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• W3081655922 abstract "Breast cancer genome-wide association studies (GWASs) have identified 150 genomic risk regions containing more than 13,000 credible causal variants (CCVs). The CCVs are predominantly noncoding and enriched in regulatory elements. However, the genes underlying breast cancer risk associations are largely unknown. Here, we used genetic colocalization analysis to identify loci at which gene expression could potentially explain breast cancer risk phenotypes. Using data from the Breast Cancer Association Consortium (BCAC) and quantitative trait loci (QTL) from the Genotype-Tissue Expression (GTEx) project and The Cancer Genome Project (TCGA), we identify shared genetic relationships and reveal novel associations between cancer phenotypes and effector genes. Seventeen genes, including NTN4, were identified as potential mediators of breast cancer risk. For NTN4, we showed the rs61938093 CCV at this region was located within an enhancer element that physically interacts with the NTN4 promoter, and the risk allele reduced NTN4 promoter activity. Furthermore, knockdown of NTN4 in breast cells increased cell proliferation in vitro and tumor growth in vivo. These data provide evidence linking risk-associated variation to genes that may contribute to breast cancer predisposition. Breast cancer genome-wide association studies (GWASs) have identified 150 genomic risk regions containing more than 13,000 credible causal variants (CCVs). The CCVs are predominantly noncoding and enriched in regulatory elements. However, the genes underlying breast cancer risk associations are largely unknown. Here, we used genetic colocalization analysis to identify loci at which gene expression could potentially explain breast cancer risk phenotypes. Using data from the Breast Cancer Association Consortium (BCAC) and quantitative trait loci (QTL) from the Genotype-Tissue Expression (GTEx) project and The Cancer Genome Project (TCGA), we identify shared genetic relationships and reveal novel associations between cancer phenotypes and effector genes. Seventeen genes, including NTN4, were identified as potential mediators of breast cancer risk. For NTN4, we showed the rs61938093 CCV at this region was located within an enhancer element that physically interacts with the NTN4 promoter, and the risk allele reduced NTN4 promoter activity. Furthermore, knockdown of NTN4 in breast cells increased cell proliferation in vitro and tumor growth in vivo. These data provide evidence linking risk-associated variation to genes that may contribute to breast cancer predisposition. The influence of common genetic variation on gene expression underlies a considerable proportion of the heritability associated with complex traits. Mapping of expression QTL (eQTL), where genetic variants are tested for association with gene expression levels, is widely used to identify genes that are regulated by trait-associated variants. Several studies have shown that eQTLs are enriched in cell types relevant to the trait of interest.1Ongen H. Brown A.A. Delaneau O. Panousis N.I. Nica A.C. Dermitzakis E.T. GTEx ConsortiumEstimating the causal tissues for complex traits and diseases.Nat. Genet. 2017; 49: 1676-1683Crossref PubMed Scopus (75) Google Scholar,2Raj T. Rothamel K. Mostafavi S. Ye C. Lee M.N. Replogle J.M. Feng T. Lee M. Asinovski N. Frohlich I. et al.Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes.Science. 2014; 344: 519-523Crossref PubMed Scopus (290) Google Scholar For example, T cell-specific eQTLs are over-represented for autoimmune risk alleles and monocyte-specific eQTLs for Alzheimer (MIM: 104300) and Parkinson (MIM: 168600) disease alleles.2Raj T. Rothamel K. Mostafavi S. Ye C. Lee M.N. Replogle J.M. Feng T. Lee M. Asinovski N. Frohlich I. et al.Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes.Science. 