Cancer Characterizing Genetic Susceptibility to Breast ...cebp. ? Characterizing Genetic Susceptibility

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Research ArticleCharacterizing Genetic Susceptibility to BreastCancer in Women of African AncestryYe Feng1, Suhn Kyong Rhie1, Dezheng Huo2, Edward A. Ruiz-Narvaez3,Stephen A. Haddad3, Christine B. Ambrosone4, Esther M. John5,6, Leslie Bernstein7,Wei Zheng8, Jennifer J. Hu9, Regina G. Ziegler10, Sarah Nyante11, Elisa V. Bandera12,Sue A. Ingles1, Michael F. Press13, Sandra L. Deming8, Jorge L. Rodriguez-Gil9,Yonglan Zheng14, Song Yao15, Yoo-Jeong Han14, Temidayo O. Ogundiran16,Timothy R. Rebbeck17, Clement Adebamowo18,OladosuOjengbede19, AdeyinkaG. Falusi20,Anselm Hennis21,22, Barbara Nemesure22, Stefan Ambs23,William Blot8, Qiuyin Cai8,Lisa Signorello24, Katherine L. Nathanson25, Kathryn L. Lunetta26,Lara E. Sucheston-Campbell15, Jeannette T. Bensen11, Stephen J. Chanock10,Loic Le Marchand27, Andrew F. Olshan11, Laurence N. Kolonel27, David V. Conti1,Gerhard A. Coetzee1, Daniel O. Stram1, Olufunmilayo I. Olopade14, Julie R. Palmer3, andChristopher A. Haiman1AbstractBackground:Genome-wide association studies have identifiedapproximately 100 common genetic variants associated withbreast cancer risk, the majority of which were discovered inwomen of European ancestry. Because of different patterns oflinkage disequilibrium, many of these genetic markers may notrepresent signals in populations of African ancestry.Methods: We tested 74 breast cancer risk variants and con-ducted fine-mapping of these susceptibility regions in 6,522breast cancer cases and 7,643 controls of African ancestry fromthree genetic consortia (AABC, AMBER, and ROOT).Results: Fifty-four of the 74 variants (73%) were found tohave ORs that were directionally consistent with those previ-ously reported, of which 12 were nominally statistically signif-icant (P < 0.05). Through fine-mapping, in six regions (3p24,12p11, 14q13, 16q12/FTO, 16q23, 19p13), we observed sevenmarkers that better represent the underlying risk variant foroverall breast cancer or breast cancer subtypes, whereas inanother two regions (11q13, 16q12/TOX3), we identified sug-gestive evidence of signals that are independent of the reportedindex variant. Overlapping chromatin features and regulatoryelements suggest that many of the risk alleles lie in regions withbiological functionality.Conclusions: Through fine-mapping of known susceptibilityregions, we have revealed alleles that better characterize breastcancer risk in women of African ancestry.Impact: The risk alleles identified represent genetic markers formodeling and stratifying breast cancer risk in women of Africanancestry. Cancer Epidemiol Biomarkers Prev; 26(7); 101626.2017 AACR.1Department of Preventive Medicine, Keck School of Medicine and Norris Compre-hensive Cancer Center, University of Southern California, Los Angeles, California.2Department of Public Health Sciences, University of Chicago, Chicago, Illinois.3Slone Epidemiology Center at BostonUniversity, Boston, Massachusetts. 4Depart-ment ofCancer Prevention andControl, Roswell Park Cancer Institute, Buffalo, NewYork. 5CancerPrevention Institute of California, Fremont, California. 6Department ofHealth Research and Policy (Epidemiology) and Stanford Cancer Institute, StanfordUniversity School of Medicine, Stanford, California. 7Division of Cancer Etiology,Department of Population Sciences, Beckman Research Institute, City of Hope,Duarte, California. 8Division of Epidemiology, Department of Medicine, VanderbiltEpidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt UniversitySchool of Medicine, Nashville, Tennessee. 9Sylvester Comprehensive Cancer Centerand Department of Epidemiology and Public Health, University of Miami MillerSchool of Medicine, Miami, Florida. 10Epidemiology and Biostatistics Program,Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda,Maryland. 