3K27ac – in the luminal A-like and triple-negative TN subtypes. Both luminal A-like and triple-negative TN subtypes were enriched for gastrointestinal cell types and suppression of central nervous system cells. a) Heatmap showing patterns of cell-type specific enrichment for histone marks H3K27ac in luminal A-like tumors and TN tumors L u m in al A -lik e T rip le-n eg a tiv e T N C ell ty p e b) Heatmap showing patterns of cell-type specific enrichment for histone marks H3K4me1 in luminal A-like tumors and triple-negative TN tumors c) Heatmap showing patterns of cell-type specific enrichment for histone marks H3K4me3 in luminal A-like tumors and triple-negative TN tumors d) Heatmap showing patterns of cell-type specific enrichment for histone marks H3K9ac in luminal A-like tumors and triple-negative TN tumors Trip le -n egative T N C ell ty p e L u m in al A -lik e Supplementary Note eQTL Analysis Data from breast cancer tumors and adjacent normal breast tissue were accessed from The Cancer Genome Atlas (TCGA)1. Germline variant genotypes (Affymetrix 6.0 arrays) were processed and imputed to the 1000 Genomes reference panel (October 2014) and European ancestry ascertained as previously described2. Tumor tissue copy number was estimated from the Affymetrix 6.0 and called using the GISTIC2 algorithm3. Complete genotype, RNA-seq and copy number data were available for 679 genetically European patients (78 with adjacent normal tissue). Further, RNA- seq for normal breast tissue and imputed germline genotype data were available from 80 females from the GTEx Consortium4. Genes with a median expression level of 0 RPKM across samples were removed, and RPKM values of each gene were log2 transformed. Expression values of samples were quantile normalized. Genetic variants were evaluated for association with the expression of genes located within ±2Mb of the lead variant at each risk region using linear regression models, adjusting for ESR1 expression. Tumor tissue was also adjusted for copy number variation, as previously described5. eQTL analyses were performed using the MatrixEQTL program in R6. INQUISIT target gene analysis Logic underlying INQUISIT predictions: Details of the INQUISIT pipeline have been previously described1. Briefly, genes were evaluated as potential targets of candidate causal variants through effects on: (1) distal gene regulation, (2) proximal regulation, or (3) a gene's coding sequence. We intersected CCV positions with multiple sources of genomic information, chromatin interaction analysis by paired-end tag sequencing (ChIA-PET)7 in MCF7 cells, and genome-wide chromosome conformation capture (Hi-C) in HMECs8. We used breast cell line computational enhancer–promoter correlations (PreSTIGE9, IM-PET10, FANTOM511) breast cell super-enhancer12, breast tissue-specific expression variants (eQTL) from multiple independent studies (TCGA (normal Field Code Changed Field Code Changed breast and breast tumor) and GTEx breast, See eQTL Methods), transcription factor and histone modification chromatin immunoprecipitation followed by sequencing (ChIP-seq) from the ENCODE and Roadmap Epigenomics Projects together with the genomic features found to be significantly enriched for all known breast cancer CCVs13, gene expression RNA-seq from several breast cancer lines and normal samples (ENCODE) and topologically associated domain (TAD) boundaries from T47D cells (ENCODE14). To assess the impact of intragenic variants, we evaluated their potential to alter primary protein coding sequence and splicing using Ensembl Variant Effect Predictor15 using MaxEntScan and dbscSNV modules for splicing alterations based on “ada” and “rf” scores. Nonsense and missense changes were assessed with the REVEL ensemble algorithm, with CCVs displaying REVEL scores > 0.5 deemed deleterious. Scoring hierarchy: Each target gene prediction category (distal, promoter or coding) was scored according to different criteria. Genes predicted to be distally-regulated targets of CCVs were awarded two points based on physical links (for example ChIA-PET), and one point for computational prediction methods, or eQTL associations. All CCVs were considered as potentially involved in distal regulation and all CCVs (including coding variants) were scored in this category. Intersection of a putative distal enhancer with genomic features found to be significantly enriched20 were further upweighted with an additional point. In the case of multiple, independent interactions, an additional point was awarded. CCVs in gene proximal regulatory regions were intersected with histone ChIP- Seq peaks characteristic of promoters and assigned to the overlapping transcription start sites (defined as -1.0 kb - +0.1 kb). Further points were awarded to such genes if there was evidence for an eQTL association, while a lack of expression resulted in down-weighting as potential targets. Potential coding changes including missense, nonsense and predicted splicing alterations resulted in addition of one point to the encoded gene for each type of change, while lack of expression reduced the score. We added an additional point for predicted target genes that were also breast cancer drivers (278 genes1,20). For each category, scores potentially ranged from 0-8 (distal); 0-4 (promoter) or 0-3 (coding). We converted these scores into 'confidence levels': Level 1 (highest confidence) when distal score >4, promoter score ≥3 or coding score >1; Level 2 when distal score ≤4 and ≥1, promoter score=1 or=2, coding score=1; and Level 3 when distal score <1 and >0, promoter score <1 and >0, and coding <1 and >0. For genes with multiple scores (for example, predicted as targets from multiple independent risk signals or predicted to be impacted in several categories), we recorded the highest score. Global genomic enrichment analyses We performed stratified LD score regression analyses16-18 as previously described2 for two major intrinsic-like subtypes, luminal A-like and triple-negative, using the summary statistics from the meta-analyses of OncoArray, iCOGs, and CIMBA. The analysis included all variants in the 1000 Genome Project Phase 1v3 release with MAF>1% and imputation quality score R2>0.3 in the OncoArray data. We restricted analysis to all variants present on the HapMap version 3 dataset. We first fit a model that included 24 non-cell-type-specific, publicly available annotations as well as 24 additional annotations that included a 500-bp window around each of the 24 main annotations. We also included 100-bp windows around ChIP–seq peaks and one annotation containing all variants, leading to a total of 53 overlapping annotations. In addition to the baseline model using 24 main annotations, we also performed cell-type-specific analyses using annotations of the four histone marks (H3K4me1, H3K4me3, H3K9ac and H3K27ac). Each cell-type-specific annotation corresponds to a histone mark in a single cell type (for example, H3K27ac in adipose nuclei tissues)16. There was a total of 220 such annotations. We further subdivided these 220 cell-type-specific annotations into 10 categories by aggregating the cell-type-specific annotations within each group (for example, variants related with any of the four histone modifications in any hematopoietic and immune cells were considered as one category). To estimate the enrichment of each marker, we ran 220 LD score regressions after adding each different histone mark to the baseline model. We used a Wald test to evaluate the differences in the functional enrichment between the luminal A-like and triple-negative subtypes, using the regression coefficients and standard error based on the models above. After Bonferroni correction none of the differences were significant. Notably, the Wald test assumes that the enrichment estimates of luminal A-like and triple-negative subtypes were independent, but this assumption was violated by the sharing of controls between the subtypes. Under this scenario, our Wald test statistics were less conservative than had we adjusted for the correlation between estimates. However, given the lack of significant differences observed between luminal A-like and triple-negative subtypes we had no concern about a type one error. Two-stage polytomous model The two-stage polytomous logistic regression model allows us to efficiently test for genetic associations while accounting for tumor marker correlations and large amounts of missing tumor data 19. We used this method to detect breast cancer susceptibility variants while taking account of four tumor characteristics: estrogen receptor (ER; ER-positive vs ER-negative), progesterone receptor (PR; PR-positive vs PR-negative), human epidermal growth factor receptor 2 (HER2; HER2-positive vs HER2-negative), and grade (defined as grade 1, grade 2, and grade 3). Below we describe in greater detail how we applied this method In our study, we investigated for underlying heterogenous associations according to ER, PR, HER2, and grade; however, we will first start the discussion of fitting a two-stage polytomous model by first focusing on ER, PR, and HER2, and then discuss including grade in the model. The cross combination of ER, PR, and HER2 results in eight distinct breast cancer subtypes (8 = 2x2x2). Let N denote the total sample size and let Di denote the disease status of ith subject which can take values from {0,1,2,… ,8} and i = 1,2,… ,N. Di = 0 represent a control, and Di = 𝑚 represent the ith subject with the breast cancer subtypes M. Let Gi denote the genotype of a variant for ith subject, taking values from {0,1,2}. Let 𝐗𝐢 denote the other covariates for the ith subject, for example principal components or age. In the first stage of the model, we fit a standard “saturated” polytomous logistic regression model: Pr(Di = m|Gi, Xi) = exp(𝛽𝑚𝐺𝑖 + 𝜼𝒎 𝑻 𝑿𝒊) 1 + ∑ exp(𝛽𝑚𝐺𝑖 + 𝜼𝒎𝑻 𝑿𝒊) 8 𝑚=1 , (1) where βm is the regression coefficient for a variant (G) associated with the mth subtype and 𝜂𝑚 is the vector of regression coefficients for the other covariate (X) associated with mth subtype. Each cancer subtype m is defined through a unique combination of ER, PR, and HER2; therefore, we can alternatively index the parameters βm as βs1s2s3, where s1, s2, 𝑠3 ∈ {0, 1} for the three binary tumor characteristics. Originally, β1 represented the regression coefficient of the ER-, PR-, HER2- subtype. With this indexing, β1 can be alternatively written as β000 and, thus with this reparameterization we can represent the log odds ratio of the eight subtypes as: βs1𝑠2𝑠3 = θ (0) + 𝜃1 (1)𝑠1 + 𝜃2 (1)𝑠2 + 𝜃3 (1)𝑠3 + 𝜃12 (2)𝑠1𝑠2 + 𝜃13 (2)𝑠1𝑠3 + 𝜃23 (2)𝑠1𝑠3 + 𝜃123 (3) 𝑠1𝑠2𝑠3, (2) where θ0 (0) represents the case-control log odds ratio for a reference subtypes versus the controls. We have chosen ER-, PR-, HER2- as the reference subtype, but any subtype can be chosen as the reference subtype. θk (1) represents the case-case log odds ratio for the kth tumor characteristic after adjusting for the other tumor characteristics. We also refer θk (1) as the main effect of the kth tumor characteristic. θk1k2 (2) represents how the case-case log odds ratio associated with k1th tumor characteristic is modified by levels of the k2th tumor characteristic and vice versa. We also refer to θk1k2 (2) as the pairwise interaction between the k1th tumor characteristic and the k2th tumor characteristic. 𝜃123 (3) represents the third order interaction of the three tumor characteristics. This decomposition is equivalent to the first stage polytomous logistic regression since both the first stage and second stage have eight parameters. We can specify different two stage models by assuming different second stage parameters to be equal to 0. For example, the baseline two-stage model is represented by: βs1𝑠2𝑠3 = 𝜃 (0) . (3) This baseline model assumes all of the subtypes have the same log odds ratio and is equivalent to a standard case-control logistic regression testing the association between an exposure and breast cancer, irrespective of tumor subtypes. We can also constrain all of the second stage pairwise interactions and higher order interactions to be 0: βs1𝑠2𝑠3 = θ (0) + 𝜃1 (1)𝑠1 + 𝜃2 (1)𝑠2 + 𝜃3 (1)𝑠3. (4) This additive two-stage model assumes the case-case log odds ratio of a tumor characteristic are not affected by interactions with the other tumor characteristics. By adding the second stage pairwise interactions parameters into the model, we can also construct the pairwise interaction two-stage polytomous model: βs1𝑠2𝑠3 = θ (0) + 𝜃1 (1)𝑠1 + 𝜃2 (1)𝑠2 + 𝜃3 (1)𝑠3 + 𝜃12 (2)𝑠1𝑠2 + 𝜃13 (2)𝑠1𝑠3 + 𝜃23 (2)𝑠1𝑠3. (5) This model evaluates how two tumor characteristics are modified by each other. For example, θ12 (2) measures how the case-case log odds ratio associated of ER is modified by the status of PR and vice versa. If we further add the three-way interaction term between ER, PR, and HER2, then this model becomes saturated (as shown in in Equation 2) and is equivalent to the polytomous logistic regression. When we add the three-level ordinal variable tumor grade into the model, we can define 24 (2x2x2x3) breast cancer subtypes. We can apply the same decomposition as implemented with three tumor characteristics to provide the following additive two-stage model: βs1𝑠2𝑠3𝑠4 = θ (0) + 𝜃1 (1)𝑠1 + 𝜃2 (1)𝑠2 + 𝜃3 (1)𝑠3 + 𝜃4 (1)𝑠4, (6) where 𝜃4 (1) is the main effect of grade and 𝑠4 can take the values from {1, 2, 3}. In this model, we assume the grade main effect linearly changes, meaning the average log odds ratios difference between grade 3 versus grade2 is the same the as the difference between grade 2 versus grade1. We can always describe the link between the first stage parameters and second stage parameters in Equation (6) in matrix form: ER − PR − HER2 − grade1 ER + PR − HER2 − grade1 ER − PR + HER2 − grade1 ER + PR + HER2 − grade1 ER − PR − HER2 + grade1 ER + PR − HER2 + grade1 ER − PR + HER2 + grade1 ER + PR + HER2 + grade1 … ER + PR + HER2 + grade3 𝛃 = [ 𝛽1 𝛽2 𝛽3 𝛽4 𝛽5 𝛽6 𝛽7 𝛽8 … 𝛽24] = [ 1 0 0 0 1 1 1 0 0 1 1 0 1 0 1 1 1 1 0 1 1 0 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 … … … … … 1 1 1 1 3] [ 𝜃(0) 𝜃1 (1) 𝜃2 (1) 𝜃3 (1) 𝜃4 (1) ] = 𝐙 [𝜃 (0) 𝜽𝑯 ] = 𝐙𝛉, (7) where 𝛃 is a vector of regression coefficients of the first stage parameters, 𝛉 is the vector of all the second stage parameters, and 𝜽𝑯 is a vector of second stage main effects. Hypothesis testing of two-stage polytomous logistic regression Under the two-stage model framework, there are three different tests we can construct. The first is the global association test: 𝐻0: 𝜃 (0) = 0 𝑎𝑛𝑑 𝜽𝑯 = 𝟎 𝑣𝑒𝑟𝑠𝑢𝑠 𝐻1: 𝑒𝑖𝑡ℎ𝑒𝑟 𝜃 (0) ≠ 0 or 𝜽𝑯 ≠ 𝟎 . (8) This test is designed to test whether a variant is associated with any of the 24 breast cancer subtypes. If the null hypothesis is rejected under this setting, then at least one of the first stage subtype case-control log odds ratios 𝛽𝑚 is significantly not equal to 0. The second test is the global heterogeneity test: 𝐻0: 𝜽 𝑯 = 𝟎 𝑣𝑒𝑟𝑠𝑢𝑠 𝐻1: 𝜽 𝑯 ≠ 𝟎 . (8) This test is designed to test whether the associations between a variant and any two breast cancer subtypes are significantly different from each other. If the null hypothesis is rejected under this setting, then we can conclude that at least two of the first stage subtypes case-control log odds ratios are significantly different with each other (𝛽𝑚1 ≠ 𝛽𝑚2). If the global heterogeneity test is significant, then we can construct the third hypothesis tests, the specific tumor marker heterogeneity test: 𝐻0: 𝜽(𝑘) 𝑯 = 0 𝑣𝑒𝑟𝑠𝑢𝑠 𝐻1: 𝜽(𝑘) 𝑯 ≠ 0 . (9) This test is designed to test which tumor character is the source of the observed heterogeneity in the global heterogeneity test. Under the additive two-stage model in Equation (6), for example, we can test 𝐻0: 𝜃1 (1) = 0 𝑣𝑒𝑟𝑠𝑢𝑠 𝐻0: 𝜃1 (1) ≠ 0 . This is designed to test whether the case-case log odds ratio of ER is significant not equaling to 0 after adjusting for the effects of PR, HER2 and grade. Mixed effect two-stage polytomous model Although the additive two-stage model decreases the degrees of freedoms compared to the first stage polytomous logistic regression, the degrees of freedom of the two-stage model are still penalized when additional tumor characteristics are included into the model. To address this issue, we developed the mixed effect two-stage polytomous model to enter tumor characteristic variables into the model as either fixed- or random-effect terms. In this model, we keep the second stage main effect of ER (𝜃1 (1) ) as a fixed effect since there is strong a priori evidence that ER is a common source of heterogeneity 20. On the other hand, as there is minimal evidence suggesting that tumor characteristics such as PR, HER2, and grade are sources of heterogeneity, we assume the case- case parameter of PR (𝜃2 (1) ), HER2 (𝜃3 (1) ) and grade (𝜃4 (1) ) as random effects. These random parameters have an assumed arbitrary distribution with mean 0 and variance 𝜎2 . We always keep the baseline effect 𝜃(0) as fixed since it captures the overall association between a variant and breast cancer. Under the mixed effect two stage model, the global test for association is: 𝐻0: 𝜃 (0) = 0, 𝜃1 (1) = 0, σ2 = 0 𝑣𝑒𝑟𝑠𝑢𝑠 𝐻1: 𝑒𝑖𝑡ℎ𝑒𝑟 𝜃 (0), θ1 (1) , 𝑜𝑟 𝜎2 ≠ 0 (10) The rejection of the null hypothesis implies that the variant is significantly associated with at least one of the 24 breast cancer subtypes. The global heterogeneity test under the mixed effect two-stage model would be: 𝐻0: 𝜃1 (1) = 0 𝑎𝑛𝑑 σ2 = 0 𝑣𝑒𝑟𝑠𝑢𝑠 𝐻1: 𝑒𝑖𝑡ℎ𝑒𝑟 𝜃1 (1)or σ2 ≠ 0 . (11) The rejection of the null hypothesis would imply that the variant’s associations between at least two breast cancer subtypes are significantly different. However, the specific tumor marker heterogeneity test for a specific tumor marker is not applied in the mixed effect two-stage model because it requires the estimate of case-case log odds ratio of PR, HER2 and grade which are note estimated when modeled as random effects. Two-stage model for intrinsic subtypes of breast cancer In previous sections, we showed how the first stage case control log odds ratios of breast cancer subtypes are decomposed to the case control log odds ratio of a reference subtype and the into case-case parameters of tumor characteristics. Using the hierarchical second stage decomposition, the two-stage model can also estimate the case control log odds ratio of specific breast cancer subtypes of interest. In our study we defined five intrinsic-like breast cancer subtypes based on tumor status of ER, PR, HER2 and grade: (1) luminal A-like (ER+ and/or PR+, HER2-, grade 1 & 2); (2) luminal B/HER2-negative-like (ER+ and/or PR+, HER2-, grade 3); (3) luminal B-like (ER+ and/or PR+, HER2+); (4) HER2-enriched-like (ER- and PR-, HER2+), and (5) triple- negative (TN; ER-, PR-, HER2-). To estimate the case-control log odds ratios of these five intrinsic subtypes we can construct the two-stage model as: ER − PR − HER2 − grade1 ER + PR − HER2 − grade1 ER − PR + HER2 − grade1 ER + PR + HER2 − grade1 ER − PR − HER2 + grade1 ER + PR − HER2 + grade1 ER − PR + HER2 + grade1 ER + PR + HER2 + grade1 … ER + PR + HER2 + grade3 𝛃 = [ 𝛽1 𝛽2 𝛽3 𝛽4 𝛽5 𝛽6 𝛽7 𝛽8 … 𝛽24] = [ 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 … … … … … 0 1 0 0 0] [ 𝜃1 𝜃2 𝜃3 𝜃4 𝜃5] Luminal A − like, low grade Luminal B − like Luminal B/HER2 − negative − like HER2 enriched − like Triple − negative (12) Under this model, the second stage parameters provide estimates of case-control log odds ratios for the five tumor subtypes. This model is similar to directly fitting a polytomous logistic regression. However, we have incorporated into the two-stage model an efficient missing data algorithm that allows to take advantage of subjects with incomplete tumor characteristic data. The missing data algorithm has been described in detail elsewhere [1]. Modified LD score regression Since the two-stage polytomous logistic regression implements an EM algorithm to account for missing tumor characteristics data, the effective sample size is not equivalent to the sample size of cases with complete tumor characteristic data. In this case the sample size is not available, but the log odds ratio for each variant ?̂?𝑗 and the standard error sj are given. Under a case-control study, we consider the logistic regression model 𝑙𝑜𝑔 ( 𝑝 1−𝑝 ) = 𝛼 + (𝜷(𝐽)) 𝑻 𝑿， where 𝜷(𝐽) = (𝛽1 (𝐽), 𝛽2 (𝐽), … , 𝛽𝑀 (𝐽)) are the joint effect sizes. We define the heritability as ℎ2 = 𝑣𝑎𝑟((𝜷(𝐽)) 𝑇 𝑿), assuming X is standardized with mean 0 variance 1. If X is in the original 0, 1, 2 scale, we multiply the ?̂?𝑗 and 𝑠𝑗 by √2𝑝𝑗(1 − 𝑝𝑗) to standardize, where 𝑝𝑗 is the minor allele frequency for the jth variant. Therefore, the expected chi-square statistics (𝑧𝑗 2) of variant j is E(𝑧𝑗 2|𝑙𝑗) = 𝐸(?̂?𝑗 2|𝑙𝑗) 𝑠𝑗 2 = [𝐸 {(?̂?𝑗 − 𝛽𝑗) 2 |𝑙𝑗} + 2𝐸[(?̂?𝑗 − 𝛽𝑗)𝛽𝑗|𝑙𝑗] + 𝐸(𝛽𝑗 2|𝑙𝑗)] 𝑠𝑗 2 (13) = [𝐸 {(?̂?𝑗 − 𝛽𝑗) 2 |𝑙𝑗} + 𝐸(𝛽𝑗 2|𝑙𝑗)] 𝑠𝑗 2 = 1 + 𝐸{(∑ 𝑟𝑗𝑘𝛽𝑘 (𝐽) )𝑘 2 } 𝑠𝑗 2 = 1 + ℎ2 𝑀 𝑙𝑗 𝑠𝑗 2, where 𝑙𝑗 = ∑ 𝑟𝑗𝑘 2 𝑘 is the LD score of the variant j and 1/𝑠𝑗 2 is the effective sample size for variant j. The modified LD score regression formula is: E(𝑧𝑗 2|𝑙𝑗) = 1 + ℎ2 𝑀 𝑙𝑗 𝑠𝑗 2 . (14) To estimate the genetic correlation between two traits, the expected value of 𝑧1𝑗𝑧2𝑗 for a variant j is E(z1j𝑧2𝑗|𝑙𝑗) = 𝐸(?̂?1𝑗 ?̂?2𝑗|𝑙𝑗) 𝑠1𝑗𝑠2𝑗 (15) = [𝐸{(?̂?1𝑗 − 𝛽1𝑗)(?̂?2𝑗 − 𝛽2𝑗)|𝑙𝑗} + 𝐸(𝛽1𝑗𝛽2𝑗|𝑙𝑗)] 𝑠1𝑗𝑠2𝑗 = 𝑠12𝑗 𝑠1𝑗𝑠2𝑗 + 𝐸(∑ 𝑟𝑗𝑘𝛽1𝑘 (𝐽) 𝑘 ∑ 𝑟𝑗𝑘𝛽2𝑘 (𝐽) 𝑘 |𝑙𝑗) 𝑠1𝑗𝑠2𝑗 = 𝑠12𝑗 𝑠1𝑗𝑠2𝑗 + 𝜌𝑔 𝑀 𝑙𝑗 𝑠1𝑗𝑠2𝑗 , where 𝜌𝑔 is the genetic covariance between the two different traits. Under this case, 1 𝑠1𝑗 2⁄ and 1 𝑠2𝑗 2⁄ are the effective sample size for variant j for the two traits respectively. The modified LD score regression for genetic covariance is E(z1j𝑧2𝑗|𝑙𝑗) = 𝑠12𝑗 𝑠1𝑗𝑠2𝑗 + 𝜌𝑔 𝑀 𝑙𝑗 𝑠1𝑗𝑠2𝑗 . (16) The genetic correlation is given by 𝜌𝑔 √ℎ1 2ℎ2 2 . Effective sample size of cases of two-stage polytomous model The two-stage polytomous model implements the EM algorithm to impute missing tumor characteristics; therefore, the effective sample size of cases is not equivalent to the actual number of cases with available tumor characteristic data. We estimated the effective sample sizes to help demonstrate the benefit of using the EM algorithm to impute missing tumor characteristics and to aid comparability with previous studies (Supplementary Table 4). To estimate the effective sample size, suppose we have a complete dataset with no missing tumor characteristics, the sample size is 𝑛𝑘 for the kth subtype and 𝑛0 for the control. If we fit a two-stage polytomous model for the jth variant, the corresponding log odds ratio for kth subtype is ?̂?𝑗𝑘 and the standard error is 𝑠𝑗𝑘. Then, approximately: 𝑣𝑎𝑟(?̂?𝑗𝑘|𝑝𝑗) ≈ 𝑛0 + 𝑛𝑘 2 ∗ 𝑝𝑗(1 − 𝑝𝑗)(𝑛0𝑛𝑘) , where 𝑝𝑗 is the MAF of the jth variant. Now we consider fitting a two-stage polytomous model with missing tumor characteristics. Given the standard error 𝑠𝑗𝑘 of the log odds ratio and the control sample size, we have the estimate of effective number of cases as, ?̂?𝑘 = ( 1 𝑛0 − 2𝑠𝑗𝑘 2 𝑝𝑗(1 − 𝑝𝑗)) −1 . We used the median estimates of effective sample size of cases for all variants as the final estimate. References Supplementary Note Methods 1. Cancer Genome Atlas, N. Comprehensive molecular portraits of human breast tumours. Nature 490, 61-70 (2012). 2. Michailidou, K. et al. Association analysis identifies 65 new breast cancer risk loci. Nature 551, 92-94 (2017). 3. Mermel, C.H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol 12, R41 (2011). 4. Consortium, G.T. The Genotype-Tissue Expression (GTEx) project. Nat Genet 45, 580-5 (2013). 5. Li, Q. et al. Integrative eQTL-based analyses reveal the biology of breast cancer risk loci. Cell 152, 633-41 (2013). 6. Shabalin, A.A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353-8 (2012). 7. Fullwood, M.J. et al. An oestrogen-receptor-alpha-bound human chromatin interactome. Nature 462, 58-64 (2009). 8. Rao, S.S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665-80 (2014). 9. Corradin, O. et al. Combinatorial effects of multiple enhancer variants in linkage disequilibrium dictate levels of gene expression to confer susceptibility to common traits. Genome Res 24, 1-13 (2014). 10. He, B., Chen, C., Teng, L. & Tan, K. Global view of enhancer-promoter interactome in human cells. Proc Natl Acad Sci U S A 111, E2191-9 (2014). 11. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455-461 (2014). 12. Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934-47 (2013). 13. Fachal, L. et al. Fine-mapping of 150 breast cancer risk regions identifies 178 high confidence target genes. Nat Genet 52, 56-73 (2020). 14. Dixon, J.R. et al. Integrative detection and analysis of structural variation in cancer genomes. Nat Genet 50, 1388-1398 (2018). 15. McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol 17, 122 (2016). 16. Finucane, H.K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet 47, 1228-35 (2015). 17. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat Genet 47, 1236-41 (2015). 18. Bulik-Sullivan, B.K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47, 291-5 (2015). 19. Zhang, H. et al. A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics. bioRxiv, 446039 (2018). 20. Milne, R.L. et al. Identification of ten variants associated with risk of estrogen-receptor- negative breast cancer. Nat Genet advance online publication(2017). BCAC Funding and Acknowledgments Funding BCAC is funded by Cancer Research UK [C1287/A16563, C1287/A10118], the European Union's Horizon 2020 Research and Innovation Programme (grant numbers 634935 and 633784 for BRIDGES and B-CAST respectively), and by the European Community´s Seventh Framework Programme under grant agreement number 223175 (grant number HEALTH-F2-2009-223175) (COGS). The EU Horizon 2020 Research and Innovation Programme funding source had no role in study design, data collection, data analysis, data interpretation or writing of the report. Genotyping of the OncoArray was funded by the NIH Grant U19 CA148065, and Cancer UK Grant C1287/A16563 and the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344) and, the Ministère de l’Économie, Science et Innovation du Québec through Genome Québec and the PSRSIIRI-701 grant, and the Quebec Breast Cancer Foundation. Funding for the iCOGS infrastructure came from: the European Community's Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 - the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, and Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. The DRIVE Consortium was funded by U19 CA148065. The Australian Breast Cancer Family Study (ABCFS) was supported by grant UM1 CA164920 from the National Cancer Institute (USA). