Overlap of high-risk individuals across family history, genetic & non-genetic breast cancer risk models: Analysis of 180,398 women from European & Asian ancestries

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Ahearn , Irene L. Andrulis , Natalia N. Antonenkova , Kristan J. Aronson , Annelie Augustinsson , Sabine Behrens , Clara Bodelon , Natalia V. Bogdanova , Manjeet K. Bolla , Kristen Brantley , Hermann Brenner , Helen Byers , Nicola J. Camp , Jose E. Castelao , Melissa H. Cessna , Jenny Chang-Claude , Stephen J. Chanock , Georgia Chenevix-Trench , Ji-Yeob Choi , Sarah V. Colonna , Kamila Czene , Mary B. Daly , Francoise Derouane , Thilo Dörk , A. Heather Eliassen , Christoph Engel , Mikael Eriksson , D. Gareth Evans , Olivia Fletcher , Lin Fritschi , Manuela Gago-Dominguez , Jeanine M. Genkinger , Willemina R.R. Geurts-Giele , Gord Glendon , Per Hall , Ute Hamann , Cecilia Y.S. Ho , Weang-Kee Ho , Maartje J. Hooning , Reiner Hoppe , Anthony Howell , Keith Humphreys , ABCTB Investigators , kConFab Investigators , SGBCC Investigators , MyBrCa Investigators , Hidemi Ito , Motoki Iwasaki , Anna Jakubowska , Helena Jernström , Esther M. John , Nichola Johnson , Daehee Kang , Sung-Won Kim , Cari M. Kitahara , Yon-Dschun Ko , Peter Kraft , Ava Kwong , Diether Lambrechts , Susanna Larsson , Shuai Li , Annika Lindblom , Martha Linet , Jolanta Lissowska , Artitaya Lophatananon , Robert J. MacInnis , Arto Mannermaa , Siranoush Manoukian , Sara Margolin , Keitaro Matsuo , Kyriaki Michailidou , Roger L. Milne , Nur Aishah Mohd Taib , Kenneth Muir , Rachel A. Murphy , William G. Newman , Katie M. O’Brien , Nadia Obi , Olufunmilayo I. Olopade , Mihalis I. Panayiotidis , Sue K. Park , Tjoung-Won Park-Simon , Alpa V. Patel , Paolo Peterlongo , Dijana Plaseska-Karanfilska , Katri Pylkäs , Muhammad U. Rashid , Gad Rennert , Juan Rodriguez , Emmanouil Saloustros , Dale P. Sandler , Elinor J. Sawyer , Christopher G. Scott , Shamim Shahi , Xiao-Ou Shu , Katerina Shulman , Jacques Simard , Melissa C. Southey , Jennifer Stone , Jack A. Taylor , Soo-Hwang Teo , Lauren R. Teras , Mary Beth Terry , Diana Torres , Celine M. Vachon , Maxime Van Houdt , Jelle Verhoeven , Clarice R. Weinberg , Alicja Wolk , Taiki Yamaji , Cheng Har Yip , Wei Zheng , Mikael Hartman , Jingmei Li doi: https://doi.org/10.1101/2025.02.27.25323002 Peh Joo Ho 1 Human Genetics Division, Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR) , Singapore , Singapore City, 138672 2 Saw Swee Hock School of Public Health, National University of Singapore , Singapore , Singapore City, 117549 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christine Kim Yan Loo 1 Human Genetics Division, Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR) , Singapore , Singapore City, 138672 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Meng Huang Goh 1 Human Genetics Division, Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR) , Singapore , Singapore City, 138672 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mustapha Abubakar 3 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services , USA , Bethesda, MD, 20850 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Thomas U. Ahearn 3 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services , USA , Bethesda, MD, 20850 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Irene L. Andrulis 4 Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital , Canada , Toronto, Ontario, M5G 1X5 5 Department of Molecular Genetics, University of Toronto , Canada , Toronto, Ontario, M5S 1A8 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Natalia N. Antonenkova 6 N.N. Alexandrov Research Institute of Oncology and Medical Radiology , Belarus , Minsk, 223040 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kristan J. Aronson 7 Department of Public Health Sciences, and Sinclair Cancer Research Institute, Queen’s University , Canada , Kingston, ON, K7L 3N6 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Annelie Augustinsson 8 Oncology, Department of Clinical Sciences in Lund, Lund University , Sweden , Lund, 221 85 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sabine Behrens 9 Division of Cancer Epidemiology, German Cancer Research Center (DKFZ) , Germany , Heidelberg, 69120 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Clara Bodelon 10 Department of Population Science, American Cancer Society , USA , Atlanta, GA, 30303 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Natalia V. Bogdanova 6 N.N. Alexandrov Research Institute of Oncology and Medical Radiology , Belarus , Minsk, 223040 11 Department of Radiation Oncology, Hannover Medical School , Germany , Hannover, 30625 12 Gynaecology Research Unit, Hannover Medical School , Germany , Hannover, 30625 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Manjeet K. Bolla 13 Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge , UK , Cambridge, CB1 8RN MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kristen Brantley 14 Department of Epidemiology, Harvard T.H. Chan School of Public Health , USA , Boston, MA, 02115 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hermann Brenner 15 Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ) , Germany , Heidelberg, 69120 16 German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ) , Germany , Heidelberg, 69120 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Helen Byers 17 Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology , Medicine and Health, University of Manchester, Manchester Academic Health Science Centre , UK , Manchester, M13 9WL Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nicola J. Camp 18 Department of Internal Medicine and Huntsman Cancer Institute, University of Utah , USA , Salt Lake City, UT, 84112 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jose E. Castelao 19 Oncology and Genetics Unit, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS) Foundation, Complejo Hospitalario Universitario de Santiago, SERGAS , Spain , Vigo, 36312 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Melissa H. Cessna 20 Intermountain Healthcare , USA , Salt Lake City, UT, 84143 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jenny Chang-Claude 9 Division of Cancer Epidemiology, German Cancer Research Center (DKFZ) , Germany , Heidelberg, 69120 21 Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf , Germany , Hamburg, 20246 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stephen J. Chanock 3 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services , USA , Bethesda, MD, 20850 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Georgia Chenevix-Trench 22 Cancer Research Program, QIMR Berghofer Medical Research Institute Vol. Locked Bag 2000 , Herston, QLD 4029, Australia , Brisbane, Queensland, 4006 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ji-Yeob Choi 23 Department of Biomedical Sciences, Seoul National University Graduate School , Korea , Seoul, 03080 24 Cancer Research Institute, Seoul National University , Korea , Seoul, 03080 25 Institute of Health Policy and Management, Seoul National University Medical Research Center , Korea , Seoul, 03080 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sarah V. Colonna 18 Department of Internal Medicine and Huntsman Cancer Institute, University of Utah , USA , Salt Lake City, UT, 84112 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kamila Czene 26 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Sweden , Stockholm, 171 65 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mary B. Daly 27 Department of Clinical Genetics, Fox Chase Cancer Center , USA , Philadelphia, PA, 19111 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Francoise Derouane 28 Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven , Belgium , Leuven, 3000 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Thilo Dörk 12 Gynaecology Research Unit, Hannover Medical School , Germany , Hannover, 30625 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site A. Heather Eliassen 14 Department of Epidemiology, Harvard T.H. Chan School of Public Health , USA , Boston, MA, 02115 29 Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School , USA , Boston, MA, 02115 30 Department of Nutrition, Harvard T.H. Chan School of Public Health , USA , Boston, MA, 02115 ScD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christoph Engel 31 Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig , Germany , Leipzig, 04107 32 LIFE - Leipzig Research Centre for Civilization Diseases, University of Leipzig , Germany , Leipzig, 04103 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mikael Eriksson 26 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Sweden , Stockholm, 171 65 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site D. Gareth Evans 17 Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology , Medicine and Health, University of Manchester, Manchester Academic Health Science Centre , UK , Manchester, M13 9WL 33 North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre , UK , Manchester, M13 9WL MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Olivia