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Author

Anna González-Neira

Other affiliations: University of Helsinki
Bio: Anna González-Neira is an academic researcher from Carlos III Health Institute. The author has contributed to research in topics: Breast cancer & Genome-wide association study. The author has an hindex of 40, co-authored 115 publications receiving 10402 citations. Previous affiliations of Anna González-Neira include University of Helsinki.


Papers
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Journal ArticleDOI
Douglas F. Easton1, Karen A. Pooley1, Alison M. Dunning1, Paul D.P. Pharoah1, Deborah J. Thompson1, Dennis G. Ballinger, Jeffery P. Struewing2, Jonathan J. Morrison1, Helen I. Field1, Robert Luben1, Nicholas J. Wareham1, Shahana Ahmed1, Catherine S. Healey1, Richard Bowman, Kerstin B. Meyer1, Christopher A. Haiman3, Laurence K. Kolonel, Brian E. Henderson3, Loic Le Marchand, Paul Brennan4, Suleeporn Sangrajrang, Valerie Gaborieau4, Fabrice Odefrey4, Chen-Yang Shen5, Pei-Ei Wu5, Hui-Chun Wang5, Diana Eccles6, D. Gareth Evans7, Julian Peto8, Olivia Fletcher9, Nichola Johnson9, Sheila Seal, Michael R. Stratton10, Nazneen Rahman, Georgia Chenevix-Trench11, Georgia Chenevix-Trench12, Stig E. Bojesen13, Børge G. Nordestgaard13, C K Axelsson13, Montserrat Garcia-Closas2, Louise A. Brinton2, Stephen J. Chanock2, Jolanta Lissowska14, Beata Peplonska15, Heli Nevanlinna16, Rainer Fagerholm16, H Eerola16, Daehee Kang17, Keun-Young Yoo17, Dong-Young Noh17, Sei Hyun Ahn18, David J. Hunter19, Susan E. Hankinson19, David G. Cox19, Per Hall20, Sara Wedrén20, Jianjun Liu21, Yen-Ling Low21, Natalia Bogdanova22, Peter Schu¨rmann22, Do¨rk Do¨rk22, Rob A. E. M. Tollenaar23, Catharina E. Jacobi23, Peter Devilee23, Jan G. M. Klijn24, Alice J. Sigurdson2, Michele M. Doody2, Bruce H. Alexander25, Jinghui Zhang2, Angela Cox26, Ian W. Brock26, Gordon MacPherson26, Malcolm W.R. Reed26, Fergus J. Couch27, Ellen L. Goode27, Janet E. Olson27, Hanne Meijers-Heijboer24, Hanne Meijers-Heijboer28, Ans M.W. van den Ouweland24, André G. Uitterlinden24, Fernando Rivadeneira24, Roger L. Milne29, Gloria Ribas29, Anna González-Neira29, Javier Benitez29, John L. Hopper30, Margaret R. E. McCredie31, Margaret R. E. McCredie32, Margaret R. E. McCredie12, Melissa C. Southey12, Melissa C. Southey30, Graham G. Giles33, Chris Schroen30, Christina Justenhoven34, Christina Justenhoven35, Hiltrud Brauch35, Hiltrud Brauch34, Ute Hamann36, Yon-Dschun Ko, Amanda B. Spurdle11, Jonathan Beesley11, Xiaoqing Chen11, _ kConFab37, Arto Mannermaa37, Veli-Matti Kosma37, Vesa Kataja37, Jaana M. Hartikainen37, Nicholas E. Day1, David Cox, Bruce A.J. Ponder1 
28 Jun 2007-Nature
TL;DR: To identify further susceptibility alleles, a two-stage genome-wide association study in 4,398 breast cancer cases and 4,316 controls was conducted, followed by a third stage in which 30 single nucleotide polymorphisms were tested for confirmation.
Abstract: Breast cancer exhibits familial aggregation, consistent with variation in genetic susceptibility to the disease. Known susceptibility genes account for less than 25% of the familial risk of breast cancer, and the residual genetic variance is likely to be due to variants conferring more moderate risks. To identify further susceptibility alleles, we conducted a two-stage genome-wide association study in 4,398 breast cancer cases and 4,316 controls, followed by a third stage in which 30 single nucleotide polymorphisms (SNPs) were tested for confirmation in 21,860 cases and 22,578 controls from 22 studies. We used 227,876 SNPs that were estimated to correlate with 77% of known common SNPs in Europeans at r2.0.5. SNPs in five novel independent loci exhibited strong and consistent evidence of association with breast cancer (P,1027). Four of these contain plausible causative genes (FGFR2, TNRC9, MAP3K1 and LSP1). At the second stage, 1,792 SNPs were significant at the P,0.05 level compared with an estimated 1,343 that would be expected by chance, indicating that many additional common susceptibility alleles may be identifiable by this approach.

