# Functional annotation of the 2q35 breast cancer risk locus implicates a structural variant in influencing activity of a long-range enhancer element

Joseph S Baxter, Nichola Johnson, Katarzyna Tomczyk, Andrea Gillespie, Sarah Maguire, Rachel Brough, Laura Fachal, Kyriaki Michailidou, Manjeet K Bolla, Qin Wang, et al.

► To cite this version:

Joseph S Baxter, Nichola Johnson, Katarzyna Tomczyk, Andrea Gillespie, Sarah Maguire, et al. Functional annotation of the 2q35 breast cancer risk locus implicates a structural variant in influencing activity of a long-range enhancer element. American Journal of Human Genetics, 2021, 108 (7), pp.1190-1203. 10.1016/j.ajhg.2021.05.013. hal-04687067

HAL Id: hal-04687067

https://hal.science/hal-04687067v1

Submitted on 4 Sep 2024

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Distributed under a Creative Commons Attribution 4.0 International License
Functional annotation of the 2q35 breast cancer
risk locus implicates a structural variant
in influencing activity of a long-range enhancer element

Joseph S. Baxter,<sup>1,*</sup> Nichola Johnson,<sup>1</sup> Katarzyna Tomczyk,<sup>1</sup> Andrea Gillespie,<sup>1</sup> Sarah Maguire,<sup>2</sup>
Rachel Brough,<sup>1,3</sup> Laura Fachal,<sup>4</sup> Kyriaki Michailidou,<sup>5,6,7</sup> Manjeet K. Bolla,<sup>7</sup> Qin Wang,<sup>7</sup> Joe Dennis,<sup>7</sup>
Thomas U. Ahearn,<sup>8</sup> Irene L. Andrulis,<sup>9,10</sup> Hoda Anton-Culver,<sup>11</sup> Natalia N. Antonenkova,<sup>12</sup>
Volker Arndt,<sup>13</sup> Kristan J. Aronson,<sup>14</sup> Annelie Augustinsson,<sup>15</sup> Heiko Becher,<sup>16</sup> Matthias W. Beckmann,<sup>17</sup>
Sabine Behrens,<sup>18</sup> Javier Benitez,<sup>19,20</sup> Marina Bermisheva,<sup>21</sup> Natalia V. Bogdanova,<sup>12,22,23</sup>
Stig E. Bojesen,<sup>24,25,26</sup> Hermann Brenner,<sup>13,27,28</sup> Sara Y. Brucker,<sup>29</sup> Qiuyin Cai,<sup>30</sup> Daniele Campa,<sup>18,31</sup>
Federico Canzian,<sup>32</sup> Jose E. Castelao,<sup>33</sup> Tsun L. Chan,<sup>34,35</sup> Jenny Chang-Claude,<sup>18,36</sup>
Stephen J. Chanock,<sup>8</sup> Georgia Chenevix-Trench,<sup>37</sup> Ji-Yeob Choi,<sup>38,39,40</sup> Christine L. Clarke,<sup>41</sup> NBCS
Collaborators,<sup>42,43,44,45,46,47,48,49,50,51,52</sup> Sarah Colonna,<sup>53</sup> Don M. Conroy,<sup>4</sup> Fergus J. Couch,<sup>54</sup>
Angela Cox,<sup>55</sup> Simon S. Cross,<sup>56</sup> Kamila Czene,<sup>57</sup> Mary B. Daly,<sup>58</sup> Peter Devilee,<sup>59,60</sup> Thilo Dörk,<sup>23</sup>
Laure Dossus,<sup>61</sup> Miriam Dwek,<sup>62</sup> Diana M. Eccles,<sup>63</sup> Arif B. Ekici,<sup>64</sup> A. Heather Eliassen,<sup>65,66</sup>

(Author list continued on next page)

**Abstract**

A combination of genetic and functional approaches has identified three independent breast cancer risk loci at 2q35. A recent fine-scale mapping analysis to refine these associations resulted in 1 (signal 1), 5 (signal 2), and 42 (signal 3) credible causal variants at these loci. We used publicly available *in silico* DNase I and ChIP-seq data with *in vitro* reporter gene and CRISPR assays to annotate signals 2 and 3. We identified putative regulatory elements that enhanced cell-type-specific transcription from the *IGFBP5* promoter at both signals (30- to 40-fold increased expression by the putative regulatory element at signal 2, 2- to 3-fold by the putative regulatory element at signal 3). We further identified one of the five credible causal variants at signal 2, a 1.4 kb deletion (esv3594306), as the likely causal variant; the deletion allele of this variant was associated with an average additional increase in *IGFBP5* expression of 1.3-fold (MCF-7) and 2.2-fold (T-47D). We propose a model in which the deletion allele of esv3594306 juxtaposes two transcription factor binding regions (annotated by estrogen receptor alpha ChIP-seq peaks) to generate a single extended regulatory element. This regulatory element increases cell-type- specific expression of the tumor suppressor gene *IGFBP5* and, thereby, reduces risk of estrogen receptor-positive breast cancer (odds ratio = 0.77, 95% CI 0.74–0.81, p = 3.1 × 10⁻³¹).

**Introduction**

Over the last 15 years, genome-wide association studies
have transformed our ability to map genetic variation un-

derlying complex traits.<fnref n="1" /> The vast majority of variants
identified in genome-wide association studies are non-cod-
ing and are thought to influence transcriptional regula-
tion,²,³ a process which can be highly cell type and tissue

(Affiliations continued on next page)

<footnote n="1">¹The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW7 3RP, UK; ²Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Ireland BT7 1NN, UK; ³The CRUK Gene Function Laboratory, The Institute of Cancer Research, London SW3 6JB, UK; ⁴Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK; ⁵Biostatistics Unit, The Cyprus Institute of Neurology & Genetics, Nicosia 2371, Cyprus; ⁶Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology & Genetics, Nicosia 2371, Cyprus; ⁷Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; ⁸Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20850, USA; ⁹Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada; ¹⁰Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; ¹¹Department of Medicine, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA 92617, USA; ¹²N.N. Alexandrov Research Institute of Oncology and Medical Radiology, Minsk 223040, Belarus; ¹³Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; ¹⁴Department of Public Health Sciences, and Cancer Research Institute, Queen's University, Kingston, ON K7L 3N6, Canada; ¹⁵Department of Cancer Epidemiology, Clinical Sciences, Lund University, Lund 222 42, Sweden; ¹⁶Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany; ¹⁷Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Erlangen 91054, Germany; ¹⁸Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; ¹⁹Biomedical Network on Rare Diseases (CIBERER), Madrid 28029, Spain; ²⁰Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain; ²¹Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa 450054, Russia; ²²Department of Radiation Oncology, Hannover Medical School, Hannover 30625, Germany; ²³Gynaecology Research Unit, Hannover Medical School, Hannover 30625, Germany; ²⁴Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev 2730, Denmark; ²⁵Department of Clinical Biochemistry, Herlev and</footnote>
Christoph Engel,<sup>67,68</sup> Peter A. Fasching,<sup>17,69</sup> Jonine Figueroa,<sup>8,70,71</sup> Henrik Flyger,<sup>72</sup>
Manuela Gago-Dominguez,<sup>73,74</sup> Chi Gao,<sup>66,75</sup> Montserrat García-Closas,<sup>8</sup> José A. García-Sáenz,<sup>76</sup>
Maya Ghoussaini,<sup>4,77</sup> Graham G. Giles,<sup>78,79,80</sup> Mark S. Goldberg,<sup>81,82</sup> Anna González-Neira,<sup>20</sup>
Pascal Guénel,<sup>83</sup> Melanie Gündert,<sup>84,85,86</sup> Lothar Haeberle,<sup>17</sup> Eric Hahnen,<sup>87,88</sup> Christopher A. Haiman,<sup>89</sup>
Per Hall,<sup>57,90</sup> Ute Hamann,<sup>91</sup> Mikael Hartman,<sup>92,93,94</sup> Sigrid Hatse,<sup>95</sup> Jan Hauke,<sup>87,88,96</sup>
Antoinette Hollestelle,<sup>97</sup> Reiner Hoppe,<sup>98,99</sup> John L. Hopper,<sup>79</sup> Ming-Feng Hou,<sup>100</sup> kConFab
Investigators,<sup>101,102</sup>, ABCTB investigators<sup>103</sup> Hidemi Ito,<sup>104,105</sup> Motoki Iwasaki,<sup>106</sup> Agnes Jager,<sup>97</sup>
Anna Jakubowska,<sup>107,108</sup> Wolfgang Janni,<sup>109</sup> Esther M. John,<sup>110,111</sup> Vijai Joseph,<sup>112</sup> Audrey Jung,<sup>18</sup>
Rudolf Kaaks,<sup>18</sup> Daehee Kang,<sup>113</sup> Renske Keeman,<sup>114</sup> Elza Khusnutdinova,<sup>21,115</sup> Sung-Won Kim,<sup>116</sup>
Veli-Matti Kosma,<sup>117,118,119</sup> Peter Kraft,<sup>66,75</sup> Vessela N. Kristensen,<sup>43,120</sup> Katerina Kubelka-Sabit,<sup>121</sup>
Allison W. Kurian,<sup>110,111</sup> Ava Kwong,<sup>34,122,123</sup> James V. Lacey,<sup>124,125</sup> Diether Lambrechts,<sup>126,127</sup>
Nicole L. Larson,<sup>128</sup> Susanna C. Larsson,<sup>129,130</sup> Loic Le Marchand,<sup>131</sup> Flavio Lejbkowicz,<sup>132</sup> Jingmei Li,<sup>94,133</sup>

