Rare germline copy number variants (CNVs) and breast cancer risk
Authors: Dennis Joe , Tyrer Jonathan P. , Walker Logan C. , Michailidou Kyriaki , Dorling Leila , Bolla Manjeet K. , Wang Qin , Ahearn Thomas U. , Andrulis Irene L. , Anton-Culver Hoda , Antonenkova Natalia N. , Arndt Volker , Aronson Kristan J. , Freeman Laura E. Beane , Beckmann Matthias W. , Behrens Sabine , Benitez Javier , Bermisheva Marina , Bogdanova Natalia V. , Bojesen Stig E. , Brenner Hermann , Castelao Jose E. , Chang-Claude Jenny , Chenevix-Trench Georgia , Clarke Christine L. ,   NBCS Collaborators, Kristensen Vessela N. , Sahlberg Kristine K. , Børresen-Dale Anne-Lise , Gram Inger Torhild , Engebråten Olav , Naume Bjørn , Geisler Jürgen , Alnæs Grethe I. Grenaker , Collée J. Margriet ,   CTS Consortium, Lacey James , Martinez Elena , Couch Fergus J. , Cox Angela , Cross Simon S. , Czene Kamila , Devilee Peter , Dörk Thilo , Dossus Laure , Eliassen A. Heather , Eriksson Mikael , Evans D. Gareth , Fasching Peter A. , Figueroa Jonine , Fletcher Olivia , Flyger Henrik , Fritschi Lin , Gabrielson Marike , Gago-Dominguez Manuela , García-Closas Montserrat , Giles Graham G. , González-Neira Anna , Guénel Pascal , Hahnen Eric , Haiman Christopher A. , Hall Per , Hollestelle Antoinette , Hoppe Reiner , Hopper John L. , Howell Anthony ,   ABCTB Investigators, Clarke Christine , Carpenter Jane , Marsh Deborah , Scott Rodney , Baxter Robert , Yip Desmond , Davis Alison , Pathmanathan Nirmala , Simpson Peter , Graham Dinny , Sachchithananthan Mythily ,   kConFab/AOCS Investigators, Campbell Ian , de Fazio Anna , Fox Stephen , Kirk Judy , Lindeman Geoff , Milne Roger , Southey Melissa , Spurdle Amanda , Thorne Heather , Jager Agnes , Jakubowska Anna , John Esther M. , Johnson Nichola , Jones Michael E. , Jung Audrey , Kaaks Rudolf , Keeman Renske , Khusnutdinova Elza , Kitahara Cari M. , Ko Yon-Dschun , Kosma Veli-Matti , Koutros Stella , Kraft Peter , Kristensen Vessela N. , Kubelka-Sabit Katerina , Kurian Allison W. , Lacey James V. , Lambrechts Diether , Larson Nicole L. , Linet Martha , Ogrodniczak Alicja , Mannermaa Arto , Manoukian Siranoush , Margolin Sara , Mavroudis Dimitrios , Milne Roger L. , Muranen Taru A. , Murphy Rachel A. , Nevanlinna Heli , Olson Janet E. , Olsson Håkan , Park-Simon Tjoung-Won , Perou Charles M. , Peterlongo Paolo , Plaseska-Karanfilska Dijana , Pylkäs Katri , Rennert Gad , Saloustros Emmanouil , Sandler Dale P. , Sawyer Elinor J. , Schmidt Marjanka K. , Schmutzler Rita K. , Shibli Rana , Smeets Ann , Soucy Penny , Southey Melissa C. , Swerdlow Anthony J. , Tamimi Rulla M. , Taylor Jack A. , Teras Lauren R. , Terry Mary Beth , Tomlinson Ian , Troester Melissa A. , Truong Thérèse , Vachon Celine M. , Wendt Camilla , Winqvist Robert , Wolk Alicja , Yang Xiaohong R. , Zheng Wei , Ziogas Argyrios , Simard Jacques , Dunning Alison M. , Pharoah Paul D. P. , Easton Douglas F. 
Published: January 2022

Germline copy number variants (CNVs) are pervasive in the human genome but potential disease associations with rare CNVs have not been comprehensively assessed in large datasets. We analysed rare CNVs in genes and non-coding regions for 86,788 breast cancer cases and 76,122 controls of European ancestry with genome-wide array data. Gene burden tests detected the strongest association for deletions in BRCA1 (P = 3.7E−18). Nine other genes were associated with a p-value < 0.01 including known susceptibility genes CHEK2 (P = 0.0008), ATM (P = 0.002) and BRCA2 (P = 0.008). Outside the known genes we detected associations with p-values < 0.001 for either overall or subtype-specific breast cancer at nine deletion regions and four duplication regions. Three of the deletion regions were in established common susceptibility loci. To the best of our knowledge, this is the first genome-wide analysis of rare CNVs in a large breast cancer case-control dataset. We detected associations with exonic deletions in established breast cancer susceptibility genes. We also detected suggestive associations with non-coding CNVs in known and novel loci with large effects sizes. Larger sample sizes will be required to reach robust levels of statistical significance.