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• W2800524425 abstract "Genome-wide association studies (GWASs) have identified more than 150 common genetic loci for breast cancer risk. However, the target genes and underlying mechanisms remain largely unknown. We conducted a cis-expression quantitative trait loci (cis-eQTL) analysis using normal or tumor breast transcriptome data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), The Cancer Genome Atlas (TCGA), and the Genotype-Tissue Expression (GTEx) project. We identified a total of 101 genes for 51 lead variants after combing the results of a meta-analysis of METABRIC and TCGA, and the results from GTEx at a Benjamini-Hochberg (BH)-adjusted p < 0.05. Using luciferase reporter assays in both estrogen-receptor positive (ER+) and negative (ER−) cell lines, we showed that alternative alleles of potential functional single-nucleotide polymorphisms (SNPs), rs11552449 (DCLRE1B), rs7257932 (SSBP4), rs3747479 (MRPS30), rs2236007 (PAX9), and rs73134739 (ATG10), could significantly change promoter activities of their target genes compared to reference alleles. Furthermore, we performed in vitro assays in breast cancer cell lines, and our results indicated that DCLRE1B, MRPS30, and ATG10 played a vital role in breast tumorigenesis via certain disruption of cell behaviors. Our findings revealed potential target genes for associations of genetic susceptibility risk loci and provided underlying mechanisms for a better understanding of the pathogenesis of breast cancer. Genome-wide association studies (GWASs) have identified more than 150 common genetic loci for breast cancer risk. However, the target genes and underlying mechanisms remain largely unknown. We conducted a cis-expression quantitative trait loci (cis-eQTL) analysis using normal or tumor breast transcriptome data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), The Cancer Genome Atlas (TCGA), and the Genotype-Tissue Expression (GTEx) project. We identified a total of 101 genes for 51 lead variants after combing the results of a meta-analysis of METABRIC and TCGA, and the results from GTEx at a Benjamini-Hochberg (BH)-adjusted p < 0.05. Using luciferase reporter assays in both estrogen-receptor positive (ER+) and negative (ER−) cell lines, we showed that alternative alleles of potential functional single-nucleotide polymorphisms (SNPs), rs11552449 (DCLRE1B), rs7257932 (SSBP4), rs3747479 (MRPS30), rs2236007 (PAX9), and rs73134739 (ATG10), could significantly change promoter activities of their target genes compared to reference alleles. Furthermore, we performed in vitro assays in breast cancer cell lines, and our results indicated that DCLRE1B, MRPS30, and ATG10 played a vital role in breast tumorigenesis via certain disruption of cell behaviors. Our findings revealed potential target genes for associations of genetic susceptibility risk loci and provided underlying mechanisms for a better understanding of the pathogenesis of breast cancer. To date, genome-wide association studies (GWASs) have identified more than 150 genetic susceptibility loci associated with breast cancer risk.1Antoniou A.C. Wang X. Fredericksen Z.S. McGuffog L. Tarrell R. Sinilnikova O.M. Healey S. Morrison J. Kartsonaki C. Lesnick T. et al.EMBRACEGEMO Study CollaboratorsHEBONkConFabSWE-BRCAMOD SQUADGENICAA locus on 19p13 modifies risk of breast cancer in BRCA1 mutation carriers and is associated with hormone receptor-negative breast cancer in the general population.Nat. Genet. 