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Rainer Fagerholm

Bio: Rainer Fagerholm is an academic researcher from University of Helsinki. The author has contributed to research in topics: Breast cancer & Genome-wide association study. The author has an hindex of 12, co-authored 22 publications receiving 3680 citations. Previous affiliations of Rainer Fagerholm include Helsinki University Central Hospital.

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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-Heijboer28, Hanne Meijers-Heijboer24, 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
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. Stredrick3, Denise L. Stredrick27, 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

Journal ArticleDOI
Montserrat Garcia-Closas1, Per Hall2, Heli Nevanlinna3, Karen A. Pooley4, Jonathan J. Morrison4, Douglas A. Richesson1, Stig E. Bojesen5, Børge G. Nordestgaard5, C K Axelsson5, José Ignacio Arias6, Roger L. Milne6, Gloria Ribas6, Anna González-Neira6, Javier Benitez6, P. Zamora7, Hiltrud Brauch8, Hiltrud Brauch9, Christina Justenhoven8, Christina Justenhoven9, Ute Hamann10, Yon Ko, Thomas Bruening11, Susanne Haas12, Thilo Dörk13, Peter Schürmann13, Peter Hillemanns13, Natalia Bogdanova13, Michael Bremer13, Johann H. Karstens13, Rainer Fagerholm3, Kirsimari Aaltonen3, Kristiina Aittomäki3, Karl von Smitten3, Carl Blomqvist3, Arto Mannermaa14, Matti Uusitupa14, Matti Eskelinen14, Maria Tengström14, Veli-Matti Kosma14, V. Kataja14, Georgia Chenevix-Trench15, Amanda B. Spurdle15, Jonathan Beesley15, Xiaoqing Chen15, Peter Devilee16, Christi J. van Asperen16, Catharina E. Jacobi16, Rob A. E. M. Tollenaar16, Petra E A Huijts17, Jan G. M. Klijn17, Jenny Chang-Claude10, Silke Kropp10, Tracy Slanger10, Dieter Flesch-Janys18, Elke Mutschelknauss18, Ramona Salazar, Shan Wang-Gohrke19, Fergus J. Couch20, Ellen L. Goode20, Janet E. Olson20, Celine M. Vachon20, Zachary S. Fredericksen20, Graham G. Giles21, Laura Baglietto21, Gianluca Severi21, John L. Hopper22, Dallas R. English22, Melissa C. Southey22, Christopher A. Haiman23, Brian E. Henderson23, Laurence N. Kolonel24, Loic Le Marchand24, Daniel O. Stram23, David J. Hunter25, Susan E. Hankinson25, David G. Cox25, Rulla M. Tamimi25, Peter Kraft25, Mark E. Sherman1, Stephen J. Chanock1, Jolanta Lissowska26, Louise A. Brinton1, Beata Peplonska27, Maartje J. Hooning17, Han Meijers-Heijboer17, J. Margriet Collée17, Ans M.W. van den Ouweland17, André G. Uitterlinden17, Jianjun Liu28, Yen Lin Low28, Li Yuqing28, Keith Humphreys2, Kamila Czene2, Angela Cox29, Sabapathy P. Balasubramanian29, Simon S. Cross29, Malcolm W.R. Reed29, Fiona M. Blows4, Kristy Driver4, Alison M. Dunning4, Jonathan Tyrer4, Bruce A.J. Ponder30, Suleeporn Sangrajrang, Paul Brennan31, James McKay31, Fabrice Odefrey31, Valerie Gabrieau31, Alice J. Sigurdson1, Michele M. Doody1, J. P. Struewing1, Bruce H. Alexander, Douglas F. Easton4, Paul D.P. Pharoah4 
TL;DR: The findings show that common genetic variants influence the pathological subtype of breast cancer and provide further support for the hypothesis that ER-positive and ER-negative disease are biologically distinct.
Abstract: A three-stage genome-wide association study recently identified single nucleotide polymorphisms (SNPs) in five loci (fibroblast growth receptor 2 (FGFR2), trinucleotide repeat containing 9 (TNRC9), mitogen-activated protein kinase 3 K1 (MAP3K1), 8q24, and lymphocyte-specific protein 1 (LSP1)) associated with breast cancer risk. We investigated whether the associations between these SNPs and breast cancer risk varied by clinically important tumor characteristics in up to 23,039 invasive breast cancer cases and 26,273 controls from 20 studies. We also evaluated their influence on overall survival in 13,527 cases from 13 studies. All participants were of European or Asian origin. rs2981582 in FGFR2 was more strongly related to ER-positive (per-allele OR (95%CI) = 1.31 (1.27-1.36)) than ER-negative (1.08 (1.03-1.14)) disease (P for heterogeneity = 10(-13)). This SNP was also more strongly related to PR-positive, low grade and node positive tumors (P = 10(-5), 10(-8), 0.013, respectively). The association for rs13281615 in 8q24 was stronger for ER-positive, PR-positive, and low grade tumors (P = 0.001, 0.011 and 10(-4), respectively). The differences in the associations between SNPs in FGFR2 and 8q24 and risk by ER and grade remained significant after permutation adjustment for multiple comparisons and after adjustment for other tumor characteristics. Three SNPs (rs2981582, rs3803662, and rs889312) showed weak but significant associations with ER-negative disease, the strongest association being for rs3803662 in TNRC9 (1.14 (1.09-1.21)). rs13281615 in 8q24 was associated with an improvement in survival after diagnosis (per-allele HR = 0.90 (0.83-0.97). The association was attenuated and non-significant after adjusting for known prognostic factors. Our findings show that common genetic variants influence the pathological subtype of breast cancer and provide further support for the hypothesis that ER-positive and ER-negative disease are biologically distinct. Understanding the etiologic heterogeneity of breast cancer may ultimately result in improvements in prevention, early detection, and treatment.