(Author list continued on next page)

Gentofte Hospital, Copenhagen University Hospital, Herlev 2730, Denmark; ²⁶Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark; ²⁷Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg 69120, Germany; ²⁸German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; ²⁹Department of Gynecology and Obstetrics, University of Tübingen, Tübingen 72076, Germany; ³⁰Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; ³¹Department of Biology, University of Pisa, Pisa 56126, Italy; ³²Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; ³³Oncology and Genetics Unit, Instituto de Investigación Sanitaria Galicia Sur (IISGS), Xerencia de Xestion Integrada de Vigo-SERGAS, Vigo 36312, Spain; ³⁴Hong Kong Hereditary Breast Cancer Family Registry, Hong Kong; ³⁵Department of Molecular Pathology, Hong Kong Sanatorium and Hospital, Hong Kong; ³⁶Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany; ³⁷Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia; ³⁸Department of Biomedical Sciences, Seoul National University Graduate School, Seoul 03080, Korea; ³⁹Cancer Research Institute, Seoul National University, Seoul 03080, Korea; ⁴⁰Institute of Health Policy and Management, Seoul National University Medical Research Center, Seoul 03080, Korea; ⁴¹Westmead Institute for Medical Research, University of Sydney, Sydney, NSW 2145, Australia; ⁴²Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo 0379, Norway; ⁴³Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo 0450, Norway; ⁴⁴Department of Research, Vestre Viken Hospital, Drammen 3019, Norway; ⁴⁵Section for Breast and Endocrine Surgery, Department of Cancer, Division of Surgery, Cancer and Transplantation Medicine, Oslo University Hospital-Ullevål, Oslo 0450, Norway; ⁴⁶Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo 0379, Norway; ⁴⁷Department of Pathology, Akershus University Hospital, Lørenskog 1478, Norway; ⁴⁸Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo 0379, Norway; ⁴⁹Department of Oncology, Division of Surgery, Cancer and Transplantation Medicine, Oslo University Hospital-Radiumhospitalet, Oslo 0379, Norway; ⁵⁰National Advisory Unit on Late Effects after Cancer Treatment, Oslo University Hospital-Radiumhospitalet, Oslo 0379, Norway; ⁵¹Department of Oncology, Akershus University Hospital, Lørenskog 1478, Norway; ⁵²Breast Cancer Research Consortium, Oslo University Hospital, Oslo 0379, Norway; ⁵³Department of Medicine, Huntsman Cancer Institute, Salt Lake City, UT 84112, USA; ⁵⁴Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA; ⁵⁵Sheffield Institute for Nucleic Acids (SInFoNiA), Department of Oncology and Metabolism, University of Sheffield, Sheffield S10 2TN, UK; ⁵⁶Academic Unit of Pathology, Department of Neuroscience, University of Sheffield, Sheffield S10 2TN, UK; ⁵⁷Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 171 65, Sweden; ⁵⁸Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA 19111, USA; ⁵⁹Department of Pathology, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands; ⁶⁰Department of Human Genetics, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands; ⁶¹Nutrition and Metabolism Section, International Agency for Research on Cancer (IARC-WHO), Lyon 69372, France; ⁶²School of Life Sciences, University of Westminster, London W1B 2HW, UK; ⁶³Faculty of Medicine, University of Southampton, Southampton SO17 1BJ, UK; ⁶⁴Institute of Human Genetics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen 91054, Germany; ⁶⁵Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; ⁶⁶Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; ⁶⁷Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig 04107, Germany; ⁶⁸LIFE - Leipzig Research Centre for Civilization Diseases, University of Leipzig, Leipzig 04103, Germany; ⁶⁹David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology, University of California at Los Angeles, Los Angeles, CA 90095, USA; ⁷⁰Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh EH16 4UX, UK; ⁷¹Cancer Research UK Edinburgh Centre, The University of Edinburgh, Edinburgh EH4 2XR, UK; ⁷²Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev 2730, Denmark; ⁷³Fundación Pública Galega de Medicina Xenómica, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela 15706, Spain; ⁷⁴Moores Cancer Center, University of California San Diego, La Jolla, CA 92037, USA; ⁷⁵Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; ⁷⁶Medical Oncology Department, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Centro Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid 28040, Spain; ⁷⁷Open Targets, Core Genetics Team, Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK; ⁷⁸Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia; ⁷⁹Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia; ⁸⁰Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; ⁸¹Department of Medicine, McGill University, Montréal, QC H4A 3J1, Canada; ⁸²Division of Clinical Epidemiology, Royal Victoria Hospital, McGill University, Montréal, QC H4A 3J1, Canada; ⁸³Center for Research in Epidemiology and Population Health (CESP), Team Exposome and Heredity, INSERM, University Paris-Saclay, Villejuif 94805, France; ⁸⁴Molecular Epidemiology Group, C080, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; ⁸⁵Molecular Biology of Breast Cancer, University Womens Clinic Heidelberg, University of Heidelberg, Heidelberg 69120, Germany; ⁸⁶Institute of Diabetes Research, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg 85764, Germany; ⁸⁷Center for Familial Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne 50937, Germany; ⁸⁸Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne 50937, Germany; ⁸⁹Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA; ⁹⁰Department of Oncology, Södersjukhuset, Stockholm 118 83, Sweden; ⁹¹Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; ⁹²Saw Swee Hock School of Public Health, National

(Affiliations continued on next page)
Jirong Long,³⁰ Artitaya Lophatananon,¹³⁴ Jan Lubiński,¹⁰⁷ Arto Mannermaa,¹¹⁷,¹¹⁸,¹¹⁹
Mehdi Manoochehri,⁹¹ Siranoush Manoukian,¹³⁵ Sara Margolin,⁹⁰,¹³⁶ Keitaro Matsuo,¹⁰⁴,¹⁰⁵
Dimitrios Mavroudis,¹³⁷ Rebecca Mayes,⁴ Usha Menon,¹³⁸ Roger L. Milne,⁷⁸,⁷⁹,⁸⁰ Nur Aishah Mohd Taib,¹³⁹
Kenneth Muir,¹³⁴ Taru A. Muranen,¹⁴⁰ Rachel A. Murphy,¹⁴¹,¹⁴² Heli Nevanlinna,¹⁴⁰ Katie M. O'Brien,¹⁴³
Kenneth Offit,¹¹²,¹⁴⁴ Janet E. Olson,¹²⁸ Håkan Olsson,¹⁵ Sue K. Park,³⁹,¹¹³,¹⁴⁵ Tjong-Won Park-Simon,²³
Alpa V. Patel,¹⁴⁶ Paolo Peterlongo,¹⁴⁷ Julian Peto,¹⁴⁸ Dijana Plaseska-Karanfilska,¹⁴⁹ Nadege Presneau,⁶²
Katri Pylkäs,¹⁵⁰,¹⁵¹ Brigitte Rack,¹⁰⁹ Gad Rennert,¹³² Atocha Romero,¹⁵² Matthias Ruebner,¹⁷
Thomas Rüdiger,¹⁵³ Emmanouil Saloustros,¹⁵⁴ Dale P. Sandler,¹⁴³ Elinor J. Sawyer,¹⁵⁵
Marjanka K. Schmidt,¹¹⁴,¹⁵⁶ Rita K. Schmutzler,⁸⁷,⁸⁸,⁹⁶ Andreas Schneeweiss,⁸⁵,¹⁵⁷ Minouk J. Schoemaker,¹⁵⁸
Mitul Shah,⁴ Chen-Yang Shen,¹⁵⁹,¹⁶⁰ Xiao-Ou Shu,³⁰ Jacques Simard,¹⁶¹ Melissa C. Southey,⁷⁸,⁸⁰,¹⁶²
Jennifer Stone,⁷⁹,¹⁶³ Harald Surowy,⁸⁴,⁸⁵ Anthony J. Swerdlow,¹⁵⁸,¹⁶⁴ Rulla M. Tamimi,⁶⁶,¹⁶⁵