2010; 42: 885-892Crossref PubMed Scopus (266) Google Scholar, 2Cai Q. Long J. Lu W. Qu S. Wen W. Kang D. Lee J.Y. Chen K. Shen H. Shen C.Y. et al.Genome-wide association study identifies breast cancer risk variant at 10q21.2: results from the Asia Breast Cancer Consortium.Hum. Mol. Genet. 2011; 20: 4991-4999Crossref PubMed Scopus (80) Google Scholar, 3Cox A. Dunning A.M. Garcia-Closas M. Balasubramanian S. Reed M.W. Pooley K.A. Scollen S. Baynes C. Ponder B.A. Chanock S. et al.Kathleen Cunningham Foundation Consortium for Research into Familial Breast CancerBreast Cancer Association ConsortiumA common coding variant in CASP8 is associated with breast cancer risk.Nat. Genet. 2007; 39: 352-358Crossref PubMed Scopus (447) Google Scholar, 4Easton D.F. Pooley K.A. Dunning A.M. Pharoah P.D. Thompson D. Ballinger D.G. Struewing J.P. Morrison J. Field H. 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Cheng J. et al.Identification of a functional genetic variant at 16q12.1 for breast cancer risk: results from the Asia Breast Cancer Consortium.PLoS Genet. 2010; 6: e1001002Crossref PubMed Scopus (98) Google Scholar, 8Long J. Cai Q. Sung H. Shi J. Zhang B. Choi J.Y. Wen W. Delahanty R.J. Lu W. Gao Y.T. et al.Genome-wide association study in east Asians identifies novel susceptibility loci for breast cancer.PLoS Genet. 2012; 8: e1002532Crossref PubMed Scopus (127) Google Scholar, 9Long J. Delahanty R.J. Li G. Gao Y.T. Lu W. Cai Q. Xiang Y.B. Li C. Ji B.T. Zheng Y. et al.A common deletion in the APOBEC3 genes and breast cancer risk.J. Natl. Cancer Inst. 2013; 105: 573-579Crossref PubMed Scopus (109) Google Scholar, 10Michailidou K. Beesley J. Lindstrom S. Canisius S. Dennis J. Lush M.J. Maranian M.J. Bolla M.K. Wang Q. Shah M. et al.BOCSkConFab InvestigatorsAOCS GroupNBCSGENICA NetworkGenome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer.Nat. Genet. 2015; 47: 373-380Crossref PubMed Scopus (402) Google Scholar, 11Michailidou K. Hall P. Gonzalez-Neira A. Ghoussaini M. Dennis J. Milne R.L. Schmidt M.K. Chang-Claude J. Bojesen S.E. Bolla M.K. et al.Breast and Ovarian Cancer Susceptibility CollaborationHereditary Breast and Ovarian Cancer Research Group Netherlands (HEBON)kConFab InvestigatorsAustralian Ovarian Cancer Study GroupGENICA (Gene Environment Interaction and Breast Cancer in Germany) NetworkLarge-scale genotyping identifies 41 new loci associated with breast cancer risk.Nat. Genet. 2013; 45 (e1–e2): 353-361Crossref PubMed Scopus (836) Google Scholar, 12Purrington K.S. Slager S. Eccles D. Yannoukakos D. Fasching P.A. Miron P. Carpenter J. Chang-Claude J. Martin N.G. Montgomery G.W. et al.GENICA NetworkGenome-wide association study identifies 25 known breast cancer susceptibility loci as risk factors for triple-negative breast cancer.Carcinogenesis. 2014; 35: 1012-1019Crossref PubMed Scopus (112) Google Scholar, 13Shi J. Sung H. Zhang B. Lu W. Choi J.Y. Xiang Y.B. Kim M.K. Iwasaki M. Long J. Ji B.T. et al.New breast cancer risk variant discovered at 10q25 in East Asian women.Cancer Epidemiol. Biomarkers Prev. 2013; 22: 1297-1303Crossref PubMed Scopus (6) Google Scholar, 14Siddiq A. Couch F.J. Chen G.K. Lindström S. Eccles D. Millikan R.C. Michailidou K. Stram D.O. Beckmann L. Rhie S.K. et al.Australian Breast Cancer Tissue Bank InvestigatorsFamilial Breast Cancer StudyGENICA ConsortiumA meta-analysis of genome-wide association studies of breast cancer identifies two novel susceptibility loci at 6q14 and 20q11.Hum. Mol. Genet. 2012; 21: 5373-5384Crossref PubMed Scopus (141) Google Scholar, 15Turnbull C. Ahmed S. Morrison J. Pernet D. Renwick A. Maranian M. Seal S. Ghoussaini M. Hines S. Healey C.S. et al.Breast Cancer Susceptibility Collaboration (UK)Genome-wide association study identifies five new breast cancer susceptibility loci.Nat. Genet. 