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. The ABCFS was also supported by the National Health and Medical Research Council of Australia, the New South Wales Cancer Council, the Victorian Health Promotion Foundation (Australia) and the Victorian Breast Cancer Research Consortium. J.L.H. is a National Health and Medical Research Council (NHMRC) Senior Principal Research Fellow. M.C.S. is a NHMRC Senior Research Fellow. The ABCS study was supported by the Dutch Cancer Society [grants NKI 2007-3839; 2009 4363]. The Australian Breast Cancer Tissue Bank (ABCTB) was supported by the National Health and Medical Research Council of Australia, The Cancer Institute NSW and the National Breast Cancer Foundation. The AHS study is supported by the intramural research program of the National Institutes of Health, the National Cancer Institute (grant number Z01-CP010119), and the National Institute of Environmental Health Sciences (grant number Z01-ES049030). The work of the BBCC was partly funded by ELAN- Fond of the University Hospital of Erlangen. The BBCS is funded by Cancer Research UK and Breast Cancer Now and acknowledges NHS funding to the NIHR Biomedical Research Centre, and the National Cancer Research Network (NCRN). The BCEES was funded by the National Health and Medical Research Council, Australia and the Cancer Council Western Australia and acknowledges funding from the National Breast Cancer Foundation (JS). For the BCFR-NY, BCFR-PA, BCFR-UT this work was supported by grant UM1 CA164920 from the National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government or the BCFR. For BIGGS, ES is supported by NIHR Comprehensive Biomedical Research Centre, Guy's & St. Thomas' NHS Foundation Trust in partnership with King's College London, United Kingdom. IT is supported by the Oxford Biomedical Research Centre. The BREast Oncology GAlician Network (BREOGAN) is funded by Acción Estratégica de Salud del Instituto de Salud Carlos III FIS PI12/02125/Cofinanciado FEDER; Acción Estratégica de Salud del Instituto de Salud Carlos III FIS Intrasalud (PI13/01136); Programa Grupos Emergentes, Cancer Genetics Unit, Instituto de Investigacion Biomedica Galicia Sur. Xerencia de Xestion Integrada de Vigo-SERGAS, Instituto de Salud Carlos III, Spain; Grant 10CSA012E, Consellería de Industria Programa Sectorial de Investigación Aplicada, PEME I + D e I + D Suma del Plan Gallego de Investigación, Desarrollo e Innovación Tecnológica de la Consellería de Industria de la Xunta de Galicia, Spain; Grant EC11- 192. Fomento de la Investigación Clínica Independiente, Ministerio de Sanidad, Servicios Sociales e Igualdad, Spain; and Grant FEDER-Innterconecta. Ministerio de Economia y Competitividad, Xunta de Galicia, Spain. The BSUCH study was supported by the Dietmar-Hopp Foundation, the Helmholtz Society and the German Cancer Research Center (DKFZ). CBCS is funded by the Canadian Cancer Society (grant # 313404) and the Canadian Institutes of Health Research. CCGP is supported by funding from the University of Crete. The CECILE study was supported by Fondation de France, Institut National du Cancer (INCa), Ligue Nationale contre le Cancer, Agence Nationale de Sécurité Sanitaire, de l'Alimentation, de l'Environnement et du Travail (ANSES), Agence Nationale de la Recherche (ANR). The CGPS was supported by the Chief Physician Johan Boserup and Lise Boserup Fund, the Danish Medical Research Council, and Herlev and Gentofte Hospital. The CNIO- BCS was supported by the Instituto de Salud Carlos III, the Red Temática de Investigación Cooperativa en Cáncer and grants from the Asociación Española Contra el Cáncer and the Fondo de Investigación Sanitario (PI11/00923 and PI12/00070). COLBCCC is supported by the German Cancer Research Center (DKFZ), Heidelberg, Germany. Diana Torres was in part supported by a postdoctoral fellowship from the Alexander von Humboldt Foundation. The American Cancer Society funds the creation, maintenance, and updating of the CPS-II cohort. The CTS was initially supported by the California Breast Cancer Act of 1993 and the California Breast Cancer Research Fund (contract 97-10500) and is currently funded through the National Institutes of Health (R01 CA77398, UM1 CA164917, and U01 CA199277). Collection of cancer incidence data was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885. HAC receives support from the Lon V Smith Foundation (LVS39420). The University of Westminster curates the DietCompLyf database funded by Against Breast Cancer Registered Charity No. 1121258 and the NCRN. The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by: Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l’Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), Federal Ministry of Education and Research (BMBF) (Germany); the Hellenic Health Foundation, the Stavros Niarchos Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS), PI13/00061 to Granada, PI13/01162 to EPIC-Murcia, Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, ISCIII RETIC (RD06/0020) (Spain); Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 and C8221/A19170 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk, MR/M012190/1 to EPIC-Oxford) (United Kingdom). The ESTHER study was supported by a grant from the Baden Württemberg Ministry of Science, Research and Arts. Additional cases were recruited in the context of the VERDI study, which was supported by a grant from the German Cancer Aid (Deutsche Krebshilfe). FHRISK is funded from NIHR grant PGfAR 0707-10031. The GC-HBOC (German Consortium of Hereditary Breast and Ovarian Cancer) is supported by the German Cancer Aid (grant no 110837 and 113049, coordinator: Rita K. Schmutzler, Cologne). This work was also funded by the European Regional Development Fund and Free State of Saxony, Germany (LIFE - Leipzig Research Centre for Civilization Diseases, project numbers 713-241202, 713-241202, 14505/2470, 14575/2470). The GENICA was funded by the Federal Ministry of Education and Research (BMBF) Germany grants 01KW9975/5, 01KW9976/8, 01KW9977/0 and 01KW0114, the Robert Bosch Foundation, Stuttgart, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, the Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, as well as the Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany. Generation Scotland (GENSCOT) received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006]. Genotyping of the GS:SFHS samples was carried out by the Genetics Core Laboratory at the Edinburgh Clinical Research Facility, University of Edinburgh, Scotland and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” (STRADL) Reference 104036/Z/14/Z). Funding for identification of cases and contribution to BCAC funded in part by the Wellcome Trust Seed Award “Temporal trends in incidence and mortality of molecular subtypes of breast cancer to inform public health, policy and prevention” Reference 207800/Z/17/Z. The GEPARSIXTO study was conducted by the German Breast Group GmbH. The GESBC was supported by the Deutsche Krebshilfe e. V. [70492] and the German Cancer Research Center (DKFZ). The HABCS study was supported by the Claudia von Schilling Foundation for Breast Cancer Research, by the Lower Saxonian Cancer Society, and by the Rudolf Bartling Foundation. The HEBCS was financially supported by the Helsinki University Hospital Research Fund, the Finnish Cancer Society, and the Sigrid Juselius Foundation..The HMBCS was supported by a grant from the Friends of Hannover Medical School and by the Rudolf Bartling Foundation. The HUBCS was supported by a grant from the German Federal Ministry of Research and Education (RUS08/017), B.M. was supported by grant 17-44-020498, 17-29-06014 of the Russian Foundation for Basic Research, D.P. was supported by grant 18-29-09129 of the Russian Foundation for Basic Research, E.K was supported by the program for support the bioresource collections №007-030164/2, and the study was performed as part of the assignment of the Ministry of Science and Higher Education of the Russian Federation (№АААА-А16-116020350032-1). Financial support for KARBAC was provided through the regional agreement on medical training and clinical research (ALF) between Stockholm County Council and Karolinska Institutet, the Swedish Cancer Society, The Gustav V Jubilee foundation and Bert von Kantzows foundation. The KARMA study was supported by Märit and Hans Rausings Initiative Against Breast Cancer. The KBCP was financially supported by the special Government Funding (EVO) of Kuopio University Hospital grants, Cancer Fund of North Savo, the Finnish Cancer Organizations, and by the strategic funding of the University of Eastern Finland. kConFab is supported by a grant from the National Breast Cancer Foundation, and previously by the National Health and Medical Research Council (NHMRC), the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia, and the Cancer Foundation of Western Australia. Financial support for the AOCS was provided by the United States Army Medical Research and Materiel Command [DAMD17-01-1-0729], Cancer Council Victoria, Queensland Cancer Fund, Cancer Council New South Wales, Cancer Council South Australia, The Cancer Foundation of Western Australia, Cancer Council Tasmania and the National Health and Medical Research Council of Australia (NHMRC; 400413, 400281, 199600). G.C.T. and P.W. are supported by the NHMRC. RB was a Cancer Institute NSW Clinical Research Fellow. LAABC is supported by grants (1RB-0287, 3PB-0102, 5PB-0018, 10PB-0098) from the California Breast Cancer Research Program. Incident breast cancer cases were collected by the USC Cancer Surveillance Program (CSP) which is supported under subcontract by the California Department of Health. The CSP is also part of the National Cancer Institute's Division of Cancer Prevention and Control Surveillance, Epidemiology, and End Results Program, under contract number N01CN25403. LMBC is supported by the 'Stichting tegen Kanker'. DL is supported by the FWO. The MABCS study is funded by the Research Centre for Genetic Engineering and Biotechnology "Georgi D. Efremov", MASA. The MARIE study was supported by the Deutsche Krebshilfe e.V. [70-2892-BR I, 106332, 108253, 108419, 110826, 110828], the Hamburg Cancer Society, the German Cancer Research Center (DKFZ) and the Federal Ministry of Education and Research (BMBF) Germany [01KH0402]. MBCSG is supported by grants from the Italian Association for Cancer Research (AIRC) and by funds from the Italian citizens who allocated the 5/1000 share of their tax payment in support of the Fondazione IRCCS Istituto Nazionale Tumori, according to Italian laws (INT-Institutional strategic projects “5x1000”). The MCBCS was supported by the NIH grants CA192393, CA116167, CA176785 an NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer [CA116201], and the Breast Cancer Research Foundation and a generous gift from the David F. and Margaret T. Grohne Family Foundation. The Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further augmented by Australian National Health and Medical Research Council grants 209057, 396414 and 1074383 and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database. The MEC was supported by NIH grants CA63464, CA54281, CA098758, CA132839 and CA164973. The MISS study is supported by funding from ERC-2011-294576 Advanced grant, Swedish Cancer Society, Swedish Research Council, Local hospital funds, Berta Kamprad Foundation, Gunnar Nilsson. The MMHS study was supported by NIH grants CA97396, CA128931, CA116201, CA140286 and CA177150. MSKCC is supported by grants from the Breast Cancer Research Foundation and Robert and Kate Niehaus Clinical Cancer Genetics Initiative. The work of MTLGEBCS was supported by the Quebec Breast Cancer Foundation, the Canadian Institutes of Health Research for the “CIHR Team in Familial Risks of Breast Cancer” program – grant # CRN-87521 and the Ministry of Economic Development, Innovation and Export Trade – grant # PSR-SIIRI-701. The NBCS has received funding from the K.G. Jebsen Centre for Breast Cancer Research; the Research Council of Norway grant 193387/V50 (to A-L Børresen-Dale and V.N. Kristensen) and grant 193387/H10 (to A-L Børresen-Dale and V.N. Kristensen), South Eastern Norway Health Authority (grant 39346 to A-L Børresen-Dale) and the Norwegian Cancer Society (to A-L Børresen-Dale and V.N. Kristensen). The NBHS was supported by NIH grant R01CA100374. Biological sample preparation was conducted the Survey and Biospecimen Shared Resource, which is supported by P30 CA68485. The Northern California Breast Cancer Family Registry (NC-BCFR) and Ontario Familial Breast Cancer Registry (OFBCR) were supported by grant UM1 CA164920 from the National Cancer Institute (USA). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. The Carolina Breast Cancer Study was funded by Komen Foundation, the National Cancer Institute (P50 CA058223, U54 CA156733, U01 CA179715), and the North Carolina University Cancer Research Fund. The NHS was supported by NIH grants P01 CA87969, UM1 CA186107, and U19 CA148065. The NHS2 was supported by NIH grants UM1 CA176726 and U19 CA148065. The OBCS was supported by research grants from the Finnish Cancer Foundation, the Academy of Finland (grant number 250083, 122715 and Center of Excellence grant number 251314), the Finnish Cancer Foundation, the Sigrid Juselius Foundation, the University of Oulu, the University of Oulu Support Foundation and the special Governmental EVO funds for Oulu University Hospital- based research activities. The ORIGO study was supported by the Dutch Cancer Society (RUL 1997- 1505) and the Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL CP16). The PBCS was funded by Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. Genotyping for PLCO was supported by the Intramural Research Program of the National Institutes of Health, NCI, Division of Cancer Epidemiology and Genetics. The PLCO is supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, National Cancer Institute, National Institutes of Health. The POSH study is funded by Cancer Research UK (grants C1275/A11699, C1275/C22524, C1275/A19187, C1275/A15956 and Breast Cancer Campaign 2010PR62, 2013PR044. PROCAS is funded from NIHR grant PGfAR 0707-10031. The RBCS was funded by the Dutch Cancer Society (DDHK 2004-3124, DDHK 2009-4318). The SASBAC study was supported by funding from the Agency for Science, Technology and Research of Singapore (A*STAR), the US National Institute of Health (NIH) and the Susan G. Komen Breast Cancer Foundation. The SBCS was supported by Sheffield Experimental Cancer Medicine Centre and Breast Cancer Now Tissue Bank. SEARCH is funded by Cancer Research UK [C490/A10124, C490/A16561] and supported by the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge. The University of Cambridge has received salary support for PDPP from the NHS in the East of England through the Clinical Academic Reserve. The Sister Study (SISTER) is supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01-ES044005 and Z01-ES049033). The Two Sister Study (2SISTER) was supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01-ES044005 and Z01-ES102245), and, also by a grant from Susan G. Komen for the Cure, grant FAS0703856. SKKDKFZS is supported by the DKFZ. The SMC is funded by the Swedish Cancer Foundation and the Swedish Research Council/Infrastructure gran. The SZBCS and IHCC were supported by Grant PBZ_KBN_122/P05/2004 and the program of the Minister of Science and Higher Education under the name "Regional Initiative of Excellence" in 2019- 2022 project number 002/RID/2018/19 amount of financing 12 000 000 PLN. The UKBGS is funded by Breast Cancer Now and the Institute of Cancer Research (ICR), London. ICR acknowledges NHS funding to the NIHR Biomedical Research Centre. The UKOPS study was funded by The Eve Appeal (The Oak Foundation) and supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. The USRT Study was funded by Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. The WHI program is funded by the National Heart, Lung, and Blood Institute, the US National Institutes of Health and the US Department of Health and Human Services (HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C and HHSN271201100004C). This work was also funded by NCI U19 CA148065-01. The BCINIS was supported by the BCRF (breast cancer research foundation), NY. Nilanjan Chatterjee received supported from the National Human Genome Research Institute (1 R01 HG010480-01). Acknowledgements We thank all the individuals who took part in these studies and all the researchers, clinicians, technicians and administrative staff who have enabled this work to be carried out. The COGS study would not have been possible without the contributions of the following: Rosalind A. Eeles, Ali Amin Al Olama, Zsofia Kote-Jarai, Lesley McGuffog, Andrew Lee, and Ed Dicks, Craig Luccarini and the staff of the Centre for Genetic Epidemiology Laboratory, Anna Gonzalez-Neira and the staff of the CNIO genotyping unit, Daniel C. Tessier, Francois Bacot, Daniel Vincent, Sylvie LaBoissière and Frederic Robidoux and the staff of the McGill University and Génome Québec Innovation Centre, Borge G. Nordestgaard, and the staff of the Copenhagen DNA laboratory, and Julie M. Cunningham, Sharon A. Windebank, Christopher A. Hilker, Jeffrey Meyer and the staff of Mayo Clinic Genotyping Core Facility. ABCFS thank Maggie Angelakos, Judi Maskiell, Gillian Dite. ABCS thanks the Blood bank Sanquin, The Netherlands. ABCTB Investigators: Christine Clarke, Rosemary Balleine, Robert Baxter, Stephen Braye, Jane Carpenter, Jane Dahlstrom, John Forbes, Soon Lee, Debbie Marsh, Adrienne Morey, Nirmala Pathmanathan, Rodney Scott, Allan Spigelman, Nicholas Wilcken, Desmond Yip. Samples are made available to researchers on a non-exclusive basis. BBCS thanks Eileen Williams, Elaine Ryder-Mills, Kara Sargus. BCEES thanks Allyson Thomson, Christobel Saunders, Terry Slevin, BreastScreen Western Australia, Elizabeth Wylie, Rachel Lloyd. The BCINIS study would not have been possible without the contributions of Dr. K. Landsman, Dr. N. Gronich, Dr. A. Flugelman, Dr. W. Saliba, Dr. E. Liani, Dr. I. Cohen, Dr. S. Kalet, Dr. V. Friedman, Dr. O. Barnet of the NICCC in Haifa, and all the contributing family medicine, surgery, pathology and oncology teams in all medical institutes in Northern Israel. BIGGS thanks Niall McInerney, Gabrielle Colleran, Andrew Rowan, Angela Jones. The BREOGAN study would not have been possible without the contributions of the following: Manuela Gago-Dominguez, Jose Esteban Castelao, Angel Carracedo, Victor Muñoz Garzón, Alejandro Novo Domínguez, Maria Elena Martinez, Sara Miranda Ponte, Carmen Redondo Marey, Maite Peña Fernández, Manuel Enguix Castelo, Maria Torres, Manuel Calaza (BREOGAN), José Antúnez, Máximo Fraga and the staff of the Department of Pathology and Biobank of the University Hospital Complex of Santiago-CHUS, Instituto de Investigación Sanitaria de Santiago, IDIS, Xerencia de Xestion Integrada de Santiago-SERGAS; Joaquín González-Carreró and the staff of the Department of Pathology and Biobank of University Hospital Complex of Vigo, Instituto de Investigacion Biomedica Galicia Sur, SERGAS, Vigo, Spain. BSUCH thanks Peter Bugert, Medical Faculty Mannheim. CBCS thanks study participants, co-investigators, collaborators and staff of the Canadian Breast Cancer Study, and project coordinators Agnes Lai and Celine Morissette. CCGP thanks Styliani Apostolaki, Anna Margiolaki, Georgios Nintos, Maria Perraki, Georgia Saloustrou, Georgia Sevastaki, Konstantinos Pompodakis. CGPS thanks staff and participants of the Copenhagen General Population Study. For the excellent technical assistance: Dorthe Uldall Andersen, Maria Birna Arnadottir, Anne Bank, Dorthe Kjeldgård Hansen. The Danish Cancer Biobank is acknowledged for providing infrastructure for the collection of blood samples for the cases. CNIO- BCS thanks Guillermo Pita, Charo Alonso, Nuria Álvarez, Pilar Zamora, Primitiva Menendez, the Human Genotyping-CEGEN Unit (CNIO). Investigators from the CPS-II cohort thank the participants and Study Management Group for their invaluable contributions to this research. They also acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention National Program of Cancer Registries, as well as cancer registries supported by the National Cancer Institute Surveillance Epidemiology and End Results program. The CTS Steering Committee includes Leslie Bernstein, Susan Neuhausen, James Lacey, Sophia Wang, Huiyan Ma, and Jessica Clague DeHart at the Beckman Research Institute of City of Hope, Dennis Deapen, Rich Pinder, and Eunjung Lee at the University of Southern California, Pam Horn-Ross, Peggy Reynolds, Christina Clarke Dur and David Nelson at the Cancer Prevention Institute of California, Hoda Anton-Culver, Argyrios Ziogas, and Hannah Park at the University of California Irvine, and Fred Schumacher at Case Western University. DIETCOMPLYF thanks the patients, nurses and clinical staff involved in the study. The DietCompLyf study was funded by the charity Against Breast Cancer (Registered Charity Number 1121258) and the NCRN. We thank the participants and the investigators of EPIC (European Prospective Investigation into Cancer and Nutrition). ESTHER thanks Hartwig Ziegler, Sonja Wolf, Volker Hermann, Christa Stegmaier, Katja Butterbach. FHRISK thanks NIHR for funding. GC-HBOC thanks Stefanie Engert, Heide Hellebrand, Sandra Kröber and LIFE - Leipzig Research Centre for Civilization Diseases (Markus Loeffler, Joachim Thiery, Matthias Nüchter, Ronny Baber). The GENICA Network: Dr. Margarete Fischer- Bosch-Institute of Clinical Pharmacology, Stuttgart, and University of Tübingen, Germany [HB, WYL], German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ) [HB], Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2180 - 390900677 [HB], Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany [YDK, Christian Baisch], Institute of Pathology, University of Bonn, Germany [Hans-Peter Fischer], Molecular Genetics of Breast Cancer, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany [Ute Hamann], Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, Germany [TB, Beate Pesch, Sylvia Rabstein, Anne Lotz]; and Institute of Occupational Medicine and Maritime Medicine, University Medical Center Hamburg-Eppendorf, Germany [Volker Harth]. HABCS thanks Michael Bremer. HEBCS thanks Johanna Kiiski, Rainer Fagerholm, Kirsimari Aaltonen, Karl von Smitten, Irja Erkkilä. HMBCS thanks Peter Hillemanns, Hans Christiansen and Johann H. Karstens. HUBCS thanks Shamil Gantsev. KARMA and SASBAC thank the Swedish Medical Research Counsel. KBCP thanks Eija Myöhänen, Helena Kemiläinen. kConFab/AOCS wish to thank Heather Thorne, Eveline Niedermayr, all the kConFab research nurses and staff, the heads and staff of the Family Cancer Clinics, and the Clinical Follow Up Study (which has received funding from the NHMRC, the National Breast Cancer Foundation, Cancer Australia, and the National Institute of Health (USA)) for their contributions to this resource, and the many families who contribute to kConFab. LMBC thanks Gilian Peuteman, Thomas Van Brussel, EvyVanderheyden and Kathleen Corthouts. MABCS thanks Emilija Lazarova (Clinic of Radiotherapy and Oncology), Dzengis Jasar, Mitko Karadjozov (Adzibadem-Sistina” Hospital), Andrej Arsovski and Liljana Stojanovska (Re-Medika” Hospital) for their contributions and commitment to this study. MARIE thanks Petra Seibold, Dieter Flesch-Janys, Judith Heinz, Nadia Obi, Alina Vrieling, Sabine Behrens, Ursula Eilber, Muhabbet Celik, Til Olchers and Stefan Nickels. MBCSG (Milan Breast Cancer Study Group): Irene Feroce, Aliana Guerrieri Gonzaga, Monica Marabelli and and the personnel of the Cogentech Cancer Genetic Test Laboratory. The MCCS was made possible by the contribution of many people, including the original investigators, the teams that recruited the participants and continue working on follow-up, and the many thousands of Melbourne residents who continue to participate in the study. We thank the coordinators, the research staff and especially the MMHS participants for their continued collaboration on research studies in breast cancer. MSKCC thanks Marina Corines, Lauren Jacobs. MTLGEBCS would like to thank Martine Tranchant (CHU de Québec – Université Laval Research Center), Marie-France Valois, Annie Turgeon and Lea Heguy (McGill University Health Center, Royal Victoria Hospital; McGill University) for DNA extraction, sample management and skilful technical assistance. J.S. is Chair holder of the Canada Research Chair in Oncogenetics. The following are NBCS Collaborators: Kristine K. Sahlberg (PhD), Lars Ottestad (MD), Rolf Kåresen (Prof. Em.) Dr. Ellen Schlichting (MD), Marit Muri Holmen (MD), Toril Sauer (MD), Vilde Haakensen (MD), Olav Engebråten (MD), Bjørn Naume (MD), Alexander Fosså (MD), Cecile E. Kiserud (MD), Kristin V. Reinertsen (MD), Åslaug Helland (MD), Margit Riis (MD), Jürgen Geisler (MD), OSBREAC and Grethe I. Grenaker Alnæs (MSc). NBHS and For NHS and NHS2 the study protocol was approved by the institutional review boards of the Brigham and Women’s Hospital and Harvard T.H. Chan School of Public Health, and those of participating registries as required. We would like to thank the participants and staff of the NHS and NHS2 for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data. OBCS thanks Arja Jukkola-Vuorinen, Mervi Grip, Saila Kauppila, Meeri Otsukka, Leena Keskitalo and Kari Mononen for their contributions to this study. OFBCR thanks Teresa Selander, Nayana Weerasooriya. ORIGO thanks E. Krol-Warmerdam, and J. Blom for patient accrual, administering questionnaires, and managing clinical information. The LUMC survival data were retrieved from the Leiden hospital-based cancer registry system (ONCDOC) with the help of Dr. J. Molenaar. PBCS thanks Louise Brinton, Mark Sherman, Neonila