Article Text

Physical activity, sedentary time and breast cancer risk: a Mendelian randomisation study
  1. Suzanne C Dixon-Suen1,
  2. Sarah J Lewis2,3,
  3. Richard M Martin2,3,4,
  4. Dallas R English1,5,
  5. Terry Boyle6,7,
  6. Graham G Giles1,5,8,
  7. Kyriaki Michailidou9,10,
  8. Manjeet K Bolla11,
  9. Qin Wang11,
  10. Joe Dennis11,
  11. Michael Lush11,
  12. ABCTB Investigators12,
  13. Thomas U Ahearn13,
  14. Christine B Ambrosone14,
  15. Irene L Andrulis15,16,
  16. Hoda Anton-Culver17,
  17. Volker Arndt18,
  18. Kristan J Aronson19,
  19. Annelie Augustinsson20,
  20. Päivi Auvinen21,22,
  21. Laura E Beane Freeman13,
  22. Heiko Becher23,
  23. Matthias W Beckmann24,
  24. Sabine Behrens25,
  25. Marina Bermisheva26,
  26. Carl Blomqvist27,28,
  27. Natalia V Bogdanova29,30,
  28. Stig E Bojesen31,32,
  29. Bernardo Bonanni33,
  30. Hermann Brenner18,34,
  31. Thomas Brüning35,
  32. Saundra S Buys36,
  33. Nicola J Camp36,
  34. Daniele Campa25,37,
  35. Federico Canzian38,
  36. Jose E Castelao39,
  37. Melissa H Cessna40,41,
  38. Jenny Chang-Claude25,42,
  39. Stephen J Chanock13,
  40. Christine L Clarke43,
  41. Don M Conroy44,
  42. Fergus J Couch45,
  43. Angela Cox46,
  44. Simon S Cross47,
  45. Kamila Czene48,
  46. Mary B Daly49,
  47. Peter Devilee50,51,
  48. Thilo Dörk30,
  49. Miriam Dwek52,
  50. Diana M Eccles53,
  51. A. Heather Eliassen54,55,
  52. Christoph Engel56,57,
  53. Mikael Eriksson48,
  54. D. Gareth Evans58,59,
  55. Peter A Fasching24,60,
  56. Olivia Fletcher61,
  57. Henrik Flyger62,
  58. Lin Fritschi63,
  59. Marike Gabrielson48,
  60. Manuela Gago-Dominguez64,65,
  61. Montserrat García-Closas13,
  62. José A García-Sáenz66,
  63. Mark S Goldberg67,68,
  64. Pascal Guénel69,
  65. Melanie Gündert70,71,
  66. Eric Hahnen72,73,
  67. Christopher A Haiman74,
  68. Lothar Häberle24,
  69. Niclas Håkansson75,
  70. Per Hall48,76,
  71. Ute Hamann77,
  72. Steven N Hart78,
  73. Michelle Harvie79,
  74. Peter Hillemanns30,
  75. Antoinette Hollestelle80,
  76. Maartje J Hooning80,
  77. Reiner Hoppe81,82,
  78. John Hopper5,
  79. Anthony Howell83,
  80. David J Hunter55,84,
  81. Anna Jakubowska85,86,
  82. Wolfgang Janni87,
  83. Esther M John88,89,
  84. Audrey Jung25,
  85. Rudolf Kaaks25,
  86. Renske Keeman90,
  87. Cari M Kitahara91,
  88. Stella Koutros13,
  89. Peter Kraft55,92,
  90. Vessela N Kristensen93,94,
  91. Katerina Kubelka-Sabit95,
  92. Allison W Kurian88,89,
  93. James V Lacey96,97,
  94. Diether Lambrechts98,99,
  95. Loic Le Marchand100,
  96. Annika Lindblom101,102,
  97. Sibylle Loibl103,
  98. Jan Lubiński85,
  99. Arto Mannermaa104,105,
  100. Mehdi Manoochehri77,
  101. Sara Margolin76,106,
  102. Maria Elena Martinez65,107,
  103. Dimitrios Mavroudis108,
  104. Usha Menon109,
  105. Anna Marie Mulligan110,111,
  106. Rachel A Murphy112,113,
  107. NBCS Collaborators93,94,
  108. Heli Nevanlinna114,
  109. Ines Nevelsteen115,
  110. William G Newman58,59,
  111. Kenneth Offit116,117,
  112. Andrew F Olshan118,
  113. Håkan Olsson20,
  114. Nick Orr119,
  115. Alpa Patel120,
  116. Julian Peto121,
  117. Dijana Plaseska-Karanfilska122,
  118. Nadege Presneau52,
  119. Brigitte Rack87,
  120. Paolo Radice123,
  121. Erika Rees-Punia120,
  122. Gad Rennert124,
  123. Hedy S Rennert124,
  124. Atocha Romero125,
  125. Emmanouil Saloustros126,
  126. Dale P Sandler127,
  127. Marjanka K Schmidt90,128,
  128. Rita K Schmutzler72,73,
  129. Lukas Schwentner87,
  130. Christopher Scott78,
  131. Mitul Shah44,
  132. Xiao-Ou Shu129,
  133. Jacques Simard130,
  134. Melissa C Southey1,8,
  135. Jennifer Stone5,131,
  136. Harald Surowy70,71,
  137. Anthony J Swerdlow132,133,
  138. Rulla M Tamimi55,134,
  139. William J Tapper53,
  140. Jack A Taylor127,135,
  141. Mary Beth Terry136,
  142. Rob A E M Tollenaar137,
  143. Melissa A Troester118,
  144. Thérèse Truong69,
  145. Michael Untch138,
  146. Celine M Vachon139,
  147. Vijai Joseph116,
  148. Barbara Wappenschmidt72,73,
  149. Clarice R Weinberg140,
  150. Alicja Wolk75,141,
  151. Drakoulis Yannoukakos142,
  152. Wei Zheng129,
  153. Argyrios Ziogas17,
  154. Alison M Dunning44,
  155. Paul D P Pharoah11,44,
  156. Douglas F Easton11,44,
  157. Roger L Milne1,5,8,
  158. Brigid M Lynch1,5,143
  159. on behalf of the Breast Cancer Association Consortium
  1. 1 Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
  2. 2 Bristol Medical School, Department of Population Health Sciences, University of Bristol, Bristol, UK
  3. 3 MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
  4. 4 NIHR Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, UK
  5. 5 Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
  6. 6 Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
  7. 7 Australian Centre for Precision Health, University of South Australia Cancer Research Institute, Adelaide, South Australia, Australia
  8. 8 Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
  9. 9 Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
  10. 10 Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
  11. 11 Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
  12. 12 Australian Breast Cancer Tissue Bank, Westmead Institute for Medical Research, The University of Sydney, Sydney, New South Wales, Australia
  13. 13 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
  14. 14 Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
  15. 15 Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Ontario, Canada
  16. 16 Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
  17. 17 Department of Medicine, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, California, USA
  18. 18 Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
  19. 19 Department of Public Health Sciences, and Cancer Research Institute, Queen’s University, Kingston, Ontario, Canada
  20. 20 Department of Cancer Epidemiology, Clinical Sciences, Lund University, Lund, Sweden
  21. 21 Department of Oncology, Cancer Center, Kuopio University Hospital, Kuopio, Finland
  22. 22 Institute of Clinical Medicine, Oncology, University of Eastern Finland, Kuopio, Finland
  23. 23 Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
  24. 24 Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
  25. 25 Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
  26. 26 Institute of Biochemistry and Genetics, FSBSI Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russian Federation
  27. 27 Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
  28. 28 Department of Oncology, Örebro University Hospital, Örebro, Sweden
  29. 29 Department of Radiation Oncology, Hannover Medical School, Hannover, Germany
  30. 30 Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
  31. 31 Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
  32. 32 Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
  33. 33 Division of Cancer Prevention and Genetics, European Institute of Oncology IRCCS, Milan, Italy
  34. 34 Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
  35. 35 Institute for Prevention and Occupational Medicine of the German Social Accident Insurance (IPA), Institute of the Ruhr University Bochum, Bochum, Germany
  36. 36 Department of Internal Medicine and Huntsman Cancer Institute, The University of Utah, Salt Lake City, Utah, USA
  37. 37 Department of Biology, University of Pisa, Pisa, Italy
  38. 38 Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
  39. 39 Oncology and Genetics Unit, Instituto de Investigación Sanitaria Galicia Sur (IISGS), Xerencia de Xestion Integrada de Vigo-SERGAS, Vigo, Spain
  40. 40 Department of Pathology, Intermountain Medical Center, Intermountain Healthcare, Salt Lake City, Utah, USA
  41. 41 Intermountain Biorepository, Intermountain Healthcare, Salt Lake City, Utah, USA
  42. 42 Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
  43. 43 Westmead Institute for Medical Research, The University of Sydney, Sydney, New South Wales, Australia
  44. 44 Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
  45. 45 Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
  46. 46 Sheffield Institute for Nucleic Acids (SInFoNiA), Department of Oncology and Metabolism, The University of Sheffield, Sheffield, UK
