Association analyses identify 31 new risk loci for colorectal cancer susceptibility

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Association analyses identify 31 new risk loci for colorectal cancer susceptibility © The Authors, 2019. Published version Law, Philip J.; Timofeeva, Maria; Fernandez-Rozadilla, Ceres; Broderick, Peter; Studd, James; Fernandez-Tajes, Juan; Farrington, Susan; Svinti, Victoria; Palles, Claire; Orlando, Giulia; Sud, Amit; Holroyd, Amy; Penegar, Steven; Theodoratou, Evropi; Vaughan-Shaw, Peter; Campbell, Harry; Zgaga, Lina; Hayward, Caroline; Campbell, Archie; Harris, Sarah; Deary, Ian J.; Starr, John; Gatcombe, Laura; Pinna, Maria; Briggs, Sarah; Martin, Lynn; Jaeger, Emma; Sharma-Oates, Archana; East, James; Leedham, Simon; Arnold, Roland; Johnstone, Elaine; Wang, Haitao; Kerr, David; Kerr, Rachel; Maughan, Tim; Kaplan, Richard; Al-Tassan, Nada; Palin, Kimmo; Hänninen, Ulrika A.; Cajuso, Tatiana; Tanskanen, Tomas; Kondelin, Johanna; Kaasinen, Eevi; Sarin, Antti-Pekka; Eriksson, Johan G.; Rissanen, Harri; Knekt, Paul; Pukkala, Eero; Jousilahti, Pekka; Salomaa, Veikko; Ripatti, Samuli; Palotie, Aarno; Renkonen-Sinisalo, Laura; Lepistö, Anna; Böhm, Jan; Mecklin, Jukka-Pekka; Buchanan, Daniel D.; Win, Aung-Ko; Hopper, John; Jenkins, Mark E.; Lindor, Noralane M.; Newcomb, Polly A.; Gallinger, Steven; Duggan, David; Casey, Graham; Hoffmann, Per; Nöthen, Markus M.; Jöckel, Karl-Heinz; Easton, Douglas F.; Pharoah, Paul D. P.; Peto, Julian; Canzian, Federico; Swerdlow, Anthony; Eeles, Rosalind A.; Kote-Jarai, Zsofia; Muir, Kenneth; Pashayan, Nora; consortium, The PRACTICAL; Harkin, Andrea; Allan, Karen; McQueen, John; Paul, James; Iveson, Timothy; Saunders, Mark; Butterbach, Katja; Chang-Claude, Jenny; Hoffmeister, Michael; Brenner, Hermann; Kirac, Iva; Matošević, Petar; Hofer, Philipp; Brezina, Stefanie; Gsur, Andrea; Cheadle, Jeremy P.; Aaltonen, Lauri A.; Tomlinson, Ian; Houlston, Richard S.; Dunlop, Malcolm G. Law, P. J., Timofeeva, M., Fernandez-Rozadilla, C., Broderick, P., Studd, J., Fernandez-Tajes, J., Farrington, S., Svinti, V., Palles, C., Orlando, G., Sud, A., Holroyd, A., Penegar, S., Theodoratou, E., Vaughan-Shaw, P., Campbell, H., Zgaga, L., Hayward, C., Campbell, A., . . . Dunlop, M. G. (2019). Association analyses identify 31 new risk loci for colorectal cancer susceptibility. Nature Communications, 10, Article 2154. https://doi.org/10.1038/s41467-019-09775-w 2019 ARTICLE Association analyses identify 31 new risk loci for colorectal cancer susceptibility Philip J. Law, Maria Timofeeva, Ceres Fernandez-Rozadilla et al. # Colorectal cancer (CRC) is a leading cause of cancer-related death worldwide, and has a strong heritable basis. We report a genome-wide association analysis of 34,627 CRC cases and 71,379 controls of European ancestry that identifies SNPs at 31 new CRC risk loci. We also identify eight independent risk SNPs at the new and previously reported European CRC loci, and a further nine CRC SNPs at loci previously only identified in Asian populations. We use in situ promoter capture Hi-C (CHi-C), gene expression, and in silico annotation methods to identify likely target genes of CRC SNPs. Whilst these new SNP associations implicate target genes that are enriched for known CRC pathways such as Wnt and BMP, they also highlight novel pathways with no prior links to colorectal tumourigenesis. These findings provide further insight into CRC susceptibility and enhance the prospects of applying genetic risk scores to personalised screening and prevention. https://doi.org/10.1038/s41467-019-09775-w OPEN Correspondence and requests for materials should be addressed to R.S.H. (email: [email protected]). # A full list of authors and their affiliations appears at the end of the paper. NATURE COMMUNICATIONS | (2019) 10:2154 | https://doi.org/10.1038/s41467-019-09775-w | www.nature.com/naturecommunications 1 1234567890():,; Many colorectal cancers (CRC) develop in genetically susceptible individuals1and genome-wide association studies (GWAS) of CRC have thus far reported 43 SNPs mapping to 40 risk loci in European populations2,3. In Asians, 18 SNPs mapping to 16 risk loci have been identified4,5, a number of which overlap with those reported in Europeans. Collectively across ethnicities GWAS has provided evidence for 53 unique CRC susceptibility loci. While much of the heritable risk of CRC remains unexplained, statistical modelling indicates that further common risk variants remain to be discovered6. To gain a more comprehensive insight into CRC aetiology, we conducted a GWAS meta-analysis that includes additional, unreported datasets. We examine the possible gene regulatory mechanisms underlying all GWAS risk loci by analysing in situ promoter Capture Hi-C (CHi-C) to characterise chromatin interactions between predisposition loci and target genes, examine gene expression data and integrate these data with chromatin immunoprecipitation-sequencing (ChIP-seq) data. Finally, we quantify the contribution of the loci identified in this study, together with previously identified loci to the heritable risk of CRC and estimate the sample sizes required to explain the remaining heritability. Results Association analysis. Thus far, studies have identified 61 SNPs that are associated with CRC risk in European and Asian populations (Supplementary Data 1). To identify additional CRC risk loci, we conducted five new CRC GWAS, followed by a metaanalysis with 10 published GWAS totalling 34,627 cases and 71,379 controls of European ancestry under the auspices of the COGENT (COlorectal cancer GENeTics) consortium7(Fig. 1, Supplementary Data 2). Following established quality control measures for each dataset8(Supplementary Data 3), the genotypes of over 10 million SNPs in each study were imputed, primarily using 1000 Genomes and UK10K data as reference (see Methods). After filtering out SNPs with a minor allele frequency <0.5% and imputation quality score <0.8, we assessed associations between CRC status and SNP genotype in each study using logistic regression. Risk estimates were combined through an inverse-variance weighted fixed-effects meta-analysis. We found little evidence of genomic inflation in any of the GWAS datasets (individual λ GC values 1.01–1.11; meta-analysis