(Author list continued on next page)

University of Singapore, Singapore 119077, Singapore; ⁹³Department of Surgery, National University Hospital, Singapore 119228, Singapore; ⁹⁴Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore; ⁹⁵Laboratory of Experimental Oncology (LEO), Department of Oncology, KU Leuven, Leuven Cancer Institute, Leuven 3000, Belgium; ⁹⁶Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne 50931, Germany; ⁹⁷Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam 3015 GD, the Netherlands; ⁹⁸Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart 70376, Germany; ⁹⁹University of Tübingen, Tübingen 72074, Germany; ¹⁰⁰Department of Surgery, Kaohsiung Municipal Hsiao-Kang Hospital, Kaohsiung 812, Taiwan; ¹⁰¹Research Department, Peter MacCallum Cancer Center, Melbourne, VIC 3000, Australia; ¹⁰²Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia; ¹⁰³Australian Breast Cancer Tissue Bank, Westmead Institute for Medical Research, University of Sydney, Sydney, NSW 2145, Australia; ¹⁰⁴Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya 464-8681, Japan; ¹⁰⁵Division of Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan; ¹⁰⁶Division of Epidemiology, Center for Public Health Sciences, National Cancer Center, Tokyo 104-0045, Japan; ¹⁰⁷Department of Genetics and Pathology, Pomeranian Medical University, Szczecin 71-252, Poland; ¹⁰⁸Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin 71-252, Poland; ¹⁰⁹Department of Gynaecology and Obstetrics, University Hospital Ulm, Ulm 89075, Germany; ¹¹⁰Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA 94305, USA; ¹¹¹Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94304, USA; ¹¹²Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; ¹¹³Department of Preventive Medicine, Seoul National University College of Medicine, Seoul 03080, Korea; ¹¹⁴Division of Molecular Pathology, the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam 1066 CX, the Netherlands; ¹¹⁵Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa 450000, Russia; ¹¹⁶Department of Surgery, Daerim Saint Mary's Hospital, Seoul 07442, Korea; ¹¹⁷Translational Cancer Research Area, University of Eastern Finland, Kuopio 70210, Finland; ¹¹⁸Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio 70210, Finland; ¹¹⁹Biobank of Eastern Finland, Kuopio University Hospital, Kuopio, Finland; ¹²⁰Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo 0379, Norway; ¹²¹Department of Histopathology and Cytology, Clinical Hospital Acibadem Sistina, Skopje 1000, Republic of North Macedonia; ¹²²Department of Surgery, The University of Hong Kong, Hong Kong; ¹²³Department of Surgery and Cancer Genetics Center, Hong Kong Sanatorium and Hospital, Hong Kong; ¹²⁴Department of Computational and Quantitative Medicine, City of Hope, Duarte, CA 91010, USA; ¹²⁵City of Hope Comprehensive Cancer Center, City of Hope, Duarte, CA 91010, USA; ¹²⁶VIB Center for Cancer Biology, Leuven 3001, Belgium; ¹²⁷Laboratory for Translational Genetics, Department of Human Genetics, University of Leuven, Leuven 3000, Belgium; ¹²⁸Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA; ¹²⁹Institute of Environmental Medicine, Karolinska Institutet, Stockholm 171 77, Sweden; ¹³⁰Department of Surgical Sciences, Uppsala University, Uppsala 751 05, Sweden; ¹³¹Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI 96813, USA; ¹³²Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, Haifa 35254, Israel; ¹³³Human Genetics Division, Genome Institute of Singapore, Singapore 138672, Singapore; ¹³⁴Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PL, UK; ¹³⁵Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan 20133, Italy; ¹³⁶Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm 118 83, Sweden; ¹³⁷Department of Medical Oncology, University Hospital of Heraklion, Heraklion 711 10, Greece; ¹³⁸Institute of Clinical Trials & Methodology, University College London, London WC1V 6LJ, UK; ¹³⁹Breast Cancer Research Unit, University Malaya Cancer Research Institute, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia; ¹⁴⁰Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki 00290, Finland; ¹⁴¹School of Population and Public Health, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; ¹⁴²Cancer Control Research, BC Cancer, Vancouver, BC V5Z 1L3, Canada; ¹⁴³Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC 27709, USA; ¹⁴⁴Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; ¹⁴⁵Convergence Graduate Program in Innovative Medical Science, Seoul National University College of Medicine, Seoul 03080, Korea; ¹⁴⁶Department of Population Science, American Cancer Society, Atlanta, GA 30303, USA; ¹⁴⁷Genome Diagnostics Program, IFOM - the FIRC Institute of Molecular Oncology, Milan 20139, Italy; ¹⁴⁸Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK; ¹⁴⁹Research Centre for Genetic Engineering and Biotechnology 'Georgi D. Efremov', MASA, Skopje 1000, Republic of North Macedonia; ¹⁵⁰Laboratory of Cancer Genetics and Tumor Biology, Cancer and Translational Medicine Research Unit, Biocenter Oulu, University of Oulu, Oulu 90570, Finland; ¹⁵¹Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre Oulu, Oulu 90570, Finland; ¹⁵²Medical Oncology Department, Hospital Universitario Puerta de Hierro, Madrid 28222, Spain; ¹⁵³Institute of Pathology, Staedtisches Klinikum Karlsruhe, Karlsruhe 76133, Germany; ¹⁵⁴Department of Oncology, University Hospital of Larissa, Larissa 411 10, Greece; ¹⁵⁵School of Cancer & Pharmaceutical Sciences, Comprehensive Cancer Centre, Guy's Campus, King's College London, London, UK; ¹⁵⁶Division of Psychosocial Research and Epidemiology, the Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Amsterdam 1066 CX, the Netherlands; ¹⁵⁷National Center for Tumor Diseases, University Hospital and German Cancer Research Center, Heidelberg 69120, Germany; ¹⁵⁸Division of Genetics and Epidemiology, The Institute of Cancer Research, London SM2 5NG, UK; ¹⁵⁹Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan; ¹⁶⁰School of Public Health, China Medical University, Taichung, Taiwan; ¹⁶¹Genomics Center, Centre Hospitalier Universitaire de Québec - Université Laval Research Center, Québec City, QC G1V 4G2, Canada; ¹⁶²Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC 3010, Australia; ¹⁶³Genetic Epidemiology Group, School of Population and Global Health, University of Western Australia, Perth, WA 6000, Australia; ¹⁶⁴Division of Breast Cancer Research, The Institute of Cancer Research, London SW7 3RP, UK; ¹⁶⁵Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA; ¹⁶⁶Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, NIH, Research