  47. 47 Academic Unit of Pathology, Department of Neuroscience, The University of Sheffield, Sheffield, UK
  48. 48 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
  49. 49 Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, Pennsylvania, USA
  50. 50 Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
  51. 51 Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
  52. 52 School of Life Sciences, University of Westminster, London, UK
  53. 53 Faculty of Medicine, University of Southampton, Southampton, UK
  54. 54 Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
  55. 55 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
  56. 56 Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
  57. 57 LIFE - Leipzig Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
  58. 58 Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
  59. 59 North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
  60. 60 David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology, University of California Los Angeles, Los Angeles, California, USA
  61. 61 The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
  62. 62 Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
  63. 63 School of Population Health, Curtin University, Perth, Western Australia, Australia
  64. 64 Genomic Medicine Group, International Cancer Genetics and Epidemiology Group, Fundación Pública Galega de Medicina Xenómica, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain
  65. 65 Moores Cancer Center, University of California San Diego, La Jolla, California, USA
  66. 66 Medical Oncology Department, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Centro de Investigación Biomédica en Red de Cáncer, Madrid, Spain
  67. 67 Department of Medicine, McGill University, Montréal, Québec, Canada
  68. 68 Division of Clinical Epidemiology, Royal Victoria Hospital, McGill University, Montréal, Québec, Canada
  69. 69 Team ‘Exposome and Heredity’, Center for Research in Epidemiology and Population Health (CESP), Gustave Roussy, INSERM, University Paris-Saclay, Villejuif, France
  70. 70 Molecular Epidemiology Group, C080, German Cancer Research Center (DKFZ), Heidelberg, Germany
  71. 71 Molecular Biology of Breast Cancer, University Womens Clinic Heidelberg, University of Heidelberg, Heidelberg, Germany
  72. 72 Center for Familial Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
  73. 73 Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
  74. 74 Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
  75. 75 Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
  76. 76 Department of Oncology, Södersjukhuset, Stockholm, Sweden
  77. 77 Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
  78. 78 Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
  79. 79 Prevent Breast Cancer Research Unit, Manchester University Hospital Foundation NHS Trust, Manchester, UK
  80. 80 Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
  81. 81 Dr Margarete Fischer Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
  82. 82 University of Tübingen, Tübingen, Germany
  83. 83 Division of Cancer Sciences, The University of Manchester, Manchester, UK
  84. 84 Nuffield Department of Population Health, University of Oxford, Oxford, UK
  85. 85 Department of Genetics and Pathology, Pomeranian Medical University in Szczecin, Szczecin, Poland
  86. 86 Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University in Szczecin, Szczecin, Poland
  87. 87 Department of Gynaecology and Obstetrics, University Hospital Ulm, Ulm, Germany
  88. 88 Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA
  89. 89 Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA
  90. 90 Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
  91. 91 Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
  92. 92 Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
  93. 93 Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
  94. 94 Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
  95. 95 Department of Histopathology and Cytology, Clinical Hospital Acibadem Sistina, Skopje, Macedonia (the former Yugoslav Republic of)
  96. 96 Department of Computational and Quantitative Medicine, City of Hope, Duarte, California, USA
  97. 97 City of Hope Comprehensive Cancer Center, City of Hope, Duarte, California, USA
  98. 98 VIB Center for Cancer Biology, VIB, Gent, Oost-Vlaanderen, Belgium
  99. 99 Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
  100. 100 Epidemiology Program, University of Hawai'i Cancer Center, Honolulu, Hawaii, USA
  101. 101 Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
  102. 102 Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
  103. 103 German Breast Group, Neu-Isenburg, Germany
  104. 104 Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland
  105. 105 Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland
  106. 106 Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
  107. 107 Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, California, USA
  108. 108 Department of Medical Oncology, University Hospital of Heraklion, Heraklion, Greece
  109. 109 MRC Clinical Trials Unit, Institute of Clinical Trials & Methodology, University College London, London, UK
  110. 110 Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
  111. 111 Laboratory Medicine Program, University Health Network, Toronto, Ontario, Canada
  112. 112 Cancer Control Research, BC Cancer, Vancouver, British Columbia, Canada
  113. 113 School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
  114. 114 Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
  115. 115 Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
  116. 116 Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
  117. 117 Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
  118. 118 Department of Epidemiology, Gillings School of Global Public Health and UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
  119. 119 Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, UK
  120. 120 Department of Population Science, American Cancer Society, Atlanta, Georgia, USA
  121. 121 Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
  122. 122 Research Centre for Genetic Engineering and Biotechnology 'Georgi D. Efremov', MASA, Skopje, Macedonia (the former Yugoslav Republic of)
  123. 123 Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
  124. 124 Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel
  125. 125 Medical Oncology Department, Hospital Universitario Puerta de Hierro, Madrid, Spain
  126. 126 Department of Oncology, University Hospital of Larissa, Larissa, Greece
  127. 127 Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, North Carolina, USA
  128. 128 Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
  129. 129 Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
  130. 130 Genomics Center, Centre Hospitalier Universitaire de Québec – Université Laval Research Center, Québec City, Québec, Canada
  131. 131 Genetic Epidemiology Group, School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia
  132. 132 Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
  133. 133 Division of Breast Cancer Research, The Institute of Cancer Research, London, UK
  134. 134 Department of Population Health Sciences, Weill Cornell Medicine, New York City, New York, USA
  135. 135 Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, North Carolina, USA
  136. 136 Department of Epidemiology, Mailman School of Public Health, Columbia University, New York City, New York, USA
  137. 137 Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
  138. 138 Department of Gynecology and Obstetrics, Helios Clinics Berlin-Buch, Berlin, Germany
  139. 139 Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic, Rochester, Minnesota, USA
  140. 140 Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, North Carolina, USA
  141. 141 Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
  142. 142 Molecular Diagnostics Laboratory, INRASTES, National Centre for Scientific Research-Demokritos, Athens, Greece
  143. 143 Physical Activity Laboratory, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
  1. Correspondence to Assoc. Prof. Brigid M Lynch, Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia; brigid.lynch{at}cancervic.org.au

