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. 2014 Nov 15;23(22):6096-111.
doi: 10.1093/hmg/ddu311. Epub 2014 Jun 18.

Common non-synonymous SNPs associated with breast cancer susceptibility: findings from the Breast Cancer Association Consortium

Roger L Milne  1 , Barbara Burwinkel  2 , Kyriaki Michailidou  3 , Jose-Ignacio Arias-Perez  4 , M Pilar Zamora  5 , Primitiva Menéndez-Rodríguez  4 , David Hardisson  6 , Marta Mendiola  7 , Anna González-Neira  8 , Guillermo Pita  8 , M Rosario Alonso  8 , Joe Dennis  3 , Qin Wang  3 , Manjeet K Bolla  3 , Anthony Swerdlow  9 , Alan Ashworth  10 , Nick Orr  10 , Minouk Schoemaker  11 , Yon-Dschun Ko  12 , Hiltrud Brauch  13 , Ute Hamann  14 ; GENICA Network; Irene L Andrulis  15 , Julia A Knight  16 , Gord Glendon  17 , Sandrine Tchatchou  18 ; kConFab Investigators; Australian Ovarian Cancer Study Group; Keitaro Matsuo  19 , Hidemi Ito  20 , Hiroji Iwata  21 , Kazuo Tajima  22 , Jingmei Li  23 , Judith S Brand  24 , Hermann Brenner  25 , Aida Karina Dieffenbach  25 , Volker Arndt  26 , Christa Stegmaier  27 , Diether Lambrechts  28 , Gilian Peuteman  28 , Marie-Rose Christiaens  29 , Ann Smeets  29 , Anna Jakubowska  30 , Jan Lubinski  30 , Katarzyna Jaworska-Bieniek  30 , Katazyna Durda  30 , Mikael Hartman  31 , Miao Hui  32 , Wei Yen Lim  32 , Ching Wan Chan  33 , Federick Marme  34 , Rongxi Yang  34 , Peter Bugert  35 , Annika Lindblom  36 , Sara Margolin  37 , Montserrat García-Closas  38 , Stephen J Chanock  39 , Jolanta Lissowska  40 , Jonine D Figueroa  39 , Stig E Bojesen  41 , Børge G Nordestgaard  41 , Henrik Flyger  42 , Maartje J Hooning  43 , Mieke Kriege  43 , Ans M W van den Ouweland  44 , Linetta B Koppert  45 , Olivia Fletcher  46 , Nichola Johnson  46 , Isabel dos-Santos-Silva  47 , Julian Peto  47 , Wei Zheng  48 , Sandra Deming-Halverson  48 , Martha J Shrubsole  48 , Jirong Long  48 , Jenny Chang-Claude  49 , Anja Rudolph  49 , Petra Seibold  49 , Dieter Flesch-Janys  50 , Robert Winqvist  51 , Katri Pylkäs  51 , Arja Jukkola-Vuorinen  52 , Mervi Grip  53 , Angela Cox  54 , Simon S Cross  55 , Malcolm W R Reed  54 , Marjanka K Schmidt  56 , Annegien Broeks  56 , Sten Cornelissen  56 , Linde Braaf  56 , Daehee Kang  57 , Ji-Yeob Choi  58 , Sue K Park  57 , Dong-Young Noh  59 , Jacques Simard  60 , Martine Dumont  60 , Mark S Goldberg  61 , France Labrèche  62 , Peter A Fasching  63 , Alexander Hein  64 , Arif B Ekici  65 , Matthias W Beckmann  64 , Paolo Radice  66 , Paolo Peterlongo  67 , Jacopo Azzollini  68 , Monica Barile  69 , Elinor Sawyer  70 , Ian Tomlinson  71 , Michael Kerin  72 , Nicola Miller  72 , John L Hopper  73 , Daniel F Schmidt  73 , Enes Makalic  73 , Melissa C Southey  74 , Soo Hwang Teo  75 , Cheng Har Yip  76 , Kavitta Sivanandan  77 , Wan-Ting Tay  78 , Chen-Yang Shen  79 , Chia-Ni Hsiung  80 , Jyh-Cherng Yu  81 , Ming-Feng Hou  82 , Pascal Guénel  83 , Therese Truong  83 , Marie Sanchez  83 , Claire Mulot  84 , William Blot  85 , Qiuyin Cai  85 , Heli Nevanlinna  86 , Taru A Muranen  86 , Kristiina Aittomäki  87 , Carl Blomqvist  88 , Anna H Wu  89 , Chiu-Chen Tseng  89 , David Van Den Berg  89 , Daniel O Stram  89 , Natalia Bogdanova  90 , Thilo Dörk  91 , Kenneth Muir  92 , Artitaya Lophatananon  93 , Sarah Stewart-Brown  93 , Pornthep Siriwanarangsan  94 , Arto Mannermaa  95 , Vesa Kataja  96 , Veli-Matti Kosma  95 , Jaana M Hartikainen  95 , Xiao-Ou Shu  48 , Wei Lu  97 , Yu-Tang Gao  98 , Ben Zhang  48 , Fergus J Couch  99 , Amanda E Toland  100 ; TNBCC; Drakoulis Yannoukakos  101 , Suleeporn Sangrajrang  102 , James McKay  103 , Xianshu Wang  104 , Janet E Olson  105 , Celine Vachon  105 , Kristen Purrington  105 , Gianluca Severi  106 , Laura Baglietto  106 , Christopher A Haiman  89 , Brian E Henderson  89 , Fredrick Schumacher  89 , Loic Le Marchand  107 , Peter Devilee  108 , Robert A E M Tollenaar  109 , Caroline Seynaeve  43 , Kamila Czene  24 , Mikael Eriksson  24 , Keith Humphreys  24 , Hatef Darabi  24 , Shahana Ahmed  110 , Mitul Shah  110 , Paul D P Pharoah  111 , Per Hall  24 , Graham G Giles  106 , Javier Benítez  112 , Alison M Dunning  110 , Georgia Chenevix-Trench  113 , Douglas F Easton  111
Collaborators, Affiliations