367 citations

Journal ArticleDOI
Qi Guo1, Marjanka K. Schmidt, Peter Kraft2, Sander Canisius  +160 moreInstitutions (45)
TL;DR: The results of this large meta-analysis of studies in populations of European ancestry confirm that germline genotype can provide prognostic information in addition to standard tumor prognostic factors.
Abstract: Background: Survival after a diagnosis of breast cancer varies considerably between patients, and some of this variation may be because of germline genetic variation We aimed to identify genetic markers associated with breast cancer-specific survival Methods: We conducted a large meta-analysis of studies in populations of European ancestry, including 37954 patients with 2900 deaths from breast cancer Each study had been genotyped for between 200000 and 900000 single nucleotide polymorphisms (SNPs) across the genome; genotypes for nine million common variants were imputed using a common reference panel from the 1000 Genomes Project We also carried out subtype-specific analyses based on 6881 estrogen receptor (ER)-negative patients (920 events) and 23059 ER-positive patients (1333 events) All statistical tests were two-sided Results: We identified one new locus (rs2059614 at 11q242) associated with survival in ER-negative breast cancer cases (hazard ratio HR = 195, 95% confidence interval CI = 155 to 247, P = 191 x 10-8) Genotyping a subset of 2113 case patients, of which 300 were ER negative, provided supporting evidence for the quality of the imputation The association in this set of case patients was stronger for the observed genotypes than for the imputed genotypes A second locus (rs148760487 at 2q242) was associated at genome-wide statistical significance in initial analyses; the association was similar in ER-positive and ER-negative case patients Here the results of genotyping suggested that the finding was less robust Conclusions: This is currently the largest study investigating genetic variation associated with breast cancer survival Our results have potential clinical implications, as they confirm that germline genotype can provide prognostic information in addition to standard tumor prognostic factors © 2015 © The Author 2015 Published by Oxford University Press