2,288 citations

Journal ArticleDOI
02 Nov 2017-Nature
TL;DR: A genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry finds that heritability of Breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2–5-fold enriched relative to the genome- wide average.
Abstract: Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1, and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry. We identified 65 new loci that are associated with overall breast cancer risk at P < 5 × 10-8. The majority of credible risk single-nucleotide polymorphisms in these loci fall in distal regulatory elements, and by integrating in silico data to predict target genes in breast cells at each locus, we demonstrate a strong overlap between candidate target genes and somatic driver genes in breast tumours. We also find that heritability of breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2-5-fold enriched relative to the genome-wide average, with strong enrichment for particular transcription factor binding sites. These results provide further insight into genetic susceptibility to breast cancer and will improve the use of genetic risk scores for individualized screening and prevention.

1,014 citations

Journal ArticleDOI
Liisa M. Pelttari1, Sofia Khan1, Mikko Vuorela2, Johanna I. Kiiski1, Sara Vilske1, Viivi Nevanlinna1, Salla Ranta1, Johanna Schleutker3, Johanna Schleutker4, Johanna Schleutker5, Robert Winqvist2, Anne Kallioniemi4, Thilo Dörk6, Natalia Bogdanova6, Jonine Figueroa, Paul D.P. Pharoah7, Marjanka K. Schmidt8, Alison M. Dunning7, Montserrat Garcia-Closas9, Manjeet K. Bolla7, Joe Dennis7, Kyriaki Michailidou7, Qin Wang7, John L. Hopper10, Melissa C. Southey10, Efraim H. Rosenberg8, Peter A. Fasching11, Peter A. Fasching12, Matthias W. Beckmann12, Julian Peto13, Isabel dos-Santos-Silva13, Elinor J. Sawyer14, Ian Tomlinson15, Barbara Burwinkel16, Barbara Burwinkel17, Harald Surowy16, Harald Surowy17, Pascal Guénel18, Thérèse Truong18, Stig E. Bojesen19, Stig E. Bojesen20, Børge G. Nordestgaard19, Børge G. Nordestgaard20, Javier Benitez, Anna González-Neira, Susan L. Neuhausen21, Hoda Anton-Culver22, Hermann Brenner16, Volker Arndt16, Alfons Meindl23, Rita K. Schmutzler24, Hiltrud Brauch25, Hiltrud Brauch26, Hiltrud Brauch16, Thomas Brüning27, Annika Lindblom28, Sara Margolin28, Arto Mannermaa29, Jaana M. Hartikainen29, Georgia Chenevix-Trench30, kConFab10, kConFab30, Aocs Investigators31, Laurien Van Dyck31, Hilde Janssen32, Hilde Janssen16, Jenny Chang-Claude16, Anja Rudolph, Paolo Radice, Paolo Peterlongo33, Emily Hallberg33, Janet E. Olson10, Janet E. Olson34, Graham G. Giles10, Graham G. Giles34, Roger L. Milne35, Christopher A. Haiman35, Fredrick Schumacher36, Jacques Simard36, Martine Dumont37, Martine Dumont38, Vessela N. Kristensen38, Vessela N. Kristensen37, Anne Lise Børresen-Dale39, Wei Zheng39, Alicia Beeghly-Fadiel40, Mervi Grip41, Mervi Grip42, Irene L. Andrulis41, Gord Glendon43, Peter Devilee44, Caroline Seynaeve44, Maartje J. Hooning45, Margriet Collée46, Angela Cox46, Simon S. Cross7, Mitul Shah7, Robert Luben16, Ute Hamann16, Ute Hamann47, Diana Torres48, Anna Jakubowska48, Jan Lubinski33, Fergus J. Couch, Drakoulis Yannoukakos9, Nick Orr9, Anthony J. Swerdlow28, Hatef Darabi28, Jingmei Li28, Kamila Czene28, Per Hall7, Douglas F. Easton1, Johanna Mattson1, Carl Blomqvist1, Kristiina Aittomäki1, Heli Nevanlinna 
05 May 2016-PLOS ONE
TL;DR: It is suggested that loss-of-function mutations in RAD 51B are rare, but common variation at the RAD51B region is significantly associated with familial breast cancer risk.
Abstract: Common variation on 14q24.1, close to RAD51B, has been associated with breast cancer: rs999737 and rs2588809 with the risk of female breast cancer and rs1314913 with the risk of male breast cancer. The aim of this study was to investigate the role of RAD51B variants in breast cancer predisposition, particularly in the context of familial breast cancer in Finland. We sequenced the coding region of RAD51B in 168 Finnish breast cancer patients from the Helsinki region for identification of possible recurrent founder mutations. In addition, we studied the known rs999737, rs2588809, and rs1314913 SNPs and RAD51B haplotypes in 44,791 breast cancer cases and 43,583 controls from 40 studies participating in the Breast Cancer Association Consortium (BCAC) that were genotyped on a custom chip (iCOGS). We identified one putatively pathogenic missense mutation c.541C>T among the Finnish cancer patients and subsequently genotyped the mutation in additional breast cancer cases (n = 5259) and population controls (n = 3586) from Finland and Belarus. No significant association with breast cancer risk was seen in the meta-analysis of the Finnish datasets or in the large BCAC dataset. The association with previously identified risk variants rs999737, rs2588809, and rs1314913 was replicated among all breast cancer cases and also among familial cases in the BCAC dataset. The most significant association was observed for the haplotype carrying the risk-alleles of all the three SNPs both among all cases (odds ratio (OR): 1.15, 95% confidence interval (CI): 1.11-1.19, P = 8.88 x 10-16) and among familial cases (OR: 1.24, 95% CI: 1.16-1.32, P = 6.19 x 10-11), compared to the haplotype with the respective protective alleles. Our results suggest that loss-of-function mutations in RAD51B are rare, but common variation at the RAD51B region is significantly associated with familial breast cancer risk.