(Affiliations continued on next page)
William J. Tapper,<sup>63</sup> Jack A. Taylor,<sup>143,166</sup> Soo Hwang Teo,<sup>167,168</sup> Lauren R. Teras,<sup>146</sup> Mary Beth Terry,<sup>169</sup>
Amanda E. Toland,<sup>170</sup> Ian Tomlinson,<sup>171,172</sup> Thérèse Truong,<sup>83</sup> Chiu-Chen Tseng,<sup>89</sup> Michael Untch,<sup>173</sup>
Celine M. Vachon,<sup>174</sup> Ans M.W. van den Ouweland,<sup>175</sup> Sophia S. Wang,<sup>124,125</sup> Clarice R. Weinberg,<sup>176</sup>
Camilla Wendt,<sup>136</sup> Stacey J. Winham,<sup>177</sup> Robert Winqvist,<sup>150,151</sup> Alicja Wolk,<sup>129,130</sup> Anna H. Wu,<sup>89</sup>
Taiki Yamaji,<sup>106</sup> Wei Zheng,<sup>30</sup> Argyrios Ziogas,<sup>11</sup> Paul D.P. Pharoah,<sup>4,7</sup> Alison M. Dunning,<sup>4</sup>
Douglas F. Easton,<sup>4,7</sup> Stephen J. Pettitt,<sup>1,3</sup> Christopher J. Lord,<sup>1,3</sup> Syed Haider,<sup>1</sup> Nick Orr,<sup>2</sup>
and Olivia Fletcher<sup>1,*</sup>

specific.⁴ Our ability to translate these findings into a greater understanding of the mechanisms that influence an individual woman's risk will require the identification of causal variants (as opposed to correlative variants), the targets of these functional variants (the genes or non-coding RNAs that mediate the associations observed in genome-wide association studies) and an understanding of the disease causal cell types and processes.¹ Genome-wide association studies of breast cancer coupled with large-scale replication and fine-mapping studies have led to the identification of approximately 200 breast cancer risk loci;<sup>3,5-9</sup> two of these loci, annotated by rs13387042¹⁰ and rs16857609,⁵ map to a gene desert at chromosome 2q35. Fine-scale mapping, combined with *in silico* annotation, reporter gene assays, and allele-specific qRT-PCR led to the identification of a putative causal variant (rs4442975) at the rs13387042 locus.¹¹,¹² rs4442975, which is highly correlated with the tag SNP rs13387042 (r² = 0.92, D' = 0.96), maps to a consensus binding site for the transcription factor (TF) forkhead box A1 (FOXA1 [MIM: 602294]) with the alternative T-allele promoting binding of FOXA1.¹¹,¹² To date, no putative causal variant at the rs16857609 locus has been reported. Chromatin interaction methods implicate *IGFBP5* (MIM: 146734) as the target gene at both loci¹¹⁻¹³ and for the rs13387042 locus, eQTL analyses demonstrated association of the protective T-allele with slightly increased *IGFBP5* levels in normal breast tissue¹¹ and estrogen receptor-positive (ER⁺) breast cancers.¹²

Taking a functional approach based on chromosome conformation capture (3C) assays that were anchored at the *IGFBP5* promoter, Wyszynski and colleagues identified a putative regulatory element centered on a structural variant (SV; esv3594306) that maps approximately 400 kb telomeric to *IGFBP5*.¹⁴ Allele-specific expression analyses and follow-up genotyping identified 14 highly correlated variants (all r² > 0.8 with the top SNP, rs34005590) associated with breast cancer risk, which represent a third risk signal (OR = 0.82, p = 5.6 × 10⁻¹⁷).¹⁴

In this analysis we report fine-scale mapping of the 2q35 region in European and Asian individuals with breast cancer and control subjects from the Breast Cancer Association Consortium. We confirm three independent, high-confidence signals at 2q35 annotated by rs13387042 (signal 1), rs138522813 (signal 2), and rs16857609 (signal 3). We carry out functional annotation of credible variants at signals 2 and 3 and implicate the deletion variant (esv3594306) at signal 2 as causally associated with increased *IGFBP5* expression and reduced breast cancer risk.

## Material and methods

### Fine-scale mapping of the 2q35 breast cancer risk locus

Fine-scale mapping of the 2q35 breast cancer risk locus was carried out as part of a large collaborative project; full details have been published.³ Briefly, for the current analysis we accessed data from 94,391 individuals with invasive breast cancer and 83,477 individuals of European ancestry and 12,481 individuals with invasive breast cancer and 12,758 control subjects of Asian ancestry from 87 studies participating in the Breast Cancer Association Consortium. All participating studies were approved by their appropriate ethics review board and all subjects provided informed consent.

Directly genotyped or imputed (info score > 0.8) calls for 10,314 SNPs mapping to a 1.4 Mb region at 2q35 (chr2:217,405,832–218,796,508; GRCh37/hg19) were available for analysis. At this threshold, the proportions of common variants (MAF ≥ 0.05), low-frequency variants (0.01 ≤ MAF < 0.05), and rare variants (0.001 ≤ MAF < 0.01)³ that could be analyzed were 89.7%, 68.5%, and 3.6%, respectively, for OncoArray and 64.2%, 40.5%, and 0.8%, respectively, for iCOGS. Analysis of the association between each SNP and risk of breast cancer was performed using unconditional logistic regression assuming a log-additive genetic model, adjusted for study and up to 15 ancestry-informative principal components. p values were calculated using Wald tests. Forward stepwise logistic regression was used to explore whether additional loci in the fine-mapping region were independently associated with breast cancer risk. We carried out stratified analyses to determine whether each of the independent associations

Triangle Park, NC 27709, USA; ¹⁶⁷Breast Cancer Research Programme, Cancer Research Malaysia, Subang Jaya, Selangor 47500, Malaysia; ¹⁶⁸Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia; ¹⁶⁹Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, USA; ¹⁷⁰Department of Cancer Biology and Genetics, The Ohio State University, Columbus, OH 43210, USA; ¹⁷¹Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK; ¹⁷²Wellcome Trust Centre for Human Genetics and Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford OX3 7BN, UK; ¹⁷³Department of Gynecology and Obstetrics, Helios Clinics Berlin-Buch, Berlin 13125, Germany; ¹⁷⁴Department of Health Science Research, Division of Epidemiology, Mayo Clinic, Rochester, MN 55905, USA; ¹⁷⁵Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam 3015 GD, the Netherlands; ¹⁷⁶Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC 27709, USA; ¹⁷⁷Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55905, USA

*Correspondence: joseph.baxter@icr.ac.uk (J.S.B.), olivia.fletcher@icr.ac.uk (O.F.)
https://doi.org/10.1016/j.ajhg.2021.05.013.
differed according to estrogen receptor (ER) status; heterogeneity between stratum-specific estimates was assessed using Cochran's Q-test. All statistical analyses were carried out using R version 3.6.1.

## In silico annotation of credible variants

Credible variants at each of the three independent signals were aligned with DNase I and ChIP-seq data (P300 [EP300 (MIM: 602700)], H3K27Ac, H3K4me1, FOXA1, GATA3 [MIM: 131320], ERα [ESR1 (MIM: 133430)]) generated in T-47D and MCF-7 breast cancer cells¹⁵⁻¹⁷ (Table S1).

## Cloning of reporter assay constructs

All reporter assay plasmids were derived using the pGL4 reporter vector (Promega). Reporter vectors were constructed using a restriction digest-based cloning approach. The *IGFBP5* promoter and putative regulatory element regions (containing WT alleles) were synthesized as gBlocks (Integrated DNA Technologies, full details in Table S2). Double restriction digests of plasmid or gBlock were performed using BglII and XhoI (for *IGFBP5* promoter) or Sall and BamHI (for putative regulatory element regions) according to the manufacturer's instructions (New England Biolabs [NEB]). Ligations were performed in a 3:1 insert:vector ratio using T4 DNA ligase (NEB), according to manufacturer's instructions. Correct cloning was validated by Sanger sequencing using a commercially available service (Eurofins Genomics). Alternative (ALT) alleles of each variant were introduced into reporter vectors using QuikChange Lightning Site-directed Mutagenesis kit (Agilent Technologies), according to the manufacturer's instructions. Accurate mutagenesis was confirmed by Sanger sequencing (Eurofins Genomics). All reporter gene constructs are shown in Figure S1.

## Cell Culture

T-47D cells were grown in RPMI (GIBCO) supplemented with 10% FBS (GIBCO), 10 µg/mL human insulin (Sigma), and 100 U/mL penicillin with 100 µg/mL streptomycin (Sigma). HCT116 cells were grown in RPMI supplemented with 10% FBS, 100 U/mL penicillin, and 100 µg/mL streptomycin. HepG2 cells were grown in EMEM (LGC Standards-ATCC) supplemented with 10% FBS and 100 U/mL penicillin with 100 µg/mL streptomycin. MCF-7 cells (including derivative Cas9-expressing cell lines) and 293T cells were grown in DMEM (GIBCO) supplemented with 10% FBS and 100 U/mL penicillin with 100 µg/mL streptomycin. All cell lines were routinely short tandem repeat (STR)-typed and tested for mycoplasma contamination.

