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Meta-Analysis
. 2012 Jan 22;44(3):312-8.
doi: 10.1038/ng.1049.

Genome-wide association analysis identifies three new breast cancer susceptibility loci

Maya Ghoussaini  1 , Olivia Fletcher, Kyriaki Michailidou, Clare Turnbull, Marjanka K Schmidt, Ed Dicks, Joe Dennis, Qin Wang, Manjeet K Humphreys, Craig Luccarini, Caroline Baynes, Don Conroy, Melanie Maranian, Shahana Ahmed, Kristy Driver, Nichola Johnson, Nicholas Orr, Isabel dos Santos Silva, Quinten Waisfisz, Hanne Meijers-Heijboer, Andre G Uitterlinden, Fernando Rivadeneira; Netherlands Collaborative Group on Hereditary Breast and Ovarian Cancer (HEBON); Per Hall, Kamila Czene, Astrid Irwanto, Jianjun Liu, Heli Nevanlinna, Kristiina Aittomäki, Carl Blomqvist, Alfons Meindl, Rita K Schmutzler, Bertram Müller-Myhsok, Peter Lichtner, Jenny Chang-Claude, Rebecca Hein, Stefan Nickels, Dieter Flesch-Janys, Helen Tsimiklis, Enes Makalic, Daniel Schmidt, Minh Bui, John L Hopper, Carmel Apicella, Daniel J Park, Melissa Southey, David J Hunter, Stephen J Chanock, Annegien Broeks, Senno Verhoef, Frans B L Hogervorst, Peter A Fasching, Michael P Lux, Matthias W Beckmann, Arif B Ekici, Elinor Sawyer, Ian Tomlinson, Michael Kerin, Frederik Marme, Andreas Schneeweiss, Christof Sohn, Barbara Burwinkel, Pascal Guénel, Thérèse Truong, Emilie Cordina-Duverger, Florence Menegaux, Stig E Bojesen, Børge G Nordestgaard, Sune F Nielsen, Henrik Flyger, Roger L Milne, M Rosario Alonso, Anna González-Neira, Javier Benítez, Hoda Anton-Culver, Argyrios Ziogas, Leslie Bernstein, Christina Clarke Dur, Hermann Brenner, Heiko Müller, Volker Arndt, Christa Stegmaier; Familial Breast Cancer Study (FBCS); Christina Justenhoven, Hiltrud Brauch, Thomas Brüning; Gene Environment Interaction of Breast Cancer in Germany (GENICA) Network; Shan Wang-Gohrke, Ursula Eilber, Thilo Dörk, Peter Schürmann, Michael Bremer, Peter Hillemanns, Natalia V Bogdanova, Natalia N Antonenkova, Yuri I Rogov, Johann H Karstens, Marina Bermisheva, Darya Prokofieva, Elza Khusnutdinova, Annika Lindblom, Sara Margolin, Arto Mannermaa, Vesa Kataja, Veli-Matti Kosma, Jaana M Hartikainen, Diether Lambrechts, Betul T Yesilyurt, Giuseppe Floris, Karin Leunen, Siranoush Manoukian, Bernardo Bonanni, Stefano Fortuzzi, Paolo Peterlongo, Fergus J Couch, Xianshu Wang, Kristen Stevens, Adam Lee, Graham G Giles, Laura Baglietto, Gianluca Severi, Catriona McLean, Grethe Grenaker Alnaes, Vessela Kristensen, Anne-Lise Børrensen-Dale, Esther M John, Alexander Miron, Robert Winqvist, Katri Pylkäs, Arja Jukkola-Vuorinen, Saila Kauppila, Irene L Andrulis, Gord Glendon, Anna Marie Mulligan, Peter Devilee, Christie J van Asperen, Rob A E M Tollenaar, Caroline Seynaeve, Jonine D Figueroa, Montserrat Garcia-Closas, Louise Brinton, Jolanta Lissowska, Maartje J Hooning, Antoinette Hollestelle, Rogier A Oldenburg, Ans M W van den Ouweland, Angela Cox, Malcolm W R Reed, Mitul Shah, Ania Jakubowska, Jan Lubinski, Katarzyna Jaworska, Katarzyna Durda, Michael Jones, Minouk Schoemaker, Alan Ashworth, Anthony Swerdlow, Jonathan Beesley, Xiaoqing Chen; kConFab Investigators; Australian Ovarian Cancer Study Group; Kenneth R Muir, Artitaya Lophatananon, Suthee Rattanamongkongul, Arkom Chaiwerawattana, Daehee Kang, Keun-Young Yoo, Dong-Young Noh, Chen-Yang Shen, Jyh-Cherng Yu, Pei-Ei Wu, Chia-Ni Hsiung, Annie Perkins, Ruth Swann, Louiza Velentzis, Diana M Eccles, Will J Tapper, Susan M Gerty, Nikki J Graham, Bruce A J Ponder, Georgia Chenevix-Trench, Paul D P Pharoah, Mark Lathrop, Alison M Dunning, Nazneen Rahman, Julian Peto, Douglas F Easton
Affiliations
Meta-Analysis