Abstract

Objectives Physical inactivity and sedentary behaviour are associated with higher breast cancer risk in observational studies, but ascribing causality is difficult. Mendelian randomisation (MR) assesses causality by simulating randomised trial groups using genotype. We assessed whether lifelong physical activity or sedentary time, assessed using genotype, may be causally associated with breast cancer risk overall, pre/post-menopause, and by case-groups defined by tumour characteristics.

Methods We performed two-sample inverse-variance-weighted MR using individual-level Breast Cancer Association Consortium case-control data from 130 957 European-ancestry women (69 838 invasive cases), and published UK Biobank data (n=91 105–377 234). Genetic instruments were single nucleotide polymorphisms (SNPs) associated in UK Biobank with wrist-worn accelerometer-measured overall physical activity (nsnps=5) or sedentary time (nsnps=6), or accelerometer-measured (nsnps=1) or self-reported (nsnps=5) vigorous physical activity.

Results Greater genetically-predicted overall activity was associated with lower breast cancer overall risk (OR=0.59; 95% confidence interval (CI) 0.42 to 0.83 per-standard deviation (SD;~8 milligravities acceleration)) and for most case-groups. Genetically-predicted vigorous activity was associated with lower risk of pre/perimenopausal breast cancer (OR=0.62; 95% CI 0.45 to 0.87,≥3 vs. 0 self-reported days/week), with consistent estimates for most case-groups. Greater genetically-predicted sedentary time was associated with higher hormone-receptor-negative tumour risk (OR=1.77; 95% CI 1.07 to 2.92 per-SD (~7% time spent sedentary)), with elevated estimates for most case-groups. Results were robust to sensitivity analyses examining pleiotropy (including weighted-median-MR, MR-Egger).

Conclusion Our study provides strong evidence that greater overall physical activity, greater vigorous activity, and lower sedentary time are likely to reduce breast cancer risk. More widespread adoption of active lifestyles may reduce the burden from the most common cancer in women.

  • Breast
  • Physical activity
  • Sedentary Behaviour
  • Genetics

Data availability statement

Data are available upon reasonable request. The data used in this study are de-identified patient data from 76 studies participating in the Breast Cancer Association Consortium (BCAC). Enquiries about accessing BCAC data can be directed to the BCAC coordinators at the University of Cambridge: https://bcac.ccge.medschl.cam.ac.uk/

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WHAT IS ALREADY KNOWN ON THIS TOPIC

  • Observational studies have reported that active lifestyles are associated with lower breast cancer risk, but whether activity is the protective (causative) factor cannot be conclusively determined from observational evidence.

WHAT THIS STUDY ADDS

  • This study, using individual-level data from the Breast Cancer Association Consortium, provides strong evidence that greater levels of physical activity and less sedentary time are likely to reduce breast cancer risk, with results generally consistent across breast cancer subtypes.

  • A systematic Mendelian randomisation approach enhanced the ability to draw causal conclusions by minimising the effect of biases such as confounding, which are likely to have affected previous studies.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

  • On triangulating multiple evidence types, there is now robust evidence that insufficiently active lifestyles are a modifiable cause of breast cancer risk, and a stronger focus on promoting active lifestyles is likely to reduce the high burden from breast cancer.

  • It would be of public health benefit for physical activity researchers to establish whether Mendelian randomisation supports the observational findings regarding active lifestyles and cancer risk for other cancer types.

Introduction

Observational studies have shown greater physical activity and less sedentary time are associated with lower breast cancer risk. International and national cancer agencies have concluded that physical activity may reduce breast cancer risk, particularly postmenopausal disease, with associations strongest for vigorous activity.1–3 Sedentary time – defined as time spent sitting or reclining– is a distinct exposure affecting ‘active’ and ‘inactive’ people. It has been less well-studied, with conflicting findings.4 5 Physical inactivity or excess sitting may influence breast cancer initiation and/or growth. However, whether observed associations are causal or produced by biases (eg, confounding, selection bias, reverse causation) is unclear. Mendelian randomisation (MR) can simulate randomised controlled trials using observational data by substituting genotypes, which are randomly assigned at meiosis (before conception), as instruments (proxies) for exposures of interest.6 Subject to meeting specific assumptions of instrumental variable analysis,7 some of which can be investigated using sensitivity analyses (see Methods), MR can minimise confounding and reverse causation, potentially providing stronger evidence for causal inference.

A recent MR study assessed physical activity and breast cancer risk overall and by oestrogen-receptor (ER) status,8 but did not examine other breast tumour types, vigorous activity, or sedentary time. We aimed to appraise the causal nature of associations between overall activity, vigorous activity, and sedentary time, and breast cancer risk, overall and by menopausal status, stage, grade, morphology, and molecular subtypes defined by hormone-receptor (ER, progesterone (PR)) and human epidermal growth factor receptor-2 (HER2) status.

