Publications by authors named "Michael Lush"

28 Publications

  • Page 1 of 1

Association of germline genetic variants with breast cancer-specific survival in patient subgroups defined by clinic-pathological variables related to tumor biology and type of systemic treatment.

Breast Cancer Res 2021 08 18;23(1):86. Epub 2021 Aug 18.

Department of Medicine, Huntsman Cancer Institute, Salt Lake City, UT, USA.

Background: Given the high heterogeneity among breast tumors, associations between common germline genetic variants and survival that may exist within specific subgroups could go undetected in an unstratified set of breast cancer patients.

Methods: We performed genome-wide association analyses within 15 subgroups of breast cancer patients based on prognostic factors, including hormone receptors, tumor grade, age, and type of systemic treatment. Analyses were based on 91,686 female patients of European ancestry from the Breast Cancer Association Consortium, including 7531 breast cancer-specific deaths over a median follow-up of 8.1 years. Cox regression was used to assess associations of common germline variants with 15-year and 5-year breast cancer-specific survival. We assessed the probability of these associations being true positives via the Bayesian false discovery probability (BFDP < 0.15).

Results: Evidence of associations with breast cancer-specific survival was observed in three patient subgroups, with variant rs5934618 in patients with grade 3 tumors (15-year-hazard ratio (HR) [95% confidence interval (CI)] 1.32 [1.20, 1.45], P = 1.4E-08, BFDP = 0.01, per G allele); variant rs4679741 in patients with ER-positive tumors treated with endocrine therapy (15-year-HR [95% CI] 1.18 [1.11, 1.26], P = 1.6E-07, BFDP = 0.09, per G allele); variants rs1106333 (15-year-HR [95% CI] 1.68 [1.39,2.03], P = 5.6E-08, BFDP = 0.12, per A allele) and rs78754389 (5-year-HR [95% CI] 1.79 [1.46,2.20], P = 1.7E-08, BFDP = 0.07, per A allele), in patients with ER-negative tumors treated with chemotherapy.

Conclusions: We found evidence of four loci associated with breast cancer-specific survival within three patient subgroups. There was limited evidence for the existence of associations in other patient subgroups. However, the power for many subgroups is limited due to the low number of events. Even so, our results suggest that the impact of common germline genetic variants on breast cancer-specific survival might be limited.
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http://dx.doi.org/10.1186/s13058-021-01450-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371820PMC
August 2021

A case-only study to identify genetic modifiers of breast cancer risk for BRCA1/BRCA2 mutation carriers.

Nat Commun 2021 02 17;12(1):1078. Epub 2021 Feb 17.

Copenhagen General Population Study, Herlev and Gentofte Hospital Copenhagen University Hospital, Herlev, Denmark.

Breast cancer (BC) risk for BRCA1 and BRCA2 mutation carriers varies by genetic and familial factors. About 50 common variants have been shown to modify BC risk for mutation carriers. All but three, were identified in general population studies. Other mutation carrier-specific susceptibility variants may exist but studies of mutation carriers have so far been underpowered. We conduct a novel case-only genome-wide association study comparing genotype frequencies between 60,212 general population BC cases and 13,007 cases with BRCA1 or BRCA2 mutations. We identify robust novel associations for 2 variants with BC for BRCA1 and 3 for BRCA2 mutation carriers, P < 10, at 5 loci, which are not associated with risk in the general population. They include rs60882887 at 11p11.2 where MADD, SP11 and EIF1, genes previously implicated in BC biology, are predicted as potential targets. These findings will contribute towards customising BC polygenic risk scores for BRCA1 and BRCA2 mutation carriers.
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http://dx.doi.org/10.1038/s41467-020-20496-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890067PMC
February 2021

Breast Cancer Risk Factors and Survival by Tumor Subtype: Pooled Analyses from the Breast Cancer Association Consortium.

Cancer Epidemiol Biomarkers Prev 2021 04 26;30(4):623-642. Epub 2021 Jan 26.

Gynaecology Research Unit, Hannover Medical School, Hannover, Germany.

Background: It is not known whether modifiable lifestyle factors that predict survival after invasive breast cancer differ by subtype.

Methods: We analyzed data for 121,435 women diagnosed with breast cancer from 67 studies in the Breast Cancer Association Consortium with 16,890 deaths (8,554 breast cancer specific) over 10 years. Cox regression was used to estimate associations between risk factors and 10-year all-cause mortality and breast cancer-specific mortality overall, by estrogen receptor (ER) status, and by intrinsic-like subtype.