Fletcher 34 The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research , UK , London, SW7 3RP PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lin Fritschi 35 School of Population Health, Curtin University , Australia , Perth, Western Australia, 6102 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Manuela Gago-Dominguez 36 Cancer Genetics and Epidemiology Group, Genomic Medicine Group, Fundación Instituto de Investigación Sanitaria de Santiago de Compostela (FIDIS), Complejo Hospitalario Universitario de Santiago, SERGAS , Spain , Santiago de Compostela, 15706 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jeanine M. Genkinger 37 Department of Epidemiology, Mailman School of Public Health, Columbia University , USA , New York, NY, 10032 38 Herbert Irving Comprehensive Cancer Center , USA , New York, NY Find this author on Google Scholar Find this author on PubMed Search for this author on this site Willemina R.R. Geurts-Giele 39 Department of Clinical Genetics, Erasmus University Medical Center Vol. P.O. Box 2040, 3000 CA, the Netherlands , Rotterdam, 3015 CN Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gord Glendon 4 Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital , Canada , Toronto, Ontario, M5G 1X5 MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Per Hall 26 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Sweden , Stockholm, 171 65 40 Department of Oncology , Södersjukhuset, Sweden , Stockholm, 118 83 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ute Hamann 41 Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ) , Germany , Heidelberg, 69120 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cecilia Y.S. Ho 42 Department of Molecular Pathology, Hong Kong Sanatorium and Hospital , Hong Kong Find this author on Google Scholar Find this author on PubMed Search for this author on this site Weang-Kee Ho 43 School of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia , Malaysia , Semenyih, Selangor, 43500 44 Cancer Research Malaysia , Malaysia , Subang Jaya, Selangor, 47500 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maartje J. Hooning 39 Department of Clinical Genetics, Erasmus University Medical Center Vol. P.O. Box 2040, 3000 CA, the Netherlands , Rotterdam, 3015 CN 45 Department of Medical Oncology, Erasmus MC Cancer Institute Vol. P.O. Box 2040, 3000 CA, the Netherlands , Rotterdam, 3015 GD PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Reiner Hoppe 46 Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology , Germany , Stuttgart, 70376 47 University of Tübingen , Germany , Tübingen, 72074 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anthony Howell 48 Division of Cancer Sciences, University of Manchester , UK , Manchester, M13 9PL MBBS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Keith Humphreys 26 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Sweden , Stockholm, 171 65 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site 49 Australian Breast Cancer Tissue Bank, Westmead Institute for Medical Research, University of Sydney , Australia , Sydney, New South Wales, 2145 50 Research Department, Peter MacCallum Cancer Center , Australia , Melbourne, Victoria, 3000 51 Sir Peter MacCallum Department of Oncology, The University of Melbourne , Australia , Parkville, Victoria, 3010 1 Human Genetics Division, Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR) , Singapore , Singapore City, 138672 2 Saw Swee Hock School of Public Health, National University of Singapore , Singapore , Singapore City, 117549 52 Department of Surgery, National University Health System , Singapore , Singapore City, 119228 53 Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore , Singapore , Singapore City, 119077 54 Cancer Genetics Service, National Cancer Centre , Singapore , Singapore City, 169610 55 Breast Department, KK Women’s and Children’s Hospital , Singapore , Singapore City, 229899 56 SingHealth Duke-NUS Breast Centre , Singapore , Singapore City, 168753 57 Department of General Surgery, Tan Tock Seng Hospital , Singapore , Singapore City, 308433 58 Division of Surgery and Surgical Oncology, National Cancer Centre , Singapore , Singapore City, 169610 59 Department of General Surgery, Singapore General Hospital , Singapore , Singapore City, 169608 60 Division of Breast Surgery, Department of General Surgery, Changi General Hospital , Singapore , Singapore City, 529889 61 Division of Radiation Oncology, National Cancer Centre , Singapore , Singapore City, 169610 62 Division of Medical Oncology, National Cancer Centre , Singapore , Singapore City, 169610 43 School of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia , Malaysia , Semenyih, Selangor, 43500 44 Cancer Research Malaysia , Malaysia , Subang Jaya, Selangor, 47500 63 Breast Cancer Research Unit, University Malaya Cancer Research Institute, Faculty of Medicine, University of Malaya , Malaysia , Kuala Lumpur, 50603 Hidemi Ito 64 Division of Cancer Information and Control, Aichi Cancer Center Research Institute , Japan , Nagoya, 464-8681 65 Division of Cancer Epidemiology, Nagoya University Graduate School of Medicine , Japan , Nagoya, 466-8550 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Motoki Iwasaki 66 Division of Epidemiology, National Cancer Center Institute for Cancer Control , Japan , Tokyo, 104-0045 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anna Jakubowska 67 Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University , Poland , Szczecin, 171-252 68 International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University in Szczecin , Poland , Szczecin, 70-115 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Helena Jernström 8 Oncology, Department of Clinical Sciences in Lund, Lund University , Sweden , Lund, 221 85 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Esther M. John 69 Department of Epidemiology and Population Health, Stanford University School of Medicine , USA , Stanford, CA, 94305 70 Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine , USA , Stanford, CA, 94304 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nichola Johnson 34 The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research , UK , London, SW7 3RP DPhil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daehee Kang 24 Cancer Research Institute, Seoul National University , Korea , Seoul, 03080 71 Department of Preventive Medicine, Seoul National University College of Medicine , Korea , Seoul, 03080 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sung-Won Kim 72 Department of Surgery, Daerim Saint Mary’s Hospital , Korea , Seoul, 07442 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cari M. Kitahara 73 Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute , USA , Bethesda, MD, 20892 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yon-Dschun Ko 74 Department of Internal Medicine, Johanniter GmbH Bonn, Johanniter Krankenhaus , Germany , Bonn, 53177 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Peter Kraft 3 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services , USA , Bethesda, MD, 20850 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ava Kwong 75 Hong Kong Hereditary Breast Cancer Family Registry , Hong Kong 76 Department of Surgery, The University of Hong Kong , Hong Kong 77 Department of Surgery and Cancer Genetics Center, Hong Kong Sanatorium and Hospital , Hong Kong Find this author on Google Scholar Find this author on PubMed Search for this author on this site Diether Lambrechts 78 Laboratory for Translational Genetics, Department of Human Genetics , KU Leuven, Belgium , Leuven, 3000 79 VIB Center for Cancer Biology , VIB, Belgium , Leuven, 3001 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Susanna Larsson 80 Institute of Environmental Medicine, Karolinska Institutet , Sweden , Stockholm, 171 77 81 Department of Surgical Sciences, Uppsala University , Sweden , Uppsala, 751 05 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shuai Li 13 Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge , UK , Cambridge, CB1 8RN 82 Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne , Australia , Melbourne, Victoria, 3010 83 Precision Medicine, School of Clinical Sciences at Monash Health, Monash University , Australia , Clayton, Victoria, 3168 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Annika Lindblom 84 Department of Molecular Medicine and Surgery, Karolinska Institutet , Sweden , Stockholm, 171 76 85 Department of Clinical Genetics and Genomics, Karolinska University Hospital , Sweden , Stockholm, 171 76 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Martha Linet 73 Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute , USA , Bethesda, MD, 20892 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jolanta Lissowska 86 Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie National Research Oncology Institute , Poland , Warsaw, 02-034 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Artitaya Lophatananon 87 Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester , UK , Manchester, M13 9PL PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Robert J. MacInnis 82 Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne , Australia , Melbourne, Victoria, 3010 88 Cancer Epidemiology Division, Cancer Council Victoria , Australia , Melbourne, Victoria, 3004 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Arto Mannermaa 89 Translational Cancer Research Area, University of Eastern Finland , Finland , Kuopio, 70210 90 Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland , Finland , Kuopio, 70210 91 Biobank of Eastern Finland, Kuopio University Hospital , Finland , Kuopio PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Siranoush Manoukian 92 Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano , Italy , Milan, 20133 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sara Margolin 40 Department of Oncology , Södersjukhuset, Sweden , Stockholm, 118 83 93 Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet , Sweden , Stockholm, 118 83 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Keitaro Matsuo 65 Division of Cancer Epidemiology, Nagoya University Graduate School of Medicine , Japan , Nagoya, 466-8550 94 Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute , Japan , Nagoya, 464-8681 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kyriaki Michailidou 13 Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge , UK , Cambridge, CB1 8RN 95 Biostatistics Unit, The Cyprus Institute of Neurology and Genetics Vol. P.O.Box 23462, 1683, Nicosia, Cyprus , Cyprus, Nicosia, 2371 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Roger L. Milne 82 Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne , Australia , Melbourne, Victoria, 3010 83 Precision Medicine, School of Clinical Sciences at Monash Health, Monash University , Australia , Clayton, Victoria, 3168 88 Cancer Epidemiology Division, Cancer Council Victoria , Australia , Melbourne, Victoria, 3004 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nur Aishah Mohd Taib 96 Department of Surgery, Faculty of Medicine, University of Malaya, UM Cancer Research Institute , Malaysia , Kuala Lumpur, 50603 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kenneth Muir 87 Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester , UK , Manchester, M13 9PL PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rachel A. Murphy 97 School of Population and Public Health, University of British Columbia , Canada , Vancouver, BC, V6T 1Z4 98 Cancer Control Research, BC Cancer Agency , Canada , Vancouver, BC, V5Z 1L3 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site William G. Newman 17 Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology , Medicine and Health, University of Manchester, Manchester Academic Health Science Centre , UK , Manchester, M13 9WL 33 North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre , UK , Manchester, M13 9WL Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katie M. O’Brien 99 Epidemiology Branch, National Institute of Environmental Health Sciences, NIH , USA , Research Triangle Park , NC, 27709 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nadia Obi 100 Institute for Occupational and Maritime Medicine, University Medical Center Hamburg-Eppendorf , Germany , Hamburg, 20246 101 Institute for Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf , Germany , Hamburg, 20246 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Olufunmilayo I. Olopade 102 Center for Clinical Cancer Genetics, The University of Chicago , USA , Chicago, IL, 60637 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mihalis I. Panayiotidis 103 Department of Cancer Genetics, Therapeutics and Ultrastructural Pathology, The Cyprus Institute of Neurology & Genetics Vol. P.O.Box 23462, 1683, Nicosia, Cyprus , Cyprus, Nicosia, 2371 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sue K. Park 24 Cancer Research Institute, Seoul National University , Korea , Seoul, 03080 71 Department of Preventive Medicine, Seoul National University College of Medicine , Korea , Seoul, 03080 104 Integrated Major in Innovative Medical Science, Seoul National University College of Medicine , Korea , Seoul, 03080 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tjoung-Won Park-Simon 12 Gynaecology Research Unit, Hannover Medical School , Germany , Hannover, 30625 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alpa V. Patel 10 Department of Population Science, American Cancer Society , USA , Atlanta, GA, 30303 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Paolo Peterlongo 105 Genome Diagnostics Program, IFOM ETS - the AIRC Institute of Molecular Oncology , Italy , Milan, 20139 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dijana Plaseska-Karanfilska 106 Research Centre for Genetic Engineering and Biotechnology ‘Georgi D. Efremov’, MASA, Republic of North Macedonia , Skopje, 1000 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katri Pylkäs 107 Laboratory of Cancer Genetics and Tumor Biology, Translational Medicine Research Unit, Biocenter Oulu, University of Oulu , Finland , Oulu, 90220 108 Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre Oulu , Finland , Oulu, 90220 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Muhammad U. Rashid 41 Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ) , Germany , Heidelberg, 69120 109 Department of Basic Sciences, Shaukat Khanum Memorial Cancer Hospital and Research Centre (SKMCH & RC) , Pakistan , Lahore, 54000 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gad Rennert 110 Faculty of Medicine, Technion – Israel Institute of Technology and Association for Promotion of Research in Precision Medicine , Israel , Haifa, 35254 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Juan Rodriguez 26 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Sweden , Stockholm, 171 65 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emmanouil Saloustros 111 Division of Oncology, Faculty of Medicine, School of Health Sciences, University of Thessaly , Greece , Larissa, 411 10 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dale P. Sandler 99 Epidemiology Branch, National Institute of Environmental Health Sciences, NIH , USA , Research Triangle Park , NC, 27709 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elinor J. Sawyer 112 School of Cancer & Pharmaceutical Sciences, Comprehensive Cancer Centre, Guy’s Campus, King’s College London , UK , London PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christopher G. Scott 113 Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic , USA , Rochester, MN, 55905 MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shamim Shahi 97 School of Population and Public Health, University of British Columbia , Canada , Vancouver, BC, V6T 1Z4 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xiao-Ou Shu 114 Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine , USA , Nashville, TN, 37232 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katerina Shulman 115 Clalit Regional Oncology Unit, Haifa and Western Galilee District, Israel , Haifa MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jacques Simard 116 Genomics Center, Centre Hospitalier Universitaire de Québec – Université Laval Research Center , Canada , Québec City, Québec, G1V 4G2 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Melissa C. Southey 83 Precision Medicine, School of Clinical Sciences at Monash Health, Monash University , Australia , Clayton, Victoria, 3168 88 Cancer Epidemiology Division, Cancer Council Victoria , Australia , Melbourne, Victoria, 3004 117 Department of Clinical Pathology, The University of Melbourne , Australia , Melbourne, Victoria, 3010 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jennifer Stone 82 Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne , Australia , Melbourne, Victoria, 3010 118 Genetic Epidemiology Group, School of Population and Global Health, University of Western Australia , Australia , Perth, Western Australia, 6009 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jack A. Taylor 99 Epidemiology Branch, National Institute of Environmental Health Sciences, NIH , USA , Research Triangle Park , NC, 27709 119 Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences , NIH, USA , Research Triangle Park, NC, 27709 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Soo-Hwang Teo 44 Cancer Research Malaysia , Malaysia , Subang Jaya, Selangor, 47500 96 Department of Surgery, Faculty of Medicine, University of Malaya, UM Cancer Research Institute , Malaysia , Kuala Lumpur, 50603 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lauren R. Teras 10 Department of Population Science, American Cancer Society , USA , Atlanta, GA, 30303 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mary Beth Terry 37 Department of Epidemiology, Mailman School of Public Health, Columbia University , USA , New York, NY, 10032 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Diana Torres 41 Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ) , Germany , Heidelberg, 69120 120 Institute of Human