## Reporter assays

Reporter assays were performed in T-47D, MCF-7, 293T, HCT116, and HepG2 cell lines. Antibiotics were removed from standard growth media 24 h before transfection to improve viability. For assays performed under standard conditions, approximately 16,000 cells were seeded per well of a 96-well plate for T-47D, MCF-7, and HepG2, and approximately 8,000 cells were seeded per well of a 96-well plate for 293T and HCT116. Transfection was performed upon reaching 70% confluency (~24 h after cell seeding). For assays performed after 17β-estradiol treatment, cells were first hormone starved for 48 h. Approximately 10,000 cells (T-47D) and 8,000 cells (MCF-7) were seeded, per well of a 96-well plate, in standard growth media and cultured for 24 h. The media was then replaced with phenol red-free media (GIBCO) supplemented with 10% charcoal-stripped FBS (GIBCO), 100 U/mL penicillin

with 100 µg/mL streptomycin, 10 nM fulvestrant (I4409, Sigma), and 10 µg/mL human insulin (T-47D only). After 48 h, growth media was replaced with phenol red-free media supplemented with 10% charcoal-stripped FBS, 10 µg/mL human insulin (T-47D only), with the addition of either (1) 10 nM 17β-estradiol (E2758, Sigma) or (2) vehicle (ethanol). Transfection was performed upon reaching 80% confluency (6 h after 17β-estradiol or vehicle treatment).

Transfection was performed using X-treme GENE HP DNA transfection reagent (Roche). Equimolar amounts of the test pGL4-based firefly luciferase vector and pRL-TK renilla luciferase control (Promega) were combined in a 3:1 reagent:DNA ratio in OptiMEM (Fisher Scientific). After a 30 min incubation at room temperature, 10 µL transfection mixture was added per well. Each biological replicate was performed in technical triplicates with non-transfected, mock-transfected, and pEGFP-transfected controls (Takara Bio Inc). Cells were screened for luciferase activity 48 h after transfection using the Dual-Glo Luciferase Assay System (Promega) according to the manufacturer's instructions.

## Confirmatory genotyping and sequencing of putative regulatory element 2 (PRE2)

Four of the five variants mapping to PRE2 (rs72951831, rs199804270, rs138522813, and esv3594306) are highly correlated based on 1000 Genomes data (1KGP), with the ALT alleles of rs72951831, rs199804270, and rs138522813 all predicted to occur in combination with the ALT (deletion) allele of esv3594306 (esv3594306: rs72951831 r² = 1.0, D' = 1.0; esv3594306: rs199804270 r² = 0.95, D' = 1.0; esv3594306: rs138522813 r² = 1.0, D' = 1.0). However, rs572022984 (hg19, chr2:217955897) theoretically maps within the esv3594306 deleted region (chr2:217,955,891–217,957,273) casting doubt on whether the (imputed) rs572022984-del allele could occur in combination with the esv3594306 deletion allele. To clarify this, we genotyped all five variants in 300 randomly selected women participating in the Generations Study¹⁸ using MassARRAY (Agena Bioscience; full details of primers available on request). The number of carriers of the alternative (A->) allele at rs572022984 (MAF = 0.035) was 0 (expected number = 21; p = 0.00002). To confirm our genotyping, we carried out Sanger sequencing (Eurofins) of a 2.4 kb region spanning (chr2:217,955,586–217,958,000) in two individuals who were heterozygous at the linked PRE2 SNP rs138522813. Primers were: forward 5'-CGCTTCCCCTTCATCACTTG-3' and, reverse 5'-TCTCTCAGGCCAAGTCACAG-3'. Sequencing confirmed the presence of REF and ALT alleles of esv3594306, rs72951831, and rs199804270 (rs138522813 maps just outside the amplified region) but only REF alleles at rs572022984; on this basis we excluded rs572022984 from further analyses.

## Cloning of guides for CRISPR-based enhancer perturbation

Guides were designed using the online design tool CHOPCHOP (http://chopchop.cbu.uib.no). Guides were selected based on their proximity to variants of interest and specificity scores. Full details are provided in Table S3. Cloning was performed essentially as described in Ran et al.¹⁹ Briefly, guides were produced as two complementary oligonucleotides with overhangs to facilitate cloning. Oligos were annealed with T4 Polynucleotide Kinase (NEB). The expression vector pKLV-U6gRNA(BbsI)-PGKpuro2ABFP (Addgene #50946) was digested using BbsI (NEB), and ligation performed using T4 DNA ligase (NEB). Cloning was validated by sequencing (Eurofins Genomics).
## CRISPR-based enhancer perturbation

All CRISPR cell lines were derived from a parental MCF-7 cell line. Expression of each dCas9 construct was introduced by transduction with a specific Cas9-expressing lentivirus: pGH125_dCas9-Blast (Addgene #85417) for dCas9; pHR-SFFV-KRAB-dCas9-P2A-mCherry (Addgene #60954) for dCas9-KRAB; Lenti-hEF1-BLAST-dCas9-VPR (Dharmacon, CAS11916) for dCas9-VPR. Successfully transduced cells were then selected for by mCherry expression (dCas9-KRAB) or treatment with 10 µg/mL blasticidin (dCas9 and dCas9-VPR; GIBCO). Cells were then seeded into 24-well plates at a density of 50,000 cells per well. 100 µL of sgRNA lentivirus was added. After 24 h, media was replaced and after 48 h cells were lysed using the Cells-to-Ct kit (Life Technologies) for subsequent gene expression analysis by RT-PCR.

## Real-time PCR

Real-time PCR analysis of gene expression in cDNA samples was performed using Taqman probes (Life Technologies) for *IGFPB2* (MIM: 146731), *IGFBP5*, and *RPL37A* (MIM: 613314) normalized to the housekeeping gene *GAPDH* (ThermoFisher; *IGFBP2*: Hs 01040719_m1, *IGFBP5*: Hs00181213_m1, *RPL37A*: Hs011023 45_m1, *GAPDH*: Hs03929097_g1). Reactions of 5 µL were established using Taqman Universal Master Mix II, without UNG (Applied Biosystems) according to the manufacturer's instructions.

## Statistical analysis of reporter gene assays and CRISPR-based enhancer perturbation

Firefly luciferase activity was internally normalized to renilla luciferase activity, and each test condition normalized to the "IGFBP5 promoter-alone" (IGFBP5-PROM) construct. Setting IGFBP5-PROM to 1.0, for each putative enhancer-containing reporter gene construct we used t tests to test (1) $H_0$: the mean dual luciferase ratio does not differ from 1.0 and (2) $H_0$: the ALT construct does not differ from the REF construct. To compare mean dual luciferase ratios for each combination of SNP and SV at PRE2, we used three-way analysis of variance adjusting each variant for all other variants. To account for multiple testing, we used a Bonferroni corrected p value of 0.0056 (individual constructs, Figure 2; 9 tests) and 0.017 (PRE2 combinations, Figure 3; 3 tests).

Relative gene expression was calculated using the ΔΔC~T~ method. For the negative control sgRNAs (TAG-1 and TAG-2), we used t tests to test $H_0$: the relative gene expression does not differ from 1.0. To maximize the power of subsequent analyses, we then combined the negative control data and for each of the other sgRNAs we tested $H_0$: relative gene expression does not differ from the combined negative control relative gene expression. To account for multiple testing, we used a Bonferroni corrected p value of 0.017 (PROM sgRNAs Figures 4A; 3 tests per gene) and 0.0056 (PRE2 sgRNAs, Figures 4B and 4C; 9 tests per gene).

## Ethics approval and consent to participate

All participating studies were approved by their appropriate ethics review board and all subjects provided informed consent.