Szeszenia-Dabrowska, Beata Peplonska, Witold Zatonski, Pei Chao, Michael Stagner. The ethical approval for the POSH study is MREC /00/6/69, UKCRN ID: 1137. We thank staff in the Experimental Cancer Medicine Centre (ECMC) supported Faculty of Medicine Tissue Bank and the Faculty of Medicine DNA Banking resource. PREFACE thanks Sonja Oeser and Silke Landrith. PROCAS thanks NIHR for funding. RBCS thanks Jannet Blom, Saskia Pelders, Annette Heemskerk and the Erasmus MC Family Cancer Clinic. SBCS thanks Sue Higham, Helen Cramp, Dan Connley, Ian Brock, Sabapathy Balasubramanian and Malcolm W.R. Reed. We thank the SEARCH and EPIC teams. SGBCC thanks the participants and research coordinator Ms Tan Siew Li. SKKDKFZS thanks all study participants, clinicians, family doctors, researchers and technicians for their contributions and commitment to this study. We thank the SUCCESS Study teams in Munich, Duessldorf, Erlangen and Ulm. SZBCS thanks Ewa Putresza. UCIBCS thanks Irene Masunaka. UKBGS thanks Breast Cancer Now and the Institute of Cancer Research for support and funding of the Breakthrough Generations Study, and the study participants, study staff, and the doctors, nurses and other health care providers and health information sources who have contributed to the study. We acknowledge NHS funding to the Royal Marsden/ICR NIHR Biomedical Research Centre. DGE, and AH, are supported by the all Manchester NIHR Biomedical Research Centre (IS-BRC-1215-20007). The authors thank the WHI investigators and staff for their dedication and the study participants for making the program possible. Support for title page creation and format was provided by AuthorArranger, a tool developed at the National Cancer Institute. Members of consortia listed as authors kConFab/AOCS Investigators Stephen Fox, Ian Campbell (Peter MacCallum Cancer Centre, Melbourne, Australia); Georgia Chenevix-Trench, Amanda Spurdle, Penny Webb (QIMR Berghofer Medical Research Institute, Brisbane, Australia); Anna de Fazio (Westmead Millenium Institute, Sydney, Australia); Margaret Tassell (BCNA delegate, Community Representative); Judy Kirk (Westmead Hospital, Sydney, Australia); Geoff Lindeman (Walter and Eliza Hall Institute, Melbourne, Australia); Melanie Price (University of Sydney, Sydney, Australia); Melissa Southey (University of Melbourne, Melbourne, Australia); Roger Milne (Cancer Council Victoria, Melbourne, Australia); Sid Deb (Melbourne Health, Melbourne, Australia); David Bowtell (Garvan Institute of Medical Research, Sydney, Australia). ABCTB Investigators Christine Clarke (Westmead Institute for Medical Research, University of Sydney, NSW, Australia); Rosemary Balleine (Pathology West ICPMR, Westmead, NSW, Australia); Robert Baxter (Kolling Institute of Medical Research, University of Sydney, Royal North Shore Hospital, NSW, Australia); Stephen Braye (Pathology North, John Hunter Hospital, Newcastle, NSW, 2305, Australia); Jane Carpenter (Westmead Institute for Medical Research, University of Sydney); Jane Dahlstrom (Department of Anatomical Pathology, ACT Pathology, Canberra Hospital, ACT, Australia; ANU Medical School, Australian National University, ACT, Australia); John Forbes (Department of Surgical Oncology, Calvary Mater Newcastle Hospital, Australian New Zealand Breast Cancer Trials Group, and School of Medicine and Public Health, University of Newcastle, NSW, Australia); C Soon Lee (School of Science and Health, The University of Western Sydney, Sydney, Australia); Deborah Marsh (Hormones and Cancer Group, Kolling Institute of Medical Research, Royal North Shore Hospital, University of Sydney, NSW, Australia); Adrienne Morey (SydPath St Vincent's Hospital, Sydney, NSW, Australia); Nirmala Pathmanathan (Department of Tissue Pathology and Diagnostic Oncology, Pathology West; Westmead Breast Cancer Institute, Westmead Hospital, NSW, Australia); Rodney Scott (Centre for Information Based Medicine, Hunter Medical Research Institute, NSW, 2305, Australia; Priority Research Centre for Cancer, School of Biomedical Sciences and Pharmacy, Faculty of Health, University of Newcastle, NSW, Australia); Peter Simpson (The University of Queensland: UQ Centre for Clinical Research and School of Medicine, QLD, Australia); Allan Spigelman (Hereditary Cancer Clinic, St Vincent's Hospital, The Kinghorn Cancer Centre, Sydney, New South Wales, 2010, Australia); Nicholas Wilcken (Crown Princess Mary Cancer Centre, Westmead Hospital, Westmead, Australia; Sydney Medical School - Westmead, University of Sydney, NSW, Australia); Desmond Yip (Department of Medical Oncology, The Canberra Hospital, ACT, Australia; ANU Medical School, Australian National University, ACT, Australia); Nikolajs Zeps (St John of God Perth Northern Hospitals, Perth, WA, Australia). CIMBA Funding and Acknowledgements Funding CIMBA: The CIMBA data management and data analysis were supported by Cancer Research – UK grants C12292/A20861, C12292/A11174. ACA is a Cancer Research -UK Senior Cancer Research Fellow. GCT and ABS are NHMRC Research Fellows. iCOGS: the European Community's Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 - the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer (CRN-87521), and the Ministry of Economic Development, Innovation and Export Trade (PSR-SIIRI- 701), Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. The PERSPECTIVE project was supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the Ministry of Economy, Science and Innovation through Genome Québec, and The Quebec Breast Cancer Foundation. BCFR: UM1 CA164920 from the National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government or the BCFR. BFBOCC: Lithuania (BFBOCC-LT): Research Council of Lithuania grant SEN-18/2015. BIDMC: Breast Cancer Research Foundation. BMBSA: Cancer Association of South Africa (PI Elizabeth J. van Rensburg). CNIO: Spanish Ministry of Health PI16/00440 supported by FEDER funds, the Spanish Ministry of Economy and Competitiveness (MINECO) SAF2014-57680-R and the Spanish Research Network on Rare diseases (CIBERER). COH-CCGCRN: Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under grant number R25CA112486, and RC4CA153828 (PI: J. Weitzel) from the National Cancer Institute and the Office of the Director, National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. CONSIT TEAM: Associazione Italiana Ricerca sul Cancro (AIRC; IG2015 no.16732) to P. Peterlongo. DEMOKRITOS: European Union (European Social Fund – ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program of the General Secretariat for Research & Technology: SYN11_10_19 NBCA. Investing in knowledge society through the European Social Fund. DFKZ: German Cancer Research Center. EMBRACE: Cancer Research UK Grants C1287/A10118 and C1287/A11990. D. Gareth Evans and Fiona Lalloo are supported by an NIHR grant to the Biomedical Research Centre, Manchester. The Investigators at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust are supported by an NIHR grant to the Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. Ros Eeles and Elizabeth Bancroft are supported by Cancer Research UK Grant C5047/A8385. Ros Eeles is also supported by NIHR support to the Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. FCCC: The University of Kansas Cancer Center (P30 CA168524) and the Kansas Bioscience Authority Eminent Scholar Program. A.K.G. was funded by R0 1CA140323, R01 CA214545, and by the Chancellors Distinguished Chair in Biomedical Sciences Professorship. A.Vega is supported by the Spanish Health Research Foundation, Instituto de Salud Carlos III (ISCIII), partially supported by FEDER funds through Research Activity Intensification Program (contract grant numbers: INT15/00070, INT16/00154, INT17/00133), and through Centro de Investigación Biomédica en Red de Enferemdades Raras CIBERER (ACCI 2016: ER17P1AC7112/2018); Autonomous Government of Galicia (Consolidation and structuring program: IN607B), and by the Fundación Mutua Madrileña. GC-HBOC: German Cancer Aid (grant no 110837, Rita K. Schmutzler) and the European Regional Development Fund and Free State of Saxony, Germany (LIFE - Leipzig Research Centre for Civilization Diseases, project numbers 713-241202, 713-241202, 14505/2470, 14575/2470). GEMO: Ligue Nationale Contre le Cancer; the Association “Le cancer du sein, parlons-en!” Award, the Canadian Institutes of Health Research for the "CIHR Team in Familial Risks of Breast Cancer" program, the Fondation ARC pour la recherche sur le cancer (grant PJA 20151203365) and the French National Institute of Cancer (INCa grants AOR 01 082, 2001-2003, 2013-1-BCB-01-ICH-1 and SHS-E-SP 18-015). GEORGETOWN: the Non- Therapeutic Subject Registry Shared Resource at Georgetown University (NIH/NCI grant P30- CA051008), the Fisher Center for Hereditary Cancer and Clinical Genomics Research, and Swing Fore the Cure. G-FAST: Bruce Poppe is a senior clinical investigator of FWO. Mattias Van Heetvelde obtained funding from IWT. HCSC: Spanish Ministry of Health PI15/00059, PI16/01292, and CB- 161200301 CIBERONC from ISCIII (Spain), partially supported by European Regional Development FEDER funds. HEBCS: Helsinki University Hospital Research Fund, the Finnish Cancer Society and the Sigrid Juselius Foundation. HEBON: the Dutch Cancer Society grants NKI1998-1854, NKI2004- 3088, NKI2007-3756, the Netherlands Organization of Scientific Research grant NWO 91109024, the Pink Ribbon grants 110005 and 2014-187.WO76, the BBMRI grant NWO 184.021.007/CP46 and the Transcan grant JTC 2012 Cancer 12-054. HEBON thanks the registration teams of Dutch Cancer Registry (IKNL; S. Siesling, J. Verloop) and the Dutch Pathology database (PALGA; L. Overbeek) for part of the data collection. HUNBOCS: Hungarian Research Grants KTIA-OTKA CK-80745 and NKFI_OTKA K-112228. ICO: The authors would like to particularly acknowledge the support of the Asociación Española Contra el Cáncer (AECC), the Instituto de Salud Carlos III (organismo adscrito al Ministerio de Economía y Competitividad) and “Fondo Europeo de Desarrollo Regional (FEDER), una manera de hacer Europa” (PI10/01422, PI13/00285, PIE13/00022, PI15/00854, PI16/00563 and CIBERONC) and the Institut Català de la Salut and Autonomous Government of Catalonia (2009SGR290, 2014SGR338 and PERIS Project MedPerCan). IHCC: PBZ_KBN_122/P05/2004. INHERIT: Canadian Institutes of Health Research for the “CIHR Team in Familial Risks of Breast Cancer” program – grant # CRN-87521 and the Ministry of Economic Development, Innovation and Export Trade – grant # PSR-SIIRI-701. IOVHBOCS: Ministero della Salute and “5x1000” Istituto Oncologico Veneto grant. IPOBCS: Liga Portuguesa Contra o Cancro. kConFab: The National Breast Cancer Foundation, and previously by the National Health and Medical Research Council (NHMRC), the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia, and the Cancer Foundation of Western Australia. MAYO: NIH grants CA116167, CA192393 and CA176785, an NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201),and a grant from the Breast Cancer Research Foundation. MCGILL: Jewish General Hospital Weekend to End Breast Cancer, Quebec Ministry of Economic Development, Innovation and Export Trade. Marc Tischkowitz is supported by the funded by the European Union Seventh Framework Program (2007Y2013)/European Research Council (Grant No. 310018). MODSQUAD: MH CZ - DRO (MMCI, 00209805), MEYS - NPS I - LO1413 to LF, and by Charles University in Prague project UNCE204024 (MZ). MSKCC: the Breast Cancer Research Foundation, the Robert and Kate Niehaus Clinical Cancer Genetics Initiative, the Andrew Sabin Research Fund and a Cancer Center Support Grant/Core Grant (P30 CA008748). NAROD: 1R01 CA149429-01. NCI: the Intramural Research Program of the US National Cancer Institute, NIH, and by support services contracts NO2-CP-11019-50, N02-CP-21013-63 and N02-CP-65504 with Westat, Inc, Rockville, MD. NICCC: Clalit Health Services in Israel, the Israel Cancer Association and the Breast Cancer Research Foundation (BCRF), NY. NNPIO: the Russian Foundation for Basic Research (grants 17-00-00171, 18-515-45012 and 19-515-25001). NRG Oncology: U10 CA180868, NRG SDMC grant U10 CA180822, NRG Administrative Office and the NRG Tissue Bank (CA 27469), the NRG Statistical and Data Center (CA 37517) and the Intramural Research Program, NCI, KAP is an Australian National Breast Cancer Foundation Fellow. OSUCCG: Ohio State University Comprehensive Cancer Center. PBCS: Italian Association of Cancer Research (AIRC) [IG 2013 N.14477] and Tuscany Institute for Tumors (ITT) grant 2014-2015-2016. SMC: the Israeli Cancer Association. SWE-BRCA: the Swedish Cancer Society. UCHICAGO: NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA125183), R01 CA142996, 1U01CA161032 and by the Ralph and Marion Falk Medical Research Trust, the Entertainment Industry Fund National Women's Cancer Research Alliance and the Breast Cancer research Foundation. OIO is an ACS Clinical Research Professor. UCSF: UCSF Cancer Risk Program and Helen Diller Family Comprehensive Cancer Center. UKFOCR: Cancer Researc h UK. UPENN: Breast Cancer Research Foundation (to SMD, KLN); Susan G. Komen Foundation for the cure (SMD), Basser Research Center for BRCA (SMD, KLN). UPITT/MWH: Hackers for Hope Pittsburgh. VFCTG: Victorian Cancer Agency, Cancer Australia, National Breast Cancer Foundation. WCP: Dr Karlan is funded by the American Cancer Society Early Detection Professorship (SIOP-06-258-01-COUN) and the National Center for Advancing Translational Sciences (NCATS), Grant UL1TR000124. Tracy A. O’Mara was supported by NHMRC Early Career Research Fellow. Acknowledgements All the families and clinicians who contribute to the studies; Catherine M. Phelan for her contribution to CIMBA until she passed away on 22 September 2017; Sue Healey, in particular taking on the task of mutation classification with the late Olga Sinilnikova; Maggie Angelakos, Judi Maskiell, Gillian Dite, Helen Tsimiklis; members and participants in the New York site of the Breast Cancer Family Registry; members and participants in the Ontario Familial Breast Cancer Registry; Vilius Rudaitis and Laimonas Griškevičius; Drs Janis Eglitis, Anna Krilova and Aivars Stengrevics; Yuan Chun Ding and Linda Steele for their work in participant enrollment and biospecimen and data management; Bent Ejlertsen and Anne-Marie Gerdes for the recruitment and genetic counseling of participants; Alicia Barroso, Rosario Alonso and Guillermo Pita; all the individuals and the researchers who took part in CONSIT TEAM (Consorzio Italiano Tumori Ereditari Alla Mammella), in particular: Dario Zimbalatti, Daniela Zaffaroni, Laura Ottini, Giuseppe Giannini, Liliana Varesco, Viviana Gismondi, Maria Grazia Tibiletti, Daniela Furlan, Antonella Savarese, Aline Martayan, Stefania Tommasi, Brunella Pilato and the personnel of the Cogentech Cancer Genetic Test Laboratory, Milan, Italy. Ms. JoEllen Weaver and Dr. Betsy Bove; FPGMX: members of the Cancer Genetics group (IDIS): Ana Blanco, Miguel Aguado, Uxía Esperón and Belinda Rodríguez.; IFE - Leipzig Research Centre for Civilization Diseases (Markus Loeffler, Joachim Thiery, Matthias Nüchter, Ronny Baber); We thank all participants, clinicians, family doctors, researchers, and technicians for their contributions and commitment to the DKFZ study and the collaborating groups in Lahore, Pakistan (Noor Muhammad, Sidra Gull, Seerat Bajwa, Faiz Ali Khan, Humaira Naeemi, Saima Faisal, Asif Loya, Mohammed Aasim Yusuf) and Bogota, Colombia (Diana Torres, Ignacio Briceno, Fabian Gil). Genetic Modifiers of Cancer Risk in BRCA1/2 Mutation Carriers (GEMO) study is a study from the National Cancer Genetics Network UNICANCER Genetic Group, France. We wish to pay a tribute to Olga M. Sinilnikova, who with Dominique Stoppa-Lyonnet initiated and coordinated GEMO until she sadly passed away on the 30th June 2014. The team in Lyon (Olga Sinilnikova, Mélanie Léoné, Laure Barjhoux, Carole Verny-Pierre, Sylvie Mazoyer, Francesca Damiola, Valérie Sornin) managed the GEMO samples until the biological resource centre was transferred to Paris in December 2015 (Noura Mebirouk, Fabienne Lesueur, Dominique Stoppa-Lyonnet). We want to thank all the GEMO collaborating groups for their contribution to this study: Coordinating Centre, Service de Génétique, Institut Curie, Paris, France: Muriel Belotti, Ophélie Bertrand, Anne-Marie Birot, Bruno Buecher, Sandrine Caputo, Anaïs Dupré, Emmanuelle Fourme, Marion Gauthier-Villars, Lisa Golmard, Claude Houdayer, Marine Le Mentec, Virginie Moncoutier, Antoine de Pauw, Claire Saule, Dominique Stoppa-Lyonnet, and Inserm U900, Institut Curie, Paris, France: Fabienne Lesueur, Noura Mebirouk.Contributing Centres : Unité Mixte de Génétique Constitutionnelle des Cancers Fréquents, Hospices Civils de Lyon - Centre Léon Bérard, Lyon, France: Nadia Boutry-Kryza, Alain Calender, Sophie Giraud, Mélanie Léone. Institut Gustave Roussy, Villejuif, France: Brigitte Bressac-de- Paillerets, Olivier Caron, Marine Guillaud-Bataille. Centre Jean Perrin, Clermont–Ferrand, France: Yves-Jean Bignon, Nancy Uhrhammer. Centre Léon Bérard, Lyon, France: Valérie Bonadona, Christine Lasset. Centre François Baclesse, Caen, France: Pascaline Berthet, Laurent Castera, Dominique Vaur. Institut Paoli Calmettes, Marseille, France: Violaine Bourdon, Catherine Noguès, Tetsuro Noguchi, Cornel Popovici, Audrey Remenieras, Hagay Sobol. CHU Arnaud-de-Villeneuve, Montpellier, France: Isabelle Coupier, Pascal Pujol. Centre Oscar Lambret, Lille, France: Claude Adenis, Aurélie Dumont, Françoise Révillion. Centre Paul Strauss, Strasbourg, France: Danièle Muller. Institut Bergonié, Bordeaux, France: Emmanuelle Barouk-Simonet, Françoise Bonnet, Virginie Bubien, Michel Longy, Nicolas Sevenet, Institut Claudius Regaud, Toulouse, France: Laurence Gladieff, Rosine Guimbaud, Viviane Feillel, Christine Toulas. CHU Grenoble, France: Hélène Dreyfus, Christine Dominique Leroux, Magalie Peysselon, Rebischung. CHU Dijon, France: Amandine Baurand, Geoffrey Bertolone, Fanny Coron, Laurence Faivre, Caroline Jacquot, Sarab Lizard. CHU St-Etienne, France: Caroline Kientz, Marine Lebrun, Fabienne Prieur. Hôtel Dieu Centre Hospitalier, Chambéry, France: Sandra Fert Ferrer. Centre Antoine Lacassagne, Nice, France: Véronique Mari. CHU Limoges, France: Laurence Vénat-Bouvet. CHU Nantes, France: Stéphane Bézieau, Capucine Delnatte. CHU Bretonneau, Tours and Centre Hospitalier de Bourges France: Isabelle Mortemousque. Groupe Hospitalier Pitié-Salpétrière, Paris, France: Chrystelle Colas, Florence Coulet, Florent Soubrier, Mathilde Warcoin. CHU Vandoeuvre-les-Nancy, France: Myriam Bronner, Johanna Sokolowska. CHU Besançon, France: Marie-Agnès Collonge-Rame, Alexandre Damette. CHU Poitiers, Centre Hospitalier d’Angoulême and Centre Hospitalier de Niort, France: Paul Gesta. Centre Hospitalier de La Rochelle : Hakima Lallaoui. CHU Nîmes Carémeau, France : Jean Chiesa. CHI Poissy, France: Denise Molina-Gomes. CHU Angers, France : Olivier Ingster; Ilse Coene en Brecht Crombez; Ilse Coene and Brecht Crombez; Alicia Tosar and Paula Diaque; Irja Erkkilä and Virpi Palola; The Hereditary Breast and Ovarian Cancer Research Group Netherlands (HEBON) consists of the following Collaborating Centers: Coordinating center: Netherlands Cancer Institute, Amsterdam, NL: M.A. Rookus, F.B.L. Hogervorst, F.E. van Leeuwen, S. Verhoef, M.K. Schmidt, N.S. Russell, D.J. Jenner; Erasmus Medical Center, Rotterdam, NL: J.M. Collée, A.M.W. van den Ouweland, M.J. Hooning, C. Seynaeve, C.H.M. van Deurzen, I.M. Obdeijn; Leiden University Medical Center, NL: C.J. van Asperen, J.T. Wijnen, R.A.E.M. Tollenaar, P. Devilee, T.C.T.E.F. van Cronenburg; Radboud University Nijmegen Medical Center, NL: C.M. Kets, A.R. Mensenkamp; University Medical Center Utrecht, NL: M.G.E.M. Ausems, R.B. van der Luijt, C.C. van der Pol; Amsterdam Medical Center, NL: C.M. Aalfs, T.A.M. van Os; VU University Medical Center, Amsterdam, NL: J.J.P. Gille, Q. Waisfisz, H.E.J. Meijers-Heijboer; University Hospital Maastricht, NL: E.B. Gómez-Garcia, M.J. Blok; University Medical Center Groningen, NL: J.C. Oosterwijk, A.H. van der Hout, M.J. Mourits, G.H. de Bock; The Netherlands Foundation for the detection of hereditary tumours, Leiden, NL: H.F. Vasen; The Netherlands Comprehensive Cancer Organization (IKNL): S. Siesling, J.Verloop; The Dutch Pathology Registry (PALGA): L.I.H. Overbeek; Hong Kong Sanatorium and Hospital; the Hungarian Breast and Ovarian Cancer Study Group members (Janos Papp, Aniko Bozsik, Timea Pocza, Zoltan Matrai, Miklos Kasler, Judit Franko, Maria Balogh, Gabriella Domokos, Judit Ferenczi, Department of Molecular Genetics, National Institute of Oncology, Budapest, Hungary) and the clinicians and patients for their contributions to this study; the Oncogenetics Group (VHIO) and the High Risk and Cancer Prevention Unit of the University Hospital Vall d’Hebron, Miguel Servet Progam (CP10/00617), and the Cellex Foundation for providing research facilities and equipment; the ICO Hereditary Cancer Program team led by Dr. Gabriel Capella; the ICO Hereditary Cancer Program team led by Dr. Gabriel Capella; Dr Martine Dumont for sample management and skillful assistance; Catarina Santos and Pedro Pinto; members of the Center of Molecular Diagnosis, Oncogenetics Department and Molecular Oncology Research Center of Barretos Cancer Hospital; Heather Thorne, Eveline Niedermayr, all the kConFab research nurses and staff, the heads and staff of the Family Cancer Clinics, and the Clinical Follow Up Study (which has received funding from the NHMRC, the National Breast Cancer Foundation, Cancer Australia, and the National Institute of Health (USA)) for their contributions to this resource, and the many families who contribute to kConFab; the KOBRA Study Group; (National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA); Eva Machackova (Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute and MF MU, Brno, Czech Republic); and Michal Zikan, Petr Pohlreich and Zdenek Kleibl (Oncogynecologic Center and Department of Biochemistry and Experimental Oncology, First Faculty of Medicine, Charles University, Prague, Czech Republic); Anne Lincoln, Lauren Jacobs; the participants in Hereditary Breast/Ovarian Cancer Study and Breast Imaging Study for their selfless contributions to our research; the NICCC National Familial Cancer Consultation Service team led by Sara Dishon, the lab team led by Dr. Flavio Lejbkowicz, and the research field operations team led by Dr. Mila Pinchev; the investigators of the Australia New Zealand NRG Oncology group; members and participants in the Ontario Cancer Genetics Network; Kevin Sweet, Caroline Craven, Julia Cooper, Amber Aielts, and Michelle O'Conor; Yip Cheng Har, Nur Aishah Mohd Taib, Phuah Sze Yee, Norhashimah Hassan and all the research nurses, research assistants and doctors involved in the MyBrCa Study for assistance in patient recruitment, data collection and sample preparation, Philip Iau, Sng Jen-Hwei and Sharifah Nor Akmal for contributing samples from the Singapore Breast Cancer Study and the HUKM-HKL Study respectively; the Meirav Comprehensive breast cancer center team at the Sheba Medical Center; Christina Selkirk; Helena Jernström, Karin Henriksson, Katja Harbst, Maria Soller, Ulf Kristoffersson; from Gothenburg Sahlgrenska University Hospital: Anna Öfverholm, Margareta Nordling, Per Karlsson, Zakaria Einbeigi; from Stockholm and Karolinska University Hospital: Anna von Wachenfeldt, Annelie Liljegren, Brita Arver, Gisela Barbany Bustinza; from Umeå University Hospital: Beatrice Melin, Christina Edwinsdotter Ardnor, Monica Emanuelsson; from Uppsala University: Hans Ehrencrona, Maritta Hellström Pigg, Richard Rosenquist; from Linköping University Hospital: Marie Stenmark- Askmalm, Sigrun Liedgren; Cecilia Zvocec, Qun Niu; Joyce Seldon and Lorna Kwan; Dr. Robert Nussbaum, Beth Crawford, Kate Loranger, Julie Mak, Nicola Stewart, Robin Lee, Amie Blanco and Peggy Conrad and Salina Chan; Carole Pye, Patricia Harrington and Eva Wozniak; Geoffrey Lindeman, Marion Harris, Martin Delatycki, Sarah Sawyer, Rebecca Driessen, and Ella Thompson for performing all DNA amplification. Members of consortia listed as authors EMBRACE Helen Gregory (North of Scotland Regional Genetics Service, NHS Grampian & University of Aberdeen, Foresterhill, Aberdeen, UK); Zosia Miedzybrodzka (North of Scotland Regional Genetics Service, NHS Grampian & University of Aberdeen, Foresterhill, Aberdeen, UK); Patrick J. Morrison (Northern Ireland Regional Genetics Centre, Belfast Health and Social Care Trust, and Department of Medical Genetics, Queens University Belfast, Belfast, UK); Kai-ren Ong (West Midlands Regional Genetics Service, Birmingham Women’s Hospital Healthcare NHS Trust, Edgbaston, Birmingham, UK); Alan Donaldson (Clinical Genetics Department, St Michael’s Hospital, Bristol, UK); Marc Tischkowitz (Department of Medical Genetics, University of Cambridge, UK); Mark T. Rogers (All Wales Medical Genetics Services, University Hospital of Wales, Cardiff, UK); M. John Kennedy (Academic Unit of Clinical and Molecular Oncology, Trinity College Dublin and St James's Hospital, Dublin, Eire); Mary E. Porteous (South East of Scotland Regional Genetics Service, Western General Hospital, Edinburgh, UK); Carole Brewer (Department of Clinical Genetics, Royal Devon & Exeter Hospital, Exeter, UK); Rosemarie Davidson (Clinical Genetics, Southern General Hospital, Glasgow, UK); Louise Izatt (Clinical Genetics, Guy’s and St. Thomas’ NHS Foundation Trust, London, UK); Angela Brady (North West Thames Regional Genetics Service, Kennedy-Galton Centre, Harrow, UK); Julian Barwell (Leicestershire Clinical Genetics Service, University Hospitals of Leicester NHS Trust, UK); Julian Adlard (Yorkshire Regional Genetics Service, Leeds, UK); Claire Foo (Department of Clinical Genetics, Alder Hey Hospital, Eaton Road, Liverpool, UK); D. Gareth Evans (Genetic Medicine, Manchester Academic Health Sciences Centre, Central Manchester University Hospitals NHS Foundation Trust, Manchester, UK); Fiona Lalloo (Genetic Medicine, Manchester Academic Health Sciences Centre, Central Manchester University Hospitals NHS Foundation Trust, Manchester, UK); Lucy E. Side (North East Thames Regional Genetics Service, Great Ormond Street Hospital for Children NHS Trust, London, UK); Jacqueline Eason (Nottingham Clinical Genetics Service, Nottingham University Hospitals NHS Trust, UK); Alex Henderson (Institute of Genetic Medicine, Centre for Life, Newcastle Upon Tyne Hospitals NHS Trust, Newcastle upon Tyne, UK); Lisa Walker (Oxford Regional Genetics Service, Churchill Hospital, Oxford, UK); Rosalind A. Eeles (Oncogenetics Team, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, UK); Jackie Cook (Sheffield Clinical Genetics Service, Sheffield Children’s Hospital, Sheffield, UK); Katie Snape (South West Thames Regional Genetics Service, St.Georges Hospital, Cranmer Terrace, Tooting, London, UK); Diana Eccles (University of Southampton Faculty of Medicine, Southampton University Hospitals NHS Trust, Southampton, UK); Alex Murray (All Wales Medical Genetics Services, Singleton Hospital, Swansea, UK); Emma McCann (All Wales Medical Genetics Service, Glan Clwyd Hospital, Rhyl, UK). GEMO Study Collaborators Dominique Stoppa-Lyonnet, Muriel Belotti, Anne-Marie Birot, Bruno Buecher, Emmanuelle Fourme, Marion Gauthier-Villars, Lisa Golmard, Claude Houdayer, Virginie Moncoutier, Antoine de Pauw, Claire Saule (Service de Génétique, Institut Curie, Paris, France); Fabienne Lesueur, Noura Mebirouk (Inserm U900, Institut Curie, Paris, France); Olga Sinilnikova†, Sylvie Mazoyer, Francesca Damiola, Laure Barjhoux, Carole Verny-Pierre, Mélanie Léone, Nadia Boutry-Kryza, Alain Calender, Sophie Giraud (Unité Mixte de Génétique Constitutionnelle des Cancers Fréquents, Hospices Civils de Lyon - Centre Léon Bérard, Lyon, France); Olivier Caron, Marine GuillaudBataille (Institut Gustave Roussy, Villejuif, France: Brigitte Bressac-de-Paillerets); YvesJean Bignon, Nancy Uhrhammer (Centre Jean Perrin, Clermont–Ferrand, France); Christine Lasset, Valérie Bonadona (Centre Léon Bérard, Lyon, France); Pascaline Berthet, Dominique Vaur, Laurent Castera (Centre François Baclesse, Caen, France); Hagay Sobol, Violaine Bourdon, Tetsuro Noguchi, Audrey Remenieras, François Eisinger, Catherine Noguès (Institut Paoli Calmettes, Marseille, France); Isabelle Coupier, Pascal Pujol (CHU Arnaud-de-Villeneuve, Montpellier, France); Jean-Philippe Peyrat, Joëlle Fournier, Françoise Révillion, Claude Adenis (Centre Oscar Lambret, Lille, France); Danièle Muller, Jean-Pierre Fricker (Centre Paul Strauss, Strasbourg, France); Emmanuelle Barouk-Simonet, Françoise Bonnet, Virginie Bubien, Nicolas Sevenet, Michel Longy (Institut Bergonié, Bordeaux, France); Christine Toulas, Rosine Guimbaud, Laurence Gladieff, Viviane Feillel (Institut Claudius Regaud, Toulouse, France); Dominique Leroux, Hélène Dreyfus, Christine Rebischung, Magalie Peysselon (CHU Grenoble, France); Fanny Coron, Laurence Faivre, Amandine Baurand, Caroline Jacquot, Geoffrey, Bertolone, Sarab Lizard (CHU Dijon, France); Fabienne Prieur, Marine Lebrun, Caroline Kientz (CHU St- Etienne, France); Sandra Fert Ferrer (Hôtel Dieu Centre Hospitalier, Chambéry, France); Véronique Mari (Centre Antoine Lacassagne, Nice, France); Laurence Vénat-Bouvet (CHU Limoges, France); Capucine Delnatte, Stéphane Bézieau (CHU Nantes, France); Isabelle Mortemousque (CHU Bretonneau, Tours and Centre Hospitalier de Bourges France); Florence Coulet, Chrystelle Colas, Florent Soubrier, Mathilde Warcoin (Groupe Hospitalier PitiéSalpétrière, Paris, France); Johanna Sokolowska, Myriam Bronner (CHU Vandoeuvreles-Nancy, France); Marie-Agnès Collonge-Rame, Alexandre Damette (CHU Besançon, France); Paul Gesta (CHU Poitiers, Centre Hospitalier d’Angoulême and Centre Hospitalier de Niort, France); Hakima Lallaoui (Centre Hospitalier de La Rochelle); Jean Chiesa (CHU Nîmes Carémeau, France); Denise Molina-Gomes (CHI Poissy, France); Olivier Ingster (CHU Angers, France). 