2014; 344: 519-523Crossref PubMed Scopus (290) Google Scholar For breast cancer (MIM: 114480), several studies have used eQTL data from tumor and normal tissues datasets to identify candidate target genes.3Michailidou 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 (498) Google Scholar, 4Li 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 (231) Google Scholar, 5Guo X. Lin W. Bao J. Cai Q. Pan X. Bai M. Yuan Y. Shi J. Sun Y. Han M.R. et al.A comprehensive cis-eQTL Analysis revealed target genes in breast cancer susceptibility loci identified in genome-wide association studies.Am. J. Hum. Genet. 2018; 102: 890-903Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar, 6Wu L. Shi W. Long J. Guo X. Michailidou K. Beesley J. Bolla M.K. Shu X.O. Lu Y. Cai Q. et al.NBCS CollaboratorskConFab/AOCS InvestigatorsA transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer.Nat. Genet. 2018; 50: 968-978Crossref PubMed Scopus (80) Google Scholar Recent studies have also showed that breast cancer risk variants could regulate genes in cells of the tumor microenvironment, such as immune cells and fibroblasts.7Ferreira M.A. Gamazon E.R. Al-Ejeh F. Aittomäki K. Andrulis I.L. Anton-Culver H. Arason A. Arndt V. Aronson K.J. Arun B.K. et al.EMBRACE CollaboratorsGC-HBOC Study CollaboratorsGEMO Study CollaboratorsABCTB InvestigatorsHEBON InvestigatorsBCFR InvestigatorsGenome-wide association and transcriptome studies identify target genes and risk loci for breast cancer.Nat. Commun. 2019; 10: 1741Crossref PubMed Scopus (33) Google Scholar,8Geeleher P. Nath A. Wang F. Zhang Z. Barbeira A.N. Fessler J. Grossman R.L. Seoighe C. Stephanie Huang R. Cancer expression quantitative trait loci (eQTLs) can be determined from heterogeneous tumor gene expression data by modeling variation in tumor purity.Genome Biol. 2018; 19: 130Crossref PubMed Scopus (10) Google Scholar Because eQTLs are widespread, overlap between GWAS and eQTL signals is likely to occur by chance when using nominal significance levels. To mitigate false positive findings, it is therefore important to show that the same genetic signal underlies gene expression and disease susceptibility. Several statistical colocalization approaches have been developed to determine whether molecular traits (e.g., gene expression) and a disease trait share common causal variants. The simplest Bayesian model used in tools such as QTLMatch9Plagnol V. Smyth D.J. Todd J.A. Clayton D.G. Statistical independence of the colocalized association signals for type 1 diabetes and RPS26 gene expression on chromosome 12q13.Biostatistics. 2009; 10: 327-334Crossref PubMed Scopus (48) Google Scholar and COLOC10Giambartolomei C. Vukcevic D. Schadt E.E. Franke L. Hingorani A.D. Wallace C. Plagnol V. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics.PLoS Genet. 2014; 10: e1004383Crossref PubMed Scopus (567) Google Scholar tests for colocalization for two traits and determines whether they are driven by distinct variants or share a single causal signal. For example, Parker et al. used COLOC to identify 32 emphysema-associated (MIM: 130700) regions where it is likely that colocalized GWAS and eQTL signals arise from the same causal variant.11Parker M.M. Hao Y. Guo F. Pham B. Chase R. Platig J. Cho M.H. Hersh C.P. Thannickal V.J. Crapo J. et al.Identification of an emphysema-associated genetic variant near TGFB2 with regulatory effects in lung fibroblasts.eLife. 2019; 8: 8Crossref Scopus (7) Google Scholar Additional functional studies then showed that the emphysema-associated variant rs1690789 regulates TGFB2 (encoding transforming growth factor beta 2 [MIM: 190220]) expression in human lung fibroblasts. A recent implementation of COLOC, called HyPrColoc (Hypothesis Prioritization in multi-trait Colocalization), identifies colocalized association signals using summary statistics on large number of traits.12Foley C.N. Staley J.R. Breen P.G. Sun B.B. Kirk P.D.W. Burgess S. Howson J.M.M. A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits.bioRxiv. 