11Departmentof Epidemiology,Gillings SchoolofGlobal PublicHealth andLineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill,North Carolina. 12Rutgers Cancer Institute of New Jersey, New Brunswick, NewJersey. 13Department of Pathology, Keck School of Medicine and Norris Compre-hensive Cancer Center, University of Southern California, Los Angeles, California.14Department of Medicine, University of Chicago, Chicago, Illinois. 15Roswell ParkCancer Institute, Buffalo, New York. 16Department of Surgery, College of Medicine,University of Ibadan, Ibadan, Nigeria. 17Dana Farber Cancer Institute & Harvard T. H.Chan School of Public Health, Boston, Maryland. 18Department of Epidemiology &Preventive Medicine, University of Maryland, Baltimore, Maryland. 19Center forPopulation and Reproductive Health, College of Medicine, University of Ibadan,Ibadan, Nigeria. 20Institute for Medical Research and Training, College of Medicine,University of Ibadan, Ibadan, Nigeria. 21Chronic Disease Research Centre, TropicalMedicine Research Institute, University of the West Indies, Bridgetown, Barbados.22Department of PreventiveMedicine, State University of NewYork at Stony Brook,Stony Brook, New York. 23Laboratory of Human Carcinogenesis, National CancerInstitute, Bethesda, Maryland. 24Cancer Prevention Fellowship Program, NationalCancer Institute, Bethesda, Maryland. 25Department of Medicine, University ofPennsylvania, Philadelphia, Pennsylvania. 26Department of Biostatistics, BostonUniversity School of Public Health, Boston, Massachusetts. 27Epidemiology Pro-gram, University of Hawaii Cancer Center, Honolulu, Hawaii.Note: Supplementary data for this article are available at Cancer Epidemiology,Biomarkers & Prevention Online ( Feng and S.K. Rhie contributed equally to this article.Y. Feng and S.K. Rhie are the co-first authors of this article.Current address for L.E. Sucheston-Campbell: The Ohio State University, Colum-bus, OH; and current address for G.A. Coetzee: Van Andel Research Institute,Grand Rapids, MI.Corresponding Authors: Christopher A. Haiman, 1450 Biggy Street, LosAngeles, CA, 90033. Phone: 323-442-7755; Fax: 323-442-7749;; and Ye Feng, 1441 Eastlake Ave. Los Angeles, CA, 90033.Phone: 609-252-6198; E-mail: yefeng@usc.edudoi: 10.1158/1055-9965.EPI-16-05672017 American Association for Cancer Research.CancerEpidemiology,Biomarkers& PreventionCancer Epidemiol Biomarkers Prev; 26(7) July 20171016on July 8, 2018. 2017 American Association for Cancer Research. Downloaded from Published OnlineFirst April 4, 2017; DOI: 10.1158/1055-9965.EPI-16-0567 association (GWAS) and large-scale fine-mapping studies have led to the identification of >100 breastcancer susceptibility loci that are estimated to explain approx-imately 20% of the 2-fold familial risk of breast cancer inwomen of European descendant (113). For populations ofAfrican ancestry, where the span of linkage disequilibrium (LD)has been shortened by recombination events (more genera-tions), a weaker correlation between an "index" marker (fromGWAS in Asian and European ancestry populations) and bio-logically relevant risk variants is expected. As a consequence,the index marker might not accurately capture the risk associ-ated with the biologically functional variant in African ancestrypopulations. Comprehensive testing in a large African ancestrysample is needed to identify a set of markers that better capturerisk associated with the functional allele at known risk regions,which is an important prerequisite for constructing genetic riskmodels for this population. Previous fine-mapping investiga-tions in women of African ancestry have been limited in size,with the largest study including 3,016 breast cancer cases and2,745 controls and having 80% power to detect reported effectsizes for only 10 of 72 variants examined (115).To obtain greater statistical power for fine-mapping of knownbreast cancer susceptibility regions, we combined genotype andimputed data for 6,522 breast cancer