Genetics, Pontificia Universidad Javeriana , Colombia , Bogota, 110231 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Celine M. Vachon 121 Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic , USA , Rochester, MN, 55905 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maxime Van Houdt 28 Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven , Belgium , Leuven, 3000 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jelle Verhoeven 28 Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven , Belgium , Leuven, 3000 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Clarice R. Weinberg 122 Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, NIH , USA , Research Triangle Park, NC, 27709 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alicja Wolk 80 Institute of Environmental Medicine, Karolinska Institutet , Sweden , Stockholm, 171 77 DrMedSci Find this author on Google Scholar Find this author on PubMed Search for this author on this site Taiki Yamaji 66 Division of Epidemiology, National Cancer Center Institute for Cancer Control , Japan , Tokyo, 104-0045 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cheng Har Yip 96 Department of Surgery, Faculty of Medicine, University of Malaya, UM Cancer Research Institute , Malaysia , Kuala Lumpur, 50603 123 Subang Jaya Medical Centre , Malaysia , Subang Jaya, Selangor, 47500 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wei Zheng 114 Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine , USA , Nashville, TN, 37232 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mikael Hartman 2 Saw Swee Hock School of Public Health, National University of Singapore , Singapore , Singapore City, 117549 52 Department of Surgery, National University Health System , Singapore , Singapore City, 119228 53 Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore , Singapore , Singapore City, 119077 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jingmei Li 1 Human Genetics Division, Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR) , Singapore , Singapore City, 138672 53 Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore , Singapore , Singapore City, 119077 124 National Cancer Centre , Singapore , Singapore City, 168583 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: lijm1{at}gis.a-star.edu.sg Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT Background Breast cancer is multifactorial. Focusing on limited risk factors may miss high-risk individuals. Methods We assessed the performance and overlap of various risk factors in identifying high-risk individuals for invasive breast cancer (BrCa) and ductal carcinoma in situ (DCIS) in 161,849 European-ancestry and 18,549 Asian-ancestry women. Discriminatory ability was evaluated using the area under the receiver operating characteristic curve (AUC). High-risk criteria included: 5-year absolute risk ≥1·66% by the Gail model [GAIL binary ]; first-degree family history of breast cancer [FH binary ]; 5-year absolute risk ≥1·66% by a 313-variants polygenic risk score [PRS binary ]; and carriers of pathogenic variants in breast cancer predisposition genes [PTV binary ]. Findings The 5-year absolute risk by PRS outperformed the Gail model in predicting BrCa (Europeans vs controls : AUC PRS =0·635 [0·632-0·638] vs AUC Gail =0·492 [0·489-0·495]; Asians vs controls : AUC PRS =0·564 [0·556-0·573] vs AUC Gail =0·506 [0·497-0·514]). PRS binary and GAIL binary identified more high-risk European than Asia individuals. High-risk proportions were higher among BrCa (16-26%) and DCIS (20-33%) compared to controls (9-15%) among young Europeans and all Asians. Fewer than 7% of BrCa, 10% of DCIS, and 3% of controls were classified as high-risk by multiple risk classifiers. Overlap between PRS binary and PTV binary was minimal (<0·65% Europeans, <0·15% Asians) compared to the proportion at high risk using PTV binary alone (Europeans: 4·6%, Asians: 4·4%) and PRS binary alone (Europeans: 13·9%, Asians: 8·5%). PRS binary and FH binary uniquely identified 5-6% and 9-11% of young BrCa, respectively. Interpretation The incomplete overlap between high-risk individuals identified by PRS binary , GAIL binary , FH binary, and PTV binary highlights the need for a comprehensive approach to breast cancer risk prediction. SIGNIFICANCE This study shows that different ways of predicting breast cancer risk do not always flag the same people, suggesting that combining multiple risk factors could improve early detection and screening. INTRODUCTION A worldwide increase of 31% in the number of breast cancer cases is projected over the next two decades. 1 Early detection significantly improves survival rates. 2 , 3 Multiple studies have shown that mammogram screenings reduce mortality rates for women above 50 years of age, while the benefits of screening for those younger are less clear. 4 , 5 Current screening guidelines are based on age, yet many patients are diagnosed before reaching the recommended screening age. 6 Advances in breast cancer research suggest the potential for more risk-based approaches to cost-effective screening programs. 7 Developed in the 1980s, the Gail model, a validated statistical tool, uses personal information to estimate breast cancer risk over the next five years. Originally developed for White females in the United States without a history of in situ or invasive breast cancer, its accuracy for non-White populations is debated. 8 – 10 Common issues include both underestimation and overestimation in non-European populations, leading to unclear recommendations for diverse ethnic groups. 9 , 11 In addition to non-genetic risk factors, studies have explored the use of polygenic risk scores (PRS) to enhance existing prediction models. 12 , 13 Breast cancer has a significant heritable component. While PRS has added value to prediction models, its implementation, particularly in Asian populations, remains inconclusive. 15 This is partly because PRS training datasets have predominantly included European populations due to their larger representation in research. 16 Protein-truncating variants (PTVs) are another genetic factor used in risk prediction. Unlike PRS, which aggregates the associated effects of numerous, relatively common genetic variants across the genome, PTVs specifically target variants that lead to premature protein termination, potentially disrupting gene function. This distinction means that PTVs are specific genetic changes with known functional impacts, while PRS provides an overview of associated genetic risks of small effect sizes across the genome. PTVs in nine breast cancer predisposition genes, ATM, BRCA1, BRCA2, CHEK2, PALB2, BARD1, RAD51C, RAD51D, or TP53 have been shown clinically useful for inclusion on breast cancer risk prediction panels in a large analytical cohort comprising over 113,000 subjects and has been included in BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm). 17 , 18 Studying the overlap of genetic and non-genetic risk factors in identifying high-risk individuals will provide us with information on complementary risk factors that will enhance our ability to identify the subgroup of the population who would benefit from risk reduction interventions. In this case-control analysis involving the Breast Cancer Consortium (BCAC) dataset, we explore how prediction tools— such as the Gail model, PRS, PTVs in known breast cancer predisposition genes, and family history— apply to both European and Asian populations across non-screening and screening age groups. METHODS Study population BCAC is an international collaboration that was formed to provide large sample sizes for investigating genetic associations. 19 Women diagnosed with invasive breast cancer (BrCa) or ductal carcinoma in situ patients (DCIS), and women with no prior diagnosis of breast cancer (controls) were recruited by study groups across the globe and collectively studied under BCAC. 20 Our study focuses on individuals who are genetically Asian or European-White (from here on referred to as “European”). To reduce the influence of missing values on the performance of the Gail model, studies with missing values for 50% or more for each of at least two of the three risk factors in the Gail model 8 –age of menarche, age at first live birth, and first-degree family history of breast cancer–were excluded. The studies included are listed in Supplementary Table 1· Exclusion was done separately for individual studies and each disease status (BrCa, DCIS, and controls). Further exclusions were made on an individual level ( Supplementary Figure 1 ). Women with unknown age at enrolment for controls (n=5,566) and unknown age at diagnosis for BrCa and DCIS cases (n=2,103) were excluded. Women below the age of 30 years (n=2,360) and women above 80 years (n=1,897) for whom the Gail model prediction is not valid were excluded. A total of 180,398 individuals were included in our study. We compared demographic differences between the included and excluded individuals to assess potential selection bias. The result is presented in Additional Material - Supplementary Table 2· Criteria to identify women at high risk of breast cancer Four criteria were used to identify women at high risk of breast cancer: 1) 5-year absolute risk ≥1·66% by the Gail model [GAIL binary ], 2) first-degree family history for breast cancer [FH binary , yes/no], 3) 5-year absolute risk ≥1·66% by a 313-variant breast cancer polygenic risk score 14 [PRS binary ], and in a subset of women 4) carriers of pathogenic variants in breast cancer predisposition genes [PTV binary ]. The 1.66% five-year absolute risk threshold for breast cancer is widely adopted in clinical and research settings to reflect the level of risk at which women are considered for additional screening or preventive interventions, such as tamoxifen or raloxifene (from here onwards the high-risk category). 