2010; 42: 504-507Crossref PubMed Scopus (581) Google Scholar, 16Michailidou K. Lindström S. Dennis J. Beesley J. Hui S. Kar S. Lemaçon A. Soucy P. Glubb D. Rostamianfar A. et al.NBCS CollaboratorsABCTB InvestigatorsConFab/AOCS InvestigatorsAssociation analysis identifies 65 new breast cancer risk loci.Nature. 2017; 551: 92-94Crossref PubMed Scopus (681) Google Scholar, 17Milne R.L. Kuchenbaecker K.B. Michailidou K. Beesley J. Kar S. Lindström S. Hui S. Lemaçon A. Soucy P. Dennis J. et al.ABCTB InvestigatorsEMBRACEGEMO Study CollaboratorsHEBONkConFab/AOCS InvestigatorsNBSC CollaboratorsIdentification of ten variants associated with risk of estrogen-receptor-negative breast cancer.Nat. Genet. 2017; 49: 1767-1778Crossref PubMed Scopus (181) Google Scholar Approximately 90% of the single-nucleotide polymorphisms (SNPs) or variants initially identified by GWASs in these risk loci are located in intergenic, or non-coding, regions, and they are either not in linkage disequilibrium (LD) or have weak LD with coding variants. For the large majority of these risk variants, the mechanisms and biological relevance for their associations with breast cancer remain unclear. It is believed that most of these risk variants confer breast cancer pathogenesis by regulating the expression of genes, especially nearby genes.18Pierce B.L. Tong L. Chen L.S. Rahaman R. Argos M. Jasmine F. Roy S. Paul-Brutus R. Westra H.J. Franke L. et al.Mediation analysis demonstrates that trans-eQTLs are often explained by cis-mediation: a genome-wide analysis among 1,800 South Asians.PLoS Genet. 2014; 10: e1004818Crossref PubMed Scopus (55) Google Scholar, 19Innocenti F. Cooper G.M. Stanaway I.B. Gamazon E.R. Smith J.D. Mirkov S. Ramirez J. Liu W. Lin Y.S. Moloney C. et al.Identification, replication, and functional fine-mapping of expression quantitative trait loci in primary human liver tissue.PLoS Genet. 2011; 7: e1002078Crossref PubMed Scopus (172) Google Scholar, 20Nicolae D.L. Gamazon E. Zhang W. Duan S. Dolan M.E. Cox N.J. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS.PLoS Genet. 2010; 6: e1000888Crossref PubMed Scopus (913) Google Scholar, 21Ward L.D. Kellis M. Interpreting noncoding genetic variation in complex traits and human disease.Nat. Biotechnol. 2012; 30: 1095-1106Crossref PubMed Scopus (331) Google Scholar, 22Maurano M.T. Humbert R. Rynes E. Thurman R.E. Haugen E. Wang H. Reynolds A.P. Sandstrom R. Qu H. Brody J. et al.Systematic localization of common disease-associated variation in regulatory DNA.Science. 2012; 337: 1190-1195Crossref PubMed Scopus (2217) Google Scholar A recent study has shown that approximately 80% of the heritability of disease risk for 11 common diseases can be explained by variants in DNase I hypersensitivity sites, indicating that these variants, including the GWAS-identified risk variants, may play a regulatory role in gene expression.23Gusev A. Lee S.H. Trynka G. Finucane H. Vilhjálmsson B.J. Xu H. Zang C. Ripke S. Bulik-Sullivan B. Stahl E. et al.Schizophrenia Working Group of the Psychiatric Genomics ConsortiumSWE-SCZ ConsortiumSchizophrenia Working Group of the Psychiatric Genomics ConsortiumSWE-SCZ ConsortiumPartitioning heritability of regulatory and cell-type-specific variants across 11 common diseases.Am. J. Hum. Genet. 