cases and 7,643 controlsfrom three large consortia of African ancestry breast cancertheAfrican American Breast Cancer GWAS Consortium (AABC;ref. 16), the African American Breast Cancer Epidemiology andRisk Consortium (AMBER; refs. 17, 18), and the Genome-WideAssociation Study of Breast Cancer in the African Diaspora Con-sortium (ROOT; ref. 19). In addition to testing the reported indexvariants from previous GWAS, we conducted association analysesand functional annotation across each region in search ofmarkersthat might best define breast cancer risk in women of Africanancestry.Materials and MethodsStudiesThe genetic data included in this analysis were from threeconsortia of breast cancer in women of African ancestry (AABC,AMBER, and ROOT). For this analysis, the African AmericanBreast Cancer Consortium (AABC) included seven epidemiologicstudies: TheMultiethnic Cohort study (MEC), 734/1,003; The LosAngeles component of The Women's Contraceptive and Repro-ductive Experiences (CARE) Study, 380/224; The San FranciscoBay Area Breast Cancer Study (SFBCS), 172/23; The NorthernCalifornia site of the Breast Cancer Family Registry (NC-BCFR),440/53; The Prostate, Lung, Colorectal, and Ovarian CancerScreening Trial (PLCO) Cohort, 64/133; The Nashville BreastHealth Study (NBHS), 310/186; and The Wake Forest UniversityBreast Cancer Study (WFBC), 125/153). The current analysisincludes GWAS data for 2,225 invasive cases and 1,983 controlsfrom AABC (14). Although the Women's Circle of Health Study(WCHS) and The Carolina Breast Cancer Study (CBCS) partici-pated in AABC, samples from those studies are included as part ofthe AMBER consortium described below.The AMBER consortium (18) included three studies for a totalof 2,754 invasive breast cancer cases and 3,698 controls: the BlackWomen's Health Study (BWHS; ref. 20; 752/2249); WCHS (681/834; ref. 21); and CBCS (1321/615; ref. 22).The ROOT consortium (19) included six studies and a total of1,657 cases and 2,028 controls of African ancestry: The NigerianBreast Cancer Study (NBCS), 711/623; The Barbados NationalCancer Study (BNCS), 92/229; TheRacial Variability inGenotypicDeterminants of Breast Cancer Risk Study (RVGBC), 145/257; TheBaltimore Breast Cancer Study (BBCS), 95/102; The ChicagoCancer Prone Study (CCPS), 394/387; and The Southern Com-munity Cohort (SCCS), 220/430.Genotyping and quality controlGenotyping inAABCwas conductedusing the IlluminaHuman1M-Duo BeadChip as described in Chen and colleagues (14). TheROOT samples were genotyped using the Illumina 2.5 M array(23). Samples in AMBER were genotyped using an IlluminaInfinium custom160 K SNP array which included approximate-ly 45,000 SNPs selected primarily for fine-mapping of knownbreast cancer susceptibility regions.Statistical analysisImputation in AABC and AMBER was conducted usingIMPUTE2 (24) to a cosmopolitan panel of all 1000 GenomeProject subjects (March 2012 release). IMPUTE2 (24) was alsoused to impute the untyped SNPs in ROOT using a cosmopolitanpanel of all 1000 Genome Project subjects (October 2011release). Imputed SNPs with imputation quality score > 0.7 andaminor allele frequency >0.01 in each studywere used in thefine-mapping analysis. We examined 74 risk variants for breast cancerin 72 regions that had been reported at the time this study wasinitiated (111). One additional variant, rs11571833 at chromo-some 13q13, was not genotyped and could not be imputed in allthree studies; this variant had aminor allele frequency of 0.006 inthe 1000Genomes AFRpopulation. These 74 risk variants includestronger markers than the index SNP found in GWAS as well asindependent signals discovered through subsequent fine-map-ping studies (Supplementary Table S1; refs. 1, 3, 4, 8, 10, 11).A total of 6,522 breast cancer cases (2,933 ER and 1,876 ER)and 7,643 controls were included in the analysis. For each typedand imputed SNP, ORs and 95% confidence intervals (95% CI)were estimated using unconditional logistic