Methods

Data sources

We performed two-sample MR using individual-level data from 130 957 European-ancestry women (69 838 with invasive breast cancers; 6667 with in situ breast cancers; 54 452 controls) from 76 Breast Cancer Association Consortium (BCAC) studies (table 1, online supplemental table S1)(outcome dataset). We used genetic estimates for movement-related exposures from published genome-wide association studies (GWAS) using UK Biobank data (exposure datasets; n=91 105–3 77 234).9–11 Instruments were single-nucleotide polymorphisms (SNPs) associated in the UK Biobank GWAS with overall physical activity (all movement), vigorous physical activity, or sedentary time (online supplemental table S2).

Supplemental material

Table 1

Characteristics of 76 Breast Cancer Association Consortium studies, and 130 957 study participants, included in the individual-level analysis

Exposures

Overall physical activity

As our primary physical activity instrument we used five SNPs associated with overall activity (p<5×10-8) in a previous GWAS of accelerometer-assessed movement in the UK Biobank (n=91 105),9 which explain 0.10% of the variance in activity. Doherty and colleagues assessed overall activity as average vector magnitude (milligravities) per 30 second period,9 12 with mean (standard deviation (SD)) 29.0 (8.0) milligravities among women in the UK Biobank.13 One SD (8 milligravities) corresponds to ~50 min of moderate (eg, brisk walking) activity per week.8

For comparability with the previous MR study on this topic,8 we used an expanded set of 10 SNPs as a secondary instrument for overall activity. These SNPs were associated at relaxed significance (p<5×10-7) with the accelerometer-assessed overall activity phenotype in a separate UK Biobank GWAS of physical activity by Klimentidis et al.10 11

Vigorous physical activity

Klimentidis et al. identified one SNP associated (p<5×10-9) with high-intensity movement, assessed as the fraction of 30 second intervals containing accelerations over 425 milligravities.10 This threshold approximates expenditure output for vigorous activity (>6 metabolic equivalents of task (METs)).14 This SNP explains approximately 0.02% of variance in high-intensity movement. Klimentidis et al. identified five SNPs associated (p<5×10-9) with self-reported engagement in vigorous activity for at least 10 minutes ≥3 vs. 0 days/week (n=3 77 234),10 which explain approximately 0.06% of variance in this exposure. We examined both instruments as complementary measures for vigorous activity, each likely subject to a different error (weak instrument or reporting bias).

Sedentary time

Doherty et al. applied machine-learning models, trained using body-camera and diary data, to UK Biobank accelerometry data to identify sedentary periods (sitting/reclining; MET-value typically ≤1.5).9 13 They identified six SNPs associated (p<5×10-8) with the probability of engaging in sedentary behaviours, defined as the ratio of sedentary-to-total 30 s periods.9 On average UK Biobank women spent 34.6% (SD=7.2%) of their time sedentary.13 We used these six variants, explaining 0.12% of variance in sedentariness, as our sedentary time instrument.

Outcomes

We estimated breast cancer risk overall, by menopausal status, and by case-groups defined by molecular/morphological subtype, stage, or grade at diagnosis. We used BCAC clinical data to assign case-groups according to hypotheses arising from the literature. We defined separate case/control groups for invasive pre/peri-menopausal (n=23 999 cases; 17 686 controls) and postmenopausal (n=45 839 cases; 36 766 controls) breast cancers. We used age at diagnosis/interview (</≥50 years) to assign missing menopausal status (27%). We examined subtypes separately by hormone-receptor (HR) status (ER±n=46 528/11 246; PR±n=34 891/16,432) and HER2 status (±n=6945/33 214), and jointly including HER2-enriched (ER-/PR-/HER2+; n=1974) and triple-negative (ER-/PR-/HER2-; n=4964) cancers. We examined invasive ductal/lobular cancers (n=42 223/8,795), ductal carcinoma in situ (n=3510), and risk by stage (stage 1, n=17 583; stage 2, n=15 992; stages 3/4, n=4553) and grade (well/moderately differentiated, n=34 647; poorly/undifferentiated, n=16 432).

SNP-exposure (UK Biobank) and SNP-outcome (BCAC) associations

We extracted or derived estimates of association (beta coefficients, standard errors (SEs)) between SNPs and exposures from the UK Biobank GWAS publications,9 10 standardised to refer to the trait-increasing allele. Where required,10 we converted estimates to per-SD changes in activity/sedentary time using UK Biobank activity data.

We determined genotypes in BCAC using the OncoArray, an Illumina custom array, and imputed using IMPUTE2.15 We harmonised UK Biobank and BCAC data so SNP-exposure and SNP-outcome estimates related to the same allele, using allele frequency information to resolve strand-ambiguous SNPs where possible (ie, unless allele frequencies were 45%–55%). For each SNP, we derived trait-specific effect-allele dosages (range 0–2) by summing alleles predicting more activity (activity instruments) or sedentary time (sitting instrument). We assessed the association between each SNP and each outcome from individual-level BCAC data by fitting logistic regression models, adjusted for age at diagnosis (cases) or interview (controls), country, and 10 principal components of genetic population structure (accounting for genetic substructure within Europeans), obtaining beta coefficients and SEs for use in the MR analysis. Table 1 summarises the BCAC studies and participants.

Statistical analysis

We used SNP-exposure and SNP-outcome beta coefficients and SEs to estimate odds ratios (OR) and 95% CI of the effect of each trait on each outcome. For single SNPs, we divided the SNP-outcome association by the SNP-exposure association to obtain the causal estimate (Wald ratio). For multi-SNP instruments, we used inverse-variance weighted (IVW)-MR, which averages Wald ratios across SNPs, weighted by SNP-exposure beta coefficients.16–18 IVW-MR assumes all instruments are valid or that pleiotropy is balanced,17 and we assumed linearity in the associations between the SNPs and exposure, and between SNPs and outcome. We performed case-only analyses to test for differences between subtypes.