λ 1000 =1.01, Supplementary Figure 1). Excluding flanking regions of 500 kb around each previously identified CRC risk SNP, we identified 623 SNPs associated with CRC at genome-wide significance (logistic regression, P<5×10 −8). After implementing a stepwise model selection, these SNPs were resolved into 31 novel risk loci, with 27 exhibiting Bayesian False Discovery Probabilities (BFDPs)9<0.1 (Table 1,Fig.2,Supplementary Figure 2). The association at 20q13.13 (rs6066825) had only been previously identified as significant in a multi-ethnic study10. Two new associations (rs3131043 and rs9271770) were identified within the 6p21.33 major histocompatibility (MHC) region, with rs3131043 located 470 kb 5′of HLA-C,andrs9271770located between HLA-DRB1 and HLA-DQA1. Imputation of the MHC region using SNP2HLA11 provided no evidence for additional MHC risk loci. We confirmed 28 of the 40 risk loci for CRC published as genome-wide significant in Europeans (i.e. P<5×10 −8) (Supplementary Data 1). For four previously reported risk loci2,12–14,we observed associations that were just below genome-wide significance (3q26.2, rs10936599, P=1.41 × 10−7; 12p13.32, rs3217810, P=1.09 × 10−6; 16q22.1, rs9929218, P=4.96 × 10−7; 16q24.1, rs2696839, 1.28 × 10−6). In contrast, there was limited support in our current study for eight of the associations previously reported by others2,10,15–17 (2q32.3, rs11903757, P=0.23; 3p14.1, rs812481, P=0.44; 4q22.2, rs1370821, P=3.41 × 10−5; 4q26, rs3987, P= 0.10; 4q32.2, rs35509282, P=0.24; 10q11.23, rs10994860, P= 3.65 × 10−4; 12q24.22, rs73208120, P=0.03; 20q11.22, rs2295444, P=0.03), all having a BFDP >0.99 (Supplementary Data 1). Of the 16 reported Asian-specificloci 4,5, nine harboured genome-wide significant signals in the current study (all BFDP <0.06), albeit sometimes at SNPs with low r2but high D′with the original SNP in Europeans, consistent with differences in allele frequencies in the different populations (Supplementary Data 1). Conditioning on the reported Asian SNPs, five of the nine European risk SNPs were independent of the Asian SNP (P conditional <5×10 −8,Supplementary Data 4). We found no evidence of association signals at the remaining previously reported Asian SNPs. Next, we performed an analysis conditioned on the sentinel SNP (r2< 0.1 and P conditional <5×10 −8; Table 2) to search for further independent signals at these new and previously reported risk loci. We confirmed the presence of previously reported dual signals at 14q22.2, 15q13.3 and 20p12.318. For the new risk loci, an additional independent signal was identified at 5p15.3. In addition, a further seven signals were found at five previously reported risk loci: 11q13.4, 12p13.32, 15q13.3, 16q24.1, 20q13.13. Two of these signals were at the 15q13.3 locus, of which one was 5′of GREM1 and the other intronic to FMN1. A further two signals were proximal and distal of 20q13.13. At 12p13.32 and 16q24.1, genome-wide associations marked by rs12818766 and rs899244, respectively, were shown. These were independent of the previously reported associations2,14 at rs3217810 and rs2696839 (pairwise r2=0.0). In total, we identified 39 new independent SNPs associated with CRC susceptibility at genome-wide significance in Europeans. Together with the nine associations previously identified in Asian populations, and the 31 previously identified SNPs that were confirmed here, this brought the number of identified CRC association signals in Europeans to 79. Several of these risk loci map to regions previously identified in other cancers. In particular, three regions harbour susceptibility loci for multiple cancers19, specifically 5p15.33 (TERT-CLPTM1L), 9p21.3 (CDKN2A) and 20q13.33 (RTEL1) (Supplementary Data 5). Functional annotation and biological inference of risk loci.To theextentthattheyhavebeendeciphered,mostGWASrisklocimap to non-coding regions of the genome influencing gene regulation19. Consistent with this, we found evidence that the CRC risk SNPs mapped to regions enriched for active enhancer marks (H3K4me1 and H3K27ac) in colonic crypts (permutation test, P=0.034 and 0.033, respectively) and colorectal tumours (P=4.2 × 10−3and 4.0 × 10−5) (Supplementary Figure 3). To determine whether the CRC SNPs overlapped with active regulatory regions in a cell-type specificmanner 20, we analysed the H3K4me3, H3K27ac, H3K4me1, H3K27me3, H3K9ac, H3K9me3 and H3K36me3 chromatin marks across multiple cell types from the NIH Roadmap Epigenomics project21. Colonic and rectal mucosa cells showed the strongest enrichment of risk SNPs at active enhancer and promoter regions (H3K4me3, H3K4me1 and H3K27ac marks, P<5×10 −4) (Supplementary Figure 3). Given our observation that the risk loci map to putative regulatory regions, we examined both histone modifications and transcription factor (TF) binding sites in LoVo and HT29 CRC cells across the risk SNPs. Using variant set enrichment22,we identified regions of strong LD (defined as r2>0.8andD′>0.8) with each risk SNP and determined the overlap with ChIP-seq data from the Systems Biology of Colorectal cancer (SYSCOL) study and inhouse-generated histone data. We identified an over-representation of binding for MYC, ETS2, cohesin loading ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-09775-w 2NATURE COMMUNICATIONS | (2019) 10:2154 | https://doi.org/10.1038/s41467-019-09775-w | www.nature.com/naturecommunications factor NIPBL and cohesin-related proteins RAD21, SMC1A and SMC3 (Supplementary Figure 4). About 87% (69/79) of the risk SNPs were predicted to disrupt binding motifs of specificTFs, notably CTCF, SOX and FOX, with 35% located within TF binding peaks from LoVo, HT29 or ENCODE ChIP-seq data (Supplementary Data 6). The upstream mechanisms by which predisposition SNPs influence disease risk is often through effects on cis-regulatory transcriptional networks, specifically through chromatin-looping interactions that are fundamental for regulation of gene expression. Therefore, to link regulatory regions containing risk SNPs to promoters of candidate target genes, we applied in situ promoter capture Hi-C (CHi-C) data in LoVo and HT29 cells (Supplementary Data 9). About 38% of the risk SNPs mapped to regions that showed statistically significant chromatin-looping interactions with