21 Details of each risk factor are presented in Additional Materials - Methods · Due to the large number of studies with varying degrees of missing data for different risk factors, the parsimonious Gail model, which most studies would have information on, was selected. 8 The R package “BCRA” (version 2.1.2) was used to calculate 5-year absolute risk. 8 Implementation is described in Additional Material-Genetic breast cancer risk factor. In our analysis, those with unknown family history were considered to have no family history. We studied genetic risk based on common germline variants associated with breast cancer, using the breast cancer PRS with 313 variants calculated with PLINK (version 3) with the scoresum option. 22 , 23 The 5-year absolute risk was calculated by estimating the theoretical odds ratio of this percentile in relation to the 40-60 percentile, which is taken to represent the general population. 24 A subgroup of individuals (n European =56,387, n Asian =3,617) had both genotyping and targeted-sequencing data. Nine breast cancer predisposition genes (PTVs in ATM, BRCA1, BRCA2, CHEK2, PALB2, BARD1, RAD51C, RAD51D, or TP53 ) were studied collectively. Statistical analysis Differences in genetic and non-genetic breast cancer risk factors of BrCa, DCIS, and controls were assessed using the Chi-squared test (categorical variables) and Kruskal-Wallis test (continuous variables). Venn diagrams (R package “VennDiagram”) were used to visualise the overlaps in high-risk individuals identified by the high-risk criteria. We subset the population by ancestry (European or Asian) and age (30-49 or 50-80 years). Overlaps between pairs of high-risk criteria were further considered by country. In the subset of individuals with both genotyping and targeted sequencing information (n=60,004), PRS binary and PTV binary (yes/no) were analysed for their ability to uniquely identify high-risk individuals. Evaluating the drivers of Gail risk score in differentiating breast cancer cases from controls Although studies with high missingness rates for variables required to compute the Gail risk score were excluded (see “Excluded participants” above), there were still individuals with missing values. Hence, we studied the potential drivers of the Gail risk score in discriminating BrCa cases from controls using logistic regression models. All combinations of risk factors, where available, were assessed. Discriminatory ability was assessed by the area under the receiver operator curve (AUC). Missing values were coded in accordance to the “BCRA”. 8 All analyses were performed in R version 4.2.2. RESULTS Cohort description A total of 180,398 women were included, where 161,849 (90%) women were of European-ancestry and 18,549 (10%) were Asian-ancestry ( Table 1 ). Of the European women, 68,540 (42%) were controls, and 83,685 (52%) were diagnosed with BrCa. Of the 18,549 Asian women, 8,347 (45%) were controls and 9,222 (50%) were BrCa cases ( Table 1 ). In addition, there were 9,624 (6%) and 980 (5%) DCIS cases in European and Asian women, respectively ( Table 1 ). View this table: View inline View popup Download powerpoint Table 1. Characteristics of 161,849 European-ancestry and 18,549 Asian-ancestry individuals (non-breast cancer controls (controls), patients diagnosed with invasive breast cancer (BrCa) and patients diagnosed with DCIS (DCIS)) between ages 30 and 80 years. IQR interquartile range. Family history: number of first-degree relatives with breast cancer. European-ancestry study population The median age at diagnosis for BrCa cases of European ancestry was 57 years [interquartile range [IQR]: 49-65]. The corresponding age at interview for European-ancestry controls was 57 years [IQR: 50-64] ( Table 1 ). BrCa cases were more likely to have a family history than controls (14% vs 9%, respectively). The distribution of the 5-year absolute risk by the Gail model was very similar between BrCa cases (median 1·25% [IQR: 0·89-1·73]) and controls (1·25% [IQR: 0·92-1·64]). The median 5-year absolute risk by PRS in BrCa cases was 0·95% [IQR: 0·62-1·44], significantly greater than that of controls 0·69% [IQR: 0·46-1·05]. The PRS distributions (scoresum) and 5-year absolute risks were similar across countries ( Supplementary Figure 2 ). Less than 40% of the population had PTV information. Out of 29,853 European BrCa patients, 1,927 (6%) were PTV carriers, which is three times the proportion found in the control group (583 out of 24,798, or 2%). The observations were largely similar for DCIS ( Table 1 ). Asian-ancestry study population The median age at diagnosis for BrCa cases was 49 years [IQR: 43-57], and the age at enrolment was 50 years [IQR: 44-58] for controls ( Table 1 ). Of the BrCa cases, 10% reported positive family history, while a smaller proportion of controls (6%) reported so. The distribution of 5-year absolute risk by the Gail model was not significantly different between BrCa cases and controls. The distribution of 5-year absolute risk by PRS for BrCa patients was shifted rightwards of controls (p<0.001). The distribution of PRS (scoresum) and 5-year absolute risks varied by country ( Supplementary Figure 3). Among BrCa patients with known PTV information (n=2,178) 6% were mutation carriers. A smaller percentage (2%) of controls (n=1,115) were PTV carriers. As with the Europeans, the observations were mostly similar for DCIS in Asians ( Table 1 ) . and controls. Associations between risk stratifiers, BrCa and DCIS Table 2 and Table 3 display the strengths of association of different risk stratifiers (PRS binary , GAIL binary , and FH binary ) with BrCa and DCIS, respectively, stratified by ancestry and age groups. Using both PRS binary and GAIL binary (i.e. individuals is stratified as high risk when either PRS or GAIL is ≥1.66%) improves the discriminatory ability as compared to using GAIL binary alone (in Europeans: AUC BrCa-PRS_GAIL =0·554 [0·552 to 0·557] vs AUC BrCa-GAIL =0·522 [0·520-0·524]; in Asian: AUC BrCa-PRS_GAIL =0·527 [0·523 to 0·532] vs AUC BrCa-GAIL =0·506 [0·503-0·508]) ( Table 2 ), In Europeans, the odds ratios and corresponding 95% confidence intervals for PRS binary and GAIL binary (≥1·66% 5-year absolute risk threshold) were 2·60 [2·52-2·69] and 1·25 [1·22-1·28], respectively, for BrCa, and 2·21 [2·08-2·35] and 1·21 [1·15-1·27], respectively, for DCIS. In Asians, PRS binary showed significant associations with BrCa (1·83 [1·64-2·05]) and DCIS (2·30 [1·88-2·83]). The GAIL binary showed associations with BrCa (1·59 [1·31-1·92]) and DCIS (2·13 [1·51-3·00]) in Asians. The effect sizes for the associations between PRS binary , GAIL binary and FH binary were larger for younger Europeans than the older Europeans. In Asians, the same trend was observed for PRS binary and BrCa, and FH binary and DCIS. View this table: View inline View popup Download powerpoint Table 2. Association between high-risk criteria and case-control status (invasive breast cancer cases/ non-breast cancer controls), using univariate logistic regression. Analysis was repeated by age categories 30 to 49 years (n European =40,306, n Asian =8,456) and 50 to 80 years (n European =111,919, n Asian =9,113). PRS: 5-year absolute risk using polygenic risk score ≥1·66%. GAIL: 5-year absolute risk using the Gail model ≥1·66%. FH: having at least one first-degree family history of breast cancer. * High: individuals who were identified by any of the criteria were classified as “Yes”. OR: odds ratio, CI: confidence interval, P: p-value. 5-yr abs risk: 5-year absolute risk (continuous). View this table: View inline View popup Download powerpoint Table 3. Association between high-risk criteria and case-control status (DCIS cases/ non-breast cancer controls), using univariate logistic regression. Analysis was repeated by age categories 30 to 49 years (n European =19,424, n Asian =4,322) and 50 to 80 years (n European = 58,740, n Asian =5,005). PRS: 5-year absolute risk using polygenic risk score ≥1·66%. GAIL: 5-year absolute risk using the Gail model ≥1·66%. FH: having at least one first-degree family history of breast cancer. * High: individuals who were identified by any of the three criteria (PRS, GAIL, FH) were classified as “Yes”. OR: odds ratio, CI: confidence interval, P: p-value. 