Results: There was no evidence of heterogeneous associations between risk factors and mortality by subtype ( > 0.30). The strongest associations were between all-cause mortality and BMI ≥30 versus 18.5-25 kg/m [HR (95% confidence interval (CI), 1.19 (1.06-1.34)]; current versus never smoking [1.37 (1.27-1.47)], high versus low physical activity [0.43 (0.21-0.86)], age ≥30 years versus <20 years at first pregnancy [0.79 (0.72-0.86)]; >0-<5 years versus ≥10 years since last full-term birth [1.31 (1.11-1.55)]; ever versus never use of oral contraceptives [0.91 (0.87-0.96)]; ever versus never use of menopausal hormone therapy, including current estrogen-progestin therapy [0.61 (0.54-0.69)]. Similar associations with breast cancer mortality were weaker; for example, 1.11 (1.02-1.21) for current versus never smoking.

Conclusions: We confirm associations between modifiable lifestyle factors and 10-year all-cause mortality. There was no strong evidence that associations differed by ER status or intrinsic-like subtype.

Impact: Given the large dataset and lack of evidence that associations between modifiable risk factors and 10-year mortality differed by subtype, these associations could be cautiously used in prognostication models to inform patient-centered care.
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http://dx.doi.org/10.1158/1055-9965.EPI-20-0924DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8026532PMC
April 2021

Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes.

Nat Genet 2020 01 7;52(1):56-73. Epub 2020 Jan 7.

Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy.

Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes.
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http://dx.doi.org/10.1038/s41588-019-0537-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6974400PMC
January 2020

The :p.Arg658* truncating variant is associated with risk of triple-negative breast cancer.

NPJ Breast Cancer 2019 1;5:38. Epub 2019 Nov 1.

25University of Texas MD Anderson Cancer Center, Department of Breast Medical Oncology, Houston, TX USA.

Breast cancer is a common disease partially caused by genetic risk factors. Germline pathogenic variants in DNA repair genes , , , , and are associated with breast cancer risk. , which encodes for a DNA translocase, has been proposed as a breast cancer predisposition gene, with greater effects for the ER-negative and triple-negative breast cancer (TNBC) subtypes. We tested the three recurrent protein-truncating variants :p.Arg658*, p.Gln1701*, and p.Arg1931* for association with breast cancer risk in 67,112 cases, 53,766 controls, and 26,662 carriers of pathogenic variants of or . These three variants were also studied functionally by measuring survival and chromosome fragility in patient-derived immortalized fibroblasts treated with diepoxybutane or olaparib. We observed that :p.Arg658* was associated with increased risk of ER-negative disease and TNBC (OR = 2.44,  = 0.034 and OR = 3.79;  = 0.009, respectively). In a country-restricted analysis, we confirmed the associations detected for :p.Arg658* and found that also :p.Arg1931* was associated with ER-negative breast cancer risk (OR = 1.96;  = 0.006). The functional results indicated that all three variants were deleterious affecting cell survival and chromosome stability with :p.Arg658* causing more severe phenotypes. In conclusion, we confirmed that the two rare deleterious variants p.Arg658* and p.Arg1931* are risk factors for ER-negative and TNBC subtypes. Overall our data suggest that the effect of truncating variants on breast cancer risk may depend on their position in the gene. Cell sensitivity to olaparib exposure, identifies a possible therapeutic option to treat -associated tumors.
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http://dx.doi.org/10.1038/s41523-019-0127-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6825205PMC
November 2019

Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes.

Am J Hum Genet 2019 01 13;104(1):21-34. Epub 2018 Dec 13.

Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki 00290, Finland; Department of Oncology, Örebro University Hospital, Örebro 70185, Sweden.

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.
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http://dx.doi.org/10.1016/j.ajhg.2018.11.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323553PMC
January 2019

- a novel candidate breast cancer susceptibility locus on 6q14.1.

Oncotarget 2017 Nov 12;8(61):102769-102782. Epub 2017 Oct 12.

VIB Center for Cancer Biology, VIB, Leuven, Belgium.