2019; https://doi.org/10.1101/592238Crossref Scopus (0) Google Scholar This method has been used to identify regulatory loci underlying quantitative hematopoietic traits.13Thom C.S. Voight B.F. Genetic colocalization atlas points to common regulatory sites and genes for hematopoietic traits and hematopoietic contributions to disease phenotypes.BMC Med. Genomics. 2020; 13: 89Crossref PubMed Scopus (1) Google Scholar In this study, we extracted eQTL association effect estimates and standard errors for all variants at the 150 breast cancer risk loci previously analyzed by BCAC14Fachal L. Aschard H. Beesley J. Barnes D.R. Allen J. Kar S. Pooley K.A. Dennis J. Michailidou K. Turman C. et al.GEMO Study CollaboratorsEMBRACE CollaboratorsKConFab InvestigatorsHEBON InvestigatorsABCTB InvestigatorsFine-mapping of 150 breast cancer risk regions identifies 191 likely target genes.Nat. Genet. 2020; 52: 56-73Crossref PubMed Scopus (39) Google Scholar (mean region size = 1.09 Mb). GWAS summary data were available for overall breast cancer risk from 122,977 case subjects and 105,974 control subjects;3Michailidou 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 (498) Google Scholar and for estrogen receptor negative (ER−) breast cancer risk from 21,468 case subjects and 100,594 control subjects, combined with 18,908 BRCA1 mutation carriers (9,414 with breast cancer),15Milne 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 (144) Google Scholar all of European ancestries. Variant IDs were converted to GRCH38 build co-ordinates16Pärn K. Nunez Fontarnau J. Isokallio M.A. Sipilä T. Kilpelainen E. Palotie A. Ripatti S. Palta P. Genotyping chip data lift-over to reference genome build GRCh38/hg38. Protocols io.2019https://doi.org/10.17504/protocols.io.xbhfij6Crossref Google Scholar and harmonized with GTEx data (0.86% failed conversion and were dropped from the analysis). The GTEx v.8 release includes data from normal breast tissue from 396 individuals. GTEx eQTL association data for variants within ±1 Mb windows of transcription start sites were extracted based on the variants present in the breast cancer risk data. Colocalization of the GWAS and eQTL signals were calculated using the HyPrColoc R package.12Foley C.N. Staley J.R. Breen P.G. Sun B.B. Kirk P.D.W. Burgess S. Howson J.M.M. A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits.bioRxiv. 2019; https://doi.org/10.1101/592238Crossref Scopus (0) Google Scholar Breast cancer risk phenotypes and each proximal gene were analyzed separately with default parameters. Signals were considered to be plausibly colocalizing if posterior probability for colocalization (PPFC) > 0.7. We identified 17 genes at 14 loci where the GTEx eQTL association p values are < 10−6 (Table 1). For every locus, all candidate SNPs met the GWAS significance p value threshold (5 × 10−8) for overall or ER− breast cancer risk (Table 1). For 11 loci (NTN4 [MIM: 610401], PIDD1 [MIM: 605247], CBX8 [MIM: 617354], L3MBTL3 [MIM: 618844], RCCD1 [MIM: 617997], PRC1-AS1, SSBP4 [MIM: 607391], MARCH11 [MIM: 613338], ZNF596, RP5-855D21.3, and RP11-53O19.1), the candidate colocalized SNPs have been previously nominated as strong candidate causal signals using multivariate logistic regression14Fachal L. Aschard H. Beesley J. Barnes D.R. Allen J. Kar S. Pooley K.A. Dennis J. Michailidou K. Turman C. et al.GEMO Study CollaboratorsEMBRACE CollaboratorsKConFab InvestigatorsHEBON InvestigatorsABCTB InvestigatorsFine-mapping of 150 breast cancer risk regions identifies 191 likely target genes.Nat. Genet. 