regression adjustingfor age (at diagnosis for cases and age at the reference date forcontrols), study, and the first 10 eigenvectors from a principalcomponents analysis (25). For each SNP that existed in all threestudies, we tested for allele dosage effects separately in each of thethree studies, applying a 1-degree-of-freedom Wald c2 trend test.Results were then combined using inverse varianceweightedfixed-effects meta-analysis, as implemented in METAL (26). Wetested for effect heterogeneity between studies using Cochran Q-test as implemented in METAL. Power calculations were con-ducted using Quanto ( using the OR inprevious GWAS and the allele frequency in African Americans.To identify alleles thatmight capture thebiologically functionalvariant at 70 of the known breast cancer risk regions, we searchedand tested LD proxies among the genotyped and imputed SNPsthat were correlated (r2 0.4) with the index SNP [within 250 kbor larger if the index signal was contained within an LD block(based on the D' statistic) of >250 kb] in European ancestrypopulations, resulting in a total of 157,920 SNPs included in theanalysis. Two regions, 5p15 and 20q11, were excluded from fine-mapping because the AABC sample was involved in the discoveryof the risk loci in these regions (27, 28). The GWAS arrays andimputation in AAPC, AMBER, and ROOTprovided good coverageBreast Cancer in Women of African Cancer Epidemiol Biomarkers Prev; 26(7) July 2017 1017on July 8, 2018. 2017 American Association for Cancer Research. Downloaded from Published OnlineFirst April 4, 2017; DOI: 10.1158/1055-9965.EPI-16-0567 common variation (>5%) in the fine-mapped regions inAfrican ancestry populations. For AABC, an average of 96% ofcommon SNPs with a MAF >5% in the phase III 1000 GenomeAFR population were tagged (at r2 > 0.8) by the genotyped andimputed SNPs. For ROOT and AMBER, these averages were each97%. For each study, the coverage was >90% for all regions, withthe exception of chromosome 1p11 (45% in each) and chromo-some 6q25 in AMBER (82%).Locus-specific significance levels were calculated asdescribed in Feng and colleagues, 2014 (ref. 15; Supplemen-tary Table S2). More specifically, locus-specific significancelevels were calculated as 0.05 divided by the number of tagSNPs in the African population (1000 Genomes, AFR, March2012 Release) that capture (r2 0.8) all SNPs correlated withthe index signal in the European population (1000 Genomes,EUR, March 2012 Release). To reduce false-positive signals, werequired the P value of all better markers to be less than 0.01.In an attempt to eliminate minor fluctuations in P values forcorrelated SNPs, we also required the P value to decrease bymore than one order of magnitude compared with the asso-ciation with the index signal. If multiple variants satisfied theabove criterion in each region, only the most statisticallysignificant variant was reported.We also tested for novel independent associations, focusingon all genotyped and imputed SNPs in each region that wereuncorrelated with the index signal in European ancestry popu-lations (r2 < 0.1), and applied a significance criterion of a 5 106 for defining suggestive novel associations, as used in priorstudies (14). This a is not as conservative as genome-widesignificance and is an approximation of the number of teststo capture (at r2 0.8) common risk alleles across all regions.To confirm independent associations, conditional analyseswere performed that included the index SNP or better markerplus the most significant uncorrelated allele. The analysis wasfirst conducted in each separate study and then combined usingfixed-effects meta-analysis. Haplotype analysis on 16q12 wasconducted applying the "haplo.stats" package in R ( procedures described above were applied to the analysis ofoverall breast cancer as well as in secondary analyses stratified byER status.Functional annotationsWe assessed whether any of the signals colocalized with 