the promoters of respective target genes. Notably, as well as confirming the interaction between rs6983267 and MYC at 8q24.21 (Supplementary Figure 2), the looping interaction from an active enhancer region at 10q25.2 implicates TCF7L2 as the target gene of rs12255141 variation (Fig. 3). TCF7L2 (previously known as TCF4) is a key transcription factor in the Wnt pathway and plays an important role in the development and progression of CRC23. Intriguingly, TCF7L2 has been shown to bind to a MYC enhancer containing rs698326724 and to a GREM1 enhancer near rs1696968125. Based on ChromHMM, this region is annotated as a promoter in HCT116 cells, but not in normal colonic and rectal mucosa. Additionally this locus has been implicated in lung cancer26 and low-grade glioma27. Similarly, the 9p21.3 chromatin interaction provides evidence to support CDKN2B as the target gene for rs1412834 variation, a region of somatic loss. We sought to gain further insight into the target genes at each locus, and hence the biological mechanisms for the associations, Ca. 31,197 Co. 61,770 Published GWAS Validation of imputed genotypes Sample QC New GWAS Enrichment Chromatin state annotation eQTL NSCCG-OncoArray GWAS Ca. 7281 Co. 7519 UK Biobank GWAS Ca. 6360 Co. 25,440 11 Published studies Ca. 12,101 Co. 20,391 SOCCS/LBC GWAS Ca. 1037 Co.1522 SCOT GWAS Ca. 3076 Co. 4349 SOCCS/GS GWAS Ca. 4772 Co. 12,158 Promoter-capture Hi-C TCF7L2 MYC rs4776316 Active enhancer Active promoter SMAD6 67.02 Mb 67.03 Mb GWAS effect 67.01 Mb Chromosome 6 (Mb) 67.00 Mb66.99 Mb Stage C CRC tumours Stage D Normal crypt Adenoma 114.20 Mb 114.40 Mb 114.60 Mb 114.80 Mb Chromosome 10 (Mb) VTI1A TCF7L2 Metastatis H3K27ac H3K4me1 eQTL effect Meta-analysis GWAS Fig. 1 Study design NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-09775-w ARTICLE NATURE COMMUNICATIONS | (2019) 10:2154 | https://doi.org/10.1038/s41467-019-09775-w | www.nature.com/naturecommunications 3 by performing expression quantitative trait locus (eQTL) analysis in colorectal tissue. We analysed inhouse eQTL data generated from samples of normal colonic mucosa (INTERMPHEN study, n=131 patients) and GTEx data from transverse colon (n= 246). For the previously identified risk loci, there were eQTLs for rs4546885 and LAMC1 (1q25.3), rs13020391 and lnc-RNA RP11–378A13.1 (2q35), and rs3087967 and COLCA1,COLCA2 and C11orf53 (11q23.1). Amongst the eQTL associations at the new risk loci, pre-eminent eQTLs were rs9831861 and SFMBT1 (3p21.1), rs12427600 and SMAD9 (13q13.3), and rs12979278 and FUT2 and MAMSTR (19q13.33) (Supplementary Data 7). However, while multiple nominally significant cis-eQTLs were present, nearly half of all loci had no evidence of cis-eQTLs in the sample sets used. In addition to eQTL analysis, we performed Summary-databased Mendelian Randomization (SMR) analysis28 as a more stringent test for causal differences in gene transcription (Supplementary Data 8). There was support for the 11q23.1 locus SNP influencing CRC risk through differential expression of one or more of COLCA1, COLCA2 and C11orf53 transcripts (P SMR <10−10). There was also evidence that the 3p21.1 and 19q13.33 SNPs acted through SFMBT1 and FUT2, respectively, (P SMR <10 −5), as well as the 6p21.31 SNP acted through class II HLA expression (P SMR <5×10 −4). Based on genetic fine-mapping and functional annotation, our data indicated several candidate target genes with functions previously unconnected to colorectal tumourigenesis (Supplementary Data 9). The SFMBT1 protein (3p21.1) acts as a histone reader and a component of a transcriptional repressor complex29.TNS3 at 7p12.3 encodes the focal adhesion protein TENSIN3, to which the intestinal stem cell marker protein Musashi1 has been reported to bind. Tns3-null mice exhibit impaired intestinal epithelial development, probably because of defects in Rho GTPase signalling and cell adhesion30. LRP1 (12q13.3, LDL receptor-related protein 1) (Fig. 3) may be involved in Wnt-signalling31, although its role in the intestines has not previously been conclusively demonstrated. FUT2 at 19q13.33 encodes fucosyltransferase II. Variation at this locus is associated with differential interactions with intestinal bacteria and viruses. Our data thus provide evidence for a role of the microbiome in CRC risk32.PTPN1 (20q13.13), also known as PTP1B, encodes a non-receptor tyrosine phosphatase involved in regulating JAK-signalling, IR, c-Src, CTNNB1, and EGFR. We annotated all risk loci with five types of functional data: (i) presence of a CHi-C contact linking to a gene promoter, (ii) presence of an association from eQTL, (iii) presence of a regulatory state, (iv) evidence of TF binding, and (v) presence of a nonsynonymous coding change (Supplementary Data 9). Collectively this analysis suggested three primary candidate disease mechanisms across a number of risk loci: firstly, genes linked to BMP/TGF-βsignalling (e.g. GREM1,BMP2,BMP4, SMAD7,SMAD9); secondly, genes with roles either directly or indirectly linked to MYC (e.g. MYC,TCF7L2); and thirdly genes with roles in maintenance of chromosome integrity (e.g. Table 1 Summary results for the new colorectal cancer risk loci in Europeans SNP Cytoband Position (bp, GRCh37) Risk/alt allele RAF OR 95% CI P-value BFDP I2(%) P het Average info score rs61776719 1p34.3 38,461,319 C/A 0.45 1.07 (1.05; 1.10) 2.19 × 10−10 1.98 × 10−31 0.44 0.89 rs12143541 1p32.3 55,247,852 G/A 0.15 1.10 (1.06; 1.13) 9.44 × 10−10 7.44 × 10−316 0.28 0.95 rs11692435 2q11.2 98,275,354 G/A 0.90 1.12 (1.07; 1.16) 1.22 × 10−80.079 29 0.14 0.97 rs11893063 2q33.1 199,601,925 A/G 0.47 1.07 (1.04; 1.09) 9.34 × 10−90.069 43 0.04 0.96 rs7593422 2q33.1 200,131,695 T/A 0.55 1.07 (1.05; 1.10) 3.56 × 10−11 3.50 × 10−415 0.28 0.99 rs9831861 3p21.1 53,088,285 G/T 0.59 1.07 (1.05; 1.09) 4.17 × 10−10 3.72 × 10−30 0.87 0.99 rs12635946 3q13.2 112,916,918 C/T 0.62 1.08 (1.06; 1.10) 1.02 × 10−11 1.03 × 10−411 0.33 0.97 rs17035289 4q24 106,048,291 T/C 0.83 1.10 (1.07; 1.13) 2.73 × 10−10 2.30 × 10−30 0.95 1.00 rs75686861 4q31.21 145,621,328 A/G 0.10 1.12 (1.08; 1.16) 1.76 × 10−90.014 0 0.49 0.92 rs2070699 6p24.1 12,292,772 T/G 0.48 1.07 (1.04; 1.09) 3.88 × 10−90.031 29 0.14 0.95 rs3131043 6p21.33 30,758,466 G/A 0.43 1.07 (1.05; 1.1) 2.67 × 10−80.159 60 0.01 0.91 rs9271770 6p21.32 