2014; 95: 535-552Abstract Full Text Full Text PDF PubMed Scopus (370) Google Scholar Large genomics data consortia, including the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), The Cancer Genome Atlas (TCGA), and the Genotype-Tissue Expression (GETx) project, have generated massive quantities of high-dimensional genomic data, including both matched genetic and transcriptome profiles from thousands of samples of breast cancer tumor tissue and normal tissue. These data provide an unprecedented opportunity for expression quantitative trait loci (eQTL) analysis, which evaluates the association of a variant genotype with gene expression levels measured in cells or tissues from individual subjects. Li et al. conducted a cis-eQTL analysis focused on 15 breast cancer index variants to identify potential nearby regulatory transcription factor (TF) targets.24Li Q. Seo J.H. Stranger B. McKenna A. Pe’er I. Laframboise T. Brown M. Tyekucheva S. Freedman M.L. Integrative eQTL-based analyses reveal the biology of breast cancer risk loci.Cell. 2013; 152: 633-641Abstract Full Text Full Text PDF PubMed Scopus (248) Google Scholar They subsequently expanded their cis-eQTL analysis to include risk loci for multiple cancer types using a subset of TCGA data.25Li Q. Stram A. Chen C. Kar S. Gayther S. Pharoah P. Haiman C. Stranger B. Kraft P. Freedman M.L. Expression QTL-based analyses reveal candidate causal genes and loci across five tumor types.Hum. Mol. Genet. 2014; 23: 5294-5302Crossref PubMed Scopus (61) Google Scholar Recently, the GTEx project systematically identified thousands of eQTL target genes by evaluating the association between transcriptome variation and genome-wide variants across 43 types of normal tissues, including normal breast tissue from hundreds of individuals.26Melé M. Ferreira P.G. Reverter F. DeLuca D.S. Monlong J. Sammeth M. Young T.R. Goldmann J.M. Pervouchine D.D. Sullivan T.J. et al.GTEx ConsortiumHuman genomics. The human transcriptome across tissues and individuals.Science. 2015; 348: 660-665Crossref PubMed Scopus (769) Google Scholar, 27Consortium G.T. GTEx ConsortiumHuman genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans.Science. 2015; 348: 648-660Crossref PubMed Scopus (3098) Google Scholar In another work, Castro and colleagues reported 36 TF regulons, described as a set of highly co-expressed genes regulated by potential TFs associated with breast cancer index variants, using variant and transcriptome data in breast tumor tissues from METABRIC.28Castro M.A. de Santiago I. Campbell T.M. Vaughn C. Hickey T.E. Ross E. Tilley W.D. Markowetz F. Ponder B.A. Meyer K.B. Regulators of genetic risk of breast cancer identified by integrative network analysis.Nat. Genet. 2016; 48: 12-21Crossref PubMed Scopus (111) Google Scholar Most recently, Michailidou and colleagues reported 65 new breast cancer risk loci. They performed eQTL analysis using 458 tumor tissues from TCGA and 138 normal tissue samples from METABRIC.16Michailidou K. Lindström S. Dennis J. Beesley J. Hui S. Kar S. Lemaçon A. Soucy P. Glubb D. Rostamianfar A. et al.NBCS CollaboratorsABCTB InvestigatorsConFab/AOCS InvestigatorsAssociation analysis identifies 65 new breast cancer risk loci.Nature. 2017; 551: 92-94Crossref PubMed Scopus (681) Google Scholar In addition to this large-scale analysis of index variants, over the past several years, we and other groups have conducted fine-mapping and cis-eQTL analyses to identify target genes in selected loci, including ESR1 (6q25),29Sun Y. Ye C. Guo X. Wen W. Long J. Gao Y.T. Shu X.O. Zheng W. Cai Q. Evaluation of potential regulatory function of breast cancer risk locus at 6q25.1.Carcinogenesis. 2016; 37: 163-168Crossref PubMed Scopus (16) Google Scholar, 30Dunning A.M. Michailidou K. Kuchenbaecker K.B. Thompson D. French J.D. Beesley J. Healey C.S. Kar S. Pooley K.A. Lopez-Knowles E. et al.EMBRACEGEMO Study CollaboratorsHEBONkConFab InvestigatorsBreast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170.Nat. Genet. 