0.14 0.29 0.39 0.49 0.72 1.00 1.34 1.82 2.42 2.80 5.63 0.16 0.27 0.39 0.54 0.74 1.00 1.36 1.85 2.31 2.84 5.27 0.16 0.28 0.39 0.51 0.70 1.00 1.33 1.82 2.42 2.67 5.88 Intrinsic subtypes PRS ORs1 Overall PRS ORs2 ER Specific PRS ORs3 OR per SD (95%CI) AUC5 1.83 (1.78-1.88) 66.09 1.80 (1.75-1.86) 65.73 1.82 (1.77-1.87) 65.95 1 Intrinsic-like subtypes PRS based on 330 SNPs (Online Methods, Supplementary Table 19) 2 Overall breast cancer PRS with 313 SNPs previously reported22 3 ER-specific PRS with 313 SNPs previously reported22 4 Luminal A-like (ER+ and/or PR+, HER2-, grade 1 & 2). 5Area under the curve 0.05 0.35 0.39 0.65 0.71 1.00 1.20 1.51 2.14 2.17 3.37 0.20 0.27 0.44 0.54 0.64 1.00 1.10 1.42 1.87 2.19 3.14 0.15 0.27 0.48 0.44 0.67 1.00 1.09 1.35 1.82 2.16 3.43 Intrinsic subtypes PRS ORs1 Overall PRS ORs2 ER Specific PRS ORs3 OR per SD (95%CI) AUC5 1.62 (1.54-1.70) 63.30 1.62 (1.55-1.71) 63.36 1.62 (1.54-1.70) 63.23 1 Intrinsic-like subtypes PRS based on 330 SNPs (Online Methods, Supplementary Table 19) 2 Overall breast cancer PRS with 313 SNPs previously reported22 3 ER-specific PRS with 313 SNPs previously reported22 4 Luminal A-like (ER+ and/or PR+, HER2-, grade 1 & 2). 5Area under the curve 0.35 0.49 0.56 0.66 0.83 1.00 1.50 1.80 2.53 3.07 4.93 0.42 0.49 0.66 0.65 0.84 1.00 1.45 1.85 2.47 3.10 4.70 0.35 0.62 0.58 0.70 0.81 1.00 1.44 1.83 2.60 3.10 4.47 Intrinsic subtypes PRS ORs1 Overall PRS ORs2 ER Specific PRS ORs3 OR per SD (95%CI) AUC5 1.69 (1.61-1.78) 64.31 1.68 (1.60-1.77) 64.32 1.66 (1.58-1.75) 64.00 1 Intrinsic-like subtypes PRS based on 330 SNPs (Online Methods, Supplementary Table 19) 2 Overall breast cancer PRS with 313 SNPs previously reported22 3 ER-specific PRS with 313 SNPs previously reported22 4 Luminal A-like (ER+ and/or PR+, HER2-, grade 1 & 2). 5Area under the curve 0.29 0.40 0.58 0.67 0.70 1.00 1.21 1.83 1.52 1.81 3.65 0.43 0.39 0.43 0.66 0.81 1.00 1.15 1.52 1.72 2.02 2.91 0.00 0.57 0.45 0.59 0.78 1.00 1.33 1.61 1.73 2.89 2.74 Intrinsic subtypes PRS ORs1 Overall PRS ORs2 ER Specific PRS ORs3 OR per SD (95%CI) AUC5 1.53 (1.42-1.65) 62.08 1.49 (1.38-1.60) 60.93 1.59 (1.48-1.71) 62.91 1 Intrinsic-like subtypes PRS based on 330 SNPs (Online Methods, Supplementary Table 19) 2 Overall breast cancer PRS with 313 SNPs previously reported22 3 ER-specific PRS with 313 SNPs previously reported22 4 Luminal A-like (ER+ and/or PR+, HER2-, grade 1 & 2). 5Area under the curve 0.41 0.44 0.42 0.59 0.77 1.00 1.24 1.63 2.11 3.18 3.02 0.38 0.63 0.70 0.66 0.80 1.00 1.07 1.31 1.65 1.81 2.70 0.48 0.44 0.47 0.70 0.83 1.00 1.28 1.91 2.05 2.31 4.01 Intrinsic subtypes PRS ORs1 Overall PRS ORs2 ER Specific PRS ORs3 OR per SD (95%CI) AUC5 1.65 (1.57-1.73) 63.58 1.38 (1.31-1.44) 58.77 1.59 (1.51-1.66) 62.76 1 Intrinsic-like subtypes PRS based on 330 SNPs (Online Methods, Supplementary Table 19) 2 Overall breast cancer PRS with 313 SNPs previously reported22 3 ER-specific PRS with 313 SNPs previously reported22 4 Luminal A-like (ER+ and/or PR+, HER2-, grade 1 & 2). 5Area under the curve Supplementary Table 1: BCAC studies contributing data 1, by genotyping initiative Acronym Study Name Country Study design Controls Invasive In Situ Unknown Invasiveness Controls Invasive In Situ Unknown Invasiveness Controls Cases 2SISTER The Two Sister Study USA Case‐only study 919 151 1 ABCFS Australian Breast Cancer Family Study Australia Case‐control study 551 322 187 1117 285 282 ABCS Amsterdam Breast Cancer Study Netherlands Case‐control study 1628 771 189 347 ABCS‐F Amsterdam Breast Cancer Study – Familial Netherlands Case‐only study 1002 106 ABCTB Australian Breast Cancer Tissue Bank Australia Case‐control study 375 947 6 AHS Agricultural Health Study USA Prospective cohorts: nested case‐control studies 1137 513 1 BBCC Bavarian Breast Cancer Cases and Controls Germany Case‐control study 453 438 8 253 403 8 BBCS British Breast Cancer Study UK Case‐control study 1396 1404 106 2 442 122 1224 1609 BCEES Breast Cancer Employment and Environment Study Australia Case‐control study 835 783 BCFR Breast Cancer Family Registry USA, Canada, Australia Case‐control study 2251 3129 BCFR‐NY New York site of the Breast Cancer Family Registry USA Case‐control study 27 401 53 BCFR‐PA Philadelphia site of the Breast Cancer Family Registry USA Case‐control study 63 6 70 BCFR‐UTAH Utah site of the Breast Cancer Family Registry USA Case‐control study 101 1 BCINIS Breast Cancer in Northern Israel Study Israel Case‐control study 724 1334 100 3 BIGGS Breast Cancer in Galway Genetic Study Ireland Case‐control study 719 793 43 BPC3 Breast and Prostate Cancer Cohort Consortium International Prospective cohorts: nested case‐control studies 2305 1998 BREOGAN Breast Oncology Galicia Network Spain Case‐control study 725 1259 99 19 BSUCH Breast Cancer Study of the University of Heidelberg Germany Case‐control study 951 738 28 5 168 252 1 24 CBCS Canadian Breast Cancer Study Canada Case‐control study 817 568 108 CCGP Crete Cancer Genetics Program Greece Case‐control study 332 665 7 CECILE CECILE Breast Cancer Study France Case‐control study 843 630 93 159 280 26 CGPS Copenhagen General Population Study Denmark Case‐control study 4525 2867 80 716 1408 3 CNIO‐BCS Spanish National Cancer Centre Breast Cancer Study Spain Case‐control study 871 866 33 CPSII Cancer Prevention Study‐II Nutrition Cohort USA Prospective cohort: nested case‐control study 294 138 9 38 3028 2388 597 68 CTS California Teachers Study USA Prospective cohort: nested case‐control study 37 19 610 1156 DIETCOMPLYF DietCompLyf Breast Cancer Survival Study UK Prospective cohort: nested case‐control study 708 3 EPIC European Prospective Investigation Into Cancer and NutritionInternational (Europe) Prospective cohort: nested case‐control study 3644 3435 412 ESTHER ESTHER Breast Cancer Study Germany Case‐control study 318 184 1 187 291 3 2 FHRISK Family History Risk Study UK Case‐control study 296 102 25 19 GC‐HBOC German Consortium for Hereditary Breast & Ovarian Cancer Germany Case‐control study 139 1593 3416 218 477 634 GENICA Gene Environment Interaction and Breast Cancer in GermanyGermany Case‐control study 426 453 9 284 459 1 GEPARSIXTO Randomized phase II trial Germany Case‐only study 387 GESBC Genetic Epidemiology Study of Breast Cancer by Age 50 Germany Case‐control study 181 312 39 7 HABCS Hannover Breast Cancer Study Germany Case‐control study 866 909 19 HCSC Hospital Clinico San Carlos Spain Case‐control study 423 3 HEBCS Helsinki Breast Cancer Study Finland Case‐control study 1059 1515 147 177 281 1012 726 HMBCS# Hannover‐Minsk Breast Cancer Study Belarus Case‐control study 95 532 2 249 212 HUBCS Hannover‐Ufa Breast Cancer Study Russia Case‐control study 120 211 KARBAC Karolinska Breast Cancer Study Sweden Case‐control study 658 307 498 5 KARMA Karolinska Mammography Project for Risk Prediction of BreaSweden Case‐control study 6026 2366 279 KBCP Kuopio Breast Cancer Project Finland Case‐control study 188 22 3 245 522 34 KCONFAB/AOCS Kathleen Cuningham Foundation Consortium for research intAustralia and New Zeland Case‐control study 896 463 68 25 LMBC Leuven Multidisciplinary Breast Centre Belgium Case‐control study 1268 783 22 MABCS Macedonian Breast Cancer Study Macedonia Case‐control study 92 89 1 MARIE Mammary Carcinoma Risk Factor Investigation Germany Case‐control study 1776 1137 154 289 506 6 470 652 MBCSG Milan Breast Cancer Study Group Italy Case‐control study 400 188 37 263 366 549 72 167 MCBCS Mayo Clinic Breast Cancer Study USA Case‐control study 1829 1323 253 221 749 167 10 MCCS Melbourne Collaborative Cohort Study Australia Prospective cohort: nested case‐control study 228 197 978 861 189 MEC Multiethnic Cohort USA Prospective cohort: nested case‐control study 129 105 25 724 668 5 MISS Melanoma Inquiry of Southern Sweden Sweden Prospective cohort: nested case‐control study 1545 599 102 MMHS Mayo Mammography Health Study USA Prospective cohort: nested case‐control study 1635 275 99 10 MSKCC Memorial Sloan‐Kettering Cancer Center Study USA Case‐control study 136 2 MTLGEBCS Montreal Gene‐Environment Breast Cancer Study Canada Case‐control study 295 192 170 341 NBCS Norwegian Breast Cancer Study Norway Case‐control study 277 1295 9 69 NBHS Nashville Breast Health Study USA Case‐control study 79 89 652 483 112 82 NC‐BCFR Northern California Breast Cancer Family Registry USA Case‐control study 151 753 21 NCBCS North Carolina Breast Cancer study USA Case‐control study 1006 2074 315 NHS Nurses Health Study USA Prospective cohort: nested case‐control study 1804 1103 333 154 NHS2 Nurses Health Study 2 USA Prospective cohort: nested case‐control study 1905 1112 409 86 OBCS Oulu Breast Cancer Study Finland Case‐control study 414 499 7 1 OFBCR Ontario Familial Breast Cancer Registry Canada Case‐control study 353 487 17 375 1655 9 ORIGO Leiden University Medical Centre Breast Cancer Study Netherlands Prospective cohort: nested case‐control study 326 319 32 660 922 110 21 PBCS NCI Polish Breast Cancer Study Poland Case‐control study 37 27 2045 1740 111 80 PKARMA Karolinska Mammography Project for Risk Prediction of BreaSweden Case‐control study 5406 4247 436 48 740 94 PLCO The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Sc USA Prospective cohort: nested case‐control study 2595 1822 483 POSH Prospective Study of Outcomes in Sporadic Versus Hereditar UK Case‐only study 1088 PREFACE Evaluation of Predictive Factors regarding the Effectivity of AGermany Case‐only study 2981 8 PROCAS Predicting the Risk Of Cancer At Screening Study UK Prospective cohort: nested case‐control study 1656 323 83 241 RBCS Rotterdam Breast Cancer Study Netherlands Case‐control study 688 596 34 2 240 452 22 SASBAC Singapore and Sweden Breast Cancer Study Sweden Case‐control study 1373 1129 756 790 SBCS Sheffield Breast Cancer Study UK Case‐control study 848 746 58 39 SEARCH Study of Epidemiology and Risk factors in Cancer Heredity UK Case‐control study 6236 8747 181 64 2673 4057 SISTER The Sister Study USA Prospective cohort: nested case‐control study 1562 1502 496 13 SKKDKFZS Städtisches Klinikum Karlsruhe Deutsches KrebsforschungszeGermany Case‐only study 29 134 2 1086 9 SMC Swedish Mammography Cohort Sweden Prospective cohort: nested case‐control study 704 1509 SUCCESSB Simultaneous Study of Gemcitabine‐Docetaxel Combination  Germany Case‐only study 440 SUCCESSC Simultaneous Study of Docetaxel Based Anthracycline Free AGermany Case‐only study 2836 SZBCS IHCC‐Szczecin Breast Cancer Study Poland Case‐control study 298 325 13 27 174 338 9 40 TNBCC Triple‐Negative Breast Cancer Consortium International Case‐control studies 423 475 89 113 507 2890 998 UCIBCS UCI Breast Cancer Study USA Case‐control study 258 425 76 UK2 UK2 GWAS UK Case‐control study 2663 3628 UKBGS UK Breakthrough Generations Study UK Prospective cohort: nested case‐control study 327 6 4 705 1048 584 UKOPS UK Ovarian Cancer Population Study UK Case‐control study 974 USRT US Radiologic Technologists Study USA Case‐control study 1699 1354 338 VUMC 3255 464 WHI Women's Health Initiative USA Prospective cohort: nested case‐control study 4617 4930 6 37818 35727 2087 535 58383 72000 6501 1624 17588 14910 1 We excluded the OncoArray data from Norway (the Norwegian Breast Cancer Study) because there were no controls available from Norway  iCOGS OncoArray Other GWAS Acronym Study Name1 Country Study design Controls Negative Positive Unknown Negative Positive Unknown Negative Positive Unknown 1 2 3 Unknown 2SISTER The Two Sister Study USA Case‐only study 185 729 5 230 679 10 695 201 23 919 ABCFS Australian Breast Cancer Family Study Australia Case‐control study 738 331 698 410 298 729 412 17 8 1414 1439 ABCS Amsterdam Breast Cancer Study Netherlands Case‐control study 1565 227 617 274 320 498 300 512 254 352 135 409 333 241 ABCS‐F Amsterdam Breast Cancer Study – Familial Netherlands Case‐only study 84 233 684 112 196 693 228 53 720 68 208 164 561 ABCTB Australian Breast Cancer Tissue Bank Australia Case‐control study 375 389 553 5 445 495 7 837 88 22 123 293 351 180 AHS Agricultural Health Study USA Prospective cohort: nested case‐control study 1137 91 377 45 133 332 48 55 4 454 119 185 153 56 BBCC Bavarian Breast Cancer Cases and Controls Germany Case‐control study 706 127 698 16 372 428 41 692 101 48 134 431 260 16 BBCS British Breast Cancer Study UK Case‐control study 1768 117 557 851 129 296 1100 243 69 1213 146 382 292 705 BCEES Breast Cancer Employment and Environment Study Australia Case‐control study 834 116 552 115 783 529 73 181 175 268 146 194 BCFR‐NY New York site of the Breast Cancer Family Registry USA Case‐control study 27 55 109 237 401 401 401 BCFR‐PA Philadelphia site of the Breast Cancer Family Registry USA Case‐control study 28 22 13 33 17 13 63 1 4 5 53 BCFR‐UTAH Utah site of the Breast Cancer Family Registry USA Case‐control study 12 30 59 19 23 59 8 1 92 8 21 20 52 BCINIS Breast Cancer in Northern Israel Study Israel Case‐control study 724 233 1080 21 634 678 22 1078 136 120 308 602 296 128 BIGGS Breast Cancer in Galway Genetic Study Ireland Case‐control study 49 146 473 164 124 388 271 326 89 368 78 262 210 233 BREOGAN Breast Oncology Galicia Network Spain Case‐control study 725 232 985 42 379 831 49 836 216 207 217 622 336 84 BSUCH Breast Cancer Study of the University of Heidelberg Germany Case‐control study 1119 210 711 66 278 643 66 680 201 106 118 462 311 96 CBCS Canadian Breast Cancer Study Canada Case‐control study 817 108 443 17 147 372 49 359 168 41 568 CCGP Crete Cancer Genetics Program Greece Case‐control study 322 177 483 5 199 458 8 532 120 13 49 278 276 62 CECILE CECILE Breast Cancer Study France Case‐control study 1002 133 756 21 253 626 31 716 112 82 910 CGPS Copenhagen General Population Study Denmark Case‐control study 5241 565 3006 704 853 1716 1706 1537 515 2223 875 1357 577 1466 CNIO‐BCS Spanish National Cancer Centre Breast Cancer Study Spain Case‐control study 829 97 310 343 163 256 331 201 108 441 65 108 120 457 CPSII Cancer Prevention Study‐II Nutrition Cohort USA Prospective cohort: nested case‐control study 3322 74 1960 492 398 1569 559 830 129 1567 598 1011 502 415 CTS California Teachers Study USA Prospective cohort: nested case‐control study 630 194 981 100 1075 100 1075 368 508 267 32 DIETCOMPLY DietCompLyf Breast Cancer Survival Study UK Prospective cohort: nested case‐control study 108 596 4 145 335 228 353 111 244 111 325 269 3 EPIC European Prospective Investigation Into Cancer and Nutrition International (Europe) Prospective cohort: nested case‐control study 3597 181 2004 1250 511 1349 1575 856 206 2373 361 998 658 1418 ESTHER ESTHER Breast Cancer Study Germany Case‐control study 505 99 305 71 137 262 76 130 51 294 30 217 198 30 FHRISK Family History Risk Study UK Case‐control study 6 43 53 12 37 53 33 5 64 17 29 51 5 GC‐HBOC German Consortium for Hereditary Breast & Ovarian Cancer Germany Case‐control study 1732 389 1149 1878 419 1109 1888 863 209 2344 250 1379 875 912 GENICA Gene Environment Interaction and Breast Cancer in Germany Germany Case‐control study 710 191 712 9 264 638 10 465 184 263 78 531 280 23 GEPARSIXTO Randomized phase II trial Germany Case‐only study 274 112 316 70 208 178 7 137 242 GESBC Genetic Epidemiology Study of Breast Cancer by Age 50 Germany Case‐control study 181 110 177 25 119 166 27 312 23 156 119 14 HABCS Hannover Breast Cancer Study Germany Case‐control study 863 158 653 98 196 602 111 132 19 758 64 388 272 185 HCSC Hospital Clinico San Carlos Spain Case‐control study 107 289 27 147 241 35 223 92 108 29 251 92 51 HEBCS Helsinki Breast Cancer Study Finland Case‐control study 1236 288 1465 43 579 1170 47 862 156 778 479 787 452 78 HUBCS Hannover‐Ufa Breast Cancer Study Russia Case‐control study 116 17 34 160 22 29 160 28 22 161 17 68 38 88 KARBAC Karolinska Breast Cancer Study Sweden Case‐control study 73 363 367 99 279 425 26 5 772 112 199 111 381 KARMA Karolinska Mammography Project for Risk Prediction of Breast Cancer – Cohort Study Sweden Case‐control study 6026 171 1293 902 355 1062 949 1099 171 1096 286 622 424 1034 KBCP Kuopio Breast Cancer Project Finland Case‐control study 433 116 375 53 151 262 131 381 102 61 127 221 130 66 KCONFAB/AOKathleen Cuningham Foundation Consortium for research into Familial Breast Cancer/Australian Ovarian Cancer Study Australia and New Zeland Case‐control study 896 73 210 180 76 177 210 79 30 354 84 132 131 116 LMBC Leuven Multidisciplinary Breast Centre Belgium Case‐control study 1268 142 641 215 564 4 649 101 33 123 352 308 MABCS Macedonian Breast Cancer Study Macedonia Case‐control study 90 17 69 3 26 45 18 52 19 18 3 28 41 17 MARIE Mammary Carcinoma Risk Factor Investigation Germany Case‐control study 2065 362 1274 7 551 1084 8 1215 281 147 338 808 476 21 MBCSG Milan Breast Cancer Study Group Italy Case‐control study 766 105 351 281 142 313 282 234 121 382 46 189 189 313 MCBCS Mayo Clinic Breast Cancer Study USA Case‐control study 2041 314 1713 45 519 1496 57 1497 277 298 572 903 532 65 MCCS Melbourne Collaborative Cohort Study Australia Prospective cohort: nested case‐control study 1206 203 705 150 312 594 152 738 112 208 182 431 296 149 MEC Multiethnic Cohort USA Prospective cohort: nested case‐control study 853 95 582 96 161 481 131 89 25 659 209 319 170 75 MISS Melanoma Inquiry of Southern Sweden Sweden Prospective cohort: nested case‐control study 1529 75 352 172 137 295 167 312 30 257 9 14 5 571 MMHS Mayo Mammography Health Study USA Prospective cohort: nested case‐control study 1635 33 238 4 64 207 4 223 20 32 83 116 54 22 MSKCC Memorial Sloan‐Kettering Cancer Center Study USA Case‐control study 136 136 136 136 MTLGEBCS Montreal Gene‐Environment Breast Cancer Study Canada Case‐control study 465 66 460 7 132 393 8 457 54 22 533 NBCS Norwegian Breast Cancer Study Norway Case‐control study 271 273 879 78 428 713 89 816 95 319 181 435 267 347 NBHS Nashville Breast Health Study USA Case‐control study 731 258 256 58 294 216 62 340 121 111 572 NC‐BCFR Northern California Breast Cancer Family Registry USA Case‐control study 150 246 396 111 248 393 112 185 18 550 94 247 284 128 NCBCS North Carolina Breast Cancer study USA Case‐control study 1006 495 1457 122 618 1262 194 1536 283 255 395 603 510 566 NHS Nurses Health Study USA Prospective cohort: nested case‐control study 1804 167 827 109 307 662 134 503 77 523 207 370 248 278 NHS2 Nurses Health Study 2 USA Prospective cohort: nested case‐control study 1905 190 868 54 292 753 67 665 137 310 243 458 316 95 OBCS Oulu Breast Cancer Study Finland Case‐control study 414 96 403 143 355 1 430 69 86 215 182 16 OFBCR Ontario Familial Breast Cancer Registry Canada Case‐control study 728 525 1170 447 616 1050 476 549 81 1512 373 640 594 535 ORIGO Leiden University Medical Centre Breast Cancer Study Netherlands Prospective cohort: nested case‐control study 294 744 202 317 474 449 117 59 1064 158 396 357 329 PBCS NCI Polish Breast Cancer Study Poland Case‐control study 2082 529 1079 159 756 846 165 984 211 572 334 843 433 157 PKARMA Karolinska Mammography Project for Risk Prediction of Breast Cancer ‐ Case‐Control Study Sweden Case‐control study 5454 693 3786 508 1311 3087 589 1061 162 3764 622 1615 873 1877 PLCO The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial USA Prospective cohort: nested case‐control study 2595 220 1372 230 388 1139 295 1021 136 665 511 753 401 157 POSH Prospective Study of Outcomes in Sporadic Versus Hereditary Breast Cancer UK Case‐only study 222 861 5 241 584 263 152 128 808 82 422 559 25 Grade iCOGS and Oncoarray Combined 2 Estrongen Receptor status iCOGS and Oncoarray Combined 2 Progesterone Receptor status iCOGS and Oncoarray Combined 2 HER2 status iCOGS and Oncoarray Combined 2 Supplementary Table 2: BCAC studies contributing data to the two‐stage model polytomous model investigating for susceptibility SNPs while accounting for heterogeneity according to estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2),  and grade Supplementary Table 3: CIMBA studies contributing data on BRCA1  mutation carriers, by genotyping initiative Unaffected Breast cancer Unaffected Breast cancer BCFR‐AU Australian site of the Breast Cancer Family Registry AUSTRALIA 13 25 0 2 BCFR‐NC Northern California site of the Breast Cancer Family Registry USA 3 12 1 1 BCFR‐NY New York site of the Breast Cancer Family Registry USA 24 37 4 5 BCFR‐ON Ontario site of the Breast Cancer Family Registry CANADA 34 86 2 7 BCFR‐PA Philadelphia site of the Breast Cancer Family Registry USA 26 17 14 16 BCFR‐UT Utah site of the Breast Cancer Family Registry USA 135 64 1 0 BFBOCC Baltic Familial Breast Ovarian Cancer Consortium LITHUANIA/L ATVIA 133 111 16 8 BIDMC Beth Israel Deaconess Medical Center USA 41 44 1 1 BMBSA BRCA‐gene mutations and breast cancer in South African women SOUTH  AFRICA 21 37 2 1 BRICOH Beckman Research Institute of the City of Hope USA 96 50 11 9 CBCS Rigshospitalet DENMARK 110 75 80 57 CNIO Spanish National Cancer Centre SPAIN 32 31 49 44 COH City of Hope Cancer Center USA 84 141 6 8 CONSIT TEAM CONsorzio Studi ITaliani sui Tumori Ereditari Alla Mammella ITALY 265 271 217 234 DEMOKRITOS National Centre for Scientific Research Demokritos GREECE 85 132 12 20 DFCI Dana‐Farber Cancer Institute USA 82 65 3 4 DKFZ German Cancer Research Center GERMANY 19 36 0 2 EMBRACE Epidemiological Study of Familial Breast Cancer UK/IRELAND 907 785 14 13 FCCC Fox Chase Cancer Center USA 49 26 20 19 FPGMX Fundación Pública Galega de Medicina Xenómica SPAIN 40 61 GC‐HBOC German Familial Breast Group GERMANY 673 1145 54 111 GEMO Genetic Modifiers of cancer risk in BRCA1/2 mutation carriers FRANCE/USA 630 842 114 111 GEORGETOWN Georgetown University USA 6 5 1 2 G‐FAST Ghent University Hospital BELGIUM 69 121 91 42 HCSC Hospital Clinico San Carlos SPAIN 85 55 5 6 HEBCS Helsinki Breast Cancer Study FINLAND 67 53 3 5 HEBON Genen Omgeving studie van de werkgroep Hereditiair Borstkanker Onderzoek Nederland NETHERLAND S 500 372 220 202 HUNBOCS Molecular Genetic Studies of Breast‐ and Ovarian Cancer in Hungary HUNGARY 101 179 HVH University Hospital Vall d'Hebron SPAIN 56 62 0 1 ICO Institut Català d'Oncologia SPAIN 150 130 5 1 IHCC International Hereditary Cancer Centre POLAND 121 77 279 223 INHERIT INterdisciplinary HEalth Research Internal Team BReast CAncer susceptibility CANADA  (QUEBEC) 52 37 6 2 IOVHBOCS Istituto Oncologico Veneto ITALY 93 111 5 4 IPOBCS Portuguese Oncology Institute‐Porto Breast