Dataset Information

A large-scale assessment of two-way SNP interactions in breast cancer susceptibility using 46,450 cases and 42,461 controls from the breast cancer association consortium.

ABSTRACT: Part of the substantial unexplained familial aggregation of breast cancer may be due to interactions between common variants, but few studies have had adequate statistical power to detect interactions of realistic magnitude. We aimed to assess all two-way interactions in breast cancer susceptibility between 70,917 single nucleotide polymorphisms (SNPs) selected primarily based on prior evidence of a marginal effect. Thirty-eight international studies contributed data for 46,450 breast cancer cases and 42,461 controls of European origin as part of a multi-consortium project (COGS). First, SNPs were preselected based on evidence (P < 0.01) of a per-allele main effect, and all two-way combinations of those were evaluated by a per-allele (1 d.f.) test for interaction using logistic regression. Second, all 2.5 billion possible two-SNP combinations were evaluated using Boolean operation-based screening and testing, and SNP pairs with the strongest evidence of interaction (P < 10(-4)) were selected for more careful assessment by logistic regression. Under the first approach, 3277 SNPs were preselected, but an evaluation of all possible two-SNP combinations (1 d.f.) identified no interactions at P < 10(-8). Results from the second analytic approach were consistent with those from the first (P > 10(-10)). In summary, we observed little evidence of two-way SNP interactions in breast cancer susceptibility, despite the large number of SNPs with potential marginal effects considered and the very large sample size. This finding may have important implications for risk prediction, simplifying the modelling required. Further comprehensive, large-scale genome-wide interaction studies may identify novel interacting loci if the inherent logistic and computational challenges can be overcome.

SUBMITTER: Milne RL

PROVIDER: S-EPMC3943524 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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A large-scale assessment of two-way SNP interactions in breast cancer susceptibility using 46,450 cases and 42,461 controls from the breast cancer association consortium.

Milne Roger L RL Herranz Jesús J Michailidou Kyriaki K Dennis Joe J Tyrer Jonathan P JP Zamora M Pilar MP Arias-Perez José Ignacio JI González-Neira Anna A Pita Guillermo G Alonso M Rosario MR Wang Qin Q Bolla Manjeet K MK Czene Kamila K Eriksson Mikael M Humphreys Keith K Darabi Hatef H Li Jingmei J Anton-Culver Hoda H Neuhausen Susan L SL Ziogas Argyrios A Clarke Christina A CA Hopper John L JL Dite Gillian S GS Apicella Carmel C Southey Melissa C MC Chenevix-Trench Georgia G Swerdlow Anthony A Ashworth Alan A Orr Nicholas N Schoemaker Minouk M Jakubowska Anna A Lubinski Jan J Jaworska-Bieniek Katarzyna K Durda Katarzyna K Andrulis Irene L IL Knight Julia A JA Glendon Gord G Mulligan Anna Marie AM Bojesen Stig E SE Nordestgaard Børge G BG Flyger Henrik H Nevanlinna Heli H Muranen Taru A TA Aittomäki Kristiina K Blomqvist Carl C Chang-Claude Jenny J Rudolph Anja A Seibold Petra P Flesch-Janys Dieter D Wang Xianshu X Olson Janet E JE Vachon Celine C Purrington Kristen K Winqvist Robert R Pylkäs Katri K Jukkola-Vuorinen Arja A Grip Mervi M Dunning Alison M AM Shah Mitul M Guénel Pascal P Truong Thérèse T Sanchez Marie M Mulot Claire C Brenner Hermann H Dieffenbach Aida Karina AK Arndt Volker V Stegmaier Christa C Lindblom Annika A Margolin Sara S Hooning Maartje J MJ Hollestelle Antoinette A Collée J Margriet JM Jager Agnes A Cox Angela A Brock Ian W IW Reed Malcolm W R MW Devilee Peter P Tollenaar Robert A E M RA Seynaeve Caroline C Haiman Christopher A CA Henderson Brian E BE Schumacher Fredrick F Le Marchand Loic L Simard Jacques J Dumont Martine M Soucy Penny P Dörk Thilo T Bogdanova Natalia V NV Hamann Ute U Försti Asta A Rüdiger Thomas T Ulmer Hans-Ulrich HU Fasching Peter A PA Häberle Lothar L Ekici Arif B AB Beckmann Matthias W MW Fletcher Olivia O Johnson Nichola N dos Santos Silva Isabel I Peto Julian J Radice Paolo P Peterlongo Paolo P Peissel Bernard B Mariani Paolo P Giles Graham G GG Severi Gianluca G Baglietto Laura L Sawyer Elinor E Tomlinson Ian I Kerin Michael M Miller Nicola N Marme Federik F Burwinkel Barbara B Mannermaa Arto A Kataja Vesa V Kosma Veli-Matti VM Hartikainen Jaana M JM Lambrechts Diether D Yesilyurt Betul T BT Floris Giuseppe G Leunen Karin K Alnæs Grethe Grenaker GG Kristensen Vessela V Børresen-Dale Anne-Lise AL García-Closas Montserrat M Chanock Stephen J SJ Lissowska Jolanta J Figueroa Jonine D JD Schmidt Marjanka K MK Broeks Annegien A Verhoef Senno S Rutgers Emiel J EJ Brauch Hiltrud H Brüning Thomas T Ko Yon-Dschun YD Couch Fergus J FJ Toland Amanda E AE Yannoukakos Drakoulis D Pharoah Paul D P PD Hall Per P Benítez Javier J Malats Núria N Easton Douglas F DF