715 citations

Journal ArticleDOI
Nasim Mavaddat1, Kyriaki Michailidou1, Kyriaki Michailidou2, Joe Dennis1  +307 moreInstitutions (105)
TL;DR: This PRS, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset is developed and empirically validated and is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.
Abstract: Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.

653 citations

Journal ArticleDOI
Angela Cox1, Alison M. Dunning2, Montserrat Garcia-Closas3, Sabapathy P. Balasubramanian1, Malcolm W.R. Reed1, Karen A. Pooley2, Serena Scollen2, Caroline Baynes2, Bruce A.J. Ponder2, Stephen J. Chanock3, Jolanta Lissowska4, Louise A. Brinton3, Beata Peplonska5, Melissa C. Southey6, John L. Hopper6, Margaret R. E. McCredie7, Graham G. Giles8, Olivia Fletcher9, Nichola Johnson9, Isabel dos Santos Silva9, Lorna Gibson9, Stig E. Bojesen10, Børge G. Nordestgaard10, C K Axelsson10, Diana Torres11, Ute Hamann11, Christina Justenhoven12, Christina Justenhoven13, Hiltrud Brauch13, Hiltrud Brauch12, Jenny Chang-Claude11, Silke Kropp11, Angela Risch11, Shan Wang-Gohrke14, Peter Schürmann15, Natalia Bogdanova15, Thilo Dörk15, Rainer Fagerholm16, Kirsimari Aaltonen16, Carl Blomqvist16, Heli Nevanlinna16, Sheila Seal, Anthony Renwick, Michael R. Stratton, Nazneen Rahman, Suleeporn Sangrajrang, David J. Hughes17, Fabrice Odefrey17, Paul Brennan17, Amanda B. Spurdle18, Georgia Chenevix-Trench18, Jonathan Beesley18, Arto Mannermaa19, Jaana M. Hartikainen19, Vesa Kataja19, Veli-Matti Kosma19, Fergus J. Couch20, Janet E. Olson20, Ellen L. Goode20, Annegien Broeks21, Marjanka K. Schmidt21, Frans B. L. Hogervorst21, Laura J. van't Veer21, Daehee Kang22, Keun-Young Yoo22, Dong Young Noh22, Sei Hyun Ahn23, Sara Wedrén24, Per Hall24, Yen-Ling Low25, Jianjun Liu25, Roger L. Milne26, Gloria Ribas26, Anna González-Neira26, Javier Benitez26, Alice J. Sigurdson27, Alice J. Sigurdson3, Denise L. Stredrick27, Denise L. Stredrick3, Bruce H. Alexander27, Bruce H. Alexander3, Jeffery P. Struewing27, Jeffery P. Struewing3, Paul D.P. Pharoah2, Douglas F. Easton2 
TL;DR: It is demonstrated that common breast cancer susceptibility alleles with small effects on risk can be identified, given sufficiently powerful studies, as well as the need for further studies to confirm putative genetic associations with breast cancer.
Abstract: The Breast Cancer Association Consortium (BCAC) has been established to conduct combined case-control analyses with augmented statistical power to try to confirm putative genetic associations with breast cancer. We genotyped nine SNPs for which there was some prior evidence of an association with breast cancer: CASP8 D302H (rs1045485), IGFBP3 -202 C --> A (rs2854744), SOD2 V16A (rs1799725), TGFB1 L10P (rs1982073), ATM S49C (rs1800054), ADH1B 3' UTR A --> G (rs1042026), CDKN1A S31R (rs1801270), ICAM5 V301I (rs1056538) and NUMA1 A794G (rs3750913). We included data from 9-15 studies, comprising 11,391-18,290 cases and 14,753-22,670 controls. We found evidence of an