Cancer Study PORTUGAL 79 36 1 2 KCONFAB Kathleen Cuningham Consortium for Research into Familial Breast Cancer AUSTRALIA 355 366 24 26 KUMC University of Kansas Medical Center USA 3 11 MAYO Mayo Clinic USA 126 121 12 10 MCGILL McGill University CANADA  (QUEBEC) 30 24 MODSQUAD Modifier Study of Quantitative Effects on Disease CZECH  REPUBLIC 68 106 MSKCC Memorial Sloane Kettering Cancer Center USA 193 185 32 59 MUV General Hospital Vienna AUSTRIA 266 268 11 11 Acronym Study Name Country OncoArray  iCOGS NAROD Women's College Research Institute Hereditary Breast and Ovarian Cancer Study CANADA 100 46 NCI National Cancer Institute USA 108 42 6 1 NNPIO N.N. Petrov Institute of Oncology RUSSIA 22 44 1 4 NORTHSHORE NorthShore University HealthSystem USA 40 40 NRG_ONCOLOGY NRG Oncology USA/AUSTRA LIA 153 166 4 7 OCGN Ontario Cancer Genetics Network CANADA 133 71 6 4 OSU CCG The Ohio State University Comprehensive Cancer Center USA 34 39 8 10 OUH Odense University Hospital DENMARK 358 192 10 10 PBCS Università di Pisa ITALY 39 49 6 5 SMC Sheba Medical Centre ISRAEL 99 65 57 41 SWE‐BRCA Swedish Breast Cancer Study SWEDEN 237 188 52 38 UCHICAGO University of Chicago USA 51 43 7 0 UCSF University of California San Francisco USA 60 32 16 15 UKGRFOCR UK and Gilda Radner Familial Ovarian Cancer Registries UK 40 13 5 0 UPENN University of Pennsylvania USA 218 239 11 22 UPITT Cancer Family Registry University of Pittsburg USA 77 77 UTMDACC University of Texas MD Anderson Cancer Center USA 18 25 27 45 VFCTG Victorian Familial Cancer Trials Group AUSTRALIA 104 103 2 1 WCP Women's Cancer Program at Cedars‐Sinai Medical Center USA 137 50 10 6 7782 7784 1712 1630 Supplementary Table 4: BCAC studies and CIMBA BRCA1 mutation carriers sample size compared with previous publication Previous and current BCAC studies Control Cases Control Cases Control Cases Unaffected Cases Control Cases Michailidou et al. Nature 551, no. 7678 (2017) 42892 46785 45494 61282 14910 17588 Milne et al. Nat Genet 49, 1767‐1778 (2017)1 42468 7333 45494 9655 1712 1630 7782 7784 Data in overall analysis2 37818 38349 58383 80125 14910 17588 Data in subtypes analysis3 37628 34783 56779 71708 CIMBA‐BCAC TN meta‐analys4 37628 2,057 56779 5,121 1712 1630 7782 7784 1 Milne et al. Nat Genet 49, 1767-1778 (2017) restricted to cases with estrogen receptor negative breast cancer 2 Comapred to Michailidou et al. Nature 551, no. 7678 (2017), 5,074 controls and 8,436 cases originally genotyped by iCOGS were regenotyped by OncoArray 3 Subtype anlayses of BCAC data restricted to invasive breast cancer cases and subjects with age information 4 The effective sample (see Supplementary Note) of BCAC triple‐negative cases as a result of the EM algorithm are 8,602 for iCOGS and OncoArray together BCAC CIMBA BRCA1 mutation carriers iCOGS OncoArray Other GWAS iCOGS OncoArray Supplementary Table 5: Twenty‐two variants identified using standard logistic regression (n = 133,384 cases, n = 113,789 controls). Lead variant1 Chr. 2 Position Alleles 3 MAF4 Imputation Quality   iCOGS/ONCO5 OR6 95%CI7 P‐value8 P‐value (Michailidou  et al)9 rs5776993 1 110,222,901 C/CA 0.12 0.70/0.82 0.95 0.92‐0.96 2.6 x 10‐8 2.0 x 10‐7 rs9712235 2 67,881,757 A/G 0.26 0.86/1.00 1.04 1.02‐1.05 4.8 x 10‐8 7.2 x 10‐7 rs4602255 2 69,392,128 G/A 0.45 1.00/1.00 1.04 1.02‐1.05 2.0 x 10‐9 1.2 x 10‐7 rs1375631 3 16,778,867 G/A 0.5 1.00/1.00 0.97 0.95‐0.98 6.8 x 10‐9 2.0 x 10‐7 rs2886671 3 59,373,745 C/T 0.43 0.63/1.00 0.97 0.95‐0.98 4.3 x 10‐8 6.9 x 10‐7 rs34052812 3 156,535,958 A/AT 0.33 0.94/0.94 0.96 0.95‐0.98 3.3 x 10‐8 8.1 x 10‐7 rs7760611 6 21,903,533 C/T 0.47 1.00/1.00 0.96 0.95‐0.98 1.5 x 10‐9 3.2 x 10‐7 rs188092014 7 74,341,926 G/C 0.19 0.67/0.83 1.05 1.03‐1.07 2.0 x 10‐8 1.5 x 10‐6 rs79518236 7 98,026,554 ACT/A 0.23 1.00/1.00 0.96 0.95‐0.97 6.6 x 10‐9 1.0 x 10‐7 rs142890050 8 23,480,253 C/CTT 0.46 0.97/0.96 0.97 0.95‐0.98 3.5 x 10‐8 6.7 x 10‐8 rs13256025 8 25,831,778 C/T 0.2 0.68/1.00 1.05 1.03‐1.06 1.4 x 10‐8 2.1 x 10‐7 rs13277568 8 116,679,547 A/G 0.37 0.85/1.00 0.97 0.95‐0.98 2.2 x 10‐8 7.6 x 10‐7 rs4742903 9 106,856,793 C/G 0.44 1.00/1.00 0.97 0.96‐0.98 2.6 x 10‐8 1.8 x 10‐7 rs10838267 11 44,368,892 A/G 0.45 0.99/0.99 0.97 0.96‐0.98 4.5 x 10‐8 3.2 x 10‐7 12:2914026010 12 29,140,260 A/G 0.09 0.93/1.00 0.93 0.91‐0.95 7.7 x 10‐12 3.0 x 10‐10 rs11065822 12 111,600,134 G/T 0.37 0.88/1.00 0.96 0.95‐0.98 5.9 x 10‐9 9.2 x 10‐8 rs106165710 12 115,108,136 T/C 0.26 0.97/1.00 1.04 1.03‐1.06 2.5 x 10‐10 1.4 x 10‐9 rs11652463 17 70,405,095 C/G 0.31 0.76/0.84 0.96 0.95‐0.97 4.2 x 10‐8 8.4 x 10‐8 rs12962334 18 20,477,934 C/G 0.32 0.99/1.00 1.04 1.03‐1.05 3.8 x 10‐9 9.6 x 10‐7 rs1774305410 18 42,900,892 T/C 0.28 1.00/1.00 0.96 0.95‐0.97 1.5 x 10‐10 2.2 x 10‐10 rs13039563 20 52,296,849 G/A 0.24 1.00/0.95 1.04 1.03‐1.06 3.1 x 10‐9 2.1 x 10‐7 rs9808759 21 47,780,223 C/T 0.07 0.99/0.98 1.07 1.05‐1.09 5.8 x 10‐9 4.0 x 10‐7 1 Showing the strongest signal in each region 2 Chr., chromosome 3 Major alleles listed first 4 MAF, minor allele frequency 5 Imputation quality (r2) for iCOGS/OncoArray  6 OR, odds ratio per copy of the minor allele 7 95% CI f dd ti f th i ll l Supplementary Table 6: Sixteen variants identified using two‐stage polytomous logistic regression (n = 106,278 invasive cases, n = 91,477 controls), eight of them were also identified in overall analysis. Lead variant1 Chr.2 Position Alleles3 MAF4 Imputation Quality  iCOGS/ONCO5 Mixed effect model global  association test P6 Fixed effect model global association test P7 Global heterogeneity test P8 1:145126177 1 145,126,177 G/A 0.04 0.48/0.65 9.6 x 10‐9 4.0 x 10‐8 2.8 x 10‐6 rs495367 4 1,986,972 A/G 0.35 0.67/0.79 2.2 x 10‐8 2.9 x 10‐7 5.8 x 10‐2 rs138044103 5 67,424,121 C/CTG 0.45 0.92/1.00 2.4 x 10‐9 4.7 x 10‐9 5.2 x 10‐7 rs7924772 11 120,233,626 A/G 0.39 0.65/1.00 3.2 x 10‐5 3.6 x 10‐8 1.4 x 10‐3 rs78378222 17 7,571,752 T/G 0.01 0.90/1.00 1.8 x 10‐9 1.3 x 10‐10 9.1 x 10‐8 rs206435 18 10,354,649 C/A 0.5 1.00/0.99 1.6 x 10‐7 3.5 x 10‐8 1.1 x 10‐9 rs141526427 20 11,502,618 A/AAC 0.25 0.76/0.95 2.6 x 10‐8 5.8 x 10‐8 6.2 x 10‐5 rs6065254 20 39,248,265 G/A 0.39 0.89/0.97 1.8 x 10‐9 2.3 x 10‐9 7.3 x 10‐7 rs97122359 2 67,881,757 A/G 0.26 0.86/1.00 2.0 x 10‐7 1.4 x 10‐8 6.7 x 10‐3 rs77606119 6 21,903,533 C/T 0.47 1.00/1.00 1.7 x 10‐9 3.6 x 10‐9 1.4 x 10‐3 rs795182369 7 98,026,554 ACT/A 0.23 1.00/1.00 1.7 x 10‐8 3.9 x 10‐10 1.6 x 10‐3 12:291402609 12 29,140,260 A/G 0.09 0.93/1.00 4.3 x 10‐9 1.1 x 10‐8 3.9 x 10‐1 rs10616579 12 115,108,136 T/C 0.26 0.97/1.00 6.1 x 10‐9 9.3 x 10‐9 6.1 x 10‐2 rs129623349 18 20,477,934 C/G 0.32 0.99/1.00 4.2 x 10‐8 1.5 x 10‐7 4.4 x 10‐2 rs177430549 18 42,900,892 T/C 0.28 1.00/1.00 1.8 x 10‐8 3.9 x 10‐8 1.9 x 10‐2 rs130395639 20 52,296,849 G/A 0.24 1.00/0.95 1.4 x 10‐9 3.9 x 10‐9 4.9 x 10‐3 1 Variants were selected based on either the two‐stage mixed effect model or the two‐stage fixed effect model global association 2 Chr., chromosome 3 Major alleles listed first 4 MAF, minor allele frequency 5 Imputation quality (r2) for iCOGS/OncoArray  6 Mixed effect two‐stage polytomous model adjusted for top 10 PCs and age while accounting subtypes heterogeneity  for ER (fixed effect), PR (random effect), HER2(random effect), and grade (random effect). G 7 Fixed effect two‐stage model adjusted for top 10 PCs and age while accounting for ER, PR, HER2 and grade all as fixed effects 8 The global test for heterogeneity was performed under the mixed‐effect model tests if variants show evidence of heterogeneity with respect to any of the underlying tumor markers, ER, PR, HER2 and/or grade  9 Variants were also detected in the overall breast cancer analysis.  Supplementary Table 10: Conditional analysis of one genome‐wide significant variant from meta‐analysis of triple negative breast cancer of BCAC and CIMBA  BR Lead variant1 Chr.2 Position MAF3 Nearby known variant4 LD5 D’6 Meta‐analysis P7 Conditional analysis P8 rs24641959 12 121,435,475 0.37 rs206966 0 0 2.5 x 10‐8 2.2 x 10‐8 1 Showing the strongest signal in each region 2 Chr., chromosome 3 MAF, minor allele frequency 4 Known variantss previous published in  Michailidou et al. Nature 551, no. 7678 (2017) and  Milne et al. Nat Genet 49, 1767‐1778 (2017) 5 LD, linkage disequilibrium between lead variant and nearby known variant estimated from European‐ancestry controls in OncoArray 6 D’, D prime between lead variant and nearby known variant estimated from European‐ancestry controls in OncoArray 7 Meta‐analysis using triple negative from BCAC and BRACA1 mutation carriers from CIMBA 8 Meta‐analysis p‐value conditional on the nearby known variants, p‐value threshold of p < 1 x 10‐6 was used for conditional analyses (reason described in Online Me 9 Conditionally significant after adjusting for nearby known variant RCA1  mutation carriers data on nearby (within +/‐ 2 MB) known breast cancer variant BCAC: n = 8,602 effective triple‐negative cases, n = 91,477 contr thods). P‐values are raw p‐values from two‐tailed z‐test statistics.  rols; CIMBA BRCA1 carriers: n = 9,414 cases, n = 9,494 controls) Supplementary Table 11: Association of the 32 variants with intrinsic‐like subtypes, comparing results restricted to cases with complete tumor marker data and results from implementing the EM algorithm for missing tumor marker data. Odd ratio and 95% confidence intervals estimated from the fixed‐effect two stage model. Variant CHR1 Pos2 Major/Minor Alleles3 MAF4 Luminal A‐ like OR Luminal A‐like 95% CI Luminal A‐like p‐value9 Luminal A‐ like OR Luminal A‐like 95% CI Luminal A‐ like p‐value9 Luminal B,  HER2‐ negative‐like OR Luminal B,  HER2‐negative‐ like 95% CI Luminal B,  HER2‐negative‐ like p‐value9 Luminal B,  HER2‐negative‐like OR Luminal B,  HER2‐negative‐like 95% CI Luminal B,  HER2‐negative‐like p‐value9 Luminal B‐like OR  Luminal B‐like 95% CI Luminal B‐like p‐value9 Luminal B‐like OR Luminal B‐like 95% CI Luminal B‐like p‐value9 HER2‐enriched‐like OR (95% CI) HER2‐enriched‐like 95% CI HER2‐enriched‐like p‐value9 HER2‐enriched‐like OR HER2‐enriched‐like 95% CI HER2‐enriched‐like p‐value9 Triple‐negative OR  Triple‐negative 95% CI Triple‐negative p‐value9 Triple‐negative OR (95% CI) Triple‐negative 95% CI Triple‐negative p‐value9 rs5776993 1 110222901 C/CA 0.117 0.97 0.89‐1.06 5.40E‐01 0.94 0.91‐0.96 9.85E‐07 0.97 0.91‐1.03 3.12E‐01 0.97 0.91‐1.02 2.56E‐01 0.93 0.90‐0.96 1.86E‐05 0.94 0.89‐0.99 1.38E‐02 0.92 0.87‐0.98 9.23E‐03 0.95 0.87‐1.03 2.23E‐01 0.97 0.91‐1.03 3.07E‐01 0.96 0.91‐1.01 1.11E‐01 rs9712235 2 67881757 A/G 0.258 1.02 0.96‐1.08 5.37E‐01 1.04 1.02‐1.06 3.27E‐06 1.00 0.96‐1.05 8.98E‐01 1.00 0.96‐1.04 9.14E‐01 1.05 1.02‐1.07 6.16E‐05 1.02 0.99‐1.06 1.90E‐01 1.01 0.97‐1.05 6.27E‐01 1.02 0.96‐1.08 5.39E‐01 1.07 1.03‐1.11 7.49E‐04 1.09 1.05‐1.12 1.59E‐06 rs4602255 2 69392128 G/A 0.46 1.06 1.01‐1.12 2.97E‐02 1.03 1.01‐1.04 6.98E‐04 1.03 0.99‐1.06 1.64E‐01 1.03 1.00‐1.07 6.18E‐02 1.02 1.00‐1.04 2.81E‐02 1.05 1.02‐1.08 2.87E‐03 1.05 1.01‐1.08 1.10E‐02 1.07 1.02‐1.13 7.76E‐03 1.02 0.99‐1.06 2.18E‐01 1.03 1.00‐1.06 3.95E‐02 rs1375631 3 16778867 G/A 0.495 0.99 0.94‐1.04 6.58E‐01 0.97 0.95‐0.98 4.23E‐05 0.98 0.94‐1.01 2.01E‐01 0.98 0.95‐1.01 2.63E‐01 0.96 0.94‐0.98 5.62E‐05 0.99 0.96‐1.02 5.18E‐01 1.00 0.96‐1.03 7.93E‐01 0.97 0.93‐1.02 3.17E‐01 0.96 0.93‐1.00 4.08E‐02 0.95 0.92‐0.97 2.53E‐04 rs2886671 3 59373745 C/T 0.417 0.94 0.89‐1.00 4.43E‐02 0.96 0.94‐0.98 1.47E‐06 0.98 0.95‐1.02 3.82E‐01 0.99 0.96‐1.03 6.54E‐01 0.96 0.94‐0.98 6.30E‐05 0.95 0.92‐0.98 3.27E‐03 0.94 0.91‐0.98 1.66E‐03 0.94 0.89‐0.99 3.16E‐02 0.97 0.94‐1.01 1.04E‐01 0.97 0.94‐1.00 7.88E‐02 rs34052812 3 156535958 A/AT 0.332 0.97 0.91‐1.02 2.45E‐01 0.96 0.94‐0.97 3.04E‐07 0.97 0.93‐1.00 8.69E‐02 0.96 0.93‐1.00 4.54E‐02 0.96 0.94‐0.98 3.60E‐05 0.95 0.92‐0.98 2.56E‐03 0.98 0.94‐1.02 2.93E‐01 0.97 0.92‐1.02 2.65E‐01 0.98 0.94‐1.01 1.98E‐01 0.96 0.93‐1.00 3.12E‐02 rs7760611 6 21903533 C/T 0.455 1.01 0.96‐1.07 6.09E‐01 0.96 0.94‐0.97 1.10E‐07 0.94 0.90‐0.97 4.85E‐04 0.94 0.91‐0.97 2.81E‐04 0.96 0.94‐0.98 3.58E‐05 0.96 0.94‐0.99 1.33E‐02 0.97 0.94‐1.01 1.18E‐01 0.99 0.94‐1.04 7.45E‐01 1.02 0.98‐1.05 3.34E‐01 1.00 0.98‐1.04 7.50E‐01 rs188092014 7 74341926 G/C 0.202 1.1 1.02‐1.19 1.06E‐02 1.05 1.03‐1.07 2.02E‐05 1.03 0.98‐1.08 2.60E‐01 1.03 0.98‐1.08 2.36E‐01 1.03 1.01‐1.06 2.01E‐02 1.04 0.99‐1.08 9.34E‐02 1.04 0.99‐1.09 1.28E‐01 1.10 1.03‐1.18 6.92E‐03 1.04 0.99‐1.10 8.32E‐02 1.05 1.01‐1.09 2.61E‐02 rs79518236 7 98026554 ACT/A 0.215 0.98 0.92‐1.04 5.22E‐01 0.96 0.95‐0.98 6.54E‐05 0.93 0.89‐0.97 1.36E‐03 0.92 0.89‐0.96 2.20E‐04 0.97 0.94‐0.99 4.00E‐03 0.93 0.90‐0.96 7.93E‐05 0.94 0.91‐0.99 8.67E‐03 0.97 0.92‐1.03 4.01E‐01 0.98 0.95‐1.03 4.61E‐01 0.97 0.94‐1.01 1.20E‐01 rs142890050 8 23480253 C/CTT 0.425 0.99 0.94‐1.04 6.99E‐01 0.96 0.94‐0.97 6.66E‐08 0.96 0.92‐0.99 2.50E‐02 0.96 0.92‐0.99 1.41E‐02 0.96 0.94‐0.98 2.53E‐05 0.99 0.96‐1.02 6.29E‐01 1.00 0.97‐1.04 9.10E‐01 0.99 0.94‐1.04 5.68E‐01 0.97 0.94‐1.01 1.39E‐01 0.97 0.94‐1.00 3.97E‐02 rs13256025 8 25831778 C/T 0.187 1.07 1.00‐1.15 4.41E‐02 1.05 1.03‐1.07 2.57E‐06 1.03 0.98‐1.08 2.08E‐01 1.03 0.98‐1.08 2.13E‐01 1.06 1.03‐1.08 1.70E‐05 1.05 1.02‐1.10 5.90E‐03 1.04 1.00‐1.09 6.45E‐02 1.09 1.02‐1.16 1.03E‐02 1.02 0.98‐1.07 3.23E‐01 1.04 1.00‐1.08 6.55E‐02 rs13277568 8 116679547 A/G 0.349 0.98 0.93‐1.04 4.84E‐01 0.96 0.94‐0.98 9.68E‐07 1.00 0.97‐1.04 8.20E‐01 0.99 0.96‐1.03 7.69E‐01 0.97 0.95‐0.99 1.87E‐03 0.95 0.92‐0.98 2.10E‐03 0.95 0.92‐0.99 1.14E‐02 1.00 0.95‐1.05 9.09E‐01 0.99 0.96‐1.03 6.93E‐01 1.00 0.97‐1.03 9.61E‐01 rs4742903 9 106856793 C/G 0.445 0.97 0.92‐1.03 3.08E‐01 0.99 0.97‐1.00 8.26E‐02 0.92 0.89‐0.95 6.14E‐06 0.92 0.89‐0.95 2.67E‐06 0.97 0.95‐0.99 6.56E‐03 0.96 0.93‐0.99 4.22E‐03 0.96 0.93‐1.00 3.10E‐02 0.97 0.93‐1.02 3.07E‐01 0.95 0.92‐0.98 3.65E‐03 0.96 0.93‐0.99 3.18E‐03 rs10838267 11 44368892 A/G 0.449 0.99 0.94‐1.05 7.59E‐01 0.97 0.95‐0.98 3.88E‐05 0.97 0.94‐1.01 1.07E‐01 0.96 0.93‐0.99 2.09E‐02 0.97 0.95‐0.99 2.90E‐03 0.95 0.92‐0.98 3.04E‐04 0.95 0.92‐0.99 5.68E‐03 1.00 0.95‐1.05 8.70E‐01 0.96 0.93‐1.00 4.32E‐02 0.97 0.94‐1.00 6.48E‐02 chr12_29140260 12 29140260 A/G 0.085 0.9 0.81‐0.99 3.65E‐02 0.91 0.89‐0.94 3.57E‐10 0.93 0.87‐1.00 4.06E‐02 0.93 0.88‐0.99 3.34E‐02 0.93 0.90‐0.96 7.51E‐05 0.96 0.91‐1.01 1.35E‐01 0.94 0.88‐1.00 5.38E‐02 0.90 0.82‐0.99 2.45E‐02 0.95 0.89‐1.01 1.11E‐01 0.95 0.90‐1.01 9.33E‐02 chr12_111600134 12 111600134 G/T 0.383 0.97 0.92‐1.03 3.11E‐01 0.97 0.95‐0.98 2.38E‐05 0.97 0.94‐1.01 1.43E‐01 0.97 0.94‐1.01 1.45E‐01 0.97 0.95‐0.99 2.18E‐03 0.95 0.92‐0.98 1.71E‐03 0.95 0.92‐0.99 6.03E‐03 0.96 0.91‐1.01 1.11E‐01 0.97 0.94‐1.01 9.33E‐02 0.96 0.93‐0.99 1.97E‐02 chr12_115108136 12 115108136 T/C 0.273 0.98 0.93‐1.05 6.20E‐01 1.05 1.03‐1.07 9.55E‐09 1.03 0.99‐1.07 1.39E‐01 1.04 1.00‐1.08 4.56E‐02 1.05 1.03‐1.08 3.87E‐06 1.07 1.03‐1.10 1.02E‐04 1.05 1.01‐1.09 2.31E‐02 0.99 0.94‐1.05 7.90E‐01 1.01 0.97‐1.05 5.79E‐01 1.02 0.99‐1.05 2.71E‐01 rs11652463 17 70405095 C/G 0.322 0.96 0.90‐1.02 2.11E‐01 0.97 0.95‐0.99 5.82E‐04 0.95 0.91‐0.99 1.15E‐02 0.96 0.92‐1.00 3.02E‐02 0.97 0.94‐0.99 4.45E‐03 0.95 0.92‐0.99 1.03E‐02 0.94 0.90‐0.98 2.00E‐03 0.95 0.89‐1.01 8.98E‐02 0.94 0.90‐0.98 2.88E‐03 0.94 0.90‐0.97 3.33E‐04 rs12962334 18 20477934 C/G 0.307 1.05 0.99‐1.11 9.44E‐02 1.04 1.03‐1.06 3.32E‐07 1.03 0.99‐1.07 2.00E‐01 1.03 0.99‐1.07 1.29E‐01 1.04 1.02‐1.06 3.23E‐04 1.06 1.02‐1.09 6.13E‐04 1.06 1.02‐1.10 3.57E‐03 1.05 1.00‐1.11 5.31E‐02 1.01 0.98‐1.05 4.98E‐01 1.01 0.98‐1.05 4.24E‐01 rs17743054 18 42900892 T/C 0.283 1.02 0.97‐1.09 4.16E‐01 0.95 0.93‐0.97 1.78E‐08 0.94 0.91‐0.98 5.05E‐03 0.95 0.92‐0.99 1.71E‐02 0.94 0.92‐0.96 1.40E‐08 0.97 0.94‐1.00 7.92E‐02 0.95 0.92‐0.99 1.61E‐02 1.02 0.96‐1.08 5.00E‐01 0.97 0.93‐1.01 1.23E‐01 0.96 0.93‐0.99 1.41E‐02 rs13039563 20 52296849 G/A 0.249 1.01 0.95‐1.07 8.06E‐01 1.06 1.04‐1.08 4.93E‐10 1.05 1.01‐1.10 1.30E‐02 1.06 1.02‐1.10 5.30E‐03 1.07 1.04‐1.09 3.37E‐08 1.04 1.01‐1.08 2.27E‐02 1.02 0.98‐1.07 2.99E‐01 1.00 0.94‐1.06 9.34E‐01 1.00 0.96‐1.04 9.35E‐01 1.00 0.97‐1.04 9.44E‐01 rs9808759 21 47780223 C/T 0.07 0.99 0.90‐1.10 9.06E‐01 1.08 1.05‐1.11 1.16E‐07 1.06 0.99‐1.13 1.12E‐01 1.07 1.01‐1.14 3.33E‐02 1.06 1.03‐1.10 9.31E‐04 1.07 1.01‐1.13 1.39E‐02 1.04 0.98‐1.12 2.00E‐01 1.03 0.94‐1.14 5.09E‐01 1.07 1.01‐1.14 3.21E‐02 1.09 1.04‐1.16 1.40E‐03 chr1_145126177 1 145126177 G/A 0.037 0.92 0.77‐1.10 3.75E‐01 1.15 1.10‐1.21 7.24E‐09 0.96 0.85‐1.08 4.90E‐01 0.97 0.86‐1.08 5.75E‐01 1.11 1.05‐1.19 5.07E‐04 0.96 0.87‐1.05 3.79E‐01 0.97 0.86‐1.09 6.09E‐01 0.94 0.79‐1.11 4.55E‐01 0.98 0.88‐1.10 7.42E‐01 1.01 0.92‐1.11 8.39E‐01 rs495367 4 1986972 A/G 0.345 1.06 0.99‐1.13 8.78E‐02 1.05 1.03‐1.07 1.39E‐07 1.05 1.01‐1.10 2.74E‐02 1.05 1.01‐1.09 2.08E‐02 1.04 1.02‐1.06 1.15E‐03 1.04 1.01‐1.08 1.65E‐02 1.04 1.00‐1.09 6.00E‐02 1.06 1.00‐1.13 4.45E‐02 0.99 0.95‐1.03 6.36E‐01 1.00 0.97‐1.04 8.35E‐01 rs138044103 5 67424121 C/CTG 0.45 1.01 0.96‐1.07 6.12E‐01 1.05 1.03‐1.07 1.92E‐09 1.04 1.00‐1.08 2.83E‐02 1.03 1.00‐1.07 5.56E‐02 1.06 1.04‐1.08 2.98E‐08 0.99 0.96‐1.01 3.20E‐01 0.98 0.95‐1.02 3.21E‐01 1.00 0.95‐1.05 9.67E‐01 1.00 0.97‐1.04 9.80E‐01 0.98 0.95‐1.01 1.59E‐01 rs7924772 11 120233626 A/G 0.388 0.95 0.90‐1.01 9.82E‐02 1.05 1.03‐1.06 2.01E‐07 0.95 0.91‐0.99 7.36E‐03 0.95 0.92‐0.99 7.92E‐03 1.04 1.01‐1.06 9.29E‐04 1.02 0.99‐1.05 2.94E‐01 1.00 0.96‐1.04 9.14E‐01 0.96 0.91‐1.01 1.07E‐01 1.03 1.00‐1.07 7.31E‐02 1.04 1.01‐1.08 1.11E‐02 rs78378222 17 7571752 T/G 0.01 1.07 0.84‐1.37 5.66E‐01 1.13 1.05‐1.21 7.38E‐04 1.32 1.14‐1.53 2.62E‐04 1.34 1.16‐1.54 5.52E‐05 1.07 0.98‐1.17 1.17E‐01 1.10 0.96‐1.25 1.67E‐01 1.12 0.96‐1.31 1.41E‐01 1.15 0.92‐1.43 2.19E‐01 0.67 0.55‐0.81 4.33E‐05 0.67 0.57‐0.80 5.07E‐06 rs206435 18 10354649 C/A 0.496 0.95 0.90‐1.00 6.69E‐02 1.03 1.01‐1.05 2.15E‐04 0.98 0.95‐1.02 2.95E‐01 0.98 0.95‐1.01 2.37E‐01 1.03 1.01‐1.05 5.93E‐03 0.99 0.96‐1.02 3.62E‐01 0.99 0.95‐1.02 4.52E‐01 0.95 0.90‐1.00 4.65E‐02 0.95 0.92‐0.99 7.10E‐03 0.95 0.92‐0.98 4.81E‐04 rs141526427 20 11502618 A/AAC 0.25 0.98 0.91‐1.04 4.56E‐01 0.96 0.94‐0.98 1.94E‐05 0.94 0.90‐0.99 9.54E‐03 0.94 0.90‐0.98 2.99E‐03 0.96 0.94‐0.99 1.63E‐03 0.95 0.91‐0.98 3.73E‐03 0.95 0.91‐0.99 1.33E‐02 0.97 0.91‐1.03 2.66E‐01 1.05 1.01‐1.09 2.02E‐02 1.04 1.01‐1.08 1.96E‐02 rs6065254 20 39248265 G/A 0.391 1.01 0.95‐1.07 7.40E‐01 0.96 0.94‐0.97 7.08E‐08 0.96 0.92‐0.99 1.71E‐02 0.96 0.92‐0.99 1.75E‐02 0.96 0.94‐0.98 1.89E‐05 0.98 0.95‐1.01 1.21E‐01 0.96 0.92‐0.99 2.33E‐02 1.01 0.96‐1.06 7.80E‐01 1.04 1.00‐1.08 2.99E‐02 1.04 1.01‐1.07 1.19E‐02 rs17215231 6 33239869 C/T 0.08 1.05 0.95‐1.16 2.97E‐01 0.99 0.96‐1.02 6.07E‐01 0.97 0.91‐1.04 4.36E‐01 0.99 0.93‐1.06 8.42E‐01 0.97 0.93‐1.00 8.10E‐02 0.96 0.90‐1.01 1.20E‐01 0.94 0.88‐1.01 8.31E‐02 1.03 0.94‐1.13 5.67E‐01 0.84 0.79‐0.90 9.66E‐07 0.82 0.77‐0.87 2.37E‐10 chr12_121435475 12 121435475 G/A 0.372 0.96 0.91‐1.02 1.66E‐01 0.99 0.97‐1.00 1.05E‐01 0.96 0.92‐0.99 2.39E‐02 0.96 0.92‐0.99 1.87E‐02 0.99 0.97‐1.01 1.56E‐01 0.99 0.96‐1.02 5.71E‐01 0.99 0.96‐1.03 6.19E‐01 0.96 0.91‐1.01 1.24E‐01 0.93 0.90‐0.97 1.78E‐04 0.94 0.91‐0.97 5.37E‐05 1 Chromosome 2 Build 37 position 3 Manjor / minor allele in Europeans (forward strand) 4 MAF, minor allele frequency 5 OR, odds ratio per copy of the minor allele 6 luminal A‐like (ER+ and/or PR+, HER2‐, grade 1 & 2); luminal B,HER2‐negative‐like (ER+ and/or PR+, HER2‐, grade 3); luminal B‐like (ER+ and/or PR+, HER2+); HER2‐enriched‐like (ER‐ and PR‐, HER2+ ); TN (ER‐, PR‐, HER2‐) 7 The analysis using EM algorithm contains 106,278 invasive cases and 91,477 controls. 8 The complete data analysis  contains 50,749 invasive cases and 91,477 controls. 9 P‐values are raw p‐values from two‐tailed z‐test statistics. Case‐control intrinsic‐like odds ratios5,6 Results implementing EM algorithm for missing tumor marker data 8 Results restricted to cases with complete tumor marker data 7 Results implementing EM algorithm for missing tumor marker data 8 Results restricted to cases with complete tumor marker data 7 Results implementing EM algorithm for missing tumor data8 Results restricted to cases with complete tumor  marker data7 Results implementing EM algorithm for missing tumor marker data 8 Results restricted to cases with complete  tumor marker data7 Results implementing EM algorithm for missing tumor marker data 8 Results restricted to cases with complete tumor marker  data7 Supplementary Table 12: Fifteen variants identified with evidence of heterogeneity (n = 106,278 invasive cases, n = 91,477 controls). Global Heterogeneity  Test P1 OR3  (95%CI) P4 OR3 (95%CI) 95%CI P4 OR3 (95%CI) 95%CI P4 OR3 (95%CI) 95%CI P4 1:145126177 1 145,126,177 2.8 x 10‐6 0.97 0.87‐1.07 5.0 x 10‐1 1.09 1.00‐1.19 5.4 x 10‐2 0.92 0.83‐1.02 1.2 x 10‐1 0.92 0.87‐0.96 6.9 x 10‐4 rs9712235 2 67,881,757 6.7 x 10‐3 0.94 0.90‐0.97 3.7 x 10‐4 1.04 1.01‐1.07 2.4 x 10‐2 0.96 0.92‐1.00 2.8 x 10‐2 1.00 0.98‐1.02 8.5 x 10‐1 rs138044103 5 67,424,121 5.2 x 10‐7 1.02 0.99‐1.06 1.7 x 10‐1 1.02 0.99‐1.05 1.3 x 10‐1 1.01 0.98‐1.05 4.2 x 10‐1 0.98 0.96‐0.99 2.8 x 10‐3 rs17215231 6 33,239,869 2.4 x 10‐6 1.07 1.00‐1.14 4.4 x 10‐2 1.03 0.98‐1.09 2.5 x 10‐1 1.08 1.02‐1.15 9.1 x 10‐3 0.96 0.93‐0.99 7.9 x 10‐3 rs7760611 6 21,903,533 1.4 x 10‐3 0.96 0.93‐1.00 2.7 x 10‐2 0.98 0.96‐1.01 2.8 x 10‐1 0.98 0.95‐1.01 1.7 x 10‐1 1.00 0.99‐1.02 8.7 x 10‐1 rs79518236 7 98,026,554 1.6 x 10‐3 0.98 0.95‐1.02 3.7 x 10‐1 0.96 0.93‐0.99 1.7 x 10‐2 0.98 0.94‐1.02 3.0 x 10‐1 0.97 0.95‐0.99 1.9 x 10‐3 rs4742903 9 106,856,793 2.8 x 10‐3 0.98 0.95‐1.02 3.4 x 10‐1 1.01 0.99‐1.04 3.7 x 10‐1 0.97 0.94‐1.00 5.1 x 10‐2 0.98 0.96‐1.00 1.1 x 10‐2 rs7924772 11 120,233,626 1.4 x 10‐3 0.99 0.96‐1.03 6.7 x 10‐1 1.00 0.97‐1.03 8.3 x 10‐1 0.92 0.89‐0.95 1.4 x 10‐6 0.99 0.97‐1.00 1.0 x 10‐1 rs2464195 12 121,435,475 1.0 x 10‐2 1.03 1.00‐1.06 8.9 x 10‐2 1.00 0.97‐1.03 9.9 x 10‐1 0.99 0.95‐1.02 4.1 x 10‐1 0.99 0.98‐1.01 3.4 x 10‐1 rs78378222 17 7,571,752 9.1 x 10‐8 1.42 1.23‐1.65 7.0 x 10‐6 1.01 0.89‐1.14 9.0 x 10‐1 1.29 1.13‐1.48 2.7 x 10‐4 0.98 0.91‐1.05 5.2 x 10‐1 rs206435 18 10,354,649 1.1 x 10‐9 1.05 1.02‐1.08 2.8 x 10‐3 0.98 0.96‐1.01 2.5 x 10‐1 0.98 0.95‐1.01 1.4 x 10‐1 0.97 0.96‐0.99 2.8 x 10‐4 rs17743054 18 42,900,892 1.9 x 10‐2 1.00 0.97‐1.04 8.4 x 10‐1 0.99 0.96‐1.02 5.3 x 10‐1 1.01 0.97‐1.04 6.8 x 10‐1 1.02 1.00‐1.04 2.6 x 10‐2 rs6065254 20 39,248,265 7.3 x 10‐7 0.95 0.92‐0.98 4.3 x 10‐3 0.98 0.95‐1.01 2.0 x 10‐1 0.99 0.95‐1.02 3.9 x 10‐1 1.01 0.99‐1.03 2.7 x 10‐1 rs141526427 20 11,502,618 6.2 x 10‐5 0.94 0.90‐0.98 1.3 x 10‐3 0.99 0.95‐1.02 4.4 x 10‐1 0.97 0.93‐1.01 8.9 x 10‐2 0.99 0.97‐1.01 3.2 x 10‐1 rs13039563 20 52,296,849 4.9 x 10‐3 1.03 0.99‐1.07 1.4 x 10‐1 1.02 0.99‐1.05 2.6 x 10‐1 1.01 0.97‐1.04 8.1 x 10‐1 0.99 0.97‐1.01 4.4 x 10‐1 1 Global heterogeneity tests were evaluated using a mixed effect two‐stage polytomous model adjusted for top 10 PCs and age while accounting subtyupes heterogeneity  for ER (fixed effect), PR (random effect), HER2(random 2 The marker specific heterogeneity test was evaluated using a fixed effect two‐stage polytomous model adjusted for top 10 PCs and age while accounting subtyupes heterogeneity  for  ER, PR, HER2 and grade all as fixed effect 3 Case‐case per minor‐allele odds ratios were estimated with fixed‐effect two‐stage polytmous models, mutually adjusting for each tumor marker 4 P‐values are raw p‐values from two‐tailed z‐test statistics.  HER2 Grade Marker specific heterogeneity2 case‐case OR, 95% CI, and P‐values Lead variant CHR Position ER PR  effect), and grade (random effect). The global heterogeneity test was corrected for multiple testing using a False Discovery Rate (FDR) of 0.05 under the Benjamini‐Hochberg procedure ts Supplementary Table 7: Three novel variants identified as being associated with risk of triple negative breast cancer using meta‐analysis of BCAC and CIMBA data (BCAC: n = 8,602 effective triple‐negative cases, n = 91,477 controls; CIMB Lead variant Chr.1 Position Alleles2 MAF3 Imputation Quality  iCOGS/ONCO4 OR5 95% CI P6 HR7 95%CI P6 RR8 95%CI P6 rs17215231 6 33,239,869 C/T 0.08 0.82/1.00 0.82 0.77‐0.87 2.2 x 10‐10 0.88 0.82‐0.94 2.7 x 10‐4 0.85 0.81‐0.89 8.6 x 10‐13 rs2464195 12 121,435,475 G/A 0.37 1.00/1.00 0.94 0.91‐0.97 5.3 x 10‐5 0.93 0.90‐0.96 1.1 x 10‐4 0.93 0.91‐0.96 2.5 x 10‐8 rs783782228 17 7,571,752 T/G 0.01 0.90/1.00 0.67 0.56‐0.79 4.8 x 10‐6 0.71 0.58‐0.86 7.2 x 10‐4 0.69 0.62‐0.77 1.4 x 10‐8 1 Chr., chromosome 2 Major alleles listed first 3 MAF, minor allele frequency 4 Imputation quality (r2) for iCOGS/OncoArray  5 OR, per minor‐allele odds ratio estimated using the fixed‐effects two‐stage model 6 P‐values are raw p‐values from two‐tailed z‐test statistics. Bonferroni correction was used to account for multiple testing (cut off P‐value = 5x 10‐8). 