65chromatin features that capture open chromatin regions andregulatory elements across the genome in ER breast cancer(MCF7, T47D, HCC1954), ER breast cancer (MDAMB231) andnormal breast (HMEC, Myoepithelial, Fibroblast, Luminal epi-thelial) cells identified by the Coetzee Laboratory (2931) orobtained from the Encyclopedia of DNA Elements (ENCODE)project (32) orNIHRoadmapEpigenomicsMappingConsortium(REMC; refs. 33, 34). Enriched regions of chromatin features wereeither called by using the Sole-search program (35) or obtainedfrom GEO databases (GSE35583, GSE32970, GSE35239,GSE46074, GSE49651, GSE78913). To refine the genomic regu-latory regions, chromatin state segmentation information built byusing a HiddenMarkovModel (HMM) inMCF7 andHMECwerealso included (36, 37).We used motifbreakR (38) to search for transcription factormotifs that bind to each variant (3943). Chromatin features thatoverlapped variants and motifs that significantly altered binding(using the default setting with the score threshold, 0.9) aresummarized in Supplementary Table S3. We also included keytranscription factors for breast cancer such as FOXA1, GATA3,and ESR1 ChIP-seq data in ER breast cancer cells (MCF7, T47D)from the ENCODE project to examine the occupancy of tran-scription factors in vitro at regulatory elements where variantsreside (32).ResultsOf the 74 breast cancer risk variants, 68 were also common inwomen of African ancestry, with minor allele frequencies greaterthan 0.05 in all three studies. Of these 68 variants, we had50%and80%power (at P < 0.05) to detect previously reported effectsizes for 51 and 36 variants, respectively. The ORs observed for 54(73%) of the 74 SNPs were directionally consistent with thosepreviously reported (i.e., ORswere in the same direction), with 12variants nominally statistically significant at P < 0.05 (Table 1;Supplementary Table S1). Of the 61 SNPs that were directionallyconsistent with previous GWAS findings and not replicated at P /JPEG2000ColorACSImageDict > /JPEG2000ColorImageDict > /AntiAliasGrayImages false /CropGrayImages false /GrayImageMinResolution 200 /GrayImageMinResolutionPolicy /Warning /DownsampleGrayImages true /GrayImageDownsampleType /Bicubic /GrayImageResolution 300 /GrayImageDepth -1 /GrayImageMinDownsampleDepth 2 /GrayImageDownsampleThreshold 1.50000 /EncodeGrayImages true /GrayImageFilter /DCTEncode /AutoFilterGrayImages true /GrayImageAutoFilterStrategy /JPEG /GrayACSImageDict > /GrayImageDict > /JPEG2000GrayACSImageDict > /JPEG2000GrayImageDict > /AntiAliasMonoImages false /CropMonoImages false /MonoImageMinResolution 600 /MonoImageMinResolutionPolicy /Warning /DownsampleMonoImages true /MonoImageDownsampleType /Bicubic /MonoImageResolution 900 /MonoImageDepth -1 /MonoImageDownsampleThreshold 1.50000 /EncodeMonoImages true /MonoImageFilter /CCITTFaxEncode /MonoImageDict > /AllowPSXObjects false /CheckCompliance [ /None ] /PDFX1aCheck false /PDFX3Check false /PDFXCompliantPDFOnly false /PDFXNoTrimBoxError true /PDFXTrimBoxToMediaBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXSetBleedBoxToMediaBox true /PDFXBleedBoxToTrimBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXOutputIntentProfile (None) /PDFXOutputConditionIdentifier () /PDFXOutputCondition () /PDFXRegistryName () /PDFXTrapped /False /CreateJDFFile false /Description > /Namespace [ (Adobe) (Common) (1.0) ] /OtherNamespaces [ > /FormElements false /GenerateStructure false /IncludeBookmarks false /IncludeHyperlinks false /IncludeInteractive false /IncludeLayers false /IncludeProfiles false /MarksOffset 18 /MarksWeight 0.250000 /MultimediaHandling /UseObjectSettings /Namespace [ (Adobe) (CreativeSuite) (2.0) ] /PDFXOutputIntentProfileSelector /NA /PageMarksFile /RomanDefault /PreserveEditing true /UntaggedCMYKHandling /LeaveUntagged /UntaggedRGBHandling /LeaveUntagged /UseDocumentBleed false >> > ]>> setdistillerparams> setpagedevice


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