2020; 52: 56-73Crossref PubMed Scopus (39) Google Scholar (Table 1 and Figure 1). However, at six loci (ATG10 [MIM: 610800], CCDC88C [MIM: 611204], PPM1K [MIM: 611065], RP11-250B2.3, RP1-265C24.5, and RP11-250B2.5), the colocalization events are with moderate signals based on stepwise multinomial logistic regression analysis (10−6 < p < 10−4; Figure S1).14Fachal L. Aschard H. Beesley J. Barnes D.R. Allen J. Kar S. Pooley K.A. Dennis J. Michailidou K. Turman C. et al.GEMO Study CollaboratorsEMBRACE CollaboratorsKConFab InvestigatorsHEBON InvestigatorsABCTB InvestigatorsFine-mapping of 150 breast cancer risk regions identifies 191 likely target genes.Nat. Genet. 2020; 52: 56-73Crossref PubMed Scopus (39) Google Scholar While this does not rule out causality, larger GWASs would be required to confirm genome-wide significance.14Fachal L. Aschard H. Beesley J. Barnes D.R. Allen J. Kar S. Pooley K.A. Dennis J. Michailidou K. Turman C. et al.GEMO Study CollaboratorsEMBRACE CollaboratorsKConFab InvestigatorsHEBON InvestigatorsABCTB InvestigatorsFine-mapping of 150 breast cancer risk regions identifies 191 likely target genes.Nat. Genet. 2020; 52: 56-73Crossref PubMed Scopus (39) Google Scholar We also generated LocusCompare plots for colocalizing signals using the TCGA tumor dataset (Figure S2). Data were available for nine genes from a previous TCGA eQTL analysis.3Michailidou 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 (498) Google Scholar,14Fachal L. Aschard H. Beesley J. Barnes D.R. Allen J. Kar S. Pooley K.A. Dennis J. Michailidou K. Turman C. et al.GEMO Study CollaboratorsEMBRACE CollaboratorsKConFab InvestigatorsHEBON InvestigatorsABCTB InvestigatorsFine-mapping of 150 breast cancer risk regions identifies 191 likely target genes.Nat. Genet. 2020; 52: 56-73Crossref PubMed Scopus (39) Google Scholar Only two signals (ATG10 and RCCD1) are indicative of colocalization in the TCGA tumor dataset (observing eQTL p values < 10−4; Figure S2). However, this is not unexpected since the regulatory landscape between normal tissue and tumors is vastly different.17Thurman R.E. Rynes E. Humbert R. Vierstra J. Maurano M.T. Haugen E. Sheffield N.C. Stergachis A.B. Wang H. Vernot B. et al.The accessible chromatin landscape of the human genome.Nature. 2012; 489: 75-82Crossref PubMed Scopus (1629) Google ScholarTable 1Candidate Breast Cancer Risk Genes Identified by eQTL Colocalization Analyses (PPFC > 0.7)Ensembl IDGene NameBreast Cancer Risk Association1Michailidou et al.,3 Milne et al.15Genomic Coordinates (hg19)2Regions fine-mapped in Fachal et al.14Posterior Probability3Results from HyPrColoc. The “posterior explained by SNP” value represents the proportion of the posterior probability explained by the candidate SNP.Candidate SNP3Results from HyPrColoc. The “posterior explained by SNP” value represents the proportion of the posterior probability explained by the candidate SNP.Posterior Explained by SNP3Results from HyPrColoc. The “posterior explained by SNP” value represents the proportion of the posterior probability explained by the candidate SNP.GTEx eQTL p Value4GTEx v.8 breast mammary tissue summary statistics.Breast Cancer Risk p Value1Michailidou et al.,3 Milne et al.15Signal Type2Regions fine-mapped in Fachal et al.14ENSG00000074527.11NTN4overall riskchr12:95,527,759–96,527,7590.9466rs173569070.978.01E−091.02E−39strongENSG00000141570.10CBX8overall riskchr17:77,281,387–78,281,7250.9178rs99059140.497.92E−234.00E−09strongENSG00000198945.7L3MBTL3overall riskchr6:129,849,119–130,849,1190.7998rs77401071.005.88E−402.90E−11strongENSG00000183654.8MARCH11overall riskchr5:15,687,358–16,687,5280.8369rs10130180.163.05E−091.65E−11strongENSG00000177595.17PIDD1overall riskchr11:303,017–1,303,0170.9695rs65979810.226.53E−271.35E−12strongENSG00000166965.12RCCD1overall riskchr15:91,009,215–92,009,2150.9633rs1133430950.602.44E−243.37E−15strongENSG00000130511.15SSBP4overall riskchr19:18,050,434–19,071,1410.7800rs72584650.097.87E−082.79E−28strongENSG00000172748.13ZNF596ER− riskchr8:0–670,6920.9059rs353465880.792.17E−081.39E−08strongENSG00000258725.1PRC1-AS1overall