Most non- breast cancer families have no identified genetic cause. We used linkage and haplotype analyses in familial and sporadic breast cancer cases to identify a susceptibility locus on chromosome 6q. Two independent genome-wide linkage analysis studies suggested a 3 Mb locus on chromosome 6q and two unrelated Swedish families with a LOD >2 together seemed to share a haplotype in 6q14.1. We hypothesized that this region harbored a rare high-risk founder allele contributing to breast cancer in these two families. Sequencing of DNA and RNA from the two families did not detect any pathogenic mutations. Finally, 29 SNPs in the region were analyzed in 44,214 cases and 43,532 controls from BCAC, and the original haplotypes in the two families were suggested as low-risk alleles for European and Swedish women specifically. There was also some support for one additional independent moderate-risk allele in Swedish familial samples. The results were consistent with our previous findings in familial breast cancer and supported a breast cancer susceptibility locus at 6q14.1 around the gene.
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http://dx.doi.org/10.18632/oncotarget.21800DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5732689PMC
November 2017

Body mass index and breast cancer survival: a Mendelian randomization analysis.

Int J Epidemiol 2017 12;46(6):1814-1822

Academic Unit of Pathology, Department of Neuroscience, University of Sheffield, Sheffield, UK.

Background: There is increasing evidence that elevated body mass index (BMI) is associated with reduced survival for women with breast cancer. However, the underlying reasons remain unclear. We conducted a Mendelian randomization analysis to investigate a possible causal role of BMI in survival from breast cancer.

Methods: We used individual-level data from six large breast cancer case-cohorts including a total of 36 210 individuals (2475 events) of European ancestry. We created a BMI genetic risk score (GRS) based on genotypes at 94 known BMI-associated genetic variants. Association between the BMI genetic score and breast cancer survival was analysed by Cox regression for each study separately. Study-specific hazard ratios were pooled using fixed-effect meta-analysis.

Results: BMI genetic score was found to be associated with reduced breast cancer-specific survival for estrogen receptor (ER)-positive cases [hazard ratio (HR) = 1.11, per one-unit increment of GRS, 95% confidence interval (CI) 1.01-1.22, P = 0.03). We observed no association for ER-negative cases (HR = 1.00, per one-unit increment of GRS, 95% CI 0.89-1.13, P = 0.95).

Conclusions: Our findings suggest a causal effect of increased BMI on reduced breast cancer survival for ER-positive breast cancer. There is no evidence of a causal effect of higher BMI on survival for ER-negative breast cancer cases.
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http://dx.doi.org/10.1093/ije/dyx131DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5837506PMC
December 2017

Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer.

Nat Genet 2017 Dec 23;49(12):1767-1778. Epub 2017 Oct 23.

Department of Epidemiology, University of California, Irvine, Irvine, California, USA.

Most common breast cancer susceptibility variants have been identified through genome-wide association studies (GWAS) of predominantly estrogen receptor (ER)-positive disease. We conducted a GWAS using 21,468 ER-negative cases and 100,594 controls combined with 18,908 BRCA1 mutation carriers (9,414 with breast cancer), all of European origin. We identified independent associations at P < 5 × 10 with ten variants at nine new loci. At P < 0.05, we replicated associations with 10 of 11 variants previously reported in ER-negative disease or BRCA1 mutation carrier GWAS and observed consistent associations with ER-negative disease for 105 susceptibility variants identified by other studies. These 125 variants explain approximately 16% of the familial risk of this breast cancer subtype. There was high genetic correlation (0.72) between risk of ER-negative breast cancer and breast cancer risk for BRCA1 mutation carriers. These findings may lead to improved risk prediction and inform further fine-mapping and functional work to better understand the biological basis of ER-negative breast cancer.
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http://dx.doi.org/10.1038/ng.3785DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5808456PMC
December 2017

TP53-based interaction analysis identifies cis-eQTL variants for TP53BP2, FBXO28, and FAM53A that associate with survival and treatment outcome in breast cancer.

Oncotarget 2017 Mar;8(11):18381-18398

Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global health, The University of Melbourne, Melbourne, Australia.