Core assumptions of MR, which can be investigated using sensitivity analyses, are that the instrument: predicts exposure; is not associated with confounders of the exposure/outcome association; and influences the outcome only via the exposure (no horizontal pleiotropy),6 7 19 summarised in online supplemental figure S1. We undertook sensitivity analyses to assess the robustness of our findings and the potential for violations of assumptions, most critically horizontal pleiotropy. We calculated Cochran’s Q-statistic for between-SNP heterogeneity of effects. We applied complementary methods relaxing different MR assumptions, weighted-median MR (allows invalid instruments)20 and MR-Egger (allows horizontal pleiotropy, although prone to imprecision).19 21 We inspected per-SNP causal estimates (scatter, forest plots) and leave-one-out analyses to identify SNPs distorting results. We performed MR Pleiotropy Residual Sum and Outlier (MR-PRESSO) to identify outlying SNPs with evidence of horizontal pleiotropy (global-pleiotropy and SNP-outlier tests p<0.05).22 We examined the effect of excluding two SNPs with imputation quality <0.9. We checked whether SNPs are associated with other relevant traits (possible confounders, adiposity, cancer risk) or gene expression using the National Human Genome Research Institute–European Bioinformatics Institute– (NHGRI-EBI) GWAS Catalogue23 and PhenoScanner.24 25

Data preparation and analyses were performed using R software (R Foundation for Statistical Computing, Vienna), including the ‘Mendelian Randomization’18 and ‘MR-PRESSO’ packages.22 Statistical power was calculated using the mRnd Mendelian randomisation power calculation online tool.26 Further details are in online supplemental methods (Online Resource).

Results

Overall physical activity

Greater genetically-predicted physical activity was associated with lower risk of invasive breast cancer (OR=0.48; 95% CI 0.30 to 0.78 per-SD (~8 milligravities) in overall activity), with no clearly differential effects by menopausal status, molecular subtype, morphology, stage, or grade (table 2). We observed ORs less than one for all outcomes, including ER+ (OR=0.45; 95% CI 0.25 to 0.83), PR+ (OR=0.43;95% CI 0.22 to 0.85), HER2+ (OR=0.48; 95% CI 0.26 to 0.89), and HR+/HER2+ (OR=0.42; 95% CI 0.20 to 0.88) disease. Weighted-median MR and MR-Egger results were broadly consistent (Table S3).

Table 2

Link between primary instrumental genetic variables for overall physical activity (per SD) and risk of breast cancer

Heterogeneity of causal effects between SNPs was evident for some outcomes (Cochran’s-Q phet <0.05)(table 2); this was resolved after removing outliers rs564819152 (associated previously with ovarian cancer; outlying for six outcomes) or rs6775319 (one outcome), detected by MR-PRESSO, per-SNP, and leave-one-out analyses (online supplemental figures S2–S3; table S4). Evidence of protective associations remained strong after excluding rs564819152 (table 2). Outlier-corrected results (OR (95% CI)) were 0.59 (0.42–0.83) for all invasive breast cancer, 0.60 (0.43–0.85) for ER+, and 0.58 (0.37–0.91) for PR +disease (HER2 +and HR+/HER2 +analyses had no outlying SNPs).

The protective effects were consistent across leave-one-out analyses (online supplemental table S4). SNPs were not associated in previous GWAS with confounders of the exposure/outcome relationship, but two had been identified in an ovarian cancer GWAS (online supplemental table S5). Excluding these made little difference to the results (online supplemental table S4). In the UK Biobank two SNPs have been reported to be associated (p<5×10-8) with adiposity;24 25 27 this is consistent with reduced adiposity being a downstream effect of increased activity (online supplemental table S5).

Results were similar although slightly attenuated using the expanded 10-SNP instrument10 (online supplemental S6–S7). Estimates generally remained protective on removing outlying SNPs detected by pleiotropy investigations (IVW heterogeneity tests (online supplemental table S6), MR-PRESSO, per-SNP effects (online supplemental figures S4–S5), and leave-one-out analysis (online supplemental table S8). Most estimates were similar (online supplemental table S8) on excluding one SNP with imputation quality <0.9 (online supplemental table S8). Four of the 10 SNPs were associated in previous GWAS with confounders (including height, alcohol intake, education) or cancer risk. Furthermore, rs55657917 is associated with gene expression in breast tissue, including in two genes associated with breast cancer risk (online supplemental table S5).23–25 However, results excluding potentially confounded SNPs were relatively unchanged (online supplemental table S8). For four SNPs, the activity-increasing allele is associated with reduced adiposity in the UK Biobank.27

Vigorous physical activity

There was little evidence that genetically-predicted acceleration over 425 milligravities (one SNP) was associated with risk of breast cancer, with wide confidence intervals crossing one, although most estimates were in the protective direction (table 3). The activity-increasing allele has been associated in GWAS24 25 27 with greater height and decreased adiposity (online supplemental table S5).

Table 3

Association between instrumental genetic variables for vigorous physical activity, assessed in two ways, and risk of breast cancer

There was weak evidence that genetically-predicted self-reported vigorous activity was associated with decreased breast cancer risk overall (OR=0.83; 95% CI 0.69 to 1.01,≥3 days/week vs none), and ORs for most case-groups were less than 1 (table 3). A protective association was seen for pre/perimenopausal breast cancer (OR=0.62; 95% CI 0.45 to 0.87), with little evidence for an association with postmenopausal breast cancer risk (OR=0.95; 95% CI 0.75 to 1.19) (p=0.82 for the difference in pre/peri- vs post-menopausal estimates). A protective relationship was seen for PR +disease (OR=0.77; 95% CI 0.61 to 0.98). There was little evidence of pleiotropic effects (table 3, S9-S10) except one outlier in modelling in situ cancers (online supplemental figures S6-S7), a SNP previously associated with height, age at menarche, and adiposity (online supplemental table S5).24 25 27 After excluding this SNP, the in situ OR was elevated (from OR=0.94; 95% CI 0.43 to 2.08 to OR=1.30;0.72–2.34)(table 3); other estimates remained similar (online supplemental table S10). Excluding one SNP associated in UK Biobank GWAS with past smoking and childhood height (online supplemental table S5)24 25 27 attenuated estimates slightly (online supplemental table S10). The association with pre/perimenopausal cancers remained substantially inverse (protective), with confidence intervals that did not cross the null, in all sensitivity analyses (online supplemental table S10).

Sedentary time

The estimates for genetically-predicted sedentary time were elevated (in the direction of increased risk) for almost every case-group, although CIs were wide (table 4). Greater sedentary time was associated with higher risk of hormone-receptor-negative (HR-) tumours (OR=1.77; 95% CI 1.07 to 2.92 per-SD (~7% time spent sedentary)), including triple-negative (ER-/PR-/HER2-) cancers (OR=2.04; 95% CI 1.06 to 3.93) (p=0.11 for the difference in ORs by HR-status). ORs were substantially elevated for in situ cancers (OR=1.75; 95% CI 1.00 to 3.07), specifically ductal carcinoma in situ (OR=2.11; 95% CI 0.99 to 4.49). The point estimate was elevated for stage 1 tumours (OR=1.62; 95% CI 0.99 to 2.65), with little evidence of association with stage 3/4 (OR=0.91; 95% CI 0.45 to 1.84) (p=0.25 for the difference in estimates for risk of stage 1 vs stage 3/4 tumours).