## Results

Fine-scale mapping of a 1.4 Mb region at 2q35 (chr2:217,407,297-218,770,424; GRCh37/hg19; Figure 1A)

in combined data from up to 109,900 individuals with breast cancer and 88,937 control subjects of European Ancestry from the Breast Cancer Association Consortium confirmed the presence of three independent signals ($p < 5 \times 10^{-8}$; Figure S2) at this region.³ After conditioning on the top SNP at each of these three signals (signal 1, rs4442975; signal 2, rs138522813; signal 3, rs5838651), there were no additional high-confidence signals (defined as signals for which $p < 1 \times 10^{-6}$).³ Defining credible causal variants at each signal as variants with conditional p values within two orders of magnitude of the index variant there were 1, 5, and 42 credible causal variants at PRE1, PRE2, and PRE3, respectively (Table S4). Fine-scale mapping of this region in women of Asian Ancestry (12,481 affected individuals and 12,758 control subjects) did not identify any population-specific signals (all associations $p > 5 \times 10^{-8}$; Figure S3). None of the credible causal variants at signal 2 was present in women of Asian ancestry. The published causal variant at signal 1 (rs4442975) and all of the signal 3 credible causal variants (Table S5) were nominally associated with breast cancer risk in Asian women ($p < 0.05$). At signal 3, the index variants differ between Europeans and Asians (rs5838651 and 2:218265091:G:INS:ME:ALU>:218265367, respectively) but none of the European credible causal variants could be excluded on the basis of the Asian data.

The T-allele of rs4442975 was associated with reduced breast cancer risk (per allele OR = 0.88, 95% CI 0.87-0.89, $p = 1.3 \times 10^{-75}$ and OR = 0.94, 95% CI 0.89-1.00, $p = 0.04$ in European and Asian women, respectively) and the delG-allele of rs5838651 was associated with increased risk (per allele OR = 1.07, 95% CI 1.05-1.08, $p = 1.5 \times 10^{-16}$ and OR = 1.07, 95% CI 1.03-1.11, $p = 0.0008$ in European and Asian women, respectively; Table 1). The delT-allele of rs138522813 was associated with reduced risk (carrier OR = 0.80, 95% CI 0.77-0.83, $p = 5.5 \times 10^{-32}$). Stratifying by ER status, the signal 1 (rs4442975) and signal 2 (rs138522813) SNPs were more strongly associated with ER⁺ disease; for the signal 3 SNP (rs5838651), there was no evidence that the ORs differed by ER status (Table S6).

## Prioritization of credible variants for functional follow up

Fachal and colleagues³ used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium, and enriched genomic features to determine variants with high posterior probabilities of being causal (Table S4).²⁰ rs4442975, the only credible causal variant at signal 1 (posterior probability = 0.84), has previously been proposed to have a functional effect on breast cancer risk.¹¹,¹² Four of the five variants at signal 2 had posterior probabilities ≥ 0.20 (combined posterior probability 0.997); none of the variants at signal 3 had posterior probabilities > 0.15. To further prioritize putative causal variants at signals 2 and 3, we aligned the 47 credible variants at these signals with markers of open chromatin (DNase I), active transcription (P300), active
<figure><img src="image_2.png" /><figcaption>**Figure 1.** 2q35 breast cancer risk locus</figcaption></figure>

(A) Fine-scale mapping at 2q35 identified three high-confidence (p < 1 × 10⁻⁶) signals annotated by rs4442975 (signal 1), rs138522813 (signal 2), and rs5838651 (signal 3). The putative target gene (*IGFBP5*) maps 360 kb, 399 kb, and 703 kb from signals 1, 2, and 3, respectively. All coordinates are based on GRCh37/hg19.

(B) Putative regulatory element 2 (PRE2; chr2:217,955,458–217,957,767) at signal 2 colocalizes with four highly correlated variants: three single-nucleotide polymorphisms (SNPs; rs572022984, rs199804270, and rs72951831) and a 1.4 kb insertion/deletion variant (esv3594306; indicated by a black bar). A fourth SNP (rs138522813) maps outside the proposed boundaries of PRE2. Regions of open chromatin (DNase I) and ChIP-seq binding peaks for transcription factors are shown as gray bars where the shade of gray indicates the strength of the ChIP-seq peak (light gray, weak binding; dark gray, strong binding). Also shown (yellow bars) are the coordinates of three reporter gene constructs (PRE2A, PRE2B, and PRE2DEL) and the locations of sequences targeted by nine small guide (sg)RNAs.

(C) PRE3 (chr2:218,305,944–218,306,443) indicated by a blue bar colocalizes with two SNPs (rs12694417 and rs12988242). Regions of open chromatin and ChIP-seq binding peaks are as in (B).

enhancers (H3K27Ac, H3K4me1), and breast-relevant TFs (FOXA1, GATA3, ERα) generated in T-47D and MCF-7 breast cancer cells¹⁵⁻¹⁷ (Table S4). Consistent with the PAINTOR posterior probabilities, four variants at signal 2 colocalized with at least one of these features. In addition, we identified two variants at signal 3 that colocalized with one of these features. These six variants were prioritized for further functional annotation.

## Reporter gene assays of prioritized variants

For SNPs, we generated reference (REF) and alternative (ALT) constructs in which the putative regulatory element, defined in the first instance as a 500 to 700 bp region centered on the SNP or SNP pair (PRE2A rs572022984;

PRE2B rs199804270 and rs72951831; PRE3 rs12694417 and rs12988242, Table S2; Figures 1B and 1C), was cloned upstream of a luciferase reporter gene, driven by the *IGFBP5* promoter (Figure S1). For the structural variant esv3594306, which is defined by the presence (REF) or absence (ALT) of a 1.4 kb region (chr2:217,955,891–217,957,273; GRCh37/hg19), we generated separate REF constructs for PRE2A and PRE2B and a single ALT construct in which the centromeric sequences at PRE2A were juxtaposed to the telomeric sequences at PRE2B with the intervening 1.4 kb deleted (Figure 1B). Comparing the REF construct at each region with the *IGFBP5* promoter construct (IGFBP5-PROM), there was evidence that two of the putative regulatory elements (PRE2B and PRE3) enhanced transcription from the *IGFBP5* promoter (Figure 2). For PRE2B, both alleles demonstrated
<figure><img src="image_3.png" /><figcaption>**Figure 3.** Luciferase reporter assays following transient transfection of constructs with allelic variants at PRE2B and PRE2DEL into MCF-7 and T-47D cells</figcaption></figure>

Reporter gene constructs with all possible combinations of rs199804270 and rs7295 1831 and esv3594306 were generated by site-directed mutagenesis of the naturally occurring haplotypes at PRE2B and PRE2-DEL (Material and methods) into MCF-7 (A) and T-47D (B) cells. Coordinates of the PREs are given in Table S2, diagrams are in Figure S1. Error bars denote standard deviations based on three independent experiments each done in triplicate. 3-way ANOVA was used to compare each variant, adjusted for the other two variants, a Bonferroni correction was applied to account for multiple testing. \*p < 0.017, \*\*p ≤ 0.0017.

PRE2B-REF/REF allele, this time in T-47D cells (MCF-7 FC = 1.5, p = 0.15; T-47D FC = 2.7, p = 0.002; Figure S5A).

The PRE2DEL-ALT/ALT construct comprises a haplotype of three tightly linked variants: the ALT alleles of the two SNPs (rs199804270:GA:G, rs72951831:G:T) with the ALT (deletion) allele of the structural variant (esv3594306) that brings two separate ERα, FOXA1, GATA3, and P300 ChIP-seq peaks into juxtaposition (Figure 1B). To differentiate individual effects, each allele of each SNP was introduced onto esv3594306 insertion and deletion backgrounds separately using site-directed mutagenesis. The PRE2A SNP (rs572022984) was not considered further due to technical issues (Material and methods). In a combined analysis, adjusting each variant for the other two variants, there was evidence that deletion constructs consistently showed greater activity than insertion constructs (MCF-7: DEL FC = 43.4, INS FC = 34.4, i.e., average additional FC for DEL = 1.3, p<sub>het</sub> = 0.01; T-47D: DEL FC = 47.3, INS FC = 21.6, i.e., average additional FC for DEL = 2.2, p<sub>het</sub> = 1.7 × 10<sup>-8</sup>; Figure 3).