TP53 overexpression is indicative of somatic TP53 mutations and associates with aggressive tumors and poor prognosis in breast cancer. We utilized a two-stage SNP association study to detect variants associated with breast cancer survival in a TP53-dependent manner. Initially, a genome-wide study (n = 575 cases) was conducted to discover candidate SNPs for genotyping and validation in the Breast Cancer Association Consortium (BCAC). The SNPs were then tested for interaction with tumor TP53 status (n = 4,610) and anthracycline treatment (n = 17,828). For SNPs interacting with anthracycline treatment, siRNA knockdown experiments were carried out to validate candidate genes.In the test for interaction between SNP genotype and TP53 status, we identified one locus, represented by rs10916264 (p(interaction) = 3.44 × 10-5; FDR-adjusted p = 0.0011) in estrogen receptor (ER) positive cases. The rs10916264 AA genotype associated with worse survival among cases with ER-positive, TP53-positive tumors (hazard ratio [HR] 2.36, 95% confidence interval [C.I] 1.45 - 3.82). This is a cis-eQTL locus for FBXO28 and TP53BP2; expression levels of these genes were associated with patient survival specifically in ER-positive, TP53-mutated tumors. Additionally, the SNP rs798755 was associated with survival in interaction with anthracycline treatment (p(interaction) = 9.57 × 10-5, FDR-adjusted p = 0.0130). RNAi-based depletion of a predicted regulatory target gene, FAM53A, indicated that this gene can modulate doxorubicin sensitivity in breast cancer cell lines.If confirmed in independent data sets, these results may be of clinical relevance in the development of prognostic and predictive marker panels for breast cancer.
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http://dx.doi.org/10.18632/oncotarget.15110DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5392336PMC
March 2017

Genome-Wide Meta-Analyses of Breast, Ovarian, and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by at Least Two Cancer Types.

Cancer Discov 2016 09 17;6(9):1052-67. Epub 2016 Jul 17.

Department of Epidemiology, UCI Center for Cancer Genetics Research and Prevention, School of Medicine, University of California, Irvine, Irvine, California.

Unlabelled: Breast, ovarian, and prostate cancers are hormone-related and may have a shared genetic basis, but this has not been investigated systematically by genome-wide association (GWA) studies. Meta-analyses combining the largest GWA meta-analysis data sets for these cancers totaling 112,349 cases and 116,421 controls of European ancestry, all together and in pairs, identified at P < 10(-8) seven new cross-cancer loci: three associated with susceptibility to all three cancers (rs17041869/2q13/BCL2L11; rs7937840/11q12/INCENP; rs1469713/19p13/GATAD2A), two breast and ovarian cancer risk loci (rs200182588/9q31/SMC2; rs8037137/15q26/RCCD1), and two breast and prostate cancer risk loci (rs5013329/1p34/NSUN4; rs9375701/6q23/L3MBTL3). Index variants in five additional regions previously associated with only one cancer also showed clear association with a second cancer type. Cell-type-specific expression quantitative trait locus and enhancer-gene interaction annotations suggested target genes with potential cross-cancer roles at the new loci. Pathway analysis revealed significant enrichment of death receptor signaling genes near loci with P < 10(-5) in the three-cancer meta-analysis.

Significance: We demonstrate that combining large-scale GWA meta-analysis findings across cancer types can identify completely new risk loci common to breast, ovarian, and prostate cancers. We show that the identification of such cross-cancer risk loci has the potential to shed new light on the shared biology underlying these hormone-related cancers. Cancer Discov; 6(9); 1052-67. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 932.
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http://dx.doi.org/10.1158/2159-8290.CD-15-1227DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5010513PMC
September 2016

Fine-scale mapping of 8q24 locus identifies multiple independent risk variants for breast cancer.

Int J Cancer 2016 Sep 17;139(6):1303-1317. Epub 2016 Jun 17.

Division of Molecular Genetic Epidemiology, German Cancer Research Center, Heidelberg 69120, Germany.

Previous genome-wide association studies among women of European ancestry identified two independent breast cancer susceptibility loci represented by single nucleotide polymorphisms (SNPs) rs13281615 and rs11780156 at 8q24. A fine-mapping study across 2.06 Mb (chr8:127,561,724-129,624,067, hg19) in 55,540 breast cancer cases and 51,168 controls within the Breast Cancer Association Consortium was conducted. Three additional independent association signals in women of European ancestry, represented by rs35961416 (OR = 0.95, 95% CI = 0.93-0.97, conditional p = 5.8 × 10(-6) ), rs7815245 (OR = 0.94, 95% CI = 0.91-0.96, conditional p = 1.1 × 10(-6) ) and rs2033101 (OR = 1.05, 95% CI = 1.02-1.07, conditional p = 1.1 × 10(-4) ) were found. Integrative analysis using functional genomic data from the Roadmap Epigenomics, the Encyclopedia of DNA Elements project, the Cancer Genome Atlas and other public resources implied that SNPs rs7815245 in Signal 3, and rs1121948 in Signal 5 (in linkage disequilibrium with rs11780156, r(2)  = 0.77), were putatively functional variants for two of the five independent association signals. The results highlighted multiple 8q24 variants associated with breast cancer susceptibility in women of European ancestry.
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http://dx.doi.org/10.1002/ijc.30150DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5110427PMC
September 2016

Common germline polymorphisms associated with breast cancer-specific survival.