Table 4

Association between instrumental genetic variables for sedentary time (per SD in percent time spent sedentary) and risk of breast cancer

Heterogeneity between SNPs was not detected (all phet >0.2)(table 4), all MR methods produced broadly consistent results (online supplemental table S11), and MR-PRESSO did not identify outlying SNPs. Estimates were consistently elevated across leave-one-out analyses, including after omitting: one SNP correlated with a physical activity variant; one SNP predicting greater education and adiposity in PR GWAS24 25 27 28; or one strand-ambiguous SNP with minor allele frequency ~50%, for which effect-allele harmonisation was not definitive (online supplemental table S12). After excluding a SNP with imputation quality <0.9, which may have been an outlier for PR +analyses (online supplemental figures S8–S9; MR-Egger ppleiotropy=0.046 for PR+), point estimates for PR +and most other outcomes including HR-, triple-negative, and in situ cancers, moved further from null (online supplemental table S12). Estimates for HR- and in situ cancers remained substantially elevated in all sensitivity analyses (online supplemental table S12).

Discussion

Main findings

We conducted a Mendelian randomisation study using individual-level data on 130 957 women. We found that women with genetic variants predisposing them to be more active had lower breast cancer risk overall and for most case-groups defined by tumour subtypes, stage, or grade. Effect estimates for vigorous physical activity were in the protective direction for most types of breast cancer. Reporting more frequent vigorous activity was associated with reduced risk of pre/perimenopausal breast cancer. Women with genetic variants predisposing them to more sedentary time had higher risk of HR- breast cancer, but there was no strong evidence of differences in association by subtypes and weak evidence of an increased risk overall.

Strengths and limitations

A strength of our study is the use of individual-level BCAC data, which permitted examination of more outcomes than previously possible. Large sample sizes are another strength. BCAC is the largest collaboration of breast cancer studies, and we employed the most powerful available genetic instruments identified by the largest GWAS for movement-related behaviours, likely improving the precision of our estimates. While statistical power was limited by the proportion of variation in exposure explained by the genetic instruments available (we had 52% power to detect expected effects for overall activity and overall breast cancer risk, and less power for other exposure/outcome combinations; online supplemental table S13), there were no larger datasets available to increase power. The UK Biobank studies are the only GWAS of accelerometer-assessed movement, which substantially decreases measurement error compared with self-report. Measurement error in assessing genotype is typically very low (often estimated as less than 1%29 30).

The UK Biobank GWAS which identified our instruments used wrist-worn accelerometers, which may not capture ambulation as well as hip-worn accelerometers31; while this may have slightly affected precision, no superior data are available. Gene-exposure associations were estimated from a population (UK Biobank) including men, but no strong evidence of sexual dimorphism was reported in UK Biobank,9 so we assume that SNP-exposure estimates adequately reflect associations in women. While our instruments predict only a small fraction of variance in exposure, any weak-instrument bias would have biased estimates towards the null and cannot explain our findings.19 Some contributing studies within BCAC did not provide sufficient data on cancer diagnosis to classify cases into case-groups (for example tumour subtype or stage), and therefore numbers32 included in these analyses were much lower. Women without these tumour-specific outcome data may have differed from those included in analyses. Our analyses took a conventional approach of assuming linearity in SNP-exposure and SNP-outcome relationships. Satisfying this assumption is not required for valid causal inference, so even in the presence of nonlinearity our results would still provide information on probable causality, approximating an population-average causal effect of intervening on the exposure.32–34

Due to the nature of the data and study design, we estimated ORs as the measure of effect, which in some circumstances can be prone to non-collapsibility and sparse-data bias.35 36 These issues are most severe when many covariates are included in models (which was not the case for the current analysis), and when outcomes are neither rare nor very common (many of the outcomes we investigated are rare, limiting the extent of noncollapsibility). Overall activity and sedentary time results for pre/peri- and post-menopausal breast cancer (the only sub-outcome where all participants could be classified), demonstrate a slight pattern of noncollapsibility, where the OR for all invasive breast cancers does not lie between the ORs for each group separately. This is not a bias but a mathematical property of ORs.35

Implications

This analysis extends findings from a recent MR study of overall physical activity and breast cancer risk overall and by ER-status, using BCAC summary data.8 Our study, using individual-level data, confirmed those findings, and showed that the risk reduction holds across multiple subtypes. Our study also examined vigorous activity and sedentary time, not previously studied in relation to breast cancer risk using MR. We assessed associations with multiple outcomes (overall and by case-group) and our results may be subject to false positives. There was no strong evidence of differences in association by case-group.

While MR may provide estimates which more closely reflect underlying causal relationships, core assumptions must be satisfied before causal conclusions can be drawn. We satisfied the first (instrument predicts exposure) by selecting genome-wide significant SNPs identified by the largest GWAS of our traits of interest. We maximised the possibility of meeting the second (no confounding) by checking whether the SNPs were reported in previous GWAS of possible confounders (known breast cancer risk factors), and confirming that results remained consistent after excluding any SNPs that were (eg, smoking (vigorous activity analyses), education (sedentary behaviour analyses)). We interrogated the third assumption (instrument influences outcome only through exposure) using several pleiotropy-detection approaches, acting on detected violations, and confirming consistency of results from methods relaxing this assumption. Our conclusions remained unchanged following exclusion of potentially-pleiotropic SNPs.

Several SNPs in the analyses were associated with adiposity in previous GWAS. While we cannot rule out horizontal pleiotropy (SNPs influencing adiposity independently of physical activity/sedentary time), vertical pleiotropy (same causal pathway) is more plausible; reduced adiposity is a downstream effect of increased physical activity. Vertical pleiotropy does not violate MR assumptions and excluding vertically-pleiotropic variants may distort causal estimates.19 Nevertheless, previous MR analysis has shown evidence of a bi-directional relationship between overall activity and adiposity.9

Although it is possible that our findings arose by chance, our results for physical activity are consistent with observational studies, which have suggested a 20%–25% breast cancer risk reduction for the most versus least active women, with evidence of dose-response.3 37 Our findings support this and furthermore suggest these relationships are likely to be causal. The observational evidence for risk reduction, particularly for premenopausal breast cancer, is strongest for vigorous physical activity, suggesting that vigorous activity may be particularly important in preventing carcinogenesis.3 38 Short bouts of intense activity may be more protective than equivalent energy expenditure accumulated from light activity. We found that self-reported vigorous activity was associated with lower pre/perimenopausal breast cancer risk and found weak evidence for a protective effect of vigorous activity overall. Future studies should continue to explore this with more powerful instruments.