## CRISPR-based perturbation of PRE2

Reporter gene assays do not reflect the “normal” genomic context of a regulatory element. Specifically, the assay tests whether the putative regulatory element can influence expression in an episomal context²¹ and from a distance of a few kilobases; *in vivo*, PRE2 maps approximately 400 kb from the *IGFBP5* promoter. To determine whether PRE2 acts as an enhancer element in a cellular context, we used a systematic CRISPR-based enhancer perturbation approach. We hypothesized that if PRE2 acts as an enhancer *in vivo*, targeting a catalytically inactive Cas9 (dCas9) fused to a repressive (KRAB) domain to regions within PRE2 would result in lower levels of expression of *IGFBP5* (CRISPR interference; CRISPRi); by contrast, targeting dCas9 fused to an activating VPR domain would result in higher levels of expression of *IGFBP5* (CRISPR activation; CRISPRa).²²,²³ We designed CRISPR single-guide (sg) RNAs to the ERα ChIP-seq peak at the centromeric breakpoint of the deletion (guides PRE2-1 and -2), within the

esv3594306 deletion region (guides PRE2-3 to -6) and to the ERα ChIP-seq peak at the telomeric breakpoint of the deletion (guides PRE2-7 to -9; Figure 1B). As positive controls we designed sgRNAs to target the *IGFBP5* promoter (guides PROM-1 to -3; Figure S6A) and the previously characterized causal variant (rs4442975, guide PRE1-1; Figure S6B). As negative controls we designed sgRNAs to the published genome-wide association study signal 1 tag SNP (rs13387042, guides TAG-1 and -2; Figure S6B). We used MCF-7 cell lines engineered to stably express (1) dCas9 with a repressive KRAB domain and (2) dCas9 with an activating VPR domain; as an additional control we used MCF-7 cells that expressed dCas9 without the KRAB or VPR domains.

In the dCas9 cell line, there was just one sgRNA (PROM-2) that influenced *IGFBP5* expression; this sgRNA targets the *IGFBP5* promoter, colocalizing with the transcription start site (TSS) and likely reduces expression of *IGFBP5* by steric hindrance (60% reduction, p = 0.004; Figure S7A). In the CRISPRi setting, all three sgRNAs targeting the *IGFBP5* promoter repressed *IGFBP5* expression significantly to 8%–15% of levels in the negative controls (p = 0.001, p = 0.001, and p = 0.0008 for guides PROM-1, -2, and -3, respectively; Figure S8A). No sgRNA targeting non-promoter sequences influenced *IGFBP5* expression (Figures S8A and S8B). In the CRISPRa setting, the sgRNA 5' to the *IGFBP5* promoter (PROM-3; Figure 4A) enhanced *IGFBP5* expression more than 60-fold (p = 0.00008) and the PRE-1-positive control sgRNA (PRE1-1) targeting rs442975 also enhanced *IGFBP5* expression (FC = 3.7, p = 0.006; Figure 4A). In addition, four of the nine sgRNAs targeting sequences at PRE2 enhanced *IGFBP5* expression; specifically PRE2-1 and -2 targeting the ERα ChIP-seq peak at the centromeric deletion breakpoint (PRE2-1: FC = 3.7, p = 0.0005; PRE2-2: FC = 3.1, p = 0.001), PRE2-5 at the distal end of the deletion region (PRE2-5: FC = 3.2, p = 0.002), and PRE2-8 targeting the ERα ChIP-seq peak immediately telomeric to the deletion region (PRE2-8: FC = 5.3, p = 0.002; Figures 4B and 5A). None of the sgRNAs influenced expression of two genes mapping immediately 3' to *IGFBP5* (*IGFBP2* and *RPL37A*; Figure 4C).
<figure><img src="image_4.png" /><figcaption>Figure 4. Systematic CRISPRa analysis of 2q35 putative regulatory elements</figcaption></figure>

MCF-7 cells expressing dCas9-VPR were transduced with CRISPR sgRNAs targeting: (A) the PRE1 tag SNP rs13387042 (negative control), the *IGFBP5* promoter and the PRE1 causal variant rs4442975 (positive control), and (B and C) a series of sites mapping across PRE2 (Figure 1B). Relative gene expression (compared to vector alone) was calculated using the ΔΔC~T~ method. Full details of guide RNAs are listed in Table S3. Error bars denote standard deviations based on three independent experiments each done in triplicate. *p* values were determined by t tests and a Bonferroni correction was applied to account for multiple testing; (A) *p* < 0.017, ***p** < 0.0017, **p** < 0.00017; (B and C) *p* < 0.0056, **p** ≤ 0.00056.

Discussion

Fine-scale mapping at the 2q35 breast cancer locus in women of European ancestry³ confirmed rs4442975 as the probable causal variant at signal 1 and reduced the number of credible causal variants at signal 2 from 14 to 5;³,¹⁴ at signal 3, however, there remained 42 credible causal variants that could not be excluded as causal on statistical grounds alone in either the European or the Asian data. Low-throughput functional approaches that are used to investigate putative causal variants, including reporter gene assays and CRISPR screens, become prohibitive with large numbers of credible causal variants and most single locus¹¹,¹⁴,²⁴⁻³⁸ and global³,⁶ annotation studies have used co-localization of credible causal variants with markers of open chromatin, active histone modifications, and transcription factor binding in relevant cell types to

prioritize credible causal variants for functional follow up. Of the 811 annotation tracks that were examined in a recent global fine-scale mapping analysis,³ credible causal variants were enriched at three types of genomic features that are relevant to long-range regulatory elements: (1) open chromatin in ER⁺ cell lines and normal breast, (2) the active histone marks H3K4me1 and H3K27ac in MCF-7 cells, and (3) ESR1, FOXA1, GATA3, and P300 TF binding sites. By aligning the five credible causal variants at PRE2 and the 42 credible causal variants at PRE3 with these marks (Table S4), we were able to prioritize 4 of the 5 credible causal variants at PRE2 and 2 of the 42 credible causal variants at PRE3 for follow-up studies. By taking this approach

there is, inevitably, the possibility that we have excluded one or more causal variants from our follow-up analyses. For PRE2 this seems unlikely as we selected four out of the five credible causal variants for further follow-up studies. For PRE3 it is entirely possible, or even probable, that we failed to prioritize one or more causal variant(s); improving our ability to discriminate more accurately between potentially functional variants and large numbers of correlated variants will require genome-wide datasets with functional outputs²¹,³⁹,⁴⁰ generated in more relevant cellular disease models and taking advantage of single-cell technologies.¹

Using reporter gene assays, we have demonstrated that both the distal region of PRE2 (PRE2B) and the entire PRE3 region can enhance transcription from the *IGFBP5* promoter in a cell-type-specific manner. Despite co-localizing with multiple markers, we found no evidence that
A

<figure><img src="image_5.png" /></figure>

B

<figure><img src="image_6.png" /><figcaption>**Figure 5.** Increasing the local density of activator TF domains with dCas9-VPR or by juxtaposition of two ChIP-seq peaks is associated with increased expression of *IGFBP5*</figcaption></figure>

(A) Introducing dCas9 fused to a VPR activator domain at the ERα, FOXA1, GATA3 ChIP-seq peak at the centromeric end of the deletion breakpoint (PRE2-1 and PRE2-2), proximal to, or at, the ERα, FOXA1, GATA3 ChIP-seq peak at the telomeric end of the deletion breakpoint (PRE2-5 and PRE2-8, respectively) increases expression of *IGFBP5* in MCF-7 cells.

(B) Deletion of 1.4 kb on the ALT allele of esv3594306 juxtaposes these two ERα, FOXA1, GATA3 ChIP-seq peaks.

In each case (A and B) this increases the density of activating TF domains in the region and is associated with increased expression of *IGFBP5*.

the proximal region of PRE2 (PRE2A) acts as an independent enhancer element. The ChIP-seq peaks at this region are, however, relatively weak (Figure 1B); combining data from both PRE2A alleles, in both breast cancer cell lines to increase our power (i.e., using 12 replicates rather than 3) the overall mean fold change for PRE2A was 1.14 (1.03–1.26, p = 0.01), consistent with the presence of a very modest enhancer element. Comparing REF constructs with ALT constructs, we found no evidence that either of the credible causal variants at PRE3 (rs12694417, rs12988242) altered the activity of the PRE. This does not exclude these SNPs as functional; as above, modest effects on enhancer activity may be difficult to detect and variants that, for example, influence chromatin accessibility may not be detectable in transient assays.¹¹ However, without preliminary *in vitro* evidence to suggest that one of these variants alters cell-type-specific transcription from the *IGFBP5* promoter, pursuing further functional studies that are predicated on this very assumption seems unlikely

to be fruitful. By contrast, one comparison that was consistent and significant between constructs and across the two breast cancer cell lines was that PRE2 deletion alleles had stronger enhancer activity than PRE2 insertion alleles.