Breast Cancer Res 2015 Apr 22;17:58. Epub 2015 Apr 22.

Department of Cancer Epidemiology/Clinical Cancer Registry and Institute for Medical Biometrics and Epidemiology, University Clinic Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany.

Introduction: Previous studies have identified common germline variants nominally associated with breast cancer survival. These associations have not been widely replicated in further studies. The purpose of this study was to evaluate the association of previously reported SNPs with breast cancer-specific survival using data from a pooled analysis of eight breast cancer survival genome-wide association studies (GWAS) from the Breast Cancer Association Consortium.

Methods: A literature review was conducted of all previously published associations between common germline variants and three survival outcomes: breast cancer-specific survival, overall survival and disease-free survival. All associations that reached the nominal significance level of P value <0.05 were included. Single nucleotide polymorphisms that had been previously reported as nominally associated with at least one survival outcome were evaluated in the pooled analysis of over 37,000 breast cancer cases for association with breast cancer-specific survival. Previous associations were evaluated using a one-sided test based on the reported direction of effect.

Results: Fifty-six variants from 45 previous publications were evaluated in the meta-analysis. Fifty-four of these were evaluated in the full set of 37,954 breast cancer cases with 2,900 events and the two additional variants were evaluated in a reduced sample size of 30,000 samples in order to ensure independence from the previously published studies. Five variants reached nominal significance (P <0.05) in the pooled GWAS data compared to 2.8 expected under the null hypothesis. Seven additional variants were associated (P <0.05) with ER-positive disease.

Conclusions: Although no variants reached genome-wide significance (P <5 x 10(-8)), these results suggest that there is some evidence of association between candidate common germline variants and breast cancer prognosis. Larger studies from multinational collaborations are necessary to increase the power to detect associations, between common variants and prognosis, at more stringent significance levels.
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http://dx.doi.org/10.1186/s13058-015-0570-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4484708PMC
April 2015

Identification of novel genetic markers of breast cancer survival.

J Natl Cancer Inst 2015 May 18;107(5). Epub 2015 Apr 18.

Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, UK (QG, JT, AMD, MS, JEA, DFE, PDPP); Netherlands Cancer Institute, Antoni van Leeuwenhoek hospital, Amsterdam, the Netherlands (MKS, SC, AB, FBH); Department of Epidemiology, Harvard School of Public Health, Boston, MA (PK, SH, DJH, SL); Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard School of Public Health, Boston, MA (PK, CCh, DJH, SL); Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland (SK, RF, TAM, HN); Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK (MKB, QW, JD, KM, ML, SK, DFE, PDPP); Department of Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia (JBee, GCT); Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 17177, Sweden (KC, HD, ME, JiL, JBr, KH, PH); Laboratory for Translational Genetics, Department of Oncology, University of Leuven, Leuven, Belgium (DL); Vesalius Research Center, VIB, Leuven, Belgium (DL); Oncology Department, University Hospital Gasthuisberg, Leuven, Belgium (CW, KL); Copenhagen General Population Study, Herlev Hospital, Copenhagen, Denmark (SEB, BGN, SFN); Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Denmark (SEB, BGN, SFN); Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark (SEB, BGN); Department of Breast Surgery, Herlev Hospital, Copenhagen University Hospital, Denmark (HF); Division of Cancer Epidemiology, German Cancer Research Center (Deutsches Krebsforschungszentrum), Heidelberg, Germany (JCC, AR, PS, DC, AHü, RK, MB); Department of Cancer Epidemiology/Clinical Cancer Registry and Institute for Medical Biometrics and Epidemiology, University Clinic Hamburg-Eppendorf, Hamburg, Germany (DFJ); Department of Oncology

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 11q24.2) associated with survival in ER-negative breast cancer cases (hazard ratio [HR] = 1.95, 95% confidence interval [CI] = 1.55 to 2.47, P = 1.91 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 2q24.2) 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.
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http://dx.doi.org/10.1093/jnci/djv081DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4555642PMC
May 2015

The effect of rare variants on inflation of the test statistics in case-control analyses.