riskchr15:9,100,921–92,009,2150.9302rs22902020.225.89E−101.87E−15strongENSG00000251141.5RP11-53O19.1overall riskchr5:44,013,304–45,206,4980.9347rs109416791.004.41E−075.61E−73strongENSG00000272812.1RP5-855D21.3ER− riskchr8:0–6706920.9769rs30082810.816.11E−086.23E−09strongENSG00000152348.15ATG10overall riskchr5:80,928,261–82,038,0460.7904rs1445808060.362.56E−408.07E−12moderateENSG00000015133.18CCDC88Coverall riskchr14:91,341,069–92,368,6230.9465rs80181550.509.15E−114.03E−12moderateENSG00000163644.14PPM1Koverall riskchr4:88,743,818–89,743,8180.9935rs100224620.581.60E−081.55E−09moderateENSG00000233967.6RP11-250B2.3overall riskchr6:80,594,287–81,594,2870.8473rs94489400.224.65E−119.85E−09moderateENSG00000260645.1RP11-250B2.5overall riskchr6:80,594,287–81,594,2870.8227rs14368640.081.97E−083.89E−09moderateENSG00000219392.1RP1-265C24.5overall riskchr6:26,180,698–27,180,6980.9901rs357685950.385.95E−103.16E−09moderate1 Michailidou et al.,3Michailidou 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 (498) Google Scholar Milne et al.15Milne 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 (144) Google Scholar2 Regions fine-mapped in Fachal et al.14Fachal L. Aschard H. Beesley J. Barnes D.R. Allen J. Kar S. Pooley K.A. Dennis J. Michailidou K. Turman C. et al.GEMO Study CollaboratorsEMBRACE CollaboratorsKConFab InvestigatorsHEBON InvestigatorsABCTB InvestigatorsFine-mapping of 150 breast cancer risk regions identifies 191 likely target genes.Nat. Genet. 2020; 52: 56-73Crossref PubMed Scopus (39) Google Scholar3 Results from HyPrColoc. The “posterior explained by SNP” value represents the proportion of the posterior probability explained by the candidate SNP.4 GTEx v.8 breast mammary tissue summary statistics. Open table in a new tab Published computational predictions of target genes at breast cancer risk loci using the INQUISIT pipeline (which interrogates data including ChIA-PET, Hi-C, ChIP-seq, and eQTL data independent of GTEx) provide further support for ten colocalized genes (Table S1).3Michailidou 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 (498) Google Scholar,14Fachal L. Aschard H. Beesley J. Barnes D.R. Allen J. Kar S. Pooley K.A. Dennis J. Michailidou K. Turman C. et al.GEMO Study CollaboratorsEMBRACE CollaboratorsKConFab InvestigatorsHEBON InvestigatorsABCTB InvestigatorsFine-mapping of 150 breast cancer risk regions identifies 191 likely target genes.Nat. Genet. 2020; 52: 56-73Crossref PubMed Scopus (39) Google Scholar Of these, NTN4, PIDD1, L3MBTL3, and RCCD1 have the strongest evidence from functional genomics data. Transcriptome-wide association studies also suggest that 13 of the 17 genes are regulated by breast cancer risk variants5Guo X. Lin W. Bao J. Cai Q. Pan X. Bai M. Yuan Y. Shi J. Sun Y. Han M.R. et al.A comprehensive cis-eQTL Analysis revealed target genes in breast cancer susceptibility loci identified in genome-wide association studies.Am. J. Hum. Genet. 2018; 102: 890-903Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar, 6Wu L. Shi W. Long J. Guo X. Michailidou K. Beesley J. Bolla M.K. Shu X.O. Lu Y. Cai Q. et al.NBCS CollaboratorskConFab/AOCS InvestigatorsA transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer.Nat. Genet. 2018; 50: 968-978Crossref PubMed Scopus (80) Google Scholar, 7Ferreira M.A. Gamazon E.R. Al-Ejeh F. Aittomäki K. Andrulis I.L. Anton-Culver H. Arason A. Arndt V. Aronson K.J. Arun B.K. et al.EMBRACE CollaboratorsGC-HBOC Study CollaboratorsGEMO Study CollaboratorsABCTB InvestigatorsHEBON InvestigatorsBCFR InvestigatorsGenome-wide association and transcriptome studies identify target genes and risk loci for breast cancer.Nat. Commun. 2019; 10: 1741Crossref PubMed Scopus (33) Google Scholar,20Barfield R. Feng H. Gusev A. Wu L. Zheng W. Pasaniuc B. Kraft P. Transcriptome-wide association studies accounting for colocalization using Egger regression.Genet. Epidemiol. 