7 HR, per minor‐allele hazard ratio 8 RR, per minor‐allele relative risk was estimated through fixed effect meta‐analysis of BCAC and CIMBA data 9 rs78378222 was detected in both the two‐stage model and the meta‐analysis of BCAC triple negative and CIMBA‐BRCA1  mutation carriers CIMBA BRCA1 mutation carriers Meta‐analysisBCAC triple negative BA BRCA1 carriers: n = 9,414 cases, n = 9,494 controls). Supplementary Table 8: Conditional analysis of genome‐wide significant variants from standard logistic regression analyses of overall breast cancer risk conditional on nearb Lead variant1 Chr.2 Position MAF3 Nearby known variant4 LD5 D’6 Standard analysis P7 Conditional analysis P8 rs150157076 1  120,586,681  0.47 rs11249433 0.03 0.17 1.0 x 10‐10 2.4 x 10‐3 rs11264454 1  156,153,043  0.43 rs4971059 0.02 0.27 1.7 x 10‐8 1.1 x 10‐5 rs11749176 5     44,145,931  0.14 rs10941679 0.03 0.75 8.6 x 10‐10 1.1 x 10‐2 5:45333860 5     45,333,860  0.24 rs10941679 0.06 0.72 3.9 x 10‐21 6.8 x 10‐5 rs141930488 5     51,248,274  0.02 rs35951924 0.06 0.72 3.5 x 10‐8 1.8 x 10‐6 rs6860806 5  131,640,536  0.46 rs6596100 0 0.04 2.7 x 10‐8 1.5 x 10‐6 rs77606119 6     21,903,533  0.47 rs2223621 0 0.01 1.5 x 10‐9 4.5 x 10‐9 rs132775689 8  116,679,547  0.37 rs13267382 0 0.01 2.2 x 10‐8 3.1 x 10‐7 rs12765365 10     64,848,937  0.04 rs10995201 0.05 0.46 9.1 x 10‐9 1.1 x 10‐2 12:291402609 12     29,140,260  0.09 rs7297051 0 0.02 7.7 x 10‐12 8.2 x 10‐11 rs10616579 12  115,108,136  0.26 rs1292011 0 0 2.5 x 10‐10 1.4 x 10‐11 rs177430549 18     42,900,892  0.28 rs6507583 0 0.11 1.5 x 10‐10 1.4 x 10‐10 1 Showing the strongest signal in each region 2 Chr., chromosome 3 MAF, minor allele frequency 4 Known variantss previous published in  Michailidou et al. Nature 551, no. 7678 (2017) and  Milne et al. Nat Genet 49, 1767‐1778 (2017) 5 LD, linkage disequilibrium between lead variant and nearby known variant estimated from European‐ancestry controls in OncoArray 6 D’, D prime between lead variant and nearby known variant estimated from European‐ancestry controls in OncoArray 7 Standard logistic regression p‐value 8 Standard logistic regression p‐value adjusting for nearby known variant, a conditional significance p‐value threshold of P <1 x 10‐6 was used (reason described in Online Method 9 Conditonally significant after adjusting for nearby known variant by (within +/‐ 2 MB) known breast cancer loci (n = 133,384 cases, n = 113,789 controls). ds) Supplementary Table 9: Results from conditional analyses of genome‐wide significant variants identified by two‐stage regression models that are located nearby (within +/‐ 2 MB) a known  Lead variant1 Chr.2 Position MAF3 Nearby known variant4 LD5 D’6 Mixed effect model global association test P7 Conditional analysis P8 rs6697258 1 120,485,335 0.06 rs11249433 0.06 0.68 1.5 x 10‐15 3.2 x 10‐3 1:1451261779 1 145,126,177 0.04 rs12405132 0.03 0.4 9.6 x 10‐9 6.8 x 10‐10 rs35383942 0 0.01 rs6678914 0.01 0.05 rs56826596 5 45,374,890 0.15 rs10941679 0.09 0.41 7.8 x 10‐12 1.7 x 10‐1 rs139331653 5 45,939,294 0.03 rs10941679 0.07 0.92 2.4 x 10‐9 2.7 x 10‐1 rs34044188 10 65,257,363 0.05 rs10995201 0.04 0.43 8.7 x 10‐9 5.0 x 10‐3 rs17879961 0.02 0.3 rs132390 0 0.01 1 Showing the strongest signal in each region 2 Chr., chromosome 3 MAF, minor allele frequency 4 Known variantss previous published in  Michailidou et al. Nature 551, no. 7678 (2017) and  Milne et al. Nat Genet 49, 1767‐1778 (2017) 5 LD, linkage disequilibrium between lead variant and nearby known variant estimated from European‐ancestry controls in OncoArray 6 D’, D prime between lead variant and nearby known variant estimated from European‐ancestry controls in OncoArray 7 Results from mixed effect two‐stage models adjusting for estrogen receptor (ER, fixed effect), progesterone receptor (PR, random effect), human epidermal growth factor receptor 2 (HER2, ra 8 Results from mixed effect two‐stage models global association test conditional on the nearby known variants,  p‐value threshold of p < 1 x 10‐6 was used for conditional analyses (reason descri 9 Conditionally significant after adjusting for nearby known variant 1.2 x 10‐6 rs6677545 1 200,342,046 0.35 4.3 x 10‐8 1.3 x 10‐6 rs16988381 22 30,592,808 0.02 2.8 x 10‐9 breast cancer susceptibility variant (n = 106,278 invasive cases, n = 91,477 controls). andom effect), and grade (random effect) bed in Online Methods) Supplementary Table 13: Enhancer states of candidates causal variants (CCVs) for the 32 identified variants Analysis1 Lead variant2 Variant Name3 Number of CCVs4 Number of CCVs ehancers5 OFF.PRIMED6 ACTIVE.PRIMED7 ACTIVE.OFF8 ACTIVE.OFF.PRIMED9 CCVs in ANYSWITCH enhancers10 Opposite direction variant11 Overall analysis rs5776993 1_110222901_CA_C 7 3 1 0 0 1 Y N Overall analysis rs10838267 11_44368892_G_A 14 11 1 0 1 0 Y N Overall analysis rs11065822 12_111600134_G_T 18 3 1 1 1 0 Y N Overall analysis rs1061657 12_115108136_T_C 6 0 0 0 0 0 N N Overall analysis 12:29140260 12_29140260_G_A 41 0 0 0 0 0 N N Overall analysis rs11652463 17_70405095_C_G 3 2 0 0 1 0 Y N Overall analysis rs12962334 18_20477934_G_C 128 8 1 0 1 0 Y N Overall analysis rs17743054 18_42900892_T_C 26 0 0 0 0 0 N N Overall analysis rs9712235 2_67881757_G_A 18 3 1 0 0 0 Y N Overall analysis rs4602255 2_69392128_G_A 27 9 1 0 1 1 Y N Overall analysis rs13039563 20_52296849_G_A 11 3 1 0 0 0 Y N Overall analysis rs9808759 21_47780223_T_C 38 5 1 0 1 0 Y N Overall analysis rs34052812 3_156535958_AT_A 82 1 0 0 1 0 Y N Overall analysis rs1375631 3_16778867_A_G 13 0 0 0 0 0 N N Overall analysis rs2886671 3_59373745_C_T 30 0 0 0 0 0 N N Overall analysis rs7760611 6_21903533_T_C 15 1 1 0 0 0 Y N Overall analysis rs188092014 7_74341926_G_C 56 4 0 0 0 0 N N Overall analysis rs79518236 7_98026554_ACT_A 45 10 1 1 1 0 Y N Overall analysis rs13277568 8_116679547_A_G 3 0 0 0 0 0 N N Overall analysis rs142890050 8_23480253_CTT_C 41 4 1 0 0 0 Y N Overall analysis rs13256025 8_25831778_C_T 1 1 0 0 0 0 N N Overall analysis rs4742903 9_106856793_G_C 102 0 0 0 0 0 N N Subtypes analysis 1:145126177 1_145126177_G_A 28 2 1 1 0 0 Y N Subtypes analysis rs7924772 11_120233626_A_G 93 9 1 1 1 0 Y Y Subtypes analysis rs78378222 17_7571752_T_G 2 1 0 0 0 1 Y Y Subtypes analysis rs206435 18_10354649_A_C 50 11 1 0 0 0 Y Y Subtypes analysis rs141526427 20_11502618_A_AAC 5 1 0 0 1 0 Y Y Subtypes analysis rs6065254 20_39248265_G_A 27 5 1 0 0 1 Y Y Subtypes analysis rs495367 4_1986972_A_G 2 1 0 0 1 0 Y N Subtypes analysis rs138044103 5_67424121_C_CTG 110 3 0 0 1 0 Y N TN analysis rs17215231 12_121435475_G_A 67 16 1 0 1 1 Y N TN analysis rs2464195 6_33239869_C_T 2 0 0 0 0 0 N N 1 Results from three different analysis; the overall analysis using standard logistic regression (overall analysis), the subtypes analysis using two-stage polytomous model (subtypes analysis), and the meta-analysis between BCAC TN and CIMBA BR 2 The most significant variants identified in the three different analysis 3 The variant name coded as chromosome_position_reference allele_effect allele 4 Number of CCVs within 500kb of the lead variant and with P values within 100-fold of magnitude of the most significant variants 5 Number of CCVs overlapping enhancers 6 Indicator of enhancer states in three normal breast epithelial sub-populations. If any enhancer is "OFF" in one sub-population and "PRIMED" in another sub-population, then it's coded as 1, otherwise as 0 7 Indicator of enhancer states in three normal breast epithelial sub-populations. If any enhancer is "ACTIVE" in one sub-population and "PRIMED" in another sub-population, then it's coded as 1, otherwise as 0 8 Indicator of enhancer states in three normal breast epithelial sub-populations. If any enhancer is "ACTIVE" in one sub-population and "OFF" in another sub-population, then it's coded as 1, otherwise as 0 9 Indicator of enhancer states in three normal breast epithelial sub-populations. If any enhancer is "ACTIVE" in one sub-population, "OFF" in another sub-population and "PRIMED" in the third sub-population, then it's coded as 1, otherwise as 0 10 Indicator of "ANYSWITCH" enhancers. “ANYSWITCH” enhancers exhibit different states between cell types. If there is any CCV in a "ANYSWTICH" enhancer, then it's coded as Y, otherwise as N 11 Indicator of variants with opposite associations in different breast intrinsic-like subtypes RCA1 carriers (TN analysis) Supplementary Table 14: Enhancer states for all candidate causal variants (CCVs) Analysis1 Lead variant2 Variant Name3 CCV4 Basalcells.OFF5 Basalcells.PRIMED6 Basalcells.ACTIVE7 Luminalprogenitor.OFF8 Luminalprogenitor.PRIMED9 Luminalprogenitor.ACTIVE10 Luminalcellsmature.OFF11 Luminalcellsmature.PRIMED12 Luminalcellsmature.ACTIVE13 Overall analysis rs5776993 1_110222901_CA_C 1_110169190_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs5776993 1_110222901_CA_C 1_110171525_C_G 0 1 0 1 0 0 1 0 0 Overall analysis rs5776993 1_110222901_CA_C 1_110173775_G_T 0 0 1 0 1 0 1 0 0 Overall analysis rs5776993 1_110222901_CA_C 1_110174205_C_T 0 0 1 0 1 0 1 0 0 Overall analysis rs5776993 1_110222901_CA_C 1_110222901_CA_C 0 0 0 0 0 0 0 0 0 Overall analysis rs5776993 1_110222901_CA_C 1_110230073_C_G 0 0 0 0 0 0 0 0 0 Overall analysis rs5776993 1_110222901_CA_C 1_110230075_G_ 0 0 0 0 0 0 0 0 0 Overall analysis rs10838267 11_44368892_G_A 11_44366356_A_G 1 0 0 1 0 0 1 0 0 Overall analysis rs10838267 11_44368892_G_A 11_44366518_T_C 1 0 0 1 0 0 1 0 0 Overall analysis rs10838267 11_44368892_G_A 11_44367045_A_G 1 0 0 1 0 0 1 0 0 Overall analysis rs10838267 11_44368892_G_A 11_44367897_C_G 0 0 1 0 0 1 1 0 0 Overall analysis rs10838267 11_44368892_G_A 11_44368032_A_C 0 0 1 0 0 1 1 0 0 Overall analysis rs10838267 11_44368892_G_A 11_44368381_A_G 0 0 1 0 0 1 1 0 0 Overall analysis rs10838267 11_44368892_G_A 11_44368416_G_T 1 0 0 0 1 0 1 0 0 Overall analysis rs10838267 11_44368892_G_A 11_44368892_G_A 1 0 0 0 1 0 1 0 0 Overall analysis rs10838267 11_44368892_G_A 11_44369196_A_G 1 0 0 0 1 0 1 0 0 Overall analysis rs10838267 11_44368892_G_A 11_44369477_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs10838267 11_44368892_G_A 11_44370034_C_T 1 0 0 0 1 0 1 0 0 Overall analysis rs10838267 11_44368892_G_A 11_44370140_C_T 1 0 0 0 1 0 1 0 0 Overall analysis rs10838267 11_44368892_G_A 11_44371717_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs10838267 11_44368892_G_A 11_44372916_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs11065822 12_111600134_G_T 12_111360049_A_ACCTGTAAT 0 0 0 0 0 0 0 0 0 Overall analysis rs11065822 12_111600134_G_T 12_111361137_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs11065822 12_111600134_G_T 12_111363528_T_G 0 0 0 0 0 0 0 0 0 Overall analysis rs11065822 12_111600134_G_T 12_111426615_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs11065822 12_111600134_G_T 12_111427783_GT_G 0 0 0 0 0 0 0 0 0 Overall analysis rs11065822 12_111600134_G_T 12_111495518_A_G 0 0 1 1 0 0 1 0 0 Overall analysis rs11065822 12_111600134_G_T 12_111504033_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs11065822 12_111600134_G_T 12_111600134_G_T 0 0 0 0 0 0 0 0 0 Overall analysis rs11065822 12_111600134_G_T 12_111708458_A_C 0 0 0 0 0 0 0 0 0 Overall analysis rs11065822 12_111600134_G_T 12_111720125_CA_C 0 0 0 0 0 0 0 0 0 Overall analysis rs11065822 12_111600134_G_T 12_111826477_A_AAATT 0 0 0 0 0 0 0 0 0 Overall analysis rs11065822 12_111600134_G_T 12_111833788_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs11065822 12_111600134_G_T 12_111865049_C_G 0 1 0 1 0 0 1 0 0 Overall analysis rs11065822 12_111600134_G_T 12_111884608_T_C 0 0 1 0 1 0 0 0 1 Overall analysis rs11065822 12_111600134_G_T 12_111904371_T_A 0 0 0 0 0 0 0 0 0 Overall analysis rs11065822 12_111600134_G_T 12_111907431_A_AC 0 0 0 0 0 0 0 0 0 Overall analysis rs11065822 12_111600134_G_T 12_111932800_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs11065822 12_111600134_G_T 12_112007756_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs1061657 12_115108136_T_C 12_115102482_A_C 0 0 0 0 0 0 0 0 0 Overall analysis rs1061657 12_115108136_T_C 12_115106688_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs1061657 12_115108136_T_C 12_115108136_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs1061657 12_115108136_T_C 12_115108361_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs1061657 12_115108136_T_C 12_115109223_C_G 0 0 0 0 0 0 0 0 0 Overall analysis rs1061657 12_115108136_T_C 12_115117029_G_T 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28650565_C_T 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28650613_T_C 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28651146_A_T 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28651915_T_C 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28652275_A_G 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28653024_A_G 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28653218_C_G 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28654368_A_G 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28654427_A_T 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28654464_C_G 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28655017_G_C 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28655343_G_C 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28656497_A_G 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28656512_C_T 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28656805_A_G 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28656899_C_A 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28657040_A_T 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28658039_G_C 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28658479_A_G 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28659172_A_G 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28659918_G_A 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28660527_T_G 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28661094_C_G 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28661369_T_C 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28661655_A_G 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28661995_G_A 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28662530_T_C 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28662696_G_A 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28663170_A_G 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28663667_A_G 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28666566_T_C 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28666593_A_G 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28667393_TTTTG_TGTTTG 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28737797_T_A 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_28740649_A_ATTAT 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_29140260_G_A 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_29140957_A_ACACAAATATGTATTAG 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_29159781_C_G 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_29244551_C_A 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_29247350_C_A 0 0 0 0 0 0 0 0 0 Overall analysis 12:29140260 12_29140260_G_A 12_29274538_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs11652463 17_70405095_C_G 17_70405095_C_G 0 0 1 0 0 1 1 0 0 Overall analysis rs11652463 17_70405095_C_G 17_70407856_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs11652463 17_70405095_C_G 17_70408871_C_G 1 0 0 0 0 1 0 0 1 Overall analysis rs12962334 18_20477934_G_C 18_20382974_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20383462_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20385636_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20388955_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20389215_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20395043_G_C 0 0 1 1 0 0 1 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20395774_A_C 0 0 1 1 0 0 1 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20396654_C_A 0 1 0 0 1 0 0 1 0 Overall analysis rs12962334 18_20477934_G_C 18_20404490_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20405552_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20405819_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20409207_G_GT 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20410355_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20413705_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20416736_GA_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20418285_C_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20428274_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20431157_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20436874_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20442886_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20444667_C_CCTT 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20445814_TTA_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20450135_G_T 0 1 0 1 0 0 1 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20452099_A_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20452100_G_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20453121_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20453245_GACAATGAGATTAT_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20461284_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20462056_T_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20462455_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20462792_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20464171_A_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20471369_T_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20474514_A_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20475290_G_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20476501_A_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20477848_G_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20477934_G_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20480387_G_A 0 1 0 0 1 0 1 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20487480_ATTAT_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20487706_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20491798_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20493665_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20494552_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20495998_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20496233_CTGAA_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20496750_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20496970_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20498690_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20498751_T_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20500445_T_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20502682_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20503120_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20503976_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20505479_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20506013_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20506106_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20506633_A_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20507087_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20507796_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20508604_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20508608_T_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20510161_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20516068_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20518933_G_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20520825_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20522660_A_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20526828_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20527819_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20533697_A_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20534401_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20538300_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20540098_C_T 0 0 1 1 0 0 1 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20542607_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20542950_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20543757_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20544907_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20545570_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20547095_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20547187_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20550674_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20550964_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20554136_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20554316_C_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20556324_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20556603_T_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20563621_TATGAAAGCAGC_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20564800_TTCA_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20565426_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20566384_G_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20566496_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20566973_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20568937_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20574678_A_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20575995_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20576181_G_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20577330_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20577456_T_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20582619_GTT_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20584247_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20587553_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20587911_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20588055_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20591945_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20592530_ACT_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20595102_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20598599_A_AATAGTTGTTAT 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20599869_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20601508_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20610501_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20612238_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20613270_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20613357_T_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20616210_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20616564_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20617364_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20617617_TA_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20617807_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20621298_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20621986_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20622487_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20624223_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20625140_A_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20625431_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20627691_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20628152_T_A 0 0 0 0 0 0 0 0 0 Overall analysis rs12962334 18_20477934_G_C 18_20634130_G_T 0 0 1 0 0 1 0 0 1 Overall analysis rs12962334 18_20477934_G_C 18_20634253_C_T 0 0 1 0 0 1 0 0 1 Overall analysis rs17743054 18_42900892_T_C 18_42876221_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42876934_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42877575_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42877630_G_GA 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42878436_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42878504_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42879087_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42879717_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42880242_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42880349_C_CTCTG 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42880507_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42881314_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42884026_A_T 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42888797_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42889017_C_G 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42899840_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42900582_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42900684_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42900892_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42903755_A_T 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42903987_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42907212_AT_A 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42908266_T_A 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42916139_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42916195_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs17743054 18_42900892_T_C 18_42919925_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs9712235 2_67881757_G_A 2_67872744_C_G 0 0 0 0 0 0 0 0 0 Overall analysis rs9712235 2_67881757_G_A 2_67873656_T_G 0 0 0 0 0 0 0 0 0 Overall analysis rs9712235 2_67881757_G_A 2_67875078_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs9712235 2_67881757_G_A 2_67875170_A_C 0 0 0 0 0 0 0 0 0 Overall analysis rs9712235 2_67881757_G_A 2_67876778_T_G 0 1 0 1 0 0 1 0 0 Overall analysis rs9712235 2_67881757_G_A 2_67878108_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs9712235 2_67881757_G_A 2_67879013_G_C 0 0 0 0 0 0 0 0 0 Overall analysis rs9712235 2_67881757_G_A 2_67879183_G_GA 0 0 0 0 0 0 0 0 0 Overall analysis rs9712235 2_67881757_G_A 2_67879389_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs9712235 2_67881757_G_A 2_67879834_AC_A 0 0 0 0 0 0 0 0 0 Overall analysis rs9712235 2_67881757_G_A 2_67879835_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs9712235 2_67881757_G_A 2_67881757_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs9712235 2_67881757_G_A 2_67893697_C_T 1 0 0 0 1 0 1 0 0 Overall analysis rs9712235 2_67881757_G_A 2_67893918_G_A 1 0 0 0 1 0 1 0 0 Overall analysis rs9712235 2_67881757_G_A 2_67902524_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs9712235 2_67881757_G_A 2_67908711_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs9712235 2_67881757_G_A 2_67912737_A_C 0 0 0 0 0 0 0 0 0 Overall analysis rs9712235 2_67881757_G_A 2_67913224_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69381534_T_C 0 0 1 0 1 0 1 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69381569_A_G 0 0 1 0 1 0 1 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69384107_A_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69384661_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69385921_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69387039_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69387076_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69387256_A_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69387679_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69389155_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69389332_T_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69389566_A_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69389757_T_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69390369_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69392128_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69392619_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69393185_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69394848_C_T 0 0 1 0 1 0 1 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69394936_C_T 