For sedentary time, the observational evidence is sparse and inconsistent. Our results, which minimise likelihood of confounding (eg, by unhealthy diet), are suggestive of a causal association with elevated risk of breast cancer, particularly for HR- and in situ cancer. While there is debate about the independence of physical activity and sedentary behaviour, they have different determinants and correlates and are often treated as separate traits. In our study the genetic instruments for sedentary behaviour and physical activity were mostly distinct; removing one SNP which predicted both traits did not change our findings, suggesting that both behaviours independently influence breast cancer risk.

Robust causal inference should triangulate findings across methods.39 Our findings must be considered in light of biological plausibility. A reasonable body of mechanistic evidence supports numerous causal pathways between physical activity and breast cancer risk. Pathways involving adiposity, metabolic dysfunction, sex hormones, and inflammation have been most thoroughly described.40–42 Mechanisms linking sedentary time and cancer are likely to at least partially overlap with those underpinning the physical activity relationship.43 44 Our findings cannot shed light on drivers of carcinogenesis. We saw suggestive differences by HR-status, but this may be a chance finding. Known adiposity-related SNPs did not seem to unduly influence our results, perhaps indicating that multiple pathways are important.

Conclusion

Increasing physical activity and reducing sedentary time are already recommended for cancer prevention. Our study adds further evidence that such behavioural changes are likely to lower the incidence of future breast cancer rates. A stronger cancer-control focus on physical activity and sedentary time as modifiable cancer risk factors is warranted, given the heavy burden of disease attributed to the most common cancer in women.

Data availability statement

Data are available upon reasonable request. The data used in this study are de-identified patient data from 76 studies participating in the Breast Cancer Association Consortium (BCAC). Enquiries about accessing BCAC data can be directed to the BCAC coordinators at the University of Cambridge: https://bcac.ccge.medschl.cam.ac.uk/

Ethics statements

Patient consent for publication

Ethics approval

This study involves human participants. This analysis was approved by The BCAC Data Access Committee approved the analysis (Reference ID 663), and each of the 76 studies in the outcome dataset of this multi-centre study was given approval by their Human Research Ethics Committee or equivalent Institutional Review Board. Participants gave informed consent to participate in the study before taking part.

Acknowledgments

BCAC: 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. ABCFS thank Maggie Angelakos, Judi Maskiell, Gillian Dite. ABCS thanks the Blood bank Sanquin, The Netherlands. ABCTB Investigators: Christine Clarke, Deborah Marsh, Rodney Scott, Robert Baxter, Desmond Yip, Jane Carpenter, Alison Davis, Nirmala Pathmanathan, Peter Simpson, J. Dinny Graham, Mythily Sachchithananthan. 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. F. Lejbkowicz, 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. 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. The BSUCH study acknowledges the Principal Investigator, Barbara Burwinkel, and 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. Investigators from the CPSII 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 authors would like to thank the California Teachers Study Steering Committee that is responsible for the formation and maintenance of the Study within which this research was conducted. A full list of California Teachers Study (CTS) team members is available at https://www.calteachersstudy.org/team. 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 and PROCAS thank 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-BoschInstitute of Clinical Pharmacology, Stuttgart, and University of Tübingen, Germany [Hiltrud Brauch, Wing-Yee Lo], German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Partner Site Tübingen, 72074 Tübingen, Germany [Hiltrud Brauch], gefördert durch die Deutsche Forschungsgemeinschaft (DFG) im Rahmen der Exzellenzstrategie des Bundes und der Länder - EXC 2180 - 390900677 [Hiltrud Brauch], Department of Internal Medicine, Johanniter GmbH Bonn, Johanniter Krankenhaus, Bonn, Germany [Yon-Dschun Ko, 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 [Thomas Brüning, Beate Pesch, Sylvia Rabstein, Anne Lotz]; and Institute of Occupational Medicine and Maritime Medicine, University Medical Center HamburgEppendorf, Germany [Volker Harth]. HEBCS thanks Johanna Kiiski, Taru A. Muranen, Kristiina Aittomäki, Kirsimari Aaltonen, Karl von Smitten, Irja Erkkilä. HMBCS thanks Peter Hillemanns, Hans Christiansen and Johann H. Karstens. HUBCS thanks Darya Prokofyeva and Shamil Gantsev. KARMA and SASBAC thank the Swedish Medical Research Council. KBCP thanks Eija Myöhänen. LMBC thanks Gilian Peuteman, Thomas Van Brussel, EvyVanderheyden and Kathleen Corthouts. MABCS thanks Milena Jakimovska (RCGEB “Georgi D. Efremov”), Snezhana Smichkoska, Emilija Lazarova, Marina Iljoska (University 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, Nadia Obi, Sabine Behrens, Ursula Eilber and Muhabbet Celik. MBCSG (Milan Breast Cancer Study Group): Paolo Peterlongo, Siranoush Manoukian, Bernard Peissel, Jacopo Azzollini, Erica Rosina, Daniela Zaffaroni, Irene Feroce, Mariarosaria Calvello, Aliana Guerrieri Gonzaga, Monica Marabelli, Davide Bondavalli 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), Anne-Lise Børresen-Dale (Prof. Em.), 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 SBCGS thank study participants and research staff for their contributions and commitment to the studies. 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. The OFBCR thanks Teresa Selander, Nayana Weerasooriya and Steve Gallinger. 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. The authors wish to acknowledge the roles of the Breast Cancer Now Tissue Bank in collecting and making available the samples and/or data, and the patients who have generously donated their tissues and shared their data to be used in the generation of this publication. PREFACE thanks Sonja Oeser and Silke Landrith. The RBCS thanks Jannet Blom, Saskia Pelders, Wendy J.C. Prager – van der Smissen, 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. 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. UBCS thanks all study participants, the ascertainment, laboratory and research informatics teams at Huntsman Cancer Institute and Intermountain Healthcare, and Justin Williams, Brandt Jones, Myke Madsen, Stacey Knight and Kerry Rowe for their important contributions to this study. UCIBCS thanks Irene Masunaka. UKBGS thanks Breast Cancer Now and the Institute of Cancer Research for support and funding of the 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.

References

Supplementary materials

  • Supplementary Data

    This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

Footnotes

  • RLM and BML are joint senior authors.

  • Twitter @drterryboyle, @drbrigidmlynch

  • RLM and BML contributed equally.

  • Deceased HO deceased.

  • Contributors Project conception – BML, RLM; Project design – SCD, BML, RLM, SJL, RMM, DRE, TB; Acquisition, analysis, or interpretation of data for the work – all authors; initial drafting of manuscript – SCD, BML, RLM, SJL, RMM, DRE, TB; critical input – all authors; final approval of manuscript – all authors. BML is guarantor.