227 citations

Montserrat Garcia-Closas, Per Hall, Heli Nevanlinna, Karen A. Pooley, Jonathan J. Morrison, Douglas A. Richesson, Stig E. Bojesen, Børge G. Nordestgaard, C K Axelsson, Jose Ignacio Arias Perez, Roger L. Milne, Gloria Ribas, Anna González-Neira, Javier Benítez, P. Zamora, Hiltrud Brauch, Christina Justenhoven, U Hamann, Yon-Dschun Ko, Thomas Brüning, Susanne Haas, Thilo Dörk, Peter Schürmann, Peter Hillemanns, Natalia Viktorovna Bogdanova, Michael Bremer, Johann H. Karstens, Rainer Fagerholm, Kirsimari Aaltonen, Kristiina Aittomäki, Karl von Smitten, Carl Blomqvist, Arto Mannermaa, Matti Uusitupa, Matti Eskelinen, Maria Tengström, Veli-Matti Kosma, Vesa Kataja, Georgia Chenevix-Trench, Amanda B. Spurdle, Jonathan Beesley, Xiaoqing Chen, Peter Devilee, C. J. van Asperen, Catharina E. Jacobi, Robert A.E.M. Tollenaar, Petra E A Huijts, Jan G. M. Klijn, Jenny Chang-Claude, Silke Kropp, Tracy Slanger, Dieter Flesch-Janys, Elke Mutschelknauss, Ramona Salazar, Shan Wang-Gohrke, Fergus J. Couch, Ellen L. Goode, Janet E. Olson, Celine M. Vachon, Zachary S. Fredericksen, Graham G. Giles, Laura Baglietto, Gianluca Severi, John L. Hopper, Dallas R. English, Melissa C. Southey, Christopher A. Haiman, Brian E. Henderson, Laurence N. Kolonel, Loic Le Marchand, Daniel O. Stram, David J. Hunter, Susan E. Hankinson, David G. Cox, Rulla M. Tamimi, Peter Kraft, Mark E. Sherman, Stephen J. Chanock, Jolanta Lissowska, Louise A. Brinton, Beata Peplonska, M.J. Hooning, Hanne Meijers-Heijboer, J. M. Collee, A. van den Ouweland, Andre G. Uitterlinden, Jianjun Liu, L. Y. Lin, L. Yuqing, Keith Humphreys, Kamila Czene, Angela Cox, Sabapathy P. Balasubramanian, Simon S. Cross, Malcolm W.R. Reed, Fiona M. Blows, Kristy Driver, Alison M. Dunning, Jonathan Tyrer, Bruce A.J. Ponder, Suleeporn Sangrajrang, Paul Brennan, James McKay, Fabrice Odefrey, Gabrieau, Alice J. Sigurdson, Michele M. Doody, J. P. Struewing, Bruce H. Alexander, Douglas F. Easton, Paul D.P. Pharoah 
01 Jan 2008

93 citations


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Book ChapterDOI
01 Jan 2010

5,842 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 Article
TL;DR: In this paper, the coding exons of the family of 518 protein kinases were sequenced in 210 cancers of diverse histological types to explore the nature of the information that will be derived from cancer genome sequencing.
Abstract: AACR Centennial Conference: Translational Cancer Medicine-- Nov 4-8, 2007; Singapore PL02-05 All cancers are due to abnormalities in DNA. The availability of the human genome sequence has led to the proposal that resequencing of cancer genomes will reveal the full complement of somatic mutations and hence all the cancer genes. To explore the nature of the information that will be derived from cancer genome sequencing we have sequenced the coding exons of the family of 518 protein kinases, ~1.3Mb DNA per cancer sample, in 210 cancers of diverse histological types. Despite the screen being directed toward the coding regions of a gene family that has previously been strongly implicated in oncogenesis, the results indicate that the majority of somatic mutations detected are “passengers”. There is considerable variation in the number and pattern of these mutations between individual cancers, indicating substantial diversity of processes of molecular evolution between cancers. The imprints of exogenous mutagenic exposures, mutagenic treatment regimes and DNA repair defects can all be seen in the distinctive mutational signatures of individual cancers. This systematic mutation screen and others have previously yielded a number of cancer genes that are frequently mutated in one or more cancer types and which are now anticancer drug targets (for example BRAF , PIK3CA , and EGFR ). However, detailed analyses of the data from our screen additionally suggest that there exist a large number of additional “driver” mutations which are distributed across a substantial number of genes. It therefore appears that cells may be able to utilise mutations in a large repertoire of potential cancer genes to acquire the neoplastic phenotype. However, many of these genes are employed only infrequently. These findings may have implications for future anticancer drug development.

2,737 citations

Journal ArticleDOI
TL;DR: A variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours is reported.
Abstract: Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure.

2,316 citations