The purpose of our CRISPR-based enhancer perturbation was 2-fold: specifically, to interrogate the PRE2 region within its normal genomic context and more generally to evaluate CRISPRi and CRISPRa approaches for interrogating long-range regulatory elements that harbor credible causal variants. As none of our PRE2 sgRNAs impacted *IGFBP5* expression significantly

in the CRISPRi setting, our analysis raises questions as to the utility of this approach for characterizing long-range regulatory elements (PRE2 maps approximately 400 kb telomeric to the *IGFBP5* promoter). This is at odds with results of a systematic CRISPRi screen to identify enhancer elements in K562 cells, which demonstrated CRISPRi-mediated repression of c-MYC expression by sgRNAs targeting sequences mapping up to 1.9 Mb downstream of c-MYC.²² In this analysis, however, CRISPRi-mediated repression by these distal elements was modest compared to CRISPRi-mediated repression by more proximal elements and, even based on 12 biological replicates, of borderline statistical significance.²² By contrast, using CRISPRa we were able to confirm that one or more elements within PRE2 can act as a long-range regulatory element that specifically targets *IGFBP5* (rather than *IGFBP2* or *RPL37A*). Four of the nine guide RNAs targeting dCas9-VPR to sequences at PRE2 increased expression of *IGFBP5*; three of these colocalized with ERα, FOXA1, and GATA3 ChIP-seq peaks (PRE2-1, -2,
and -8) and a fourth (PRE2-5) mapped within the esv3594306 deleted region (Figure 5A). There were also two guides which targeted dCas9-VPR to sequences that map close to the distal ERα, FOXA1, and GATA3 ChIP-seq peak (PRE2-6 and -7) but did not increase *IGFBP5* expression; this may reflect the very variable efficiency of different guide RNAs.²² We present a theoretical model in which we hypothesize that all of the PRE2 guides that increased expression of *IGFBP5* increased the local density of activating TF domains by bringing a VPR domain into the proximity of a cluster of TF ChIP-seq peaks; one implication of the increase in *IGFBP5* expression we observed with PRE2-5, which maps approximately 450 bp from the center of the nearest cluster of ChIP-seq peaks (Figure 5A), is that these regulatory elements may extend over relatively large (>1 kb) regions. This should not, perhaps, be surprising; at a subset of strongly activated E2-responsive enhancers, it has previously been shown that ERα recruits DNA-binding transcription factors in *trans*, to form a large (1–2 MDa) complex.⁴¹

It has previously been suggested that sequences mapping to PRE2 act as a repressor element which, in the presence of low-dose estradiol, acts to reduce *IGFBP5* expression.¹⁴ By contrast, our data support PRE2 acting as a powerful enhancer element with the deletion allele increasing expression of *IGFBP5* over and above that of the insertion allele with or without estradiol stimulation. Overall, our data are consistent with a hypothetical model in which the juxtaposition of the two ERα, FOXA1, GATA3 binding sites at PRE2 by deletion of approximately 1.4 kb of intervening sequence generates a single extended binding region (Figure 5B) that is causally associated with increased enhancer activity, higher levels of expression of the putative tumor suppressor gene *IGFBP5*,⁴² and a reduction in breast cancer risk (OR = 0.77, p = 2.2 × 10⁻²⁹) that is largely restricted to ER⁺ disease.

In conclusion, we have identified putative enhancer elements at two additional 2q35 breast cancer risk loci. One of these, mapping approximately 400 kb telomeric to *IGFBP5*, enhances transcription from the *IGFBP5* promoter by a factor of 30- to 40-fold. For this element we provide evidence that a deletion of 1.4 kb is causally associated with increased enhancer activity and suggest a mechanism for this increased activity.

## Data and code availability

Summary results for all variants genotyped by the Breast Cancer Association Consortium BCAC (including rs45446698) are available at http://bcac.ccge.medschl.cam.ac.uk/. Requests for data can be made to the corresponding author or the Data Access Coordination Committee (DACC) of the Breast Cancer Association Consortium via email to BCAC@medschl.cam.ac.uk.

## Supplemental information

Supplemental information can be found online at https://doi.org/10.1016/j.ajhg.2021.05.013.

## Consortia

The NBCS Collaborators are Anne-Lise Børresen-Dale, Grethe I. Grenaker Alnæs, Kristine K. Sahlberg, Lars Ottestad, Rolf Kåresen, Ellen Schlichting, Marit Muri Holmen, Toril Sauer, Vilde Haakensen, Olav Engebråten, Bjørn Naume, Alexander Fosså, Cecile E. Kiserud, Kristin V. Reinertsen, Åslaug Helland, Margit Riis, Jürgen Geisler, and OSBREAC.

The kConFab Investigators are David Amor, Lesley Andrews, Yoland Antill, Rosemary Balleine, Jonathan Beesley, Ian Bennett, Michael Bogwitz, Leon Botes, Meagan Brennan, Melissa Brown, Michael Buckley, Jo Burke, Phyllis Butow, Liz Caldon, Ian Campbell, Deepa Chauhan, Manisha Chauhan, Georgia Chenevix-Trench, Alice Christian, Paul Cohen, Alison Colley, Ashley Crook, James Cui, Margaret Cummings, Sarah Jane Dawson, Anna deFazio, Martin Delatycki, Rebecca Dickson, Joanne Dixon, Ted Edkins, Stacey Edwards, Gelareh Farshid, Andrew Fellows, Georgina Fenton, Michael Field, James Flanagan, Peter Fong, Laura Forrest, Stephen Fox, Juliet French, Michael Friedlander, Clara Gaff, Mike Gattas, Peter George, Sian Greening, Marion Harris, Stewart Hart, Nick Hayward, John Hopper, Cass Hoskins, Clare Hunt, Paul James, Mark Jenkins, Alexa Kidd, Judy Kirk, Jessica Koehler, James Kollias, Sunil Lakhani, Mitchell Lawrence, Geoff Lindeman, Lara Lipton, Liz Lobb, Graham Mann, Deborah Marsh, Sue Anne McLachlan, Bettina Meiser, Roger Milne, Sophie Nightingale, Shona O'Connell, Sarah O'Sullivan, David Gallego Ortega, Nick Pachter, Briony Patterson, Amy Pearn, Kelly Phillips, Ellen Pieper, Edwina Rickard, Bridget Robinson, Mona Saleh, Elizabeth Salisbury, Christobel Saunders, Jodi Saunus, Rodney Scott, Clare Scott, Adrienne Sexton, Andrew Shelling, Peter Simpson, Melissa Southey, Amanda Spurdle, Jessica Taylor, Renea Taylor, Heather Thorne, Alison Trainer, Kathy Tucker, Jane Visvader, Logan Walker, Rachael Williams, Ingrid Winship, and Mary Ann Young.

The ABCTB Investigators are Christine Clarke, Deborah Marsh, Rodney Scott, Robert Baxter, Desmond Yip, Jane Carpenter, Alison Davis, Nirmala Pathmanathan, Peter Simpson, Dinny Graham, and Mythily Sachchithananthan.

## Declaration of interests

M.W.B. conducts research funded by Amgen, Novartis, and Pfizer. P.A.F. conducts research funded by Amgen, Novartis, and Pfizer and received honoraria from Roche, Novartis, and Pfizer. A.W.K. received research funding to her institution from Myriad Genetics for an unrelated project (funding dates 2017-2019). U.M. has stockownership in Abcodia Ltd. All other authors declare no conflict of interest.

Received: October 20, 2020

Accepted: May 25, 2021

Published: June 18, 2021

## Web resources

1000 Genomes Project, https://www.internationalgenome.org/

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