Common non-synonymous SNPs associated with breast cancer susceptibility: findings from the Breast Cancer Association Consortium

Roger L Milne et al. Hum Mol Genet. .

Abstract

Candidate variant association studies have been largely unsuccessful in identifying common breast cancer susceptibility variants, although most studies have been underpowered to detect associations of a realistic magnitude. We assessed 41 common non-synonymous single-nucleotide polymorphisms (nsSNPs) for which evidence of association with breast cancer risk had been previously reported. Case-control data were combined from 38 studies of white European women (46 450 cases and 42 600 controls) and analyzed using unconditional logistic regression. Strong evidence of association was observed for three nsSNPs: ATXN7-K264R at 3p21 [rs1053338, per allele OR = 1.07, 95% confidence interval (CI) = 1.04-1.10, P = 2.9 × 10(-6)], AKAP9-M463I at 7q21 (rs6964587, OR = 1.05, 95% CI = 1.03-1.07, P = 1.7 × 10(-6)) and NEK10-L513S at 3p24 (rs10510592, OR = 1.10, 95% CI = 1.07-1.12, P = 5.1 × 10(-17)). The first two associations reached genome-wide statistical significance in a combined analysis of available data, including independent data from nine genome-wide association studies (GWASs): for ATXN7-K264R, OR = 1.07 (95% CI = 1.05-1.10, P = 1.0 × 10(-8)); for AKAP9-M463I, OR = 1.05 (95% CI = 1.04-1.07, P = 2.0 × 10(-10)). Further analysis of other common variants in these two regions suggested that intronic SNPs nearby are more strongly associated with disease risk. We have thus identified a novel susceptibility locus at 3p21, and confirmed previous suggestive evidence that rs6964587 at 7q21 is associated with risk. The third locus, rs10510592, is located in an established breast cancer susceptibility region; the association was substantially attenuated after adjustment for the known GWAS hit. Thus, each of the associated nsSNPs is likely to be a marker for another, non-coding, variant causally related to breast cancer risk. Further fine-mapping and functional studies are required to identify the underlying risk-modifying variants and the genes through which they act.

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Figures

Figure 1.
Figure 1.
Per-allele OR estimates for AKAP9-M463I (rs6964587) for European women by study, based on published data and new data from the Breast Cancer Association Consoritum. MAF, minor allele frequency; pHWE, P-value for departure from Hardy–Weinberg equilibrium; CI, confidence interval.
Figure 2.
Figure 2.
Per-allele OR estimates for ATXN7-K264R (rs1053338) for European women by study, based on data from the Breast Cancer Association Consortium. MAF, minor allele frequency; pHWE, P-value for departure from Hardy–Weinberg equilibrium; CI, confidence interval.
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