32,594,248 A/G 0.81 1.08 (1.05; 1.11) 3.60 × 10−80.192 0 0.91 0.93 rs6928864 6q21 105,966,894 C/A 0.91 1.13 (1.09; 1.19) 1.37 × 10−80.094 0 0.73 0.98 rs10951878 7p12.3 46,926,695 C/T 0.49 1.06 (1.04; 1.09) 1.10 × 10−80.080 0 0.65 0.99 rs3801081 7p12.3 47,511,161 G/A 0.68 1.08 (1.06; 1.11) 2.00 × 10−11 1.96 × 10−450 0.01 1.00 rs1412834 9p21.3 22,110,131 T/C 0.50 1.08 (1.06; 1.11) 4.13 × 10−14 5.05 × 10−714 0.30 1.00 rs4450168 11p15.4 10,286,755 C/A 0.17 1.10 (1.06; 1.13) 1.24 × 10−80.079 0 0.81 0.86 rs7398375 12q13.3 57,540,848 C/G 0.72 1.09 (1.06; 1.13) 3.91 × 10−10 3.23 × 10−30 0.93 0.83 rs12427600 13q13.3 37,460,648 C/T 0.24 1.09 (1.06; 1.11) 5.43 × 10−11 5.01 × 10−40 0.81 0.99 rs45597035 13q22.1 73,649,152 A/G 0.64 1.08 (1.05; 1.10) 2.16 × 10−10 1.94 × 10−30 0.53 0.96 rs1330889 13q22.3 78,609,615 C/T 0.87 1.11 (1.07; 1.14) 6.50 × 10−10 5.25 × 10−30 0.59 0.97 rs7993934 13q34 111,074,915 T/C 0.65 1.08 (1.05; 1.10) 3.03 × 10−11 2.94 × 10−40 0.55 0.99 rs4776316 15q22.31 67,007,813 A/G 0.73 1.08 (1.05; 1.10) 1.11 × 10−80.076 22 0.21 0.95 rs10152518 15q23 68,177,162 G/A 0.19 1.08 (1.05; 1.11) 3.24 × 10−80.180 0 0.84 0.97 rs7495132 15q26.1 91,172,901 T/C 0.12 1.11 (1.07; 1.14) 7.92 × 10−10 6.34 × 10−329 0.14 0.99 rs61336918 16q23.2 80,007,266 A/T 0.29 1.09 (1.06; 1.12) 2.04 × 10−12 2.14 × 10−50 0.90 0.99 rs1078643 17p12 10,707,241 A/G 0.77 1.09 (1.06; 1.12) 4.14 × 10−11 3.81 × 10−40 0.56 0.92 rs285245 19p13.11 16,420,817 T/C 0.11 1.11 (1.07; 1.15) 3.71 × 10−80.195 2 0.42 0.91 rs12979278 19q13.33 49,218,602 T/C 0.53 1.07 (1.05; 1.09) 6.11 × 10−10 5.35 × 10−315 0.28 0.96 rs6066825 20q13.13 47,340,117 A/G 0.65 1.10 (1.08; 1.13) 3.82 × 10−17 5.67 × 10−10 0 0.49 0.99 rs3787089 20q13.33 62,316,630 C/T 0.32 1.07 (1.05; 1.10) 5.80 × 10−90.043 0 0.80 0.96 Associations previously only identified in Asian populations rs639933 5q31.1 134,467,751 C/A 0.38 1.07 (1.05; 1.10) 1.14 × 10−99.50 × 10−30 0.73 0.98 rs6933790 6p21.1 41,672,769 T/C 0.83 1.10 (1.07; 1.14) 3.65 × 10−10 3.03 × 10−321 0.23 0.91 rs704017 10q22.3 80,819,132 G/A 0.60 1.10 (1.08; 1.13) 2.96 × 10−16 4.15 × 10−923 0.21 0.95 rs12255141 10q25.2 114,294,892 G/A 0.10 1.11 (1.07; 1.15) 2.97 × 10−90.022 0 0.81 0.96 rs10849438 12p13.31 6,412,036 G/T 0.12 1.12 (1.08; 1.16) 1.04 × 10−10 9.49 × 10−421 0.23 0.95 rs73975588 17p13.3 816,741 A/C 0.87 1.10 (1.06; 1.13) 8.71 × 10−90.058 33 0.11 0.97 rs9797885 19q13.2 41,873,001 G/A 0.71 1.08 (1.05; 1.10) 2.77 × 10−10 2.43 × 10−30 0.70 0.99 rs6055286 20p12.3 7,718,045 A/G 0.15 1.11 (1.07; 1.14) 9.69 × 10−11 8.61 × 10−450 0.02 0.97 rs2179593 20q13.12 42,660,286 A/C 0.72 1.07 (1.05; 1.10) 4.62 × 10−90.035 0 0.67 0.97 BFDP calculated using prior =10−5and maximum relative risk =1.2 RAF risk allele frequency in Europeans, OR odds ratio, CI confidence interval, BFDP Bayesian False Discovery Probability, I2proportion of the total variation due to heterogeneity, P het P-value for heterogeneity ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-09775-w 4NATURE COMMUNICATIONS | (2019) 10:2154 | https://doi.org/10.1038/s41467-019-09775-w | www.nature.com/naturecommunications TERT,RTEL1) and DNA repair (e.g. POLD3)(Supplementary Figure 5). Pathway gene set enrichment analyses33 revealed several growth or development related pathways were enriched, notably TGF-βsignalling and immune response pathways (Supplementary Figure 6, Supplementary Data 10). Other cancer-related themes included apoptosis and leukocyte differentiation pathways. We used Data-driven Expression-Prioritized Integration for Complex Traits (DEPICT)34 to predict gene targets based on gene functions that are shared across genome-wide significant risk loci, as well as those associated at P<10 −5as advocated to mitigate against type II error. Tissue-specificity with respect to colonic tissue was evident (permutation test, P<5×10 −3) and among the protein-coding genes predicted, there was enrichment for TGF-β and PI3K-signalling pathways, and abnormal intestinal crypt gene sub-networks (P<10 −5; Supplementary Data 11). Contribution of risk SNPs to heritability. Using Linkage Disequilibrium Adjusted Kinships (LDAK)35 in conjunction with the GWAS data generated on unselected CRC cases (i.e. COIN, CORSA, Croatia, DACHS, FIN, SCOT, Scotland1, SOCCS/LBC, SOCCS/GS, UKBB, VQ58 studies) we estimated that the heritability of CRC attributable to all common variation is 0.29 (95% confidence interval: 0.24–0.35). To estimate the sample size required to explain a greater proportion of the GWAS heritability, we implemented a likelihood-based approach using association statistics in combination with LD information to model the effect-size distribution36, which was best represented by a threecomponent model (mixture of two normal distributions). Under this model, to identify SNPs explaining 80% of the GWAS heritability, it is likely to require effective sample sizes in excess of 300,000 if solely based on GWAS associations (Supplementary Figure 7). After adjusting for winner’s curse37, the 79 SNPs thus far shown to be associated with CRC susceptibility in Europeans explain 11% of the 2.2-fold familial relative risk (FRR)38, whilst all common genetic variants identifiable through GWAS could explain 73% of the FRR. Thus, the identified susceptibility SNPs collectively account for approximately 15% of the FRR of CRC that can be explained by common genetic variation. We incorporated the newly identified SNPs into risk prediction models for CRC and derived a polygenic risk score (PRS) based on a total of 79 GWAS significant risk variants. Individuals in the top 1% have a 2.6-fold increased risk of CRC compared with the population average (Supplementary Figure 8). Risk reclassification using this PRS offers the prospect of optimising prevention programmes for CRC in the population, for example through targeting screening6, and also preventative interventions. The identification of further risk loci through the analysis of even larger GWAS is likely to improve the performance of any PRS model. Co-heritability with non-cancer traits. We implemented crosstrait LD score regression39 to investigate co-heritability globally between CRC and 41 traits with publicly available GWAS summary statistics data. None of the genetic correlations remained significant after Bonferroni correction (two-sided Z-test, Pthreshold: 0.05/41 =1.2 × 10−3). However, nominally significant positive associations with CRC risk (Supplementary Data 12) included insulin resistance, comprising raised fasting insulin, glucose and HbA1c (positive), hyperlipidaemia, comprising raised total cholesterol and low-density lipoprotein cholesterol, and ulcerative colitis, all of which are traits or diseases previously reported in observational epidemiological studies to be associated with CRC risk40,41. Discussion Here we report a comprehensive analysis that sheds new light on the molecular basis of genetic risk for a common cancer, and greatly increases the number of known CRC risk SNPs. To identify the most credible target genes at each site, we have performed detailed annotation using public databases, and have also acquired our own disease-specific data from ChIP-seq, promoter capture Hi-C and gene expression analyses. Given that there remains significant missing common heritability for CRC, additional GWAS meta-analyses are likely to lead to discovery of more risk loci. Such an assertion is directly supported a contemporaneous study42, which has reported the identification of 40 independent signals; 30 novel loci and 10 conditionally independent association signals at previously and newly identified CRC risk loci. Of these, 18 were replicated in our analysis, with an additional five exhibiting an independent signal present at the same locus (Supplementary Data 13). Overall, our findings provide new insights into the biological basis of CRC, not only confirming the importance of established gene networks, but also providing evidence that point to a role for the gut microbiome in CRC causation, and identifying several functional mechanisms previously unsuspected of any involvement in colorectal tumourigenesis. Several of the gene pathways identified through GWAS may provide potential novel targets for chemoprevention and chemotherapeutic intervention. Methods Ethics. Collection of patient samples and associated clinico-pathological information was undertaken with written informed consent and relevant ethical review board approval at respective study centres in accordance with the tenets of the Declaration of Helsinki. Specifically: (i) UK National Cancer Research Network Multi-Research Ethics Committee (02/0/097 [NSCCG], 01/0/5) [SOCCS], 05/ S1401/89 [GS:SFHS], LREC/1998/4/183 [LBC1921], 2003/2/29 [LBC1936], 17/SC/ 0079 [CORGI] and 07/S0703/136 [SCOT]); (ii) The research activities of UK Biobank were approved by the North West Multi-centre Research Ethics Committee (11/NW/0382) in relation to the process of participant invitation, assessment and follow-up procedures. Additionally, ethics approvals from the National Information Governance Board for Health & Social Care in England and Wales and approval from the Community Health Index Advisory Group in Scotland were also obtained to gain access to the information that would allow the invitation of participants. This study did not need to re-contact the participants, and no separate ethics approval was required according to the Ethics and Governance Framework (EGF) of UK Biobank; (iii) South East Ethics Committee MREC (03/1/014); (iv) Written informed consent was obtained from all participants of CORSA. The study was approved by the ethical review committee of the Medical University of Vienna (MUW, EK Nr. 703/2010) and the “Ethikkommission Burgenland”(KRAGES, 33/ 2010) and (v) Finnish National Supervisory Authority for Welfare and Health, National Institute for Health and Welfare (THL/151/5.05.00/2017), the Ethics Committee of the Hospital District of Helsinki and Uusimaa (HUS/408/13/03/03/ 09). The diagnosis of colorectal cancer (ICD-9 153, 154; ICD-10 C18.9, C19, C20) was established in all cases in accordance with World Health Organization guidelines. Primary GWAS. We analysed data from five primary GWAS (Supplementary Data 2 and Supplementary Data 3): (1) The NSCCG-OncoArray GWAS comprised 6240 cases ascertained through the National Study of Colorectal Cancer Genetics (NSCCG)43 and 1041 cases collected through the CORGI consortium, genotyped using the Illumina OncoArray. Patients were selected for having a family history of CRC (at least one first-degree relative) or age of diagnosis below 58. Controls were also genotyped using the OncoArray and comprised (i) 3031 cancer-free men recruited by the PRACTICAL Consortium—the UK Genetic Prostate Cancer Study (UKGPCS) (age <65 years), a study conducted through the Royal Marsden NHS Foundation Trust and SEARCH (Study of Epidemiology & Risk Factors in Cancer), recruited via GP practices in East Anglia (2003–2009) and (ii) 4,488 cancer-free women across the UK, recruited via the Breast Cancer Association Consortium (BCAC). (2) The SCOT GWAS comprised 3076 cases from the Short Course Oncology Treatment (SCOT) trial—a study of adjuvant chemotherapy in colorectal cancer by the CACTUS and OCTO groups44. Controls comprised 4349 cancer-free individuals from The Heinz Nixdorf Recall study45. Both cases and controls were genotyped using the Illumina Global Screening Array. NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-09775-w ARTICLE NATURE COMMUNICATIONS | (2019) 10:2154 | https://doi.org/10.1038/s41467-019-09775-w | www.nature.com/naturecommunications 5 0 102030405060 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X SF3A3-[]-FHL3 PARS2-[TTC22]-C1orf177 COX5B-[ACTR1B]-ZAP70 PLCL1-[]-SATB2 SFMBT1-[ENSG00000272305]-RFT1 CXXC4-[]-TET2 GYPA----[HHIP]--ANAPC10 HIVEP1--[EDN1]---PHACTR1 IER3-[]-DDR1 HLA-DRB5-[]-HLA-DRB1 PREP-[]-PRDM1 ENSG00000233539----[TNS3]-C7orf65 CDKN2B-[]-DMRTA1 SWAP70-[SBF2]-ADM STAT6-[LRP1]-NXPH4 RFXAP-[SMAD9]-ALG5 PIBF1-[KLF5]----KLF12 EDNRB--[]--POU4F1 COL4A1-[COL4A2]-RAB20 ENSG00000259471-[SMAD6]--SMAD3 SKOR1-[]-PIAS1 IQGAP1-[CRTC3]-BLM MAF----[]----DYNLRB2 TMEM220-[]-PIRT FUT2-[MAMSTR]-RASIP1 GTSF1L--[TOX2]-JPH2 PITX1-[C5orf66]-H2AFY MDFI-[TFEB]-ENSG00000268275 SFTPD-[] ZDHHC6-[VTI1A]--TCF7L2 CD9-[]-PLEKHG6 RNMTL1-[NXN]-ENSG00000262003 CCDC97-[ENSG00000255730]-[TMEM91] BMP2-[]-HAO1 PLCL1-[]-SATB2 SLC6A19-[SLC6A18]-TERT ENSG00000233539----[]----TNS3 POLD3-[CHRDL2]-RNF169 PARP11-[]-CCND2 GREM1-[FMN1]--RYR3 GREM1-[FMN1]--RYR3 FOXL1-[]-C16orf95 PARD6B-[]-BCAS4 CEBPB-[]-PTPN1 WNT4-[]-ZBTB40 