BMC Bioinformatics 2015 Feb 20;16:53. Epub 2015 Feb 20.

Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, 2 Worts' Causeway, Cambridge, CB1 8RN, UK.

Background: The detection of bias due to cryptic population structure is an important step in the evaluation of findings of genetic association studies. The standard method of measuring this bias in a genetic association study is to compare the observed median association test statistic to the expected median test statistic. This ratio is inflated in the presence of cryptic population structure. However, inflation may also be caused by the properties of the association test itself particularly in the analysis of rare variants. We compared the properties of the three most commonly used association tests: the likelihood ratio test, the Wald test and the score test when testing rare variants for association using simulated data.

Results: We found evidence of inflation in the median test statistics of the likelihood ratio and score tests for tests of variants with less than 20 heterozygotes across the sample, regardless of the total sample size. The test statistics for the Wald test were under-inflated at the median for variants below the same minor allele frequency.

Conclusions: In a genetic association study, if a substantial proportion of the genetic variants tested have rare minor allele frequencies, the properties of the association test may mask the presence or absence of bias due to population structure. The use of either the likelihood ratio test or the score test is likely to lead to inflation in the median test statistic in the absence of population structure. In contrast, the use of the Wald test is likely to result in under-inflation of the median test statistic which may mask the presence of population structure.
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http://dx.doi.org/10.1186/s12859-015-0496-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4339749PMC
February 2015

Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer.

Nat Genet 2015 Apr 9;47(4):373-80. Epub 2015 Mar 9.

Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Genome-wide association studies (GWAS) and large-scale replication studies have identified common variants in 79 loci associated with breast cancer, explaining ∼14% of the familial risk of the disease. To identify new susceptibility loci, we performed a meta-analysis of 11 GWAS, comprising 15,748 breast cancer cases and 18,084 controls together with 46,785 cases and 42,892 controls from 41 studies genotyped on a 211,155-marker custom array (iCOGS). Analyses were restricted to women of European ancestry. We generated genotypes for more than 11 million SNPs by imputation using the 1000 Genomes Project reference panel, and we identified 15 new loci associated with breast cancer at P < 5 × 10(-8). Combining association analysis with ChIP-seq chromatin binding data in mammary cell lines and ChIA-PET chromatin interaction data from ENCODE, we identified likely target genes in two regions: SETBP1 at 18q12.3 and RNF115 and PDZK1 at 1q21.1. One association appears to be driven by an amino acid substitution encoded in EXO1.
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http://dx.doi.org/10.1038/ng.3242DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4549775PMC
April 2015

Multiple independent variants at the TERT locus are associated with telomere length and risks of breast and ovarian cancer.

Nat Genet 2013 Apr;45(4):371-84, 384e1-2

Copenhagen General Population Study, Herlev Hospital, Copenhagen University Hospital, University of Copenhagen, Copenhagen, Denmark.

TERT-locus SNPs and leukocyte telomere measures are reportedly associated with risks of multiple cancers. Using the Illumina custom genotyping array iCOGs, we analyzed ∼480 SNPs at the TERT locus in breast (n = 103,991), ovarian (n = 39,774) and BRCA1 mutation carrier (n = 11,705) cancer cases and controls. Leukocyte telomere measurements were also available for 53,724 participants. Most associations cluster into three independent peaks. The minor allele at the peak 1 SNP rs2736108 associates with longer telomeres (P = 5.8 × 10(-7)), lower risks for estrogen receptor (ER)-negative (P = 1.0 × 10(-8)) and BRCA1 mutation carrier (P = 1.1 × 10(-5)) breast cancers and altered promoter assay signal. The minor allele at the peak 2 SNP rs7705526 associates with longer telomeres (P = 2.3 × 10(-14)), higher risk of low-malignant-potential ovarian cancer (P = 1.3 × 10(-15)) and greater promoter activity. The minor alleles at the peak 3 SNPs rs10069690 and rs2242652 increase ER-negative (P = 1.2 × 10(-12)) and BRCA1 mutation carrier (P = 1.6 × 10(-14)) breast and invasive ovarian (P = 1.3 × 10(-11)) cancer risks but not via altered telomere length. The cancer risk alleles of rs2242652 and rs10069690, respectively, increase silencing and generate a truncated TERT splice variant.
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http://dx.doi.org/10.1038/ng.2566DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3670748PMC
April 2013

BioMart Central Portal: an open database network for the biological community.