2016; 48: 374-386Crossref PubMed Scopus (99) Google Scholar IGFBP5 (2q35),31Ghoussaini M. Edwards S.L. Michailidou K. Nord S. Cowper-Sal Lari R. Desai K. Kar S. Hillman K.M. Kaufmann S. Glubb D.M. et al.Australian Ovarian Cancer Management GroupAustralian Ovarian Cancer Management GroupEvidence that breast cancer risk at the 2q35 locus is mediated through IGFBP5 regulation.Nat. Commun. 2014; 4: 4999Crossref PubMed Scopus (87) Google Scholar FGFR2 (10q26),32Meyer K.B. Maia A.T. O’Reilly M. Teschendorff A.E. Chin S.F. Caldas C. Ponder B.A. Allele-specific up-regulation of FGFR2 increases susceptibility to breast cancer.PLoS Biol. 2008; 6: e108Crossref PubMed Scopus (230) Google Scholar, 33Meyer K.B. O’Reilly M. Michailidou K. Carlebur S. Edwards S.L. French J.D. Prathalingham R. Dennis J. Bolla M.K. Wang Q. et al.GENICA NetworkkConFab InvestigatorsAustralian Ovarian Cancer Study GroupFine-scale mapping of the FGFR2 breast cancer risk locus: putative functional variants differentially bind FOXA1 and E2F1.Am. J. Hum. Genet. 2013; 93: 1046-1060Abstract Full Text Full Text PDF PubMed Scopus (79) Google Scholar, 34Zhu X. Asa S.L. Ezzat S. Histone-acetylated control of fibroblast growth factor receptor 2 intron 2 polymorphisms and isoform splicing in breast cancer.Mol. Endocrinol. 2009; 23: 1397-1405Crossref PubMed Scopus (24) Google Scholar CCND1 (11q13),35French J.D. Ghoussaini M. Edwards S.L. Meyer K.B. Michailidou K. Ahmed S. Khan S. Maranian M.J. O’Reilly M. Hillman K.M. et al.GENICA NetworkkConFab InvestigatorsFunctional variants at the 11q13 risk locus for breast cancer regulate cyclin D1 expression through long-range enhancers.Am. J. Hum. Genet. 2013; 92: 489-503Abstract Full Text Full Text PDF PubMed Scopus (164) Google Scholar MAP3K1 (5q11),36Glubb D.M. Maranian M.J. Michailidou K. Pooley K.A. Meyer K.B. Kar S. Carlebur S. O’Reilly M. Betts J.A. Hillman K.M. et al.GENICA NetworkkConFab InvestigatorsNorwegian Breast Cancer StudyFine-scale mapping of the 5q11.2 breast cancer locus reveals at least three independent risk variants regulating MAP3K1.Am. J. Hum. Genet. 2015; 96: 5-20Abstract Full Text Full Text PDF PubMed Scopus (59) Google Scholar CASP8 (2q33),37Lin W.Y. Camp N.J. Ghoussaini M. Beesley J. Michailidou K. Hopper J.L. Apicella C. Southey M.C. Stone J. Schmidt M.K. et al.GENICA NetworkkConFab InvestigatorsAustralian Ovarian Cancer Study GroupBreast and Ovarian Cancer Susceptibility (BOCS) StudyIdentification and characterization of novel associations in the CASP8/ALS2CR12 region on chromosome 2 with breast cancer risk.Hum. Mol. Genet. 2015; 24: 285-298Crossref PubMed Scopus (37) Google Scholar RCCD1 (15q26),2Cai Q. Long J. Lu W. Qu S. Wen W. Kang D. Lee J.Y. Chen K. Shen H. Shen C.Y. et al.Genome-wide association study identifies breast cancer risk variant at 10q21.2: results from the Asia Breast Cancer Consortium.Hum. Mol. Genet. 2011; 20: 4991-4999Crossref PubMed Scopus (80) Google Scholar TET2 (4q24),38Guo X. Long J. Zeng C. Michailidou K. Ghoussaini M. Bolla M.K. Wang Q. Milne R.L. Shu X.O. Cai Q. et al.kConFab InvestigatorsFine-scale mapping of the 4q24 locus identifies two independent loci associated with breast cancer risk.Cancer Epidemiol. Biomarkers Prev. 2015; 24: 1680-1691Crossref PubMed Scopus (19) Google Scholar MYC (8q24),39Shi J. Zhang Y. Zheng W. Michailidou K. Ghoussaini M. Bolla M.K. Wang Q. Dennis J. Lush M. Milne R.L. et al.Mervi GripkConFab InvestigatorsFine-scale mapping of 8q24 locus identifies multiple independent risk variants for breast cancer.Int. J. Cancer. 