2018; 42: 418-433Crossref PubMed Scopus (28) Google Scholar,21Feng H. Gusev A. Pasaniuc B. Wu L. Long J. Abu-Full Z. Aittomäki K. Andrulis I.L. Anton-Culver H. Antoniou A.C. et al.GEMO Study CollaboratorsEMBRACE CollaboratorsGC-HBOC study CollaboratorsABCTB InvestigatorsHEBON InvestigatorsBCFR InvestigatorsOCGN InvestigatorsTranscriptome-wide association study of breast cancer risk by estrogen-receptor status.Genet. Epidemiol. 2020; 44: 442-468Crossref PubMed Scopus (7) Google Scholar (Table S1). Moreover, previous eQTL analysis based on TCGA breast tumor data have identified three of these candidate genes.3Michailidou 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 (498) Google Scholar,14Fachal L. Aschard H. Beesley J. Barnes D.R. Allen J. Kar S. Pooley K.A. Dennis J. Michailidou K. Turman C. et al.GEMO Study CollaboratorsEMBRACE CollaboratorsKConFab InvestigatorsHEBON InvestigatorsABCTB InvestigatorsFine-mapping of 150 breast cancer risk regions identifies 191 likely target genes.Nat. Genet. 2020; 52: 56-73Crossref PubMed Scopus (39) Google Scholar For three genes (PIDD1, L3MBTL3, and SSBP4), CCVs are located in the promoter regions, and for PIDD1 previous reporter assays indicate that the risk haplotype increases promoter activity.3Michailidou 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 (498) Google Scholar Our recent capture Hi-C data also showed that chromatin looping occurs between putative regulatory regions containing CCVs and the promoters of four genes (NTN4, PRC1-AS1, ATG10, and RP1-265C24.5) in breast cell lines.22Beesley J. Sivakumaran H. Moradi Marjaneh M. Lima L.G. Hillman K.M. Kaufmann S. Tuano N. Hussein N. Ham S. Mukhopadhyay P. et al.Chromatin interactome mapping at 139 independent breast cancer risk signals.Genome Biol. 2020; 21: 8Crossref PubMed Scopus (8) Google Scholar For the remaining loci, multiple CCVs were located in the introns of target genes and/or intergenic regions, but lacked demonstrable CCV-gene interactions. It is possible that some cis-regulatory interactions are detected only in specific breast cell subpopulations or that CCVs are acting through other mechanisms such as perturbation of pre-messenger RNA splicing or altered noncoding RNA stability, structure, and/or function. Of note, three genes (PIDD1, CBX8, and L3MBTL3) also contain breast cancer CCVs in their exons which are predicted to change the amino acid sequence, thus we cannot rule out that these variants could also affect the protein product. One high probability colocalization signal, associated with NTN4 expression, was detected at a locus at 12q22 (Table 1, Figures 1 and 2). Genetic fine-mapping studies have identified one risk signal at 12q22 that contains two CCVs (rs61938093 and rs17356907; odds ratio = 1.094, r2 = 1).14Fachal L. Aschard H. Beesley J. Barnes D.R. Allen J. Kar S. Pooley K.A. Dennis J. Michailidou K. Turman C. et al.GEMO Study CollaboratorsEMBRACE CollaboratorsKConFab InvestigatorsHEBON InvestigatorsABCTB InvestigatorsFine-mapping of 150 breast cancer risk regions identifies 191 likely target genes.Nat. Genet. 2020; 52: 56-73Crossref PubMed Scopus (39) Google Scholar Both CCVs fall within putative regulatory elements (PREs) marked by open chromatin in B80T5 and MCF10A non-tumorigenic breast cell lines (Figure 3A). The PREs map to a large intergenic region between USP44 (MIM: 610993) (encoding ubiquitin-specific protease 44) and NTN4 (encoding Netrin 4; Figure 3A). Using promoter capture HiC data,22Beesley J. Sivakumaran H. Moradi Marjaneh M. Lima L.G. Hillman K.M. Kaufmann S. Tuano N. Hussein N. Ham S. Mukhopadhyay P. et al.Chromatin interactome mapping at 139 independent breast cancer risk signals.Genome Biol. 2020; 21: 8Crossref PubMed Scopus (8) Google Scholar we observed that the PREs frequently participate in long-range chromatin interactions with the NTN4 promoter in non-tumorigenic and tumorigenic breast cell lines (Figures 3A and S3A). Notably, no other eQTLs or chromatin interactions from the PRE to promoter regions were detected in the breast cell lines we examined (Figures 3A and S3A),22Beesley J. Sivakumaran H. Moradi Marjaneh M. Lima L.G. Hillman K.M. Kaufmann S. Tuano N. Hussein N. Ham S. Mukhopadhyay P. et al.Chromatin interactome mapping at 139 independent breast cancer risk signals.Genome Biol. 2020; 21: 8Crossref PubMed Scopus (8) Google Scholar suggesting that NTN4 is the likely target gene at this signal.Figure 3Breast Cancer CCVs Distally Regulate NTN4Show full caption(A) WashU genome browser showing topologically associating domains (TADs) as horizontal gray bars above GENCODE-annotated coding genes (blue). The promoter capture Hi-C (PCHi-C) baits are depicted as black boxes. The putative regulatory element (PRE) containing the CCVs is shown as red colored vertical lines. The ATAC-seq tracks for B80T5 and MCF10A breast cells are shown as blue histograms. PCHi-C chromatin interactions are shown as black arcs. Red arcs depict chromatin looping between CCVs and the NTN4 promoter region.(B) dCAS9-KRAB was targeted to the PRE using two different sgRNAs (sgPRE1 and sgPRE2) in Bre80-TERT1 breast cells. SgCON contains a non-targeting control guide RNA. Gene expression was measured by qPCR and normalized to beta-glucuronidase (GUSB) expression. Error bars, SEM (n = 3). p values were determined by one-way ANOVA followed by Dunnett’s multiple comparisons test (∗∗p < 0.01).(C) Luciferase reporter assays following transient transfection of MCF10A breast cells. A PRE1 containing the protective (Prot.) or risk allele of rs61938093 and a PRE2 containing the protective (Prot.) or risk allele of rs1735907 were cloned into NTN4-promoter driven luciferase constructs. Error bars, SEM (n = 3). p values were determined by two-way ANOVA followed by Dunnett’s multiple comparisons test (∗∗∗∗p < 0.0001).(D and E) Left: 3C interaction profiles between the NTN4 promoter and the genomic region containing the PRE in MCF10A (D) and T47D (E) 3C libraries generated with HindIII. A physical map of the region interrogated by 3C is shown above; the blue shading represents the position of the PRE and the anchor point set at the NTN4 promoter. Representative 3C profiles are shown. Error bars, SD (n = 3). Right: Allele-specific qPCR using primer set 1 (Table S2) and Taqman SNP assay to quantify the allelic ratio at CCV rs61938093. Error bars, SEM (n = 3). p values were determined using a Student’s t test (∗∗∗p < 0.001).(F) EMSA for oligonucleotide duplexes containing CCVs rs61938093 or rs17356907 with the risk allele (R) or protective allele (P) as indicated, assayed using Bre80-TERT1 nuclear extracts. Competitor oligonucleotides are listed above each panel and were used at 100-fold molar excess: (−) no competitor; (Neg) a non-specific competitor; (Self) an identical oligonucleotide with no biotin label. Red arrowheads indicate band mobility differences between alleles.View Large Image Figure ViewerDownload Hi-res image Download (PPT) (A) WashU genome browser showing topologically associating domains (TADs) as horizontal gray bars above GENCODE-annotated coding genes (blue). The promoter capture Hi-C (PCHi-C) baits are depicted as black boxes. The putative regulatory element (PRE) containing the CCVs is shown as red colored vertical lines. The ATAC-seq tracks for B80T5 and MCF10A breast cells are shown as blue histograms. PCHi-C chromatin interactions are shown as black arcs. Red arcs depict chromatin looping between CCVs and the NTN4 promoter region. (B) dCAS9-KRAB was targeted to the PRE using two different sgRNAs (sgPRE1 and sgPRE2) in Bre80-" @default.
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• W3081655922 title "eQTL Colocalization Analyses Identify NTN4 as a Candidate Breast Cancer Risk Gene" @default.
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