0 1 0 1 0 0 1 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69396387_G_A 0 0 1 1 0 0 0 1 0 Overall analysis rs4602255 2_69392128_G_A 2_69396497_C_G 0 0 1 1 0 0 0 1 0 Overall analysis rs4602255 2_69392128_G_A 2_69410456_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69410748_T_C 0 0 1 1 0 0 1 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69429294_G_A 1 0 0 1 0 0 1 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69429635_T_G 1 0 0 1 0 0 1 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69434227_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4602255 2_69392128_G_A 2_69435243_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs13039563 20_52296849_G_A 20_52278275_C_G 0 0 0 0 0 0 0 0 0 Overall analysis rs13039563 20_52296849_G_A 20_52278609_C_CT 1 0 0 1 0 0 0 1 0 Overall analysis rs13039563 20_52296849_G_A 20_52285250_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs13039563 20_52296849_G_A 20_52285882_C_G 0 0 0 0 0 0 0 0 0 Overall analysis rs13039563 20_52296849_G_A 20_52287610_G_T 0 0 0 0 0 0 0 0 0 Overall analysis rs13039563 20_52296849_G_A 20_52289702_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs13039563 20_52296849_G_A 20_52294096_G_GT 0 0 0 0 0 0 0 0 0 Overall analysis rs13039563 20_52296849_G_A 20_52296849_G_A 1 0 0 1 0 0 0 1 0 Overall analysis rs13039563 20_52296849_G_A 20_52297165_G_A 1 0 0 1 0 0 0 1 0 Overall analysis rs13039563 20_52296849_G_A 20_52298228_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs13039563 20_52296849_G_A 20_52298514_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47715639_C_CCT 1 0 0 1 0 0 1 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47717208_T_C 1 0 0 1 0 0 1 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47717218_A_C 1 0 0 1 0 0 1 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47717616_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47721661_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47723125_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47732574_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47744037_CG_C 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47748185_C_CCT 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47761064_G_T 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47767008_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47773177_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47777222_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47780223_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47780305_GT_G 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47785252_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47786817_C_G 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47787002_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47790235_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47792185_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47793474_G_T 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47798596_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47799152_C_T 1 0 0 0 0 1 1 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47801998_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47802000_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47802008_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47805051_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47812009_A_T 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47817669_G_GGCTGGGCCTGT 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47819009_AG_A 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47819986_A_G 1 0 0 0 1 0 1 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47820394_AGAG_A 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47820493_C_CGCAATCTTGGCTCGAGTGCAGTGGT 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47823507_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47829197_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47832012_A_AT 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47850178_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs9808759 21_47780223_T_C 21_47856670_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs34052812 3_156535958_AT_A 3_156363242_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs34052812 3_156535958_AT_A 3_156399180_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs34052812 3_156535958_AT_A 3_156421525_G_T 0 0 0 0 0 0 0 0 0 Overall analysis rs34052812 3_156535958_AT_A 3_156458671_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs34052812 3_156535958_AT_A 3_156464729_A_C 0 0 0 0 0 0 0 0 0 Overall analysis rs34052812 3_156535958_AT_A 3_156480941_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs34052812 3_156535958_AT_A 3_156489509_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs34052812 3_156535958_AT_A 3_156491160_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs34052812 3_156535958_AT_A 3_156492758_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs34052812 3_156535958_AT_A 3_156493213_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs34052812 3_156535958_AT_A 3_156493707_C_CA 0 0 1 1 0 0 1 0 0 Overall analysis rs34052812 3_156535958_AT_A 3_156501063_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs34052812 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7_98026554_ACT_A 7_98015868_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs79518236 7_98026554_ACT_A 7_98017099_G_A 1 0 0 1 0 0 0 1 0 Overall analysis rs79518236 7_98026554_ACT_A 7_98017192_AAAC_A 1 0 0 1 0 0 0 1 0 Overall analysis rs79518236 7_98026554_ACT_A 7_98017730_G_A 1 0 0 1 0 0 0 1 0 Overall analysis rs79518236 7_98026554_ACT_A 7_98022262_GC_G 0 0 0 0 0 0 0 0 0 Overall analysis rs79518236 7_98026554_ACT_A 7_98022479_G_GTA 0 0 0 0 0 0 0 0 0 Overall analysis rs79518236 7_98026554_ACT_A 7_98026554_ACT_A 1 0 0 1 0 0 0 1 0 Overall analysis rs79518236 7_98026554_ACT_A 7_98027580_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs79518236 7_98026554_ACT_A 7_98030349_G_C 0 0 0 0 0 0 0 0 0 Overall analysis rs79518236 7_98026554_ACT_A 7_98030513_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs79518236 7_98026554_ACT_A 7_98033917_CACAA_CACAAACAA 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_116679547_A_G 8_116675063_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_116679547_A_G 8_116677409_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_116679547_A_G 8_116679547_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23024091_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23451402_T_TA 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23454413_T_G 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23455599_GA_G 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23458354_T_A 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23460123_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23463020_GC_G 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23464004_G_A 1 0 0 0 1 0 1 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23464505_A_T 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23465063_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23466466_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23466845_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23466880_G_C 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23468402_A_T 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23468410_AAGAG_A 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23468650_A_T 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23469088_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23469528_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23470115_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23470583_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23470947_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23474184_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23477010_A_AAC 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23478809_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23480253_CTT_C 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23647494_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23649025_G_A 1 0 0 1 0 0 1 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23649647_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23651661_A_G 1 0 0 1 0 0 0 1 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23651702_G_T 1 0 0 1 0 0 0 1 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23655784_G_T 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23659511_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23663101_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23663205_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23663504_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23663653_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23665830_T_TCAACTGAC 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23671452_C_G 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23671498_G_T 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23681126_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs13277568 8_23480253_CTT_C 8_23685567_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs13256025 8_25831778_C_T 8_25831778_C_T 1 0 0 1 0 0 1 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106662879_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106674435_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106675148_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106681686_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106682707_G_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106684088_C_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106686172_AAAAAAT_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106852385_A_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106853118_GAACT_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106856452_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106856633_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106856691_T_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106856692_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106856793_G_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106856806_TGGCGGGA_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106856910_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106856952_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106856972_G_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106857078_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106857180_ATTGTGGAGAG_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106857908_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106858192_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106858788_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106859701_TA_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106859738_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106859811_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106860568_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106861118_T_TA 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106861191_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106861281_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106861282_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106864570_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106865438_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106865691_A_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106865692_G_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106866703_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106867106_A_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106868443_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106871050_AATCCTAGGTTTC_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106872864_A_ATGT 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106875277_G_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106877939_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106878318_TCTC_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106878839_TATCA_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106881402_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106883085_T_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106885243_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106887581_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106888838_ATTAT_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106890840_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106893334_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106894211_A_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106896809_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106896874_T_TATATTATAAA 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106897491_C_CAGATGATGCAAGTAGGAG 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106898410_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106899951_TA_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106904265_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106905277_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106906437_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106906959_T_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106906963_G_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106907332_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106907819_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106909051_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106909300_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106909577_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106911633_A_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106911946_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106912892_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106913250_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106914905_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106917770_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106919726_A_AT 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106924871_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106924872_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106926980_C_T 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106928260_G_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106928402_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106928940_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106929798_A_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106931408_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106931541_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106931890_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106932435_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106933163_C_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106933625_A_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106938280_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106939884_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106939922_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106940330_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106941903_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106945991_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106946359_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106949429_A_G 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106955686_A_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106957103_G_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106957627_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106957890_T_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106960503_T_A 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106973857_T_C 0 0 0 0 0 0 0 0 0 Overall analysis rs4742903 9_106856793_G_C 9_106974096_T_C 0 0 0 0 0 0 0 0 0 Subtypes analysis 1:145126177 1_145126177_G_A 1_144839594_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis 1:145126177 1_145126177_G_A 1_144840703_T_A 0 0 0 0 0 0 0 0 0 Subtypes analysis 1:145126177 1_145126177_G_A 1_144842642_C_A 0 0 0 0 0 0 0 0 0 Subtypes analysis 1:145126177 1_145126177_G_A 1_144858145_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis 1:145126177 1_145126177_G_A 1_144864860_GC_G 0 0 0 0 0 0 0 0 0 Subtypes analysis 1:145126177 1_145126177_G_A 1_144911200_GC_G 0 0 0 0 0 0 0 0 0 Subtypes analysis 1:145126177 1_145126177_G_A 1_144968559_T_C 0 0 0 0 0 0 0 0 0 Subtypes analysis 1:145126177 1_145126177_G_A 1_144987790_C_T 0 0 0 0 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Subtypes analysis rs6065254 20_39248265_G_A 20_39265578_G_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs6065254 20_39248265_G_A 20_39267356_G_A 1 0 0 0 1 0 0 1 0 Subtypes analysis rs6065254 20_39248265_G_A 20_39268516_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs6065254 20_39248265_G_A 20_39269074_T_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs6065254 20_39248265_G_A 20_39270816_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs6065254 20_39248265_G_A 20_39270992_G_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs6065254 20_39248265_G_A 20_39272739_C_G 1 0 0 1 0 0 1 0 0 Subtypes analysis rs6065254 20_39248265_G_A 20_39272959_C_G 1 0 0 1 0 0 1 0 0 Subtypes analysis rs6065254 20_39248265_G_A 20_39278391_T_C 1 0 0 0 1 0 0 1 0 Subtypes analysis rs495367 4_1986972_A_G 4_1984732_T_TAACA 0 0 0 0 0 0 0 0 0 Subtypes analysis rs495367 4_1986972_A_G 4_1986972_A_G 0 0 1 1 0 0 1 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67412319_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67412429_C_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67412484_T_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67412588_G_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67412596_T_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67412748_A_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67412797_G_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67412932_T_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67413143_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67413305_T_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67413612_T_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67413991_A_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67414281_A_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67414443_C_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67414657_A_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67414733_T_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67414744_A_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67414859_G_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67415013_T_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67415027_C_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67415042_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67415714_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67415779_C_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67415797_G_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67415973_A_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67416086_A_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67416371_A_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67416409_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67416832_ATTTT_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67417117_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67417177_AC_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67417280_C_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67417489_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67417508_G_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67417597_A_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67417988_C_G 1 0 0 1 0 0 0 0 1 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67418015_G_A 1 0 0 1 0 0 0 0 1 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67418507_C_G 1 0 0 1 0 0 0 0 1 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67418845_C_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67419418_T_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67419911_T_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67420468_A_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67420551_A_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67420708_G_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67421088_TGA_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67421092_GCCTACC_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67421584_A_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67421997_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67422424_CAA_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67422975_A_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67422992_G_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67423093_AAGGGATGGGGGTTAG_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67423286_A_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67423333_G_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67423346_C_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67423353_G_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67423398_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67423438_T_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67423440_CA_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67423600_C_CA 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67423766_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67423856_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67424121_C_CTG 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67425410_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67425470_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67425627_G_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67425856_G_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67425875_G_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67426147_A_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67426574_G_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67426672_C_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67426882_T_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67427981_T_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67428291_A_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67429332_G_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67430335_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67430709_G_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67430737_A_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67430899_G_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67430991_T_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67431598_T_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67431634_G_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67431901_AC_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67432791_T_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67433143_C_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67433205_C_CA 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67433507_T_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67433751_T_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67433913_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67434070_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67434112_T_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67434325_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67434442_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67434821_A_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67436395_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67436568_T_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67436716_G_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67437252_C_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67437359_T_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67437942_G_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67438439_G_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67438488_CA_C 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67438650_G_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67438729_A_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67438789_G_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67439059_A_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67439491_C_T 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67441026_A_G 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67459070_T_A 0 0 0 0 0 0 0 0 0 Subtypes analysis rs138044103 5_67424121_C_CTG 5_67471798_TA_T 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121384495_T_C 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121386122_G_A 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121388559_T_C 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121389721_A_G 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121390078_A_G 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121391671_C_A 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121392040_G_A 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121392341_C_T 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121397875_A_G 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121398654_A_C 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121398657_G_T 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121401647_AT_A 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121403724_G_A 1 0 0 0 0 1 0 1 0 TN analysis rs17215231 12_121435475_G_A 12_121404078_CT_C 1 0 0 0 0 1 0 1 0 TN analysis rs17215231 12_121435475_G_A 12_121404155_A_C 1 0 0 0 0 1 0 1 0 TN analysis rs17215231 12_121435475_G_A 12_121404243_ATT_AT 1 0 0 0 0 1 0 1 0 TN analysis rs17215231 12_121435475_G_A 12_121405126_C_A 1 0 0 1 0 0 0 1 0 TN analysis rs17215231 12_121435475_G_A 12_121406370_G_T 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121413027_G_A 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121413345_A_G 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121415390_T_C 1 0 0 1 0 0 0 0 1 TN analysis rs17215231 12_121435475_G_A 12_121416622_C_G 1 0 0 1 0 0 0 0 1 TN analysis rs17215231 12_121435475_G_A 12_121416650_A_C 1 0 0 1 0 0 0 0 1 TN analysis rs17215231 12_121435475_G_A 12_121416988_A_G 