SHCBP1L-[LAMC1]-LAMC2 DUSP10----[]----HHIPL2 AAMP-[PNKD]-TMBIM1 ZNF621-[]-CTNNB1 TERT-[]-CLPTM1L DAB2-[]-PTGER4 TULP1-[FKBP5]-ARMC12 SRSF3-[]-CDKN1A HMGCLL1--[BMP5]--COL21A1 TRPS1-[]-EIF3H FAM84B-[]-POU5F1B GATA3----[] NKX2-3--[]--SLC25A28 LIPT2-[POLD3]-CHRDL2 ARHGAP20----[C11orf53]-COLCA1 ATF1-[]-TMPRSS12 SH2B3-[ATXN2]-BRAP ENSG00000257726----[]----MED13L STARD13-[]-RFC3 DDHD1----[BMP4]---CDKN3 SCG5-[]-GREM1 CTIF-[SMAD7]-DYM C19orf40-[RHPN2]-GPATCH1 FERMT1-[]-BMP2 SULF2----[PREX1]-ARFGEF2 CEBPB-[]-PTPN1 ADRM1-[LAMA5]-RPS21 GPR143-[SHROOM2]-ENSG00000234469 BMP4----[]----CDKN3 SCG5-[GREM1]-FMN1 FERMT1-[]-BMP2 C3orf17-[]-BOC STMN3-[RTEL1-TNFRSF6B] AP1M1-[]-KLF2 –log10(P) Fig. 2 Manhattan plot showing all loci containing genetic risk variants independently associated with colorectal cancer risk at P<5×10 −8in European populations. SNPs on the left of the plot are new SNPs identified in this study, and SNPs on the right were identified in previous studies and replicated at genome-wide significance in this study. The 79 risk SNPs consisted of 31 previously reported SNPs, 39 new risk SNPs, and nine SNPs previously identified in Asian but not in European populations (denoted in dark gold). Dotted lines indicate SNPs that were identified as independent through conditional analysis. Square brackets indicate the position of the sentinel SNP relative to nearest genes (“gene1-[]-gene2”for intergenic SNPs, “[gene]”for intragenic SNPs). The distance from the sentinel SNP to each gene is proportional to the number of dashes. The red line indicates the genome-wide significance threshold. The x-axis represents the −log 10 P-values of the SNPs, and the y-axis represents the chromosomal positions ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-09775-w 6NATURE COMMUNICATIONS | (2019) 10:2154 | https://doi.org/10.1038/s41467-019-09775-w | www.nature.com/naturecommunications (3) SOCCS/Generation Scotland (SOCCS/GS) comprised 4772 cases from the Study of Colorectal Cancer in Scotland (SOCCS)12,13 and 12,158 controls including 2221 population-based controls from SOCCS and additional 9937 population controls without prior history of colorectal cancer from Generation Scotland-Scottish Family Health Study (GS:SFHS)46. (4) SOCCS/Lothian Birth Cohort (SOCCS/LBC) GWAS comprised 1037 cases from the Study of Colorectal Cancer in Scotland (SOCCS)47 and 1522 population-based controls without prior history of malignant tumours from the Lothian Birth Cohorts (LBC) of 1921 and 193648. (5) UK Biobank (UKBB) GWAS comprised 6360 cases and 25,440 populationbased control individuals. UK Biobank is a large cohort study with more than 500,000 individuals recruited. Biological samples of these participants were genotyped using the custom-designed Affymetrix UK BiLEVE Axiom array on an initial 50,000 participants and Affymetrix UK Biobank Axiom array on the remaining 450,000 participants. The two arrays had over 95% common content. Genotyping was done at the Affymetrix Research Services Laboratory in Santa Clara, California, USA. Details on genotyping and quality control were previously reported49. Self-reported cases of cancers of bowel, colon or rectum, if not confirmed by the ICD9 or ICD10 codes were excluded from the analysis. Healthy control individuals without history of cancer and/or colorectal adenoma were included in the analysis after matching one case to four controls by age, gender, date of blood draw, ethnicity and region of residence (two first letters of postal code). Published GWAS. We made use of 10 previously published GWAS (Supplementary Data 2): (1) UK1 (CORGI study) comprised 940 cases with colorectal neoplasia and 965 controls12; (2) Scotland1 (COGS study) included 1012 CRC cases and 1012 controls12; (3) VQ58 comprised 1800 cases from the UK-based VICTOR and QUASAR2 adjuvant chemotherapy clinical trials and 2690 population control genotypes from the Wellcome Trust Case Control Consortium 2 (WTCCC2) 1958 birth cohort50; (4) CCFR1 comprised 1290 familial CRC cases and 1055 controls from the Colon Cancer Family Registry (CCFR)15; (5) CCFR2 included a further 796 cases from the CCFR and 2236 controls from the Cancer Genetic Markers of Susceptibility (CGEMS) studies of breast and prostate cancer51,52; (6) COIN was based on 2244 CRC cases ascertained through two independent Medical Research Council clinical trials of advanced/metastatic CRC (COIN and COIN-B)53 and controls comprised 2162 individuals from the UK Blood Service Control Group genotyped as part of the WTCCC2; (7) Finnish GWAS (FIN)3was based on 1172 CRC cases and 8266 cancer-free controls ascertained through FINRISK, Health 2000, Finnish Twin Cohort and Helsinki Birth Cohort Studies; (8) CORSA (COloRectal cancer Study of Austria) a molecular epidemiological study of 978 cases and 855 colonoscopy-negative controls54; (9) DACHS (Darmkrebs: Chancen der Verhütung durch Screening)55 based on 1105 cases and 700 controls and (10) Croatia consisted of 764 cases and 460 populationbased controls56. The VQ58, UK1 and Scotland1 GWAS were genotyped using Illumina Hap300, Hap240S, Hap370, Hap550 or Omni2.5 M arrays. 1958BC genotyping was performed as part of the WTCCC2 study on Hap1.2M-Duo Custom arrays. The CCFR samples were genotyped using Illumina Hap1M, Hap1M-Duo or Omniexpress arrays. CGEMS samples were genotyped using Illumina Hap300 and Hap240 or Hap550 arrays. The COIN cases were genotyped using Affymetrix Axiom Arrays and the Blood Service controls were genotyped using Affymetrix 6.0 arrays. FIN cases were genotyped using Illumina HumanOmni 2.5M8v1 and controls using Illumina HumanHap 670k and 610k arrays. DACHS study samples were genotyped using the Illumina OncoArray, CORSA study sampels were genotyped on the Affymetrix Axiom Genome-Wide CEU 1 Array, and Croatia study samples were genotyped on Illumina OmniExpressExome BeadChip 8v1.1 or 8v1.3. Quality control. Standard quality control (QC) measures were applied to each GWAS8. Specifically, individuals with low SNP call rate (<95%) as well as individuals evaluated to be of non-European ancestry (using the HapMap version 2 CEU, JPT/CHB and YRI populations as a reference) were excluded (Supplementary Figure 9). For apparent first-degree relative pairs, we excluded the control from a case-control pair; otherwise, we