Genome-wide association analysis identifies three new breast cancer susceptibility loci

Maya Ghoussaini et al. Nat Genet. .

Abstract

Breast cancer is the most common cancer among women. To date, 22 common breast cancer susceptibility loci have been identified accounting for ∼8% of the heritability of the disease. We attempted to replicate 72 promising associations from two independent genome-wide association studies (GWAS) in ∼70,000 cases and ∼68,000 controls from 41 case-control studies and 9 breast cancer GWAS. We identified three new breast cancer risk loci at 12p11 (rs10771399; P = 2.7 × 10(-35)), 12q24 (rs1292011; P = 4.3 × 10(-19)) and 21q21 (rs2823093; P = 1.1 × 10(-12)). rs10771399 was associated with similar relative risks for both estrogen receptor (ER)-negative and ER-positive breast cancer, whereas the other two loci were associated only with ER-positive disease. Two of the loci lie in regions that contain strong plausible candidate genes: PTHLH (12p11) has a crucial role in mammary gland development and the establishment of bone metastasis in breast cancer, and NRIP1 (21q21) encodes an ER cofactor and has a role in the regulation of breast cancer cell growth.

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Figures

Figure 1
Figure 1
Forest plots for the 3 SNPs showing evidence of association with breast cancer. Squares represent the estimated per-allele odds ratio (OR) for individual studies. The area of square is inversely proportional to the precise of the estimate. Diamonds represent the summary OR estimates for the subgroups indicated. Horizontal lines represent 95% confidence limits.
Figures 2a, b and c
Figures 2a, b and c
Association plots for the three new breast cancer susceptibility loci at (a) 12p11 (b) 12q24 and (c) 21q21 drawn using the SNAP software35 . Genotyped and imputed SNPs are plotted based on their chromosomal position in build 36 on the X axis and their overall P values (as −log10 values) from the UK2 and BBCS GWAS on the Y axis. For each region, the most strongly associated SNP is represented by a diamond. The intensity of the red shading reflects the strength of correlation (r2) between the best SNP and the other SNPs in the region. Genes present in the region (if any) are indicated in green.
Figures 2a, b and c
Figures 2a, b and c
Association plots for the three new breast cancer susceptibility loci at (a) 12p11 (b) 12q24 and (c) 21q21 drawn using the SNAP software35 . Genotyped and imputed SNPs are plotted based on their chromosomal position in build 36 on the X axis and their overall P values (as −log10 values) from the UK2 and BBCS GWAS on the Y axis. For each region, the most strongly associated SNP is represented by a diamond. The intensity of the red shading reflects the strength of correlation (r2) between the best SNP and the other SNPs in the region. Genes present in the region (if any) are indicated in green.
Figures 2a, b and c
Figures 2a, b and c
Association plots for the three new breast cancer susceptibility loci at (a) 12p11 (b) 12q24 and (c) 21q21 drawn using the SNAP software35 . Genotyped and imputed SNPs are plotted based on their chromosomal position in build 36 on the X axis and their overall P values (as −log10 values) from the UK2 and BBCS GWAS on the Y axis. For each region, the most strongly associated SNP is represented by a diamond. The intensity of the red shading reflects the strength of correlation (r2) between the best SNP and the other SNPs in the region. Genes present in the region (if any) are indicated in green.
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