1 0 0 1 0 0 0 0 1 TN analysis rs17215231 12_121435475_G_A 12_121417536_G_GACTC 1 0 0 1 0 0 0 0 1 TN analysis rs17215231 12_121435475_G_A 12_121419926_T_C 1 0 0 1 0 0 0 0 1 TN analysis rs17215231 12_121435475_G_A 12_121420260_A_G 1 0 0 1 0 0 0 0 1 TN analysis rs17215231 12_121435475_G_A 12_121420263_A_G 1 0 0 1 0 0 0 0 1 TN analysis rs17215231 12_121435475_G_A 12_121422449_CTGACTGGCACTCAGCA_T 1 0 0 1 0 0 0 1 0 TN analysis rs17215231 12_121435475_G_A 12_121423285_T_C 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121423376_G_A 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121423386_A_G 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121423659_A_G 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121423956_C_T 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121424406_G_A 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121424490_C_T 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121424574_G_A 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121426064_C_T 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121426478_C_T 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121426594_G_A 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121428407_A_G 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121431300_C_T 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121432603_C_T 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121434833_G_A 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121435342_C_T 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121435427_G_A 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121435475_G_A 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121438311_C_T 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121438844_T_C 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121439192_G_T 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121439433_G_A 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121440731_C_T 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121441461_G_T 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121443116_A_G 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121443753_T_G 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121444441_CAT_C 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121445808_T_C 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121446446_T_A 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121450165_C_T 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121450354_C_A 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121450384_G_C 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121452249_C_T 1 0 0 1 0 0 1 0 0 TN analysis rs17215231 12_121435475_G_A 12_121454313_C_A 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121454906_G_A 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121455873_C_T 0 0 0 0 0 0 0 0 0 TN analysis rs17215231 12_121435475_G_A 12_121477359_G_T 1 0 0 0 0 1 1 0 0 TN analysis rs17215231 12_121435475_G_A 12_121489586_AT_A 0 0 0 0 0 0 0 0 0 TN analysis rs2464195 6_33239869_C_T 6_33239869_C_T 0 0 0 0 0 0 0 0 0 TN analysis rs2464195 6_33239869_C_T 6_33308380_G_A 0 0 0 0 0 0 0 0 0 1 Results from three different analysis; the overall analysis using standard logistic regression (overall analysis), the subtypes analysis using two-stage polytomous model (subtypes analysis), and the meta-analysis between BCAC TN and CIMBA BRCA1 carriers (TN analysis) 2 The most significant variants identified in the three different analysis 3 The variant name coded as chromosome_position_reference allele_effect allele 4 Number of candidates causal variants (CCVs) within 500kb of the lead variant and with P values within 100-fold of magnitude of the most significant variants 5 Indicator of "OFF" enhancer in basel cell. If this CCV overlaps with an "OFF" enhancer in basel cell, then it's coded as 1, otherwise as 0 6 Indicator of "PRIMED" enhancer in basel cell. If this CCV overlaps with an "PRIMED" enhancer in basel cell, then it's coded as 1, otherwise as 0 7 Indicator of "ACTIVE" enhancer in basel cell. If this CCV overlaps with an "ACTIVE" enhancer in basel cell, then it's coded as 1, otherwise as 0 8 Indicator of "OFF" enhancer in basel cell. If this CCV overlaps with an "OFF" enhancer in basel cell, then it's coded as 1, otherwise as 0 9 Indicator of "PRIMED" enhancer in luminal progenitor cell. If this CCV overlaps with an "PRIMED" enhancer in luminal progenitor cell, then it's coded as 1, otherwise as 0 10 Indicator of "ACTIVE" enhancer in luminal progenitor cell. If this CCV overlaps with an "ACTIVE" enhancer in luminal progenitor cell, then it's coded as 1, otherwise as 0 11 Indicator of "OFF" enhancer in mature luminal cell. If this CCV overlaps with an "OFF" enhancer in mature luminal cell, then it's coded as 1, otherwise as 0 12 Indicator of "PRIMED" enhancer in mature luminal cell. If this CCV overlaps with an "PRIMED" enhancer in mature luminal cell, then it's coded as 1, otherwise as 0 13 Indicator of "ACTIVE" enhancer in mature luminal cell. If this CCV overlaps with an "ACTIVE" enhancer in mature luminal cell, then it's coded as 1, otherwise as 0 Supplementary Table 15: INQUISIT analysis results  Analysis1 Region2 Target gene3 INQUIST category4 FINAL INQUISIT SCORE LEVEL5 Overall analysis 12_111600134_G_T ATXN2 DISTAL 1 Overall analysis 12_115108136_T_C TBX3 DISTAL 1 Overall analysis 18_20477934_G_C RBBP8 DISTAL 1 Overall analysis 20_52296849_G_A ZNF217 DISTAL 1 Overall analysis 21_47780223_T_C C21orf58 DISTAL 1 Overall analysis 21_47780223_T_C DIP2A DISTAL 1 Overall analysis 21_47780223_T_C PCNT DISTAL 1 Overall analysis 21_47780223_T_C YBEY DISTAL 1 Overall analysis 3_156535958_AT_A LEKR1 DISTAL 1 Overall analysis 3_156535958_AT_A LINC00886 DISTAL 1 Overall analysis 3_156535958_AT_A TIPARP DISTAL 1 Overall analysis 3_156535958_AT_A TIPARP‐AS1 DISTAL 1 Overall analysis 6_21903533_T_C SOX4 DISTAL 1 Overall analysis 7_74341926_G_C GTF2I DISTAL 1 Overall analysis 7_98026554_ACT_A BAIAP2L1 DISTAL 1 Overall analysis 7_98026554_ACT_A BRI3 DISTAL 1 Subtypes analysis 1_145126177_G_A PDE4DIP DISTAL 1 Subtypes analysis 1_145126177_G_A TXNIP DISTAL 1 Subtypes analysis 11_120233626_A_G ARHGEF12 DISTAL 1 Subtypes analysis 11_120233626_A_G TMEM136 DISTAL 1 Subtypes analysis 20_39248265_G_A MAFB DISTAL 1 Subtypes analysis 4_1986972_A_G C4orf48 DISTAL 1 TN analysis 12_121435475_G_A C12orf43 PROMOTER 1 Overall analysis 1_110222901_CA_C AMPD2 DISTAL 2 Overall analysis 1_110222901_CA_C GNAI3 DISTAL 2 Overall analysis 1_110222901_CA_C GSTM3 DISTAL 2 Overall analysis 1_110222901_CA_C GSTM4 DISTAL 2 Overall analysis 1_110222901_CA_C RP5‐1160K1.8 DISTAL 2 Overall analysis 1_110222901_CA_C GSTM1 PROMOTER 2 Overall analysis 12_111600134_G_T CUX2 DISTAL 2 Overall analysis 12_111600134_G_T RP11‐686G8.2 DISTAL 2 Overall analysis 12_111600134_G_T SH2B3 DISTAL 2 O ll l i 12 115108136 T C TBX5 DISTAL 2 Supplementary Table 16: Genetic correlation between the five intrinsic‐like breast cancer subtypes1 and CIMBA BRCA1 carriers estimated by LD‐score regression2 Genetic correlation (SE) Luminal A‐like Luminal B/HER2‐negative‐like Luminal B‐like  HER2‐enriched‐like  TN CIMBA BRCA1 Luminal A‐like 1 (0) Luminal B/HER2‐negative‐like 0.80 (0.05) 1 (0) Luminal B‐like  0.74 (0.05) 0.69 (0.07) 1 (0) HER2‐enriched‐like  0.57 (0.07) 0.59 (0.11) 0.35 (0.10) 1 (0) TN 0.46 (0.05) 0.40 (0.08) 0.60 (0.08) 0.56 (0.13) 1 (0) CIMBA BRCA1 0.39 (0.09) 0.31 (0.12) 0.38 (0.16) 0.80 (0.24) 0.84 (0.15) 1 (0) 1 Luminal A‐like (ER+ and/or PR+, HER2‐, grade 1 & 2); luminal B/HER2‐negative‐like (ER+ and/or PR+, HER2‐, grade 3); luminal B‐like (ER+ and/or PR+, HER2+); (4) HER2‐e 2 LD‐score regression was described in Nat Genet 47, 291‐5 (2015). and Nat Genet 47, 1236‐41 (2015) enriched‐like (ER‐ and PR‐, HER2+), and triple‐negative (ER‐, PR‐, HER2‐) Supplementary table 17: Enrichment analysis based on 53 genomic features (n = 45,253 effective luminal A‐like cases, n = 8,602 effective triple‐nega Annotation1 Proportion variants Proportion heritability Enrichment P‐value H3K27ac2, extend500bp 0.42 0.93 2.19 2.5 x 10‐14 H3K27ac2 0.39 0.85 2.17 7.6 x 10‐13 Super‐enhancers, extend500bp 0.17 0.5 2.93 1.3 x 10‐12 Super‐enhancers 0.17 0.48 2.87 1.7 x 10‐11 H3K27ac3, extend500bp 0.34 0.85 2.54 1.0 x 10‐9 H3K4me1 0.43 1.03 2.41 4.6 x 10‐8 Repressed, extend500bp 0.72 0.42 0.58 8.1 x 10‐8 TFBS, extend500bp 0.34 0.91 2.66 1.1 x 10‐6 Digital genomic footprint, extend500bp 0.54 1.07 1.98 1.6 x 10‐6 H3K4me1, extend500bp 0.61 0.93 1.53 4.5 x 10‐5 H3K4me3 0.13 0.6 4.53 4.8 x 10‐5 H3K4me3, extend500bp 0.26 0.63 2.47 9.7 x 10‐5 H3K3me peaks 0.17 0.85 4.98 1.2 x 10‐4 Transcription factor binding site 0.13 0.72 5.47 1.5 x 10‐4 Conserved 0.33 0.75 2.25 1.8 x 10‐4 H3K9ac 0.23 0.6 2.62 2.1 x 10‐4 Super‐enhancers, extend500bp 0.17 0.52 3.04 3.3 x 10‐6 H3K27ac2, extend500bp 0.42 0.87 2.05 5.2 x 10‐5 Super‐enhancers 0.17 0.49 2.91 1.2 x 10‐4 Digital genomic footprint, extend500bp 0.54 1.18 2.19 4.0 x 10‐4 H3K27ac2 0.39 0.82 2.09 4.5 x 10‐4 1 Of the 52 baseline genomic features described in Finucane, H.K. et al. Partitioning heritability by functional annotation using genome‐wide association HR+, HER2‐, low grade Triple Negative 2 Hnisz, D. et al. Super‐enhancers in the control of cell identity and disease. Cell 155, 934‐47 (2013) 3 Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia‐associated genetic loci. Nature 511,  Supplementary Table 18: Comparison  of genetic variance estimates  of invasive breast cancer subtypes between two‐stage polytomous model with missing data algorithm 1 and  st Phenotype Genetic variance for all GWAS variants The variance of genetic variance estimate for all GWAS variants4 Luminal A‐like 0.620 0.056 Luminal A‐like complete data only 0.592 0.063 Luminal B/HER2‐negative‐like 0.740 0.093 Luminal B/HER2‐negative‐like complete data only  0.751 0.093 Luminal B‐like 0.597 0.077 Luminal B‐like complete data only 0.563 0.098 HER2‐enriched‐like 0.689 0.154 HER2‐enriched‐like complete data only 0.612 0.187 Triple negative 0.492 0.072 Triple negative complete data only 0.520 0.085 1 Two‐stage polytomous model was fitted for the five intrinsic‐like subtypes using a missing data algorithm   2 Standard polytomous model was fitted for the five intrinsic‐like subtypes usig on the complete data 3 Genetic variance of all reliably genome‐wide imputable variants was estimated through LD‐score regression described in Nat Genet 47, 291‐5 (2015). and Nat Genet 47, 1236‐41 (2015 4 The variance of the genetic variance estimate of all reliably genome‐wide imputable variants was estimated through LD‐score regression described in Nat Genet 47, 291‐5 (2015). and  tandard polytomous model restricting to complete data 2 5).  Nat Genet 47, 1236‐41 (2015).  Supplementary Table 19: Effect sizes for 330 SNPs used to construct the intrinsic subtypes‐like polygenic risk score (PRS) Var name1 variant2 CHR Position Reference allele Effect allele EAF 3 Luminal A‐like Luminal B/HER2‐negative‐like Luminal B‐like HER2‐enriched‐like TN 1_100880328_A_T rs612683 1 100880328 A T 0.41 0.04 0.03 0.02 ‐0.02 0.03 1_10566215_A_G rs616488 1 10566215 A G 0.32 ‐0.03 ‐0.05 ‐0.12 ‐0.18 ‐0.08 1_110198129_CAAA_C rs56097627 1 110198129 CAAA C 0.78 0.05 0.07 0.03 0.06 0.04 1_114445880_G_A rs7513707 1 114445880 G A 0.17 0.06 0.04 0.05 0.05 0.06 1_118141492_A_C rs12406858 1 118141492 A C 0.27 0.03 0.05 0.01 0.03 0.02 1_120257110_T_C rs637868 1 120257110 T C 0.53 0.04 0.03 0.03 0.01 0.01 1_121280613_A_G rs11249433 1 121280613 A G 0.42 0.15 0.06 0.06 ‐0.02 0.02 1_121287994_A_G rs111458676 1 121287994 A G 0.10 ‐0.17 ‐0.05 ‐0.05 ‐0.05 0.01 1_145604302_C_CT rs72127681 1 145604302 C CT 0.34 ‐0.05 ‐0.05 ‐0.07 ‐0.01 ‐0.01 1_149906413_T_C rs11205303 1 149906413 T C 0.41 0.07 0.02 0.05 0.01 0.04 1_155556971_G_A rs12091730 1 155556971 G A 0.23 0.06 0.05 0.04 0.08 0.00 1_168171052_CA_C rs139315904 1 168171052 CA C 0.11 ‐0.07 ‐0.05 ‐0.10 ‐0.04 ‐0.09 1_172328767_T_TA rs11463354 1 172328767 T TA 0.33 ‐0.03 ‐0.01 ‐0.06 ‐0.10 ‐0.06 1_18807339_T_C rs2992756 1 18807339 T C 0.51 ‐0.06 ‐0.07 ‐0.08 ‐0.02 ‐0.02 1_201437832_C_T rs35383942 1 201437832 C T 0.06 0.11 0.02 0.16 0.15 0.08 1_202184600_C_T rs6686987 1 202184600 C T 0.40 0.02 0.00 0.00 ‐0.15 ‐0.06 1_203770448_T_A rs7514172 1 203770448 T A 0.28 0.06 0.02 0.07 0.08 0.04 1_204502514_T_TTCTGAAACAGGG rs11268668 1 204502514 T TTCTGAAACAGGG 0.80 0.02 ‐0.07 ‐0.03 ‐0.08 ‐0.18 1_208076291_G_A rs2785646 1 208076291 G A 0.33 ‐0.03 ‐0.09 ‐0.04 ‐0.03 ‐0.04 1_217053815_T_G rs2576261 1 217053815 T G 0.33 0.03 0.03 0.05 0.06 0.03 1_217220574_G_A rs11117758 1 217220574 G A 0.21 ‐0.06 ‐0.04 ‐0.06 0.00 ‐0.01 1_220671050_C_T rs11118563 1 220671050 C T 0.25 0.03 0.07 0.02 0.04 0.01 1_242034263_A_G rs72755295 1 242034263 A G 0.03 0.15 0.14 0.17 0.12 0.12 1_41380440_C_T rs4233486 1 41380440 C T 0.65 0.05 0.03 0.04 0.10 0.02 1_41389220_T_C rs114282204 1 41389220 T C 0.02 0.12 0.12 0.09 0.22 0.12 1_46670206_TC_T rs144105764 1 46670206 TC T 0.30 0.05 0.04 0.00 0.06 0.00 1_51467096_CT_C rs56168262 1 51467096 CT C 0.49 0.03 0.06 0.05 0.03 0.01 1_7917076_G_A rs707475 1 7917076 G A 0.38 ‐0.03 ‐0.02 ‐0.03 ‐0.04 ‐0.05 1_88156923_G_A rs17426269 1 88156923 G A 0.15 0.06 0.04 0.07 0.00 0.03 1_88428199_C_A rs2151842 1 88428199 C A 0.24 ‐0.04 ‐0.02 ‐0.06 ‐0.05 ‐0.03 2_10138983_T_C rs78425380 2 10138983 T C 0.12 0.07 0.03 0.11 0.09 0.01 2_121058254_A_G rs6746250 2 121058254 A G 0.70 ‐0.02 ‐0.03 0.00 ‐0.05 ‐0.08 2_121089731_T_C rs17625845 2 121089731 T C 0.19 ‐0.02 ‐0.04 ‐0.06 ‐0.06 ‐0.12 2_121159205_G_A rs10164550 2 121159205 G A 0.35 ‐0.06 ‐0.02 ‐0.01 ‐0.01 ‐0.03 2_121246568_T_C rs10179592 2 121246568 T C 0.90 0.09 0.13 0.12 0.10 0.14 2_172974566_C_G rs17726078 2 172974566 C G 0.47 ‐0.08 ‐0.01 ‐0.03 ‐0.04 0.01 2_174212910_A_G rs1550622 2 174212910 A G 0.85 0.05 0.08 0.14 0.08 0.00 2_192381934_C_T rs2356656 2 192381934 C T 0.86 0.01 ‐0.01 0.03 0.10 0.08 2_19315675_T_A rs6743383 2 19315675 T A 0.55 ‐0.03 ‐0.05 ‐0.02 ‐0.04 ‐0.08 2_202204741_T_C rs10197246 2 202204741 T C 0.71 ‐0.04 ‐0.04 ‐0.06 ‐0.02 ‐0.07 2_217920769_G_T rs4442975 2 217920769 G T 0.48 ‐0.18 ‐0.11 ‐0.11 ‐0.10 ‐0.04 2_217955896_GA_G 2:217955896 2 217955896 GA G 0.03 ‐0.30 ‐0.12 ‐0.13 ‐0.11 0.02 2_218292158_C_G rs11693806 2 218292158 C G 0.72 ‐0.08 ‐0.08 ‐0.06 ‐0.10 ‐0.05 2_218714845_G_A rs3791977 2 218714845 G A 0.39 ‐0.05 ‐0.03 0.00 0.03 ‐0.02 2_241388857_C_A rs4676356 2 241388857 C A 0.98 ‐0.10 ‐0.06 ‐0.18 ‐0.13 ‐0.15 2_25129473_A_G rs6725517 2 25129473 A G 0.40 ‐0.04 0.00 ‐0.08 ‐0.08 ‐0.06 2_29179452_G_C rs12472404 2 29179452 G C 0.23 0.03 ‐0.01 0.03 ‐0.05 ‐0.11 2_29615233_T_C rs4322799 2 29615233 T C 0.26 ‐0.02 ‐0.06 ‐0.05 ‐0.08 0.00 2_39699510_C_CT rs11406722 2 39699510 C CT 0.46 ‐0.03 ‐0.06 0.00 ‐0.02 ‐0.05 2_70172587_G_A rs6756513 2 70172587 G A 0.27 ‐0.04 ‐0.07 ‐0.02 0.00 ‐0.03 2_88358825_G_C rs1036759 2 88358825 G C 0.31 0.03 0.03 0.03 0.07 0.05 3_141112859_CTT_C rs34207738 3 141112859 CTT C 0.42 0.05 0.07 0.06 0.12 ‐0.01 3_172285237_G_A rs58058861 3 172285237 G A 0.22 0.07 0.00 0.05 0.00 ‐0.02 3_189774456_C_T rs9882792 3 189774456 C T 0.22 ‐0.04 ‐0.02 ‐0.02 ‐0.06 ‐0.02 Log odds ratio of effect allele for intrinsic‐like subtypes 3_27353716_C_A rs552647 3 27353716 C A 0.54 0.12 0.10 0.08 0.03 0.06 3_27388664_C_G 3:27388664 3 27388664 C G 0.29 0.11 0.09 0.08 0.02 0.07 3_29294845_C_T rs112476261 3 29294845 C T 0.02 ‐0.12 ‐0.11 ‐0.13 ‐0.52 ‐0.24 3_30684907_C_T rs17838698 3 30684907 C T 0.30 0.08 0.00 0.04 ‐0.02 0.04 3_46888198_T_C rs56387622 3 46888198 T C 0.10 ‐0.09 ‐0.04 ‐0.13 ‐0.10 ‐0.10 3_4742251_A_G rs6762558 3 4742251 A G 0.39 0.07 0.06 0.06 0.09 0.03 3_49709912_C_CT 3:49709912 3 49709912 C CT 0.28 ‐0.02 ‐0.03 0.00 ‐0.06 ‐0.07 3_55970777_A_AT rs138866686 3 55970777 A AT 0.03 ‐0.11 ‐0.13 ‐0.11 0.07 ‐0.05 3_59373745_C_T rs2886671 3 59373745 C T 0.42 ‐0.04 ‐0.04 0.01 ‐0.05 ‐0.04 3_63887449_T_TTG rs147250346 3 63887449 T TTG 0.13 0.07 0.08 0.06 0.09 0.04 3_71620370_T_G rs9825432 3 71620370 T G 0.63 ‐0.03 ‐0.05 ‐0.04 ‐0.02 ‐0.06 3_87037543_A_G rs13066793 3 87037543 A G 0.09 ‐0.08 ‐0.11 ‐0.11 ‐0.03 ‐0.06 3_99403877_G_A rs639355 3 99403877 G A 0.48 ‐0.04 ‐0.02 ‐0.02 0.02 ‐0.03 4_106069013_G_T rs62331150 4 106069013 G T 0.23 0.05 0.05 0.05 ‐0.06 0.03 4_126752992_A_AAT rs147399132 4 126752992 A AAT 0.51 ‐0.03 ‐0.03 ‐0.04 ‐0.05 ‐0.05 4_143467195_C_T rs56039025 4 143467195 C T 0.11 ‐0.05 ‐0.06 ‐0.04 ‐0.03 ‐0.07 4_151218296_CATATTT_C rs138786872 4 151218296 CATATTT C 0.66 0.04 0.01 0.03 0.07 0.05 4_175842495_G_A rs28436676 4 175842495 G A 0.11 ‐0.13 ‐0.12 ‐0.12 0.00 0.04 4_175847436_C_A rs62334414 4 175847436 C A 0.35 0.07 0.03 0.08 0.00 ‐0.03 4_187503758_A_T rs13147907 4 187503758 A T 0.45 0.04 0.05 0.05 0.01 0.03 4_38784633_G_T rs10012017 4 38784633 G T 0.25 0.05 0.03 0.00 0.01 0.05 4_84370124_TAA_TA 4:84370124 4 84370124 TAA TA 0.53 ‐0.04 ‐0.05 ‐0.10 ‐0.07 ‐0.04 4_89240476_G_A rs17014016 4 89240476 G A 0.44 0.03 0.05 0.03 0.08 0.02 4_92594859_TTCTTTC_T rs147404208 4 92594859 TTCTTTC T 0.44 ‐0.04 ‐0.04 ‐0.01 ‐0.08 ‐0.01 5_104300273_G_T rs17157372 5 104300273 G T 0.18 ‐0.04 ‐0.03 ‐0.04 0.01 ‐0.03 5_122478676_C_A rs335160 5 122478676 C A 0.74 ‐0.03 ‐0.05 ‐0.01 ‐0.03 ‐0.05 5_122705244_C_T rs1428387 5 122705244 C T 0.03 0.11 0.11 0.09 ‐0.04 0.08 5_1279790_C_T rs10069690 5 1279790 C T 0.26 0.04 0.05 0.02 0.02 0.23 5_1296255_A_AG rs3215401 5 1296255 A AG 0.30 ‐0.06 ‐0.06 0.00 ‐0.13 ‐0.14 5_131640536_A_G rs6860806 5 131640536 A G 0.55 0.04 0.05 0.05 ‐0.01 ‐0.01 5_132407058_C_T rs6596100 5 132407058 C T 0.24 ‐0.06 ‐0.05 ‐0.04 0.00 ‐0.01 5_1353077_T_C rs62329727 5 1353077 T C 0.01 0.18 0.09 0.12 0.33 0.07 5_158244083_C_T rs1432679 5 158244083 C T 0.56 ‐0.08 ‐0.03 ‐0.07 ‐0.03 ‐0.07 5_16231194_G_C rs17611291 5 16231194 G C 0.55 ‐0.05 ‐0.04 ‐0.07 ‐0.01 ‐0.05 5_169591460_T_C rs10074269 5 169591460 T C 0.34 0.04 0.07 0.05 ‐0.02 ‐0.01 5_173358154_G_A rs6864691 5 173358154 G A 0.42 0.03 0.03 0.03 0.02 0.03 5_176134882_T_C rs4868701 5 176134882 T C 0.54 0.04 0.01 ‐0.02 0.03 0.02 5_2777029_G_A rs4866496 5 2777029 G A 0.42 0.05 0.05 0.03 0.00 0.03 5_32579616_TCA_T rs35130031 5 32579616 TCA T 0.49 0.04 ‐0.01 0.04 0.05 ‐0.01 5_345109_T_C rs116095464 5 345109 T C 0.06 0.09 0.02 0.13 0.07 0.06 5_44508264_G_GT rs58166936 5 44508264 G GT 0.12 ‐0.10 ‐0.05 ‐0.05 ‐0.06 ‐0.03 5_44619502_A_G rs187108781 5 44619502 A G 0.15 ‐0.09 ‐0.07 ‐0.07 ‐0.08 ‐0.06 5_44649944_C_T rs4613718 5 44649944 C T 0.61 0.08 0.02 0.03 ‐0.01 ‐0.02 5_44706498_A_G rs10941679 5 44706498 A G 0.26 0.18 0.10 0.09 0.04 0.01 5_44853593_G_C rs17343002 5 44853593 G C 0.30 ‐0.07 ‐0.03 ‐0.04 ‐0.05 ‐0.01 5_52679539_C_CA rs199562199 5 52679539 C CA 0.10 0.05 0.05 0.06 ‐0.02 0.04 5_55662540_C_CT rs113803968 5 55662540 C CT 0.36 ‐0.04 ‐0.04 ‐0.04 ‐0.02 ‐0.01 5_55965167_C_T rs889310 5 55965167 C T 0.56 0.05 0.04 0.02 0.07 0.02 5_56023083_T_G rs16886165 5 56023083 T G 0.17 0.20 0.21 0.21 0.10 0.03 5_56042972_C_T rs76250845 5 56042972 C T 0.06 0.24 0.28 0.18 0.10 0.02 5_56045081_T_C rs11949391 5 56045081 T C 0.16 ‐0.11 ‐0.07 ‐0.07 ‐0.06 ‐0.01 5_58241712_C_T rs113778879 5 58241712 C T 0.57 ‐0.03 ‐0.05 ‐0.06 ‐0.07 ‐0.04 5_71965007_G_A rs3010266 5 71965007 G A 0.25 ‐0.05 ‐0.02 ‐0.04 ‐0.07 ‐0.01 5_73234583_T_C rs157557 5 73234583 T C 0.32 ‐0.04 ‐0.04 ‐0.05 0.02 0.00 5_77155397_GT_G rs144028731 5 77155397 GT G 0.34 ‐0.03 ‐0.06 ‐0.01 ‐0.03 ‐0.05 5_79180995_G_GA rs34525310 5 79180995 G GA 0.18 0.02 0.01 0.07 0.06 0.08 5_81512947_TA_T rs146817970 5 81512947 TA T 0.25 ‐0.07 ‐0.08 ‐0.02 ‐0.03 ‐0.01 5_90789470_G_A rs332529 5 90789470 G A 0.15 ‐0.09 ‐0.06 ‐0.03 0.00 ‐0.04 6_130341728_C_CT rs55941023 6 130341728 C CT 0.72 0.04 0.03 0.02 0.06 0.05 6_13713366_G_C rs418053 6 13713366 G C 0.56 ‐0.08 ‐0.06 ‐0.04 ‐0.04 0.01 6_149595505_T_C rs2121348 6 149595505 T C 0.20 ‐0.04 ‐0.08 ‐0.05 ‐0.07 0.00 6_151949806_A_C rs6913578 6 151949806 A C 0.32 0.06 0.08 0.11 0.18 0.16 6_151955914_A_G rs60954078 6 151955914 A G 0.08 0.12 0.20 0.22 0.35 0.28 6_152022664_CAAAAAAA_C rs57589542 6 152022664 CAAAAAAA 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‐0.04 ‐0.04 ‐0.01 0.03 ‐0.05 15_66630569_G_A rs8042593 15 66630569 G A 0.64 ‐0.03 ‐0.02 ‐0.02 0.01 ‐0.03 15_67457698_A_G rs35874463 15 67457698 A G 0.05 0.09 0.08 0.00 ‐0.05 0.06 15_75750383_T_C rs8035987 15 75750383 T C 0.26 ‐0.05 ‐0.03 0.03 ‐0.02 ‐0.04 15_91512267_G_T rs2290202 15 91512267 G T 0.13 ‐0.05 ‐0.15 ‐0.14 ‐0.11 ‐0.06 16_10706580_G_A rs34872983 16 10706580 G A 0.07 ‐0.08 ‐0.02 ‐0.09 ‐0.14 ‐0.01 16_23007047_G_T rs75753503 16 23007047 G T 0.02 0.14 0.05 0.09 ‐0.02 0.07 16_4008542_CAAAAA_C rs57920543 16 4008542 CAAAAA C 0.82 ‐0.04 ‐0.03 0.01 ‐0.10 ‐0.06 16_4106788_C_A rs11076805 16 4106788 C A 0.26 ‐0.02 ‐0.02 ‐0.02 ‐0.04 ‐0.06 16_52538825_C_A rs35668161 16 52538825 C A 0.28 0.24 0.21 0.22 0.21 0.12 16_52599188_C_T rs4784227 16 52599188 C T 0.27 0.25 0.20 0.22 0.21 0.11 16_53809123_C_T rs55872725 16 53809123 C T 0.41 ‐0.06 ‐0.06 ‐0.08 ‐0.09 ‐0.07 16_53861139_C_T rs6499648 16 53861139 C T 0.76 ‐0.03 ‐0.03 ‐0.03 ‐0.10 ‐0.07 16_53861592_G_A rs7184573 16 53861592 G A 0.36 ‐0.05 ‐0.04 ‐0.01 ‐0.10 ‐0.04 16_54682064_G_A rs28539243 16 54682064 G A 0.49 0.05 0.04 0.04 0.05 0.02 16_6963972_C_G rs12709163 16 6963972 C G 0.79 0.00 ‐0.02 0.05 0.07 0.03 16_80648296_A_G rs7500067 16 80648296 A G 0.24 0.09 0.09 0.10 0.03 0.04 16_85145977_T_C rs9931038 16 85145977 T C 0.49 ‐0.02 0.00 0.03 ‐0.05 ‐0.07 16_87086492_T_C rs12449271 16 87086492 T C 0.25 ‐0.05 ‐0.04 ‐0.05 ‐0.06 ‐0.03 17_29168077_G_T rs79461387 17 29168077 G T 0.26 ‐0.04 ‐0.04 ‐0.06 ‐0.06 ‐0.04 17_39251123_T_C rs150537328 17 39251123 T C 0.07 0.07 0.05 0.02 0.18 0.14 17_40127060_T_C rs11296 17 40127060 T C 0.06 ‐0.02 ‐0.10 ‐0.06 0.06 0.13 17_40485239_G_T rs17881320 17 40485239 G T 0.08 ‐0.05 0.01 ‐0.02 ‐0.16 ‐0.10 17_40744470_G_A rs149370081 17 40744470 G A 0.01 0.24 0.14 0.07 0.15 0.17 17_43212339_C_CT rs71363517 17 43212339 C CT 0.23 0.03 0.02 0.01 0.03 0.06 17_44283858_G_A 17:44283858 17 44283858 G A 0.19 ‐0.05 ‐0.03 ‐0.09 ‐0.10 ‐0.02 17_53209774_A_C rs2787486 17 53209774 A C 0.29 ‐0.11 ‐0.04 ‐0.08 ‐0.04 ‐0.02 17_77781725_A_G rs745570 17 77781725 A G 0.50 ‐0.04 ‐0.03 ‐0.06 ‐0.05 ‐0.05 18_11696613_C_T rs16976596 18 11696613 C T 0.14 ‐0.02 0.00 ‐0.01 ‐0.10 ‐0.06 18_20634253_C_T rs11665269 18 20634253 C T 0.64 ‐0.04 ‐0.06 ‐0.02 ‐0.05 ‐0.01 18_24125857_T_C rs1111207 18 24125857 T C 0.43 0.04 0.05 0.06 ‐0.02 0.04 18_24337424_C_G rs527616 18 24337424 C G 0.63 0.05 0.09 0.04 0.06 0.03 18_24518050_AT_A rs35369219 18 24518050 AT A 0.27 ‐0.08 ‐0.05 ‐0.06 0.02 0.03 18_25407513_C_G rs8092192 18 25407513 C G 0.71 0.02 0.02 0.07 0.08 0.07 18_29981526_G_A rs72931898 18 29981526 G A 0.04 ‐0.08 ‐0.14 ‐0.11 ‐0.07 ‐0.14 18_42411803_G_C rs9954058 18 42411803 G C 0.07 ‐0.13 ‐0.07 ‐0.08 ‐0.10 ‐0.01 18_42888797_T_C rs9952980 18 42888797 T C 0.34 ‐0.05 ‐0.02 ‐0.08 0.01 ‐0.05 19_13249921_G_T rs117922601 19 13249921 G T 0.05 0.13 0.10 0.00 0.03 0.13 19_17393925_C_A rs56069439 19 17393925 C A 0.30 ‐0.01 0.05 0.04 0.01 0.23 19_18569492_C_T rs10164323 19 18569492 C T 0.34 ‐0.09 ‐0.02 ‐0.06 ‐0.06 ‐0.06 19_19517054_C_CGGGCG rs140702307 19 19517054 C CGGGCG 0.36 0.03 0.05 0.02 0.04 0.03 19_44283031_T_C rs56681946 19 44283031 T C 0.36 0.07 0.06 0.06 0.10 0.07 19_46166073_T_C rs4399645 19 46166073 T C 0.60 ‐0.03 ‐0.05 ‐0.03 ‐0.02 ‐0.01 19_55816678_C_T rs1172821 19 55816678 C T 0.36 ‐0.03 ‐0.04 0.00 ‐0.07 ‐0.03 20_11379842_T_C rs1154723 20 11379842 T C 0.95 0.07 0.02 0.08 0.26 ‐0.01 20_41613706_C_G rs6030585 20 41613706 C G 0.79 0.03 0.01 ‐0.01 0.04 0.06 20_52296849_G_A rs13039563 20 52296849 G A 0.24 0.06 0.04 0.05 0.01 0.00 20_5948227_G_A rs16991615 20 5948227 G A 0.07 0.07 0.09 0.15 0.06 0.08 21_16364756_T_G rs2822999 21 16364756 T G 0.18 0.07 0.09 0.06 0.04 0.05 21_16566350_A_G rs2823130 21 16566350 A G 0.09 0.09 0.05 0.09 0.06 0.01 21_16574455_C_A rs2403907 21 16574455 C A 0.31 ‐0.11 ‐0.06 ‐0.08 ‐0.06 ‐0.01 21_47762932_G_A rs4818836 21 47762932 G A 0.04 0.09 0.11 0.12 0.11 0.07 22_19766137_C_T rs9798754 22 19766137 C T 0.38 ‐0.05 ‐0.03 0.00 ‐0.07 ‐0.02 22_29121087_A_G rs17879961 22 29121087 A G 0.01 0.42 0.07 0.34 0.35 ‐0.66 22_29135543_G_A rs5997390 22 29135543 G A 0.09 0.10 0.05 0.08 0.08 0.01 22_29203724_C_T rs34134147 22 29203724 C T 0.02 0.17 0.34 0.34 ‐0.06 0.02 22_29551872_A_G rs132289 22 29551872 A G 0.98 ‐0.19 ‐0.35 ‐0.37 ‐0.09 ‐0.09 22_38583315_AAAAG_AAAAGAAAG rs373038216 22 38583315 AAAAG AAAAGAAAG 0.28 ‐0.07 ‐0.05 ‐0.02 ‐0.02 0.00 22_39343916_T_A rs5750715 22 39343916 T A 0.26 0.06 0.07 0.03 0.04 0.03 22_40904707_CT_C rs66987842 22 40904707 CT C 0.12 0.13 0.04 0.11 0.14 0.12 22_43433100_C_T rs9611990 22 43433100 C T 0.11 ‐0.05 ‐0.04 ‐0.06 ‐0.14 ‐0.02 22_45319953_G_A rs112855987 22 45319953 G A 0.41 ‐0.01 ‐0.01 ‐0.04 ‐0.01 ‐0.05 22_46283297_G_A rs28512361 22 46283297 G A 0.11 0.06 0.11 0.07 0.13 0.09 2_67881757_G_A3 rs9712235 2 67881757 G A 0.74 ‐0.04 ‐0.02 0.00 ‐0.01 ‐0.08 2_69392128_G_A3 rs4602255 2 69392128 G A 0.45 0.03 0.04 0.01 0.06 0.04 3_16778867_A_G3 rs1375631 3 16778867 A G 0.50 0.03 0.01 0.02 0.00 0.06 3_156535958_AT_A3 rs34052812 3 156535958 AT A 0.67 0.04 0.05 0.05 0.02 0.04 7_74341926_G_C3 rs188092014 7 74341926 G C 0.19 0.04 0.04 0.03 0.08 0.04 8_25831778_C_T3 rs13256025 8 25831778 C T 0.21 0.04 0.06 0.04 0.08 0.02 8_116679547_A_G3 rs13277568 8 116679547 A G 0.37 ‐0.05 ‐0.06 ‐0.03 0.01 ‐0.01 9_106856793_G_C3 rs4742903 9 106856793 G C 0.56 0.01 0.04 0.07 0.00 0.05 17_70405095_C_G3 rs11652463 17 70405095 C G 0.30 ‐0.03 ‐0.05 ‐0.04 ‐0.06 ‐0.06 4_1986972_A_G3 rs495367 4 1986972 A G 0.35 0.05 0.05 0.04 0.06 0.01 5_67424121_C_CTG3 rs138044103 5 67424121 C CTG 0.48 0.05 ‐0.01 0.02 0.02 ‐0.03 11_120233626_A_G3 rs7924772 11 120233626 A G 0.39 0.05 0.01 ‐0.03 ‐0.05 0.04 17_7571752_T_G3 rs78378222 17 7571752 T G 0.01 0.12 0.03 0.26 0.18 ‐0.44 18_10354649_A_C3 rs206435 18 10354649 A C 0.51 ‐0.03 0.02 0.02 0.05 0.05 20_39248265_G_A3 rs6065254 20 39248265 G A 0.41 ‐0.04 ‐0.02 ‐0.04 0.00 0.03 6_33239869_C_T3 rs17215231 6 33239869 C T 0.08 ‐0.01 ‐0.05 0.04 0.05 ‐0.22 12_121435475_G_A3 12:121435475 12 121435475 G A 0.36 ‐0.02 0.00 ‐0.06 ‐0.05 ‐0.03 1 The variant name coded as chromosome_position_reference allele_effect allele 2 Effect allele frequency estiamted in OncoArray data 3 Seventeen out of the 32 new variants that are independent with previous reported 313 SNPs for ovearll and ER-specific breast cancer PRS (Mavaddat, Nasim, et al. The American Journal of Human Genetics 104.1 (2019): 21-34)