excluded the individual with the lower call rate. SNPs with a call rate <95% were excluded as were those with a MAF <0.5% or displaying significant deviation from Hardy–Weinberg equilibrium (P<10 −5). QC details are provided in Supplementary Data 3. All genotype analyses were performed using PLINK v1.957. Imputation and statistical analysis. Prediction of the untyped SNPs was carried out using SHAPEIT v2.83758 and IMPUTE v2.3.259. The CCFR1, CCFR2, COIN, CORSA, Croatia, NSCCG-OncoArray, SCOT, Scotland1, SOCCS/GS, SOCCS/LBC, UK1 and VQ58 samples used a merged reference panel using data from 1000 Genomes Project (phase 1, December 2013 release) and UK10K (April 2014 release). Imputation of UKBB was based on data from 1000 Genomes Project (phase 3), UK10K and Haplotype Reference Consortium. The FIN and DACHS GWAS were imputed using a reference panel comprised of 1000 Genomes Projects Table 2 Colorectal cancer variants identified in analysis conditioning on the sentinel SNP at each risk locus Conditional (Sentinel) SNPs Cytoband (position (bp, GRCh37)) Risk/ Alt Allele RAF OR (95% CI) Pvalue Conditional OR (95% CI) Conditional P-value BFDP LD with sentinel SNP (r2; D’) I2(%) P het Average info score rs77776598 (rs2735940) 5p15.33 (1,240,998) C/T 0.06 1.14 (1.09;1.20) 7.90 × 10−9 1.16 (1.11;1.21) 2.84 × 10−10 0.003 0.00; 0.33 0 0.93 0.99 rs4944940 (rs3824999) 11q13.4 (74,415,252) G/A 0.96 1.31 (1.24;1.39) 1.05 × 10−20 1.28 (1.21;1.35) 3.21 × 10−17 2.73 × 10−9 0.00; 0.19 6 0.38 0.95 rs12818766 (rs3217810) 12p13.32 (4,376,091) A/G 0.18 1.10 (1.07;1.13) 2.15 × 10−9 1.10 (1.07;1.13) 5.29 × 10−90.037 0.00; 0.06 30 0.16 0.89 rs1570405a (rs4444235) 14q22.2 (54,554,234) G/A 0.31 1.06 (1.03;1.08) 9.81 × 10−7 1.07 (1.04;1.09) 1.91 × 10−80.125 0.02; 0.19 0 0.46 1.00 rs16969681b (rs73376930) 15q13.3 (32,993,111) T/C 0.09 1.22 (1.18;1.27) 2.97 × 10−27 1.21 (1.16;1.25) 2.85 × 10−24 1.33 × 10−16 0.01; 0.32 42 0.04 0.99 rs16959063 (rs73376930) 15q13.3 (33,105,730) A/G 0.01 1.30 (1.18;1.42) 3.72 × 10−8 1.33 (1.21;1.45) 5.40 × 10−90.23 0.00; 0.40 30 0.13 0.96 rs17816465 (rs73376930) 15q13.3 (33,156,386) A/G 0.20 1.11 (1.08;1.14) 1.12 × 10−14 1.12 (1.09;1.15) 8.36 × 10−15 1.07 × 10−7 0.00; 0.11 44 0.04 0.97 rs899244 (rs2696839) 16q24.1 (86,700,030) T/C 0.21 1.09 (1.06;1.12) 1.11 × 10−10 1.09 (1.06;1.12) 1.13 × 10−10 4.06 × 10−3 0.00; 0.04 14 0.29 0.99 rs6085661c (rs961253) 20p12.3 (6,693,128) T/C 0.39 1.09 (1.06;1.11) 1.63 × 10−14 1.09 (1.07;1.11) 2.95 × 10−15 3.88 × 10−8 0.00; 0.08 0 0.96 1.00 rs4811050 (rs1810502) 20q13.13 (48,980,670) A/G 0.18 1.10 (1.07;1.13) 2.43 × 10−11 1.09 (1.06;1.12) 4.07 × 10−94.06 × 10−3 0.04; 0.45 20 0.23 0.99 rs6091213 (rs1810502) 20q13.13 (49,384,745) C/T 0.26 1.08 (1.05;1.11) 4.35 × 10−10 1.08 (1.05;1.11) 5.68 × 10−10 4.76 × 10−3 0.00; 0.05 6 0.39 0.94 BFDP calculated using prior =10−5and maximum relative risk =1.2. LD calculated based on European populations in the 1000 Genomes Project data. BFDP calculated using conditional analysis results, with prior =10−5and maximum relative risk =1.2 RAF risk allele frequency, OR odds ratio, CI confidence interval, BFDP Bayesian False Discovery Probability, I2proportion of the total variation due to heterogeneity, P het P-value for heterogeneity aTags to rs1957636 (r2=0.67, D′=1). Previously identified in Tomlinson IP, Nat Genet, 2008 (PMID:18372905) bPreviously identified in Tomlinson IP, Nat Genet, 2008 (PMID:18372905) cTags to rs4813802 (r2=0.75, D′=0.93). Previously identified in Tomlinson IP, Nat Genet, 2008 (PMID:18372905) NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-09775-w ARTICLE NATURE COMMUNICATIONS | (2019) 10:2154 | https://doi.org/10.1038/s41467-019-09775-w | www.nature.com/naturecommunications 7 Recombination rate (cM/Mb) LRP1 > LRP1−AS STAT6 AC023237.1 MIR1228 chr 12 position (Mb) 57.50 Mb 57.52 Mb 57.54 Mb 57.56 Mb 57.58 Mb rs7398375 Recombination rate (cM/Mb) 1.0 0.5 VTI1A ACSL5 TCF7L2 TECTB ZDHHC6 chr 10 position (Mb) 114.20 Mb 114.40 Mb 114.60 Mb 114.80 Mb 115.00 Mb rs12255141 SMC3 MYC RAD21 NIPBL SMC1A SMC3 RAD21 NIPBL JUN RAD21 SMAD3 SMC1A NIPBL MYC JUNB SMAD2 JUND RAD21 JUN CTCF SMC3 RAD21 NIPBL SMC1A RAD21 SMC3 JUND SMAD3 Recombination rate (cM/Mb) FUT2 MAMSTR RASIP1 chr 19 position (Mb) 49.20 Mb 49.21 Mb 49.22 Mb 49.23 Mb 49.24 Mb rs12979278 12 Recombination rate (cM/Mb) SLC6A3 SLC12A7 SLC6A19 SLC6A18 TERT CLPTM1L LPCAT1RP11-325I22.2 chr 5 position (Mb) 1.10 Mb 1.20 Mb 1.30 Mb 1.40 Mb 1.50 Mb rs2735940 rs77776598 LoVo CHi-C interactions HT29 CHi-C interactions HCT116 Sigmoid colon Colonic mucosa Rectal mucosa HT29 CHi-C interactions ROADMAP ChromHMM states ZNF genes & repeats Active TSS Flanking active TSS Transcr. at gene 5' and 3' Strong transcription Weak transcription Genic enhancers Enhancers Heterochromatin Bivalent/ poised TSS Flanking bivalent TSS/Enh Bivalent enhancer Repressed PolyComb Weak repressed PolyComb Quiescent/low HCT116 ChromHMM states Promoter Enhancer Other Open chromatin HCT116 Sigmoid colon Colonic mucosa Rectal mucosa HCT116 Sigmoid colon Colonic mucosa Rectal mucosa HCT116 Sigmoid colon Colonic mucosa Rectal mucosa HT29 CHi-C interactions −log 10 P 9 6 3 0 6 4 2 0 9 6 3 0 −log 10 P −log 10 P −log 10 P 9 6 3 0 6 4 2 0 10 8 6 4 2 0 8 4 0 12 8 4 0 r2 65 60 55 50 45 40 35 30 25 20 15 10 5 0 5 0 20 15 10 5 0 1.0 0.5 r2 1.0 0.5 r2 1.0 0.5 r2 20 15 10 5 0 CTD−3080P12.3 a b c d Fig. 3 Regional plots of exemplar colorectal cancer risk loci. In the main panel, −log 10 P-values (y-axis) of the SNPs are shown according to their chromosomal positions (x-axis). The colour intensity of each symbol reflects the extent of LD with the top SNP: white (r2=0) through to dark red (r2= 1.0), with r2estimated from EUR 1000 Genomes data. Genetic recombination rates (cM/Mb) are shown with a light blue line. Physical positions are based on GRCh37 of the human genome. Where available, the upper panel shows Hi-C contacts from HT29 or LoVo. The lower panel shows the chromatin state segmentation track from the Roadmap Epigenomics project (colonic mucosa, rectal mucosa, sigmoid colon), and HCT116. Also shown are the relative positions of genes and transcripts mapping to each region of association. ars12255141 (10q25.2); brs12979278 (19q13); crs2735940 (5p15); drs7398375 (12q13.3) ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-09775-w 8NATURE COMMUNICATIONS | (2019) 10:2154 | https://doi.org/10.1038/s41467-019-09775-w | www.nature.com/naturecommunications 75 Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm 30303, Sweden. 