Database (Oxford) 2011 18;2011:bar041. Epub 2011 Sep 18.

Ontario Institute for Cancer Research, Toronto, M5G 0A3, Canada.

BioMart Central Portal is a first of its kind, community-driven effort to provide unified access to dozens of biological databases spanning genomics, proteomics, model organisms, cancer data, ontology information and more. Anybody can contribute an independently maintained resource to the Central Portal, allowing it to be exposed to and shared with the research community, and linking it with the other resources in the portal. Users can take advantage of the common interface to quickly utilize different sources without learning a new system for each. The system also simplifies cross-database searches that might otherwise require several complicated steps. Several integrated tools streamline common tasks, such as converting between ID formats and retrieving sequences. The combination of a wide variety of databases, an easy-to-use interface, robust programmatic access and the array of tools make Central Portal a one-stop shop for biological data querying. Here, we describe the structure of Central Portal and show example queries to demonstrate its capabilities.
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http://dx.doi.org/10.1093/database/bar041DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3263598PMC
January 2012

genenames.org: the HGNC resources in 2011.

Nucleic Acids Res 2011 Jan 6;39(Database issue):D514-9. Epub 2010 Oct 6.

European Bioinformatics Institute, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK.

The HUGO Gene Nomenclature Committee (HGNC) aims to assign a unique gene symbol and name to every human gene. The HGNC database currently contains almost 30,000 approved gene symbols, over 19,000 of which represent protein-coding genes. The public website, www.genenames.org, displays all approved nomenclature within Symbol Reports that contain data curated by HGNC editors and links to related genomic, phenotypic and proteomic information. Here we describe improvements to our resources, including a new Quick Gene Search, a new List Search, an integrated HGNC BioMart and a new Statistics and Downloads facility.
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http://dx.doi.org/10.1093/nar/gkq892DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3013772PMC
January 2011

The HGNC Database in 2008: a resource for the human genome.

Nucleic Acids Res 2008 Jan 4;36(Database issue):D445-8. Epub 2007 Nov 4.

European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK.

The HUGO Gene Nomenclature Committee (HGNC) aims to assign a unique and ideally meaningful name and symbol to every human gene. The HGNC database currently comprises over 24 000 public records containing approved human gene nomenclature and associated gene information. Following our recent relocation to the European Bioinformatics Institute our homepage can now be found at http://www.genenames.org, with direct links to the searchable HGNC database and other related database resources, such as the HCOP orthology search tool and manually curated gene family webpages.
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http://dx.doi.org/10.1093/nar/gkm881DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2238870PMC
January 2008

HCOP: a searchable database of human orthology predictions.

Brief Bioinform 2007 Jan 2;8(1):2-5. Epub 2006 Sep 2.

HUGO Gene Nomenclature Committee (HGNC), Department of Biology, University College London, Wolfson House, 4 Stephenson Way, London.

The HUGO Gene Nomenclature Committee (HGNC) Comparison of Orthology Predictions (HCOP) search tool combines the human, mouse, rat and chicken orthology assertions made by PhIGs, HomoloGene, Ensembl, Inparanoid, Mouse Genome Informatics (MGI) and HGNC, enabling users to identify predicted ortholog pairs for a specified gene or genes. The HCOP resource provides a useful method to integrate, compare and access a variety of disparate sources of human orthology data. The HCOP search tool, data and documentation are available at http://www.gene.ucl.ac.uk/hcop.
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http://dx.doi.org/10.1093/bib/bbl030DOI Listing
January 2007

The HUGO Gene Nomenclature Database, 2006 updates.

Nucleic Acids Res 2006 Jan;34(Database issue):D319-21

Department of Biology, HUGO Gene Nomenclature Committee (HGNC), University College London, Wolfson House, 4 Stephenson Way, London NW1 2HE, UK.

The HUGO Gene Nomenclature Committee (HGNC) aims to give every human gene a unique and ideally meaningful name and symbol. The HGNC database, previously known as Genew, contains over 22,000 public records with approved human gene nomenclature and associated information. The database has undergone major improvements throughout the last year, is publicly available for online searching at http://www.gene.ucl.ac.uk/cgi-bin/nomenclature/searchgenes.pl and has a new custom downloads interface at http://www.gene.ucl.ac.uk/cgi-bin/nomenclature/gdlw.pl.
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http://dx.doi.org/10.1093/nar/gkj147DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1347509PMC
January 2006

HCOP: the HGNC comparison of orthology predictions search tool.