5-yr abs risk: 5-year absolute risk (continuous). Intersection of high-risk individuals identified by different risk factors Figure 1 illustrates the overlap of high-risk individuals identified by PRS binary , GAIL binary , and FH binary across different ancestry and age groups. For young Europeans and all Asians, the proportion of high-risk individuals among BrCa (16-26%) and DCIS (20-30%) cases was about twice that of the controls (9-13%). In these groups, women were primarily classified as high-risk due to FH binary and PRS binary . Less than 7%, 10%, and 3% of the BrCa, DCIS, and controls, respectively, were classified as high-risk by more than one criterion (i.e. PRS binary , GAIL binary , or FH binary ). PRS binary uniquely identified 4-7% of young BrCa and DCIS cases as high-risk, and 10-18% of older BrCa and DCIS cases as high-risk. FH binary uniquely identified 9-11% of young BrCa and DCIS cases as high-risk, and 2-5% of older BrCa and DCIS cases as high-risk. The proportion of young Europeans (aged 30-49) uniquely identified by GAIL binary to be at high-risk is 1%. Among Asians, all individuals classified as GAIL binary high-risk were also positive for FH (i.e. 0% uniquely called by GAIL binary ). Among the older Europeans, 40% of the controls were identified as high-risk, compared to 52% of BrCa cases and 47% of DCIS cases. Download figure Open in new tab Figure 1. Venn diagram depicting the overlaps between individuals identified as high risk by the three criteria. Breast cancer polygenic risk score (PRS, 5-year absolute risk using polygenic risk score ≥1·66%), the Gail model (GAIL, 5-year absolute risk using the Gail model ≥1·66%), and family history (FH, having at least one first-degree family history of breast cancer). European: women of European ancestry; Asian: women of Asian ancestry. Breast cancer predisposition genes (PTV binary ) and common variants (PRS binary ) identified different high-risk individuals In the subgroup of individuals with target-enriched sequencing data, the proportion of women identified to be at high risk by both PRS binary and PTV binary was limited (0·6% of European and 0·1% of Asian) compared to PTV binary alone (Europeans: 4·6%; Asians: 4·4%) and PRS binary alone (Europeans: 13·9%; Asians: 8·5%) ( Table 4 ). There were more older women than younger women at high risk due to PRS (2·3x in Europeans; 3·8x in Asians). Conversely, there were more younger women than older women at high risk due to PTV (1.9x in Europeans; 1.5x in Asians). View this table: View inline View popup Download powerpoint Table 4. Overlap of individuals with 5-year absolute risk by polygenic risk score ≥1·66% (PRS) and carriers of protein-truncating variants in at least one of nine breast cancer predisposition genes (i.e. PTVs in ATM, BRCA1, BRCA2, CHEK2, PALB2, BARD1, RAD51C, RAD51D, or TP53 ). Country-specific differences in high-risk individuals identified by PRS and GAIL Figure 2 shows the breakdown of BrCa/DCIS cases and controls identified to be at high risk by PRS binary and GAIL binary by country and age. Generally, both PRS binary and GAIL binary identified a higher proportion (%) of high-risk BrCa/DCIS individuals in the European-ancestry populations (median [IQR], PRS binary-young : 5 [3-9], PRS binary-old : 20 [10-23], GAIL binary-young : 3 [2-7], GAIL binary-old : 30 [24-39]) than the Asian countries (PRS binary-young : 4 [2-5], PRS binary-old : 15 [10-16], GAIL binary-young : <1 [<1 to 2], GAIL binary-old : 4 [2-10]). Download figure Open in new tab Figure 2. The proportion of individuals identified as at high risk by the breast cancer polygenic risk score (PRS) and the Gail model (GAIL), by country and age. The proportion of individuals identified as high risk by both criteria is indicated in grey. PRS: 5-year absolute risk using polygenic risk score ≥1·66%. GAIL: 5-year absolute risk using the Gail model ≥1·66%. Numbers adjacent to the bars represent the number of high-risk individuals identified by respective risk tools. European: women of European ancestry; Asian: women of Asian ancestry. Factors influencing the performance of the Gail model across different demographics Figure 2 shows that both PRS binary and GAIL binary identified a higher proportion of high-risk individuals in European-ancestry compared to Asian-ancestry populations, with variations in performance by age. By studying the factors that influence the Gail model’s performance across different demographics, we can better understand what drives the model’s effectiveness in various populations. In Figure 3A and Figure 3B , we show that in Europeans, incorporating both family history (number of first-degree relatives with breast cancer) and prior breast biopsies was sufficient to achieve the highest AUC. For younger Asians, the key factors affecting model performance are age at menarche and the number of prior biopsies, with higher discriminatory ability observed in models that did not include family history ( Figure 3C ). In older Asians, the model’s performance was not significantly different between those that included both age at first live birth and family history ( Figure 3D ). This indicates that, while the Gail model’s performance for Europeans is primarily influenced by the number of first-degree relatives with breast cancer and prior biopsies, younger Asians are more influenced by age at first menarche, and for older Asians, both age at first live birth and family history are important. Download figure Open in new tab Figure 3. Discriminatory ability of risk factor combinations in the Gail model. The five-year absolute risk was calculated using the R package “BCRA” and used to predict the invasive breast cancer case-control status of the individuals. Dots represent risk factors included in the model, and crosses indicate the model with all risk factors with the addition of atypical hyperplasia. European: women of European ancestry; Asian: women of Asian ancestry. Applying the high-risk criterion of ≥1·66% 5-year absolute risk resulted in changes in the order of the models regarding their discriminatory ability (AUC) ( Supplementary Figure 4 ). However, the AUCs of the models were small (highest AUC [95%CI]: 0·529 [0·526-0·531]) and not appreciably different (within 0·03 difference in AUCs. Excluding models with confidence intervals including 0·5, lowest AUC: 0·501 [0·500-0·501]) ( Supplementary Table 2 ). Discussion We evaluated the performance of different risk stratifiers, including PRS, GAIL, and FH, in identifying high-risk individuals for BrCa and DCIS across various demographics and to understand the overlap and unique contributions of these models in different populations. The association between different risk stratifiers (PRS, GAIL, and FH) with BrCa and DCIS varies by ancestry and age. PRS demonstrated superior discrimination compared to the Gail model for predicting both BrCa and DCIS in European- and Asian-ancestry populations. Specifically, the 5-year absolute risk from PRS showed higher AUC values than the Gail model for both conditions. In Europeans, PRS and GAIL showed significant odds ratios for identifying high-risk individuals, with larger effect sizes observed in younger populations. In Asians, PRS and GAIL also showed significant associations. The overlap of high-risk individuals identified by PRS, GAIL, and FH revealed that PRS and FH were primary contributors to high-risk classification, particularly in young Europeans and all Asians. PRS uniquely identified a notable percentage of high-risk individuals that were missed by GAIL and FH, while GAIL identified a significant portion in older Europeans. Additionally, target-enriched sequencing data showed that high-risk individuals identified by both PRS and predisposition genes (PTV) were limited, with PRS alone identifying a larger proportion of high-risk individuals compared to PTV alone. Country-specific analysis indicated that both PRS and GAIL identified a higher proportion of high-risk individuals in European-ancestry populations compared to Asian-ancestry populations, with greater variability observed for GAIL. Given the complexity and multifactorial nature of breast cancer, relying on a single risk factor or model may not sufficiently capture all high-risk individuals. 6 , 12 The analysis of the intersection between high-risk individuals identified by PRS, GAIL, and FH reveals important insights into how these risk stratifiers overlap and uniquely contribute to risk assessment. Our results derived from the analysis of 180,398 woman across diverse ancestries corroborate previous findings that report a limited overlap in the high-risk individuals identified by different risk predictors. 6 , 12 The unique contribution of PRS is particularly notable. Traditional risk models like the Gail model are less accurate in younger populations. These models often rely on risk factors which may not fully capture the risk in younger women. Younger women may not have a significant personal or reproductive history, making genetic information from PRS and specific gene mutations particularly valuable for risk assessment. 