Human molecular genetics 20131115 7

Part of the substantial unexplained familial aggregation of breast cancer may be due to interactions between common variants, but few studies have had adequate statistical power to detect interactions of realistic magnitude. We aimed to assess all two-way interactions in breast cancer susceptibility between 70,917 single nucleotide polymorphisms (SNPs) selected primarily based on prior evidence of a marginal effect. Thirty-eight international studies contributed data for 46,450 breast cancer cas ...[more]

PMID: 24242184

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Project description:BackgroundBreast cancer predisposition genes identified to date (e.g., BRCA1 and BRCA2) are responsible for less than 5% of all breast cancer cases. Many studies have shown that the cancer risks associated with individual commonly occurring single nucleotide polymorphisms (SNPs) are incremental. However, polygenic models suggest that multiple commonly occurring low to modestly penetrant SNPs of cancer related genes might have a greater effect on a disease when considered in combination.MethodsIn an attempt to identify the breast cancer risk conferred by SNP interactions, we have studied 19 SNPs from genes involved in major cancer related pathways. All SNPs were genotyped by TaqMan 5'nuclease assay. The association between the case-control status and each individual SNP, measured by the odds ratio and its corresponding 95% confidence interval, was estimated using unconditional logistic regression models. At the second stage, two-way interactions were investigated using multivariate logistic models. The robustness of the interactions, which were observed among SNPs with stronger functional evidence, was assessed using a bootstrap approach, and correction for multiple testing based on the false discovery rate (FDR) principle.ResultsNone of these SNPs contributed to breast cancer risk individually. However, we have demonstrated evidence for gene-gene (SNP-SNP) interaction among these SNPs, which were associated with increased breast cancer risk. Our study suggests cross talk between the SNPs of the DNA repair and immune system (XPD-[Lys751Gln] and IL10-[G(-1082)A]), cell cycle and estrogen metabolism (CCND1-[Pro241Pro] and COMT-[Met108/158Val]), cell cycle and DNA repair (BARD1-[Pro24Ser] and XPD-[Lys751Gln]), and within carcinogen metabolism (GSTP1-[Ile105Val] and COMT-[Met108/158Val]) pathways.ConclusionThe importance of these pathways and their communication in breast cancer predisposition has been emphasized previously, but their biological interactions through SNPs have not been described. The strategy used here has the potential to identify complex biological links among breast cancer genes and processes. This will provide novel biological information, which will ultimately improve breast cancer risk management.

Project description: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.

Dataset Information

A large-scale assessment of two-way SNP interactions in breast cancer susceptibility using 46,450 cases and 42,461 controls from the breast cancer association consortium.

Publications

A large-scale assessment of two-way SNP interactions in breast cancer susceptibility using 46,450 cases and 42,461 controls from the breast cancer association consortium.

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