association with breast cancer for CASP8 D302H (with odds ratios (OR) of 0.89 (95% confidence interval (c.i.): 0.85-0.94) and 0.74 (95% c.i.: 0.62-0.87) for heterozygotes and rare homozygotes, respectively, compared with common homozygotes; P(trend) = 1.1 x 10(-7)) and weaker evidence for TGFB1 L10P (OR = 1.07 (95% c.i.: 1.02-1.13) and 1.16 (95% c.i.: 1.08-1.25), respectively; P(trend) = 2.8 x 10(-5)). These results demonstrate that common breast cancer susceptibility alleles with small effects on risk can be identified, given sufficiently powerful studies.

567 citations


Cited by
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08 Oct 2009-Nature
TL;DR: This paper examined potential sources of missing heritability and proposed research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.
Abstract: Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, 'missing' heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.

7,797 citations

01 Feb 2015
TL;DR: In this article, the authors describe the integrative analysis of 111 reference human epigenomes generated as part of the NIH Roadmap Epigenomics Consortium, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression.
Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

4,409 citations

Journal ArticleDOI
TL;DR: This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.
Abstract: The past year has witnessed substantial advances in understanding the genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.

2,908 citations

Journal ArticleDOI
TL;DR: Improved data access is improved with the release of a new RESTful API to support high-throughput programmatic access, an improved web interface and a new summary statistics database.
Abstract: The GWAS Catalog delivers a high-quality curated collection of all published genome-wide association studies enabling investigations to identify causal variants, understand disease mechanisms, and establish targets for novel therapies. The scope of the Catalog has also expanded to targeted and exome arrays with 1000 new associations added for these technologies. As of September 2018, the Catalog contains 5687 GWAS comprising 71673 variant-trait associations from 3567 publications. New content includes 284 full P-value summary statistics datasets for genome-wide and new targeted array studies, representing 6 × 109 individual variant-trait statistics. In the last 12 months, the Catalog's user interface was accessed by ∼90000 unique users who viewed >1 million pages. We have improved data access with the release of a new RESTful API to support high-throughput programmatic access, an improved web interface and a new summary statistics database. Summary statistics provision is supported by a new format proposed as a community standard for summary statistics data representation. This format was derived from our experience in standardizing heterogeneous submissions, mapping formats and in harmonizing content. Availability: https://www.ebi.ac.uk/gwas/.

2,878 citations