2016; 139: 1303-1317Crossref PubMed Scopus (31) Google Scholar PTHLH (12p11),40Zeng C. Guo X. Long J. Kuchenbaecker K.B. Droit A. Michailidou K. Ghoussaini M. Kar S. Freeman A. Hopper J.L. et al.EMBRACEbehalf of GEMO Study CollaboratorsHEBONKConFabAOCS InvestigatorsIdentification of independent association signals and putative functional variants for breast cancer risk through fine-scale mapping of the 12p11 locus.Breast Cancer Res. 2016; 18: 64Crossref PubMed Scopus (27) Google Scholar STXBP4 (17q22),41Wright F.A. Sullivan P.F. Brooks A.I. Zou F. Sun W. Xia K. Madar V. Jansen R. Chung W. Zhou Y.H. et al.Heritability and genomics of gene expression in peripheral blood.Nat. Genet. 2014; 46: 430-437Crossref PubMed Scopus (228) Google Scholar HELQ (4q21),42Hamdi Y. Soucy P. Adoue V. Michailidou K. Canisius S. Lemaçon A. Droit A. Andrulis I.L. Anton-Culver H. Arndt V. et al.NBCS CollaboratorskConFab/AOCS InvestigatorsAssociation of breast cancer risk with genetic variants showing differential allelic expression: Identification of a novel breast cancer susceptibility locus at 4q21.Oncotarget. 2016; 7: 80140-80163Crossref PubMed Scopus (25) Google Scholar NRBF2 (10q21),43Darabi H. McCue K. Beesley J. Michailidou K. Nord S. Kar S. Humphreys K. Thompson D. Ghoussaini M. Bolla M.K. et al.German Consortium of Hereditary Breast and Ovarian CancerkConFab/AOCS InvestigatorsPolymorphisms in a putative enhancer at the 10q21.2 breast cancer risk locus regulate NRBF2 expression.Am. J. Hum. Genet. 2015; 97: 22-34Abstract Full Text Full Text PDF PubMed Scopus (28) Google Scholar and MRPS30 (5p12).44Ghoussaini M. French J.D. Michailidou K. Nord S. Beesley J. Canisus S. Hillman K.M. Kaufmann S. Sivakumaran H. Moradi Marjaneh M. et al.kConFab/AOCS InvestigatorsNBCS CollaboratorsEvidence that the 5p12 variant rs10941679 confers susceptibility to estrogen-receptor-positive breast cancer through FGF10 and MRPS30 regulation.Am. J. Hum. Genet. 2016; 99: 903-911Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar, 45Quigley D.A. Fiorito E. Nord S. Van Loo P. Alnæs G.G. Fleischer T. Tost J. Moen Vollan H.K. Tramm T. Overgaard J. et al.The 5p12 breast cancer susceptibility locus affects MRPS30 expression in estrogen-receptor positive tumors.Mol. Oncol. 2014; 8: 273-284Crossref PubMed Scopus (24) Google Scholar. While previous studies identified a large number of susceptibility gene candidates as described above, target genes for a large proportion of risk loci remain unknown. In addition, many candidate target genes were identified based on eQTL analysis at p < 0.05 in only one dataset; some false positive results can be ruled out only via independent replication using additional datasets. In particular, eQTL analysis has not been systematically performed to evaluate the associations of nearby genes and index variants using large-scale transcriptome data in tumor tissues from METABRIC. In the present study, we collected a total of 172 index variants for breast cancer risk at p < 5.0 × 10−8 from previous literature (Table S1). Using GWAS data from the Breast Cancer Association Consortium (BCAC), we identified a total of 159 breast cancer lead variants for these index variants, whereas they are not in LD (R2 < 0.1) (see Material and Methods, Table S1). We conducted a comprehensive cis-eQTL analysis of these variants to evaluate their associations with expression levels of nearby genes (1 Mb distance from the lead variant) in four transcriptome datasets from the METABRIC, TCGA, and GTEx project. Using luciferase reporter assays, we experimentally validated that alternative alleles of several