Mamm Genome 2005 Nov 11;16(11):827-8. Epub 2005 Nov 11.

HUGO Gene Nomenclature Committee, The Galton Laboratory, Department of Biology, University College London, Wolfson House, 4, Stephenson Way, London, UK.

The HGNC Comparison of Orthology Predictions search tool, HCOP (http://www.gene.ucl.ac.uk/cgi-bin/nomenclature/hcop.pl ), enables users to compare predicted human and mouse orthologs for a specified gene, or set of genes, from either species according to the ortholog assertions from the Ensembl, HGNC, Homologene, Inparanoid, MGI and PhIGs databases. Users can assess the reliability of the prediction from the number of these different sources that identify a particular orthologous pair. HCOP provides a useful one-stop resource to summarise, compare and access various sources of human and mouse orthology data.
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http://dx.doi.org/10.1007/s00335-005-0103-2DOI Listing
November 2005

Gene map of the extended human MHC.

Nat Rev Genet 2004 Dec;5(12):889-99

Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge CB10 1SA, UK.

The major histocompatibility complex (MHC) is the most important region in the vertebrate genome with respect to infection and autoimmunity, and is crucial in adaptive and innate immunity. Decades of biomedical research have revealed many MHC genes that are duplicated, polymorphic and associated with more diseases than any other region of the human genome. The recent completion of several large-scale studies offers the opportunity to assimilate the latest data into an integrated gene map of the extended human MHC. Here, we present this map and review its content in relation to paralogy, polymorphism, immune function and disease.
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http://dx.doi.org/10.1038/nrg1489DOI Listing
December 2004

Genew: the Human Gene Nomenclature Database, 2004 updates.

Nucleic Acids Res 2004 Jan;32(Database issue):D255-7

HUGO Gene Nomenclature Committee (HGNC), Department of Biology, University College London, Wolfson House, 4 Stephenson Way, London NW1 2HE, UK.

Genew, the Human Gene Nomenclature Database http://www.gene.ucl.ac.uk/cgi-bin/nomenclature/searchgenes.pl is the only resource that provides data for all human genes that have approved symbols. It is managed by the HUGO Gene Nomenclature Committee (HGNC) as a confidential database, containing over 22 000 records, 75% of which are represented online by a publicly searchable text file. Since 2002, there have been significant improvements to the Genew search engine. Additionally we have increased our capacity to analyse confidential sequence data, which has enabled us to manage the large numbers of gene symbol requests that we receive from the chromosome sequencing consortia.
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http://dx.doi.org/10.1093/nar/gkh072DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC308806PMC
January 2004

Guidelines for human gene nomenclature.

Genomics 2002 Apr;79(4):464-70

HUGO Gene Nomenclature Committee, The Galton Laboratory, Department of Biology, University College London, Wolfson House, 4, Stephenson Way, London, NW1 2HE, UK.

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http://dx.doi.org/10.1006/geno.2002.6748DOI Listing
April 2002

Genew: the human gene nomenclature database.

Nucleic Acids Res 2002 Jan;30(1):169-71

HUGO Gene Nomenclature Committee (HGNC), Department of Biology, University College London, Wolfson House, 4 Stephenson Way, London NW1 2HE, UK.

Genew, the Human Gene Nomenclature Database, is the only resource that provides data for all human genes which have approved symbols. It is managed by the HUGO Gene Nomenclature Committee (HGNC) as a confidential database, containing over 16 000 records, 80% of which are represented on the Web by searchable text files. The data in Genew are highly curated by HGNC editors and gene records can be searched on the Web by symbol or name to directly retrieve information on gene symbol, gene name, cytogenetic location, OMIM number and PubMed ID. Data are integrated with other human gene databases, e.g. GDB, LocusLink and SWISS-PROT, and approved gene symbols are carefully co-ordinated with the Mouse Genome Database (MGD). Approved gene symbols are available for querying and browsing at http://www.gene.ucl.ac.uk/cgi-bin/nomenclature/searchgenes.pl.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC99058PMC
http://dx.doi.org/10.1093/nar/30.1.169DOI Listing
January 2002
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