76 Epidemiology Research Program, American Cancer Society, 250 Williams Street, Atlanta, GA 30303, USA. 77 SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA. 78 Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane 4059 Queensland, Australia. 79 Translational Research Institute, Brisbane 4102 Queensland, Australia. 80 Department of Medical Biochemistry and Genetics, Institute of Biomedicine, University of Turku, Turku 20520, Finland. 81 Tyks Microbiology and Genetics, Department of Medical Genetics, Turku University Hospital, Turku 20520, Finland. 82 Division of Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm 171 77, Sweden. 83 Department of Surgical Sciences, Uppsala University, Uppsala 751 85, Sweden. 84 Division of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Radiotherapy Related Research, Manchester NIHR Biomedical Research Centre, The Christie Hospital NHS Foundation Trust, Manchester M13 9NT, UK. 85 Department of Epidemiology, Harvard T.H Chan School of Public Health, Boston, MA 02115, USA. 86 CeRePP, Tenon Hospital, Paris 75020, France. 87 Sorbonne Université, GRC n°5 ONCOTYPE-URO, Tenon Hospital, Paris 75970, France. 88 Department of Molecular Medicine, Aarhus University Hospital, Aarhus 8000, Denmark. 89 Department of Clinical Medicine, Aarhus University, Aarhus 8000, Denmark. 90 Department of Medical Genetics, Oslo University Hospital, Oslo 0424, Norway. 91 Department of Oncology, Addenbrooke’s Hospital, University of Cambridge, Cambridge CB2 0QQ, UK. 92 Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge CB2 0RE, UK. 93 Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 7LF, UK. 94 Faculty of Medical Science, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK. 95 School of Social and Community Medicine, University of Bristol, Bristol BS8 2PS, UK. 96 Cancer Epidemiology Unit, Nuffield Department of Population Health University of Oxford, Oxford OX3 7LF, UK. 97 Department of Surgical Oncology, Princess Margaret Cancer Centre, Toronto M5G 2M9, Canada. 98 Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. 99 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. 100 Centre for Molecular Oncology, Barts Cancer Institute, John Vane Science Centre, Queen Mary University of London, London EC1M 6BQ, UK. 101 Cancer Epidemiology & Intelligence Division, The Cancer Council Victoria, Melbourne 3004 Victoria, Australia. 102 Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne 3053, Australia. 103 Division of Urologic Surgery, Brigham and Womens Hospital, Boston, MA 02115, USA. 104 Fundación Pública Galega de Medicina Xenómica-SERGAS, Grupo de Medicina Xenómica, CIBERER, IDIS, Santiago de Compostela 15782, Spain. 105 Centre for Research in Environmental Epidemiology (CREAL), Barcelona Institute for Global Health (ISGlobal), Barcelona 60803, Spain. 106 CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain. 107 IMIM (Hospital del Mar Research Institute), Barcelona 08003, Spain. 108 Universitat Pompeu Fabra, Barcelona 08002, Spain. 109 Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital/Harvard Medical School, Boston, MA 02115, USA. 110 Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa 33612, USA. 111 Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle 98109 Washington, USA. 112 Department of Epidemiology, School of Public Health, University of Washington, Seattle 98195 Washington, USA. 113 International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin 70-001, Poland. 114 Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 1165, Denmark. 115 Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev 2900, Denmark. 116 Institute for Human Genetics, University Hospital Ulm, Ulm 89081, Germany. 117 Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 118 Cancer Prevention Institute of California, Fremont, CA 94538, USA. 119 Department of Health Research & Policy (Epidemiology) and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA. 120 Department of Genetics, Portuguese Oncology Institute of Porto, Porto 4200-072, Portugal. 121 Biomedical Sciences Institute (ICBAS), University of Porto, Porto 4200-072, Portugal. 122 Department of Population Sciences, Beckman Research Institute of the City of Hope, Duarte, CA 91016, USA. 123 Faculty of Medicine and Health Sciences, Basic Medical Sciences, Ghent University, Gent 9000, Belgium. 124 Faculty of Medicine, Department of Surgery, University of Malaya, Kuala Lumpur 50603, Malaysia. 125 Department of Urology, University of Washington, Seattle, WA 98105, USA. 126 Division of Medical Oncology, Urogenital Unit, Department of Oncology, University Hospital Centre Zagreb, 10 000 Zagreb, Croatia. 127 Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University, Sofia 1431, Bulgaria. 128 Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton T6G 2R3 Alberta, Canada. 129 Division of Radiation Oncology, Cross Cancer Institute, Edmonton T6G 1Z2 Alberta, Canada. 130 Molecular Endocrinology Laboratory, Department of Cellular and Molecular Medicine, KU Leuven 3000 Leuven, Belgium. 131 Institute of Cancer Sciences, Manchester Cancer Research Centre, University of Manchester, Manchester Academic Health Science Centre, St Mary’s Hospital, Manchester M13 9WL, UK. 132 University of California San Diego, Moores Cancer Center, La Jolla, CA 92093, USA. 133 Department of Urology, Erasmus University Medical Center, Rotterdam 3015, the Netherlands. 134 Cancer & Environment Group, Center for Research in Epidemiology and Population Health (CESP), INSERM, University Paris-Sud, University Paris-Saclay, Villejuif 94805, France. 135 Clinical Gerontology Unit, University of Cambridge, Cambridge CB2 2QQ, UK. 136 Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, UT 84108-1266, USA. 137 George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT 84148, USA. 138 The University of Surrey, Guildford GU2 7XH Surrey, UK. 139 Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-09775-w ARTICLE NATURE COMMUNICATIONS | (2019) 10:2154 | https://doi.org/10.1038/s41467-019-09775-w | www.nature.com/naturecommunications 15