functional SNPs could significantly change the promoter activities of target genes compared to their reference alleles. Using in vitro functional assays in breast cancer cell lines, our results further indicated that three candidate susceptibility genes play a vital role in breast tumorigenesis via certain disruption of cell behaviors. These findings provide additional insights into the understanding of regulatory mechanisms of genetic risk variants and genes for breast cancer development. We collected and characterized 172 index variants for breast cancer risk at p < 5.0 × 10−8 from previous literature (Table S1). We extracted 11,642 variants in strong LD with 172 index variants (R2 > 0.4). We retained any variants at p < 5.0 × 10−8 from the association results of the BCAC (122,977 breast cancer case subjects and 105,974 control subjects).16Michailidou K. Lindström S. Dennis J. Beesley J. Hui S. Kar S. Lemaçon A. Soucy P. Glubb D. Rostamianfar A. et al.NBCS CollaboratorsABCTB InvestigatorsConFab/AOCS InvestigatorsAssociation analysis identifies 65 new breast cancer risk loci.Nature. 2017; 551: 92-94Crossref PubMed Scopus (681) Google Scholar If variants in the same locus were in LD (R2 > 0.1), only one variant with the best association was defined as the lead variant for the downstream analysis. In the end, we identified a total of 159 lead variants for the downstream analysis (Table S1). We downloaded gene expression profiles generated by Illumina HT12 arrays in a total of 1,981 primary breast tumor tissues from Synapse (syn1757063) from the METABRIC project. The normalized gene expression and somatic copy alteration data were downloaded from the CbioPortal. The normalized gene expression has been described in a previous study.46Curtis C. Shah S.P. Chin S.F. Turashvili G. Rueda O.M. Dunning M.J. Speed D. Lynch A.G. Samarajiwa S. Yuan Y. et al.METABRIC GroupThe genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups.Nature. 2012; 486: 346-352Crossref PubMed Scopus (3715) Google Scholar Genetic variant data, genotyped using array-based Affymetrix SNP 6.0 in a total of 1,992 samples, were downloaded from EBI (EGAD00010000164). A total of 1,895 tumor tissue samples with matched gene expression, somatic copy number alterations, and SNP data were included in our analysis. For TCGA data, we downloaded RNA-seq V2 data (level 3), DNA methylation data, and somatic copy number alterations data from the CbioPortal. We also downloaded level 3 SNP data, genotyped using the Affymetrix SNP 6.0 array from TCGA’s data portal. A total of 536 tumor samples with matched gene expressions, DNA methylations, copy number alterations, and genetic variant data from European descendants were included. We also downloaded matched whole exome-seq and RNA-seq data in 494 tumor tissue samples from European descendants from the TCGA data portal. We extracted cis-eQTL results for lead variants and nearby genes based on 251 normal breast tissues from the most recent GTEx database (v.7). We excluded results of long non-coding RNAs and ribosomal genes from our analysis. In total, we analyzed the association results for 147 variants (140 lead variants and 7 surrogate variants in strong LD, R2 > 0.8) and their nearby genes from the GTEx project. In addition, we also extracted significant cis-eQTL results for 72 lead variants and nearby genes at p < 0.05 based on 138 normal breast tissues from METABRIC from previous literature.16Michailidou K. Lindström S. Dennis J. Beesley J. Hui S. Kar S. Lemaçon A. Soucy P. Glubb D. Rostamianfar A. et al.NBCS CollaboratorsABCTB InvestigatorsConFab/AOCS InvestigatorsAssociation analysis identifies 65 new breast cancer risk loci.Nature. 