6 , 12 No single model is suitable for every subgroup within the general population. The limited overlap and the unique contributions of each risk stratifier suggest that using a combination of these tools could provide a more comprehensive risk assessment, capturing high-risk individuals that might be missed by any single model. Comprehensive risk models such as BOADICEA improve prediction, however they can be challenging to implement at the general population level. 18 In addition, calibration and validation for populations not used in the model’s development need to be done. The evaluation of country-specific differences in high-risk identification by PRS and GAIL shows that European-ancestry populations generally had higher proportions of predicted high-risk individuals compared to Asian-ancestry populations. This is expected, as two risk predictors utilize breast cancer incidence rates that were higher in Europeans (i.e. the “White” used to develop the Gail model) than in Asians (i.e. the “Chinese”). 15 The variability in the performance of the GAIL model across different countries, with larger standard deviations compared to PRS, suggests that GAIL’s effectiveness may be more influenced by regional factors, such as differences in reproductive factors, lifestyle and healthcare practices. 11 , 25 The analysis of factors influencing the performance of the Gail model reveals differences in its effectiveness based on age and demographic factors. For Europeans, incorporating family history and prior breast biopsies achieved the highest AUC, emphasizing the importance of these factors in risk prediction. In younger Asians, age at menarche and the number of prior biopsies were more influential, with models excluding family history showing better performance. While there are likely regional differences in genetics, lifestyle, and healthcare practices, the Gail model may be compounded by variations in data quality and recall. 25 , 26 The current guidelines for breast cancer screening are based on sex and age. The recommendations typically advocate biennial mammography for women aged 50 to 69 or 70 years. 3 , 27 Previously, the US Preventive Services Task Force (USPSTF) advised that the decision to begin biennial screening before age 50 should be personalized, considering the patient’s values regarding specific benefits and harms. It is unclear if clinicians are provided with directives on the specific topics to discuss with patients regarding screening suitability. For age groups where the evidence for mammography is less definitive, integrating comprehensive risk stratification into discussions about screening would ensure that recommendations are as relevant as possible. However, in Apr 2024, the USPSTF published in its Final Recommendation Statement biennial mammogram screenings for all women aged 40 to 74 years to detect early-stage cancer (accessed Jul 23, 2024). 28 While this earlier screening will benefit many, it also raises concerns about over-screening and its potential consequences. Risk stratification could enhance the effectiveness of the new USPSTF guidelines by targeting screening efforts more precisely. By incorporating complementary individual risk factors, healthcare providers can better identify those who are genuinely at higher risk for breast cancer. As a result, risk stratification can help balance the benefits of early detection with the potential drawbacks of excessive screening. Our study uses one of the largest breast cancer association study datasets, providing high statistical power for comprehensive risk factor analyses and diverse population coverage that includes both European and Asian ancestries. The study’s multi-center scope allowed for the comparison of risk models across different countries. However, differences in study design, data collection methods, and risk factor definitions across included studies may have introduced variability and affected the consistency of results. Variations in the time of data collection and changes in clinical practices over time could affect the comparability of data across studies. Combining different studies introduced gaps in data (i.e. missingness) for some risk factors, and exclusions of certain studies affected the generalizability of our findings for those regions. In addition, while the study covers women of European and Asian ancestries, it does not represent all global populations. Regional variations in absolute risk—due to genetic differences, varying gene predispositions, lifestyle factors, and healthcare access—also impact the applicability of risk models to other settings or demographics. 11 , 29 Not all known breast cancer risk factors were considered in our analyses. Examples of other risk factors include mammographic density, physical activity, alcohol use, and smoking. 18 , 30 Overall, while PRS shows consistent superiority in breast cancer risk stratification across demographics, the complementary use of the Gail model and family history can enhance the overall risk assessment process. Integrating and calibrating these models for different ethnic populations, along with understanding their unique contributions and limitations, can lead to more precise identification of high-risk individuals who would otherwise be missed. 13 Ideally, evaluating individual breast cancer risk could lead to more precise and cost-effective early detection by tailoring screening approaches to risk levels. However, despite advancements in risk assessment, it remains important to adhere to the established consensus guidelines for minimum mammography screening, as set by nationally recognized organizations with expertise in screening methodology. DECLARATIONS Ethics approval This study was approved by the A*STAR Institutional Review Board (reference number: 2022-041). Consent to participate Informed consent was obtained by the individual studies that contributed to BCAC. Conflict of interest The authors declare no potential conflicts of interest. Availability of data and materials The data used in our analyses are available upon reasonable request through BCAC, subject to data access committee approval. Author contributions Conception and design of the study: JL; Acquisition of data: All; Analysis and interpretation of data: PJH, MHG, CKYL, JL; Drafting of the manuscript or revising it for important content: JL, PJH, CKYL; Final approval of the version submitted for publication: All. Acknowledgements This study is funded by the Agency for Science, Technology and Research (A*STAR) and PRECISION Health Research, Singapore (PRECISE). The breast cancer genome-wide association analyses in BCAC were supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the ‘Ministère de l’Économie, de la Science et de l’Innovation du Québec’ through Genome Québec and grant PSR-SIIRI-701, The National Institutes of Health (U19 CA148065, X01HG007492), Cancer Research UK (C1287/A10118, C1287/A16563, C1287/A10710), and The European Union (HEALTH-F2-2009-223175 and H2020 633784 and 634935). All studies and funders are listed in Additional Materials (BCAC Funding and Acknowledgments) . References ↵ Sedeta , E. T. , Jobre , B. & Avezbakiyev , B . Breast cancer: Global patterns of incidence, mortality, and trends . Journal of Clinical Oncology 41 , 10528 – 10528 , doi: 10.1200/JCO.2023.41.16_suppl.10528 ( 2023 ). OpenUrl CrossRef ↵ Ginsburg , O. , Yip , C. H. , Brooks , A. , Cabanes , A. , Caleffi , M. , Dunstan Yataco , J. A. et al. Breast cancer early detection: A phased approach to implementation . Cancer 126 Suppl 10 , 2379 – 2393 , doi: 10.1002/cncr.32887 ( 2020 ). OpenUrl CrossRef PubMed ↵ Lim , Y. X. , Lim , Z. L. , Ho , P. J. & Li , J . Breast Cancer in Asia: Incidence, Mortality, Early Detection, Mammography Programs, and Risk-Based Screening Initiatives . Cancers (Basel) 14 , doi: 10.3390/cancers14174218 ( 2022 ). OpenUrl CrossRef PubMed ↵ Lim , Z. L. , Ho , P. J. , Khng , A. J. , Yeoh , Y. S. , Ong , A. T. W. , Tan , B. K. T. et al. Mammography screening is associated with more favorable breast cancer tumor characteristics and better overall survival: case-only analysis of 3739 Asian breast cancer patients . BMC Med 20 , 239 , doi: 10.1186/s12916-022-02440-y ( 2022 ). OpenUrl CrossRef ↵ Evans , A. & Whelehan , P . Breast screening policy: are we heading in the right direction? Clin Radiol 66 , 915 – 919 , doi: 10.1016/j.crad.2011.03.024 ( 2011 ). OpenUrl CrossRef PubMed ↵ Ho , P. J. , Ho , W. K. , Khng , A. J. , Yeoh , Y. S. , Tan , B. K. , Tan , E. Y. et al. Overlap of high-risk individuals predicted by family history, and genetic and non-genetic breast cancer risk prediction models: implications for risk stratification . BMC Med 20 , 150 , doi: 10.1186/s12916-022-02334-z ( 2022 ). OpenUrl CrossRef ↵ Sun , L. , Legood , R. , Sadique , Z. , Dos-Santos-Silva , I. & Yang , L . Cost-effectiveness of risk-based breast cancer screening program, China . Bull World Health Organ 96 , 568 – 577 , doi: 10.2471/BLT.18.207944 ( 2018 ). OpenUrl CrossRef PubMed ↵ Gail , M. H. , Brinton , L. A. , Byar , D. P. , Corle , D. K. , Green , S. B. , Schairer , C. et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually . J Natl Cancer Inst 81 , 1879 – 1886 , doi: 10.1093/jnci/81.24.1879 ( 1989 ). OpenUrl CrossRef PubMed Web of Science ↵ Ho , P. J. , Wong , F. Y. , Chay , W. Y. , Lim , E. H. , Lim , Z. L. , Chia , K. S. et al. Breast cancer risk stratification for mammographic screening: A nationwide screening cohort of 24,431 women in Singapore . Cancer Med 10 , 8182 – 8191 , doi: 10.1002/cam4.4297 ( 2021 ). OpenUrl CrossRef PubMed ↵ Mertens , E. , Barrenechea-Pulache , A. , Sagastume , D. , Vasquez , M. S. , Vandevijvere , S. & Penalvo , J. L . Understanding the contribution of lifestyle in breast cancer risk prediction: a systematic review of models applicable to Europe . BMC Cancer 23 , 687 , doi: 10.1186/s12885-023-11174-w ( 2023 ). OpenUrl CrossRef PubMed ↵ Stevanato , K. P. , Pedroso , R. B. , Dell Agnolo , C. M. , Santos , L. D. , Pelloso , F. C. , Carvalho , M. D. B. et al. Use and Applicability of the Gail Model to Calculate Breast Cancer Risk: A Scoping Review . Asian Pac J Cancer Prev 23 , 1117 – 1123 , doi: 10.31557/APJCP.2022.23.4.1117 ( 2022 ). OpenUrl CrossRef PubMed ↵ Ho , P. J. , Lim , E. H. , Hartman , M. , Wong , F. Y. & Li , J . Breast cancer risk stratification using genetic and non-genetic risk assessment tools for 246,142 women in the UK Biobank . Genet Med 25 , 100917 , doi: 10.1016/j.gim.2023.100917 ( 2023 ). OpenUrl CrossRef PubMed ↵ Li , S. X. , Milne , R. L. , Nguyen-Dumont , T. , Wang , X. , English , D. R. , Giles , G. G. et al. Prospective Evaluation of the Addition of Polygenic Risk Scores to Breast Cancer Risk Models . JNCI Cancer Spectr 5 , doi: 10.1093/jncics/pkab021 ( 2021 ). OpenUrl CrossRef ↵ Mavaddat , N. , Michailidou , K. , Dennis , J. , Lush , M. , Fachal , L. , Lee , A. et al. Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes . Am J Hum Genet 104 , 21 – 34 , doi: 10.1016/j.ajhg.2018.11.002 ( 2019 ). OpenUrl CrossRef PubMed ↵ Ho , W. K. , Tai , M. C. , Dennis , J. , Shu , X. , Li , J. , Ho , P. J. et al. Polygenic risk scores for prediction of breast cancer risk in Asian populations . Genet Med 24 , 586 – 600 , doi: 10.1016/j.gim.2021.11.008 ( 2022 ). OpenUrl CrossRef PubMed ↵ Martin , A. R. , Kanai , M. , Kamatani , Y. , Okada , Y. , Neale , B. M. & Daly , M. J . Clinical use of current polygenic risk scores may exacerbate health disparities . Nat Genet 51 , 584 – 591 , doi: 10.1038/s41588-019-0379-x ( 2019 ). OpenUrl CrossRef PubMed ↵ Breast Cancer Association, C. , Dorling , L. , Carvalho , S. , Allen , J. , Gonzalez-Neira , A. , Luccarini , C. , et al. Breast Cancer Risk Genes - Association Analysis in More than 113,000 Women . N Engl J Med 384 , 428 – 439 , doi: 10.1056/NEJMoa1913948 ( 2021 ). OpenUrl CrossRef PubMed ↵ Lee , A. , Mavaddat , N. , Cunningham , A. , Carver , T. , Ficorella , L. , Archer , S. et al. Enhancing the BOADICEA cancer risk prediction model to incorporate new data on RAD51C, RAD51D, BARD1 updates to tumour pathology and cancer incidence . J Med Genet 59 , 1206 – 1218 , doi: 10.1136/jmedgenet-2022-108471 ( 2022 ). OpenUrl Abstract / FREE Full Text ↵ Breast Cancer Association, C. Commonly studied single-nucleotide polymorphisms and breast cancer: results from the Breast Cancer Association Consortium . J Natl Cancer Inst 98 , 1382 – 1396 , doi: 10.1093/jnci/djj374 ( 2006 ). OpenUrl CrossRef PubMed Web of Science ↵ Petridis , C. , Brook , M. N. , Shah , V. , Kohut , K. , Gorman , P. , Caneppele , M. et al. Genetic predisposition to ductal carcinoma in situ of the breast . Breast Cancer Res 18 , 22 , doi: 10.1186/s13058-016-0675-7 ( 2016 ). OpenUrl CrossRef PubMed ↵ Smith , S. G. , Sestak , I. , Forster , A. , Partridge , A. , Side , L. , Wolf , M. S. et al. Factors affecting uptake and adherence to breast cancer chemoprevention: a systematic review and meta-analysis . Ann Oncol 27 , 575 – 590 , doi: 10.1093/annonc/mdv590 ( 2016 ). OpenUrl CrossRef PubMed ↵ Purcell , S. , Neale , B. , Todd-Brown , K. , Thomas , L. , Ferreira , M. A. , Bender , D. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses . Am J Hum Genet 81 , 559 – 575 , doi: 10.1086/519795 ( 2007 ). OpenUrl CrossRef PubMed ↵ Mavaddat , N. , Pharoah , P. D. , Michailidou , K. , Tyrer , J. , Brook , M. N. , Bolla , M. K. et al. Prediction of breast cancer risk based on profiling with common genetic variants . J Natl Cancer Inst 107 , doi: 10.1093/jnci/djv036 ( 2015 ). OpenUrl CrossRef PubMed ↵ Wen , W. , Shu , X. O. , Guo , X. , Cai , Q. , Long , J. , Bolla , M. K. et al. Prediction of breast cancer risk based on common genetic variants in women of East Asian ancestry . Breast Cancer Res 18 , 124 , doi: 10.1186/s13058-016-0786-1 ( 2016 ). OpenUrl CrossRef PubMed ↵ Pruitt , W. R. , Samuels , B. & Cunningham , S . The Gail Model and Its Use in Preventive Screening: A Comparison of the Corbelli Study . Cureus 16 , e56290 , doi: 10.7759/cureus.56290 ( 2024 ). OpenUrl CrossRef ↵ Coughlin , S. S . Recall bias in epidemiologic studies . J Clin Epidemiol 43 , 87 – 91 , doi: 10.1016/0895-4356(90)90060-3 ( 1990 ). OpenUrl CrossRef PubMed Web of Science ↵ Ebell , M. H. , Thai , T. N. & Royalty , K. J . Cancer screening recommendations: an international comparison of high income countries . Public Health Rev 39 , 7 , doi: 10.1186/s40985-018-0080-0 ( 2018 ). OpenUrl CrossRef PubMed ↵ Final Recommendation Statement Breast Cancer: Screening , ( 2024 ). ↵ Yiangou , K. , Mavaddat , N. , Dennis , J. , Zanti , M. , Wang , Q. , Bolla , M. K. et al. Differences in polygenic score distributions in European ancestry populations: implications for breast cancer risk prediction . medRxiv , doi: 10.1101/2024.02.12.24302043 ( 2024 ). OpenUrl Abstract / FREE Full Text ↵ Pettersson , A. , Graff , R. E. , Ursin , G. , Santos Silva , I. D. , McCormack , V. , Baglietto , L. et al. Mammographic density phenotypes and risk of breast cancer: a meta-analysis . J Natl Cancer Inst 106 , doi: 10.1093/jnci/dju078 ( 2014 ). OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted March 03, 2025. 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Share Overlap of high-risk individuals across family history, genetic & non-genetic breast cancer risk models: Analysis of 180,398 women from European & Asian ancestries Peh Joo Ho , Christine Kim Yan Loo , Meng Huang Goh , Mustapha Abubakar , Thomas U. Ahearn , Irene L. Andrulis , Natalia N. Antonenkova , Kristan J. Aronson , Annelie Augustinsson , Sabine Behrens , Clara Bodelon , Natalia V. Bogdanova , Manjeet K. Bolla , Kristen Brantley , Hermann Brenner , Helen Byers , Nicola J. Camp , Jose E. Castelao , Melissa H. Cessna , Jenny Chang-Claude , Stephen J. Chanock , Georgia Chenevix-Trench , Ji-Yeob Choi , Sarah V. Colonna , Kamila Czene , Mary B. Daly , Francoise Derouane , Thilo Dörk , A. Heather Eliassen , Christoph Engel , Mikael Eriksson , D. Gareth Evans , Olivia Fletcher , Lin Fritschi , Manuela Gago-Dominguez , Jeanine M. Genkinger , Willemina R.R. Geurts-Giele , Gord Glendon , Per Hall , Ute Hamann , Cecilia Y.S. Ho , Weang-Kee Ho , Maartje J. Hooning , Reiner Hoppe , Anthony Howell , Keith Humphreys , ABCTB Investigators , kConFab Investigators , SGBCC Investigators , MyBrCa Investigators , Hidemi Ito , Motoki Iwasaki , Anna Jakubowska , Helena Jernström , Esther M. John , Nichola Johnson , Daehee Kang , Sung-Won Kim , Cari M. Kitahara , Yon-Dschun Ko , Peter Kraft , Ava Kwong , Diether Lambrechts , Susanna Larsson , Shuai Li , Annika Lindblom , Martha Linet , Jolanta Lissowska , Artitaya Lophatananon , Robert J. MacInnis , Arto Mannermaa , Siranoush Manoukian , Sara Margolin , Keitaro Matsuo , Kyriaki Michailidou , Roger L. Milne , Nur Aishah Mohd Taib , Kenneth Muir , Rachel A. Murphy , William G. Newman , Katie M. O’Brien , Nadia Obi , Olufunmilayo I. Olopade , Mihalis I. Panayiotidis , Sue K. Park , Tjoung-Won Park-Simon , Alpa V. Patel , Paolo Peterlongo , Dijana Plaseska-Karanfilska , Katri Pylkäs , Muhammad U. Rashid , Gad Rennert , Juan Rodriguez , Emmanouil Saloustros , Dale P. Sandler , Elinor J. 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Castelao , Melissa H. Cessna , Jenny Chang-Claude , Stephen J. Chanock , Georgia Chenevix-Trench , Ji-Yeob Choi , Sarah V. Colonna , Kamila Czene , Mary B. Daly , Francoise Derouane , Thilo Dörk , A. Heather Eliassen , Christoph Engel , Mikael Eriksson , D. Gareth Evans , Olivia Fletcher , Lin Fritschi , Manuela Gago-Dominguez , Jeanine M. Genkinger , Willemina R.R. Geurts-Giele , Gord Glendon , Per Hall , Ute Hamann , Cecilia Y.S. Ho , Weang-Kee Ho , Maartje J. Hooning , Reiner Hoppe , Anthony Howell , Keith Humphreys , ABCTB Investigators , kConFab Investigators , SGBCC Investigators , MyBrCa Investigators , Hidemi Ito , Motoki Iwasaki , Anna Jakubowska , Helena Jernström , Esther M. John , Nichola Johnson , Daehee Kang , Sung-Won Kim , Cari M. Kitahara , Yon-Dschun Ko , Peter Kraft , Ava Kwong , Diether Lambrechts , Susanna Larsson , Shuai Li , Annika Lindblom , Martha Linet , Jolanta Lissowska , Artitaya Lophatananon , Robert J. 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