  • Funding This work was supported by the following agencies. Funders had no role in study design, data collection, analysis, interpretation, writing of the report, or the decision to submit the paper for publication. BCAC is funded by the European Union's Horizon 2020 Research and Innovation Programme (grant numbers 634935 and 633784 for BRIDGES and B-CAST respectively), and the PERSPECTIVE I&I project, funded by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the Ministère de l’Économie et de l'Innovation du Québec through Genome Québec, the Quebec Breast Cancer Foundation. 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. Additional funding for BCAC is provided via the Confluence project which is funded with intramural funds from the National Cancer Institute Intramural Research Program, National Institutes of Health. Genotyping of the OncoArray was funded by the NIH Grant U19 CA148065, and Cancer Research 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 iCOGS 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 BRIDGES panel sequencing was supported by the European Union Horizon 2020 research and innovation program BRIDGES (grant number, 634935) and the Wellcome Trust (v203477/Z/16/Z). 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 ELANFond 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 organisations imply endorsement by the US Government or the BCFR. The BCINIS study is supported in part by the Breast Cancer Research Foundation (BCRF). The BREast Oncology GAlician Network (BREOGAN) is funded by Acción Estratégica de Salud del Instituto de Salud Carlos III FIS PI12/02125/Cofinanciado and FEDER PI17/00918/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 FEDERInnterconecta. 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 American Cancer Society funds the creation, maintenance, and updating of the CPS-II cohort. The California Teachers Study (CTS) and the research reported in this publication were supported by the National Cancer Institute of the National Institutes of Health under award number U01-CA199277; P30-CA033572; P30-CA023100; UM1-CA164917; and R01-CA077398. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health. The collection of cancer incidence data used in the California Teachers Study was supported by the California Department of Public Health pursuant to California Health and Safety Code Section 103885; Centers for Disease Control and Prevention’s National Program of Cancer Registries, under cooperative agreement 5NU58DP006344; the National Cancer Institute’s Surveillance, Epidemiology and End Results Program under contract HHSN261201800032I awarded to the University of California, San Francisco, contract HHSN261201800015I awarded to the University of Southern California, and contract HHSN261201800009I awarded to the Public Health Institute. The opinions, findings, and conclusions expressed herein are those of the author(s) and do not necessarily reflect the official views of the State of California, Department of Public Health, the National Cancer Institute, the National Institutes of Health, the Centers for Disease Control and Prevention or their Contractors and Subcontractors, or the Regents of the University of California, or any of its programs. 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 and PROCAS are funded from NIHR grant PGfAR 0707-10031. DGE, AH and WGN are supported by the NIHR Manchester Biomedical Research Centre (IS-BRC1215-20007). The GC-HBOC (German Consortium of Hereditary Breast and Ovarian Cancer) is supported by the German Cancer Aid (grant no 110837 and 70114178, coordinator: Rita K. Schmutzler, Cologne) and the Federal Ministry of Education and Research, Germany (grant no 01GY1901). 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, Johanniter GmbH Bonn, Johanniter Krankenhaus, Bonn, Germany. 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 Sigrid Juselius Foundation and the Cancer Foundation Finland. 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, 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 (VTR) 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. 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). The MCBCS was supported by the NIH grants R35CA253187, R01CA192393, R01CA116167, R01CA176785 a NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer [P50CA116201], and the Breast Cancer Research 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 U01CA164920 from the USA National Cancer Institute of the National Institutes of Health. 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 (NCBCS) 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 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. The RBCS was funded by the Dutch Cancer Society (DDHK 2004-3124, DDHK 2009-4318). 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 (VR 2017-00644) grant for the Swedish Infrastructure for Medical Populationbased Life-course Environmental Research (SIMPLER). The SZBCS was 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 TNBCC was supported by: a Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), a grant from the Breast Cancer Research Foundation, a generous gift from the David F. and Margaret T. Grohne Family Foundation. UBCS was supported by funding from National Cancer Institute (NCI) grant R01 CA163353 (to N.J. Camp) and the Women’s Cancer Center at the Huntsman Cancer Institute (HCI). Data collection for UBCS was supported by the Utah Population Database (UPDB) and Utah Cancer Registry (UCR). The UPDB is supported by HCI (including the Huntsman Cancer Foundation), University of Utah program in Personalized Health and Center for Clinical and Translational Science, and NCI grant P30 CA42014. The UCR is funded by the NCI’s SEER Program, Contract No. HHSN261201800016I, the US Center for Disease Control and Prevention's National Program of Cancer Registries, Cooperative Agreement No. NU58DP0063200, the University of Utah and Huntsman Cancer Foundation. The UCIBCS component of this research was supported by the NIH [CA58860, CA92044] and the Lon V Smith Foundation [LVS39420]. 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. RMM is a National Institute for Health Research Senior Investigator (NIHR202411). RMM is supported by a Cancer Research UK (C18281/A19169) programme grant (the Integrative Cancer Epidemiology Programme). RMM is also supported by the NIHR Bristol Biomedical Research Centre which is funded by the NIHR and is a partnership between University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol. RMM is affiliated with the Medical Research Council Integrative Epidemiology Unit at the University of Bristol which is supported by the Medical Research Council (MC_UU_00011/1, MC_UU_00011/3, MC_UU_00011/6, and MC_UU_00011/4) and the University of Bristol. Department of Health and Social Care disclaimer: The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. DGE, AH and WGN are supported by the NIHR Manchester Biomedical Research Centre (IS-BRC-1215-20007). BML is funded by the Victorian Cancer Agency (MCRF-18005).

  • Competing interests MWB conducts research funded by Amgen, Novartis and Pfizer. PAF conducts research funded by Amgen, Novartis and Pfizer. He received honoraria from Roche, Novartis and Pfizer. AWK declares research funding to her institution from Myriad Genetics for an unrelated project (funding dates 2017-2019). SL declares grants and honoraria paid to her institution from Amgen, Novartis, Pfizer, Roche, and, outside the submitted work, grants and/or honoraria paid to her institution from AbbVie, Celgene, Seattle Genetics, PrIME/Medscape, Daiichi-Sankyo, Lilly, Samsung, BMS, Puma, Immunomedics, AstraZeneca, Pierre Fabre, Merck, GlaxoSmithKlein, EirGenix, and Bayer, and personal fees from Chugai; SL also has a patent EP14153692.0 pending. UM declares stock ownership in Abcodia Ltd. RAM has been a consultant for Pharmavite. No other authors have conflicts to declare.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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