2017; 551: 92-94Crossref PubMed Scopus (681) Google Scholar We used the R package CRLMM to call the variant genotype for each probe from the original image array-based data in METABRIC.47Carvalho B.S. Louis T.A. Irizarry R.A. Quantifying uncertainty in genotype calls.Bioinformatics. 2010; 26: 242-249Crossref PubMed Scopus (48) Google Scholar Only those probes of high quality, with intensity greater than 3,000 at a 95% calling rate, were included. From this METABRIC data and the level 3 TCGA data, genotype data of the nearby 1 Mb region for the 159 lead variants were extracted and then imputed with the 1000 Genomes Project data using Minimac.48Howie B. Fuchsberger C. Stephens M. Marchini J. Abecasis G.R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing.Nat. Genet. 2012; 44: 955-959Crossref PubMed Scopus (1211) Google Scholar Only common variants (minor allele frequency > 0.05) with high imputation quality (R2 > 0.3) were included. We used a surrogate variant in strong LD (R2 > 0.8) instead of the lead variant if the lead variant failed to meet these criteria. In total, we included 147 variants (144 lead variants and three surrogate variants) from METABRIC and 155 variants (154 lead variants and one surrogate variant) from TCGA. We used linear regression analysis to evaluate association between lead variants and expression levels of nearby genes (1 Mb distance to the lead variant). For the METABRIC and TCGA datasets, the normalized gene expression values were analyzed. To make the data conform better to the linear model for the eQTL analysis, we further transformed the gene expression levels across samples using an inverse normalizing transformation method. A full linear regression analysis was then performed to detect eQTLs, while adjusting for methylation and copy number alterations. For the METABRIC data, only copy number alterations were adjusted due to the lack of DNA methylation data in the tumor tissue samples. BH-adjusted p values were applied to determine final eQTL target genes. To increase the statistical power, we conducted a meta-analysis of eQTL results from tumor tissues from METABRIC and TCGA using the fixed-effects model.49Normand S.L. Meta-analysis: formulating, evaluating, combining, and reporting.Stat. Med. 1999; 18: 321-359Crossref PubMed Scopus (775) Google Scholar BH-adjusted p values were applied to determine eQTL target genes. In GTEx, we identified target genes using the same cutoff. In addition, we removed the target genes that had inconsistent associations in any other datasets at a less conservative unadjusted p < 0.05 (Figure 1). In the end, we identified final target genes for lead variants after combing the results from both meta-analysis and eQTL analysis of GTEx (Figure 1). For the identified target genes, we examined their functional enrichment in the gene function category and biological pathways using the Ingenuity Pathway Analysis (IPA) tool. The most significant gene function categories and biological pathways were presented. Functional annotation was performed using data from the Encyclopedia of DNA Elements (ENCODE) or the Roadmap Epigenomics Mapping Consortium (ROADMAP). We evaluated variants for potential functional significance using chromHMM annotation across nine ENCODE cell lines: H" @default.
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• W2800524425 title "A Comprehensive cis-eQTL Analysis Revealed Target Genes in Breast Cancer Susceptibility Loci Identified in Genome-wide Association Studies" @default.
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