Article | Published:

Discovery of common and rare genetic risk variants for colorectal cancer

Nature Genetics (2018) | Download Citation

Abstract

To further dissect the genetic architecture of colorectal cancer (CRC), we performed whole-genome sequencing of 1,439 cases and 720 controls, imputed discovered sequence variants and Haplotype Reference Consortium panel variants into genome-wide association study data, and tested for association in 34,869 cases and 29,051 controls. Findings were followed up in an additional 23,262 cases and 38,296 controls. We discovered a strongly protective 0.3% frequency variant signal at CHD1. In a combined meta-analysis of 125,478 individuals, we identified 40 new independent signals at P< 5 × 10−8, bringing the number of known independent signals for CRC to ~100. New signals implicate lower-frequency variants, Krüppel-like factors, Hedgehog signaling, Hippo-YAP signaling, long noncoding RNAs and somatic drivers, and support a role for immune function. Heritability analyses suggest that CRC risk is highly polygenic, and larger, more comprehensive studies enabling rare variant analysis will improve understanding of biology underlying this risk and influence personalized screening strategies and drug development.

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Data availability

All whole-genome sequence data have been deposited in the database of Genotypes and Phenotypes (dbGaP), which is hosted by NCBI, under accession number phs001554.v1.p1. All custom Infinium OncoArray-500K array data for the studies in the stage 2 meta-analysis have been deposited at dbGaP under accession number phs001415.v1.p1. All Illumina HumanOmniExpressExome-8v1-2 array data for the studies in the stage 2 meta-analysis have been deposited at dbGaP under accession number phs001315.v1.p1. Genotype data for the studies included in the stage 1 meta-analysis have been deposited at dbGaP under accession number phs001078.v1.p1. The UK Biobank resource was accessed through application number 8614. CRC-relevant epigenome data were obtained from the NCBI Gene Expression Omnibus (GEO) database under accession number GSE77737.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.

    Ferlay, J. et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136, E359–E386 (2015).

  2. 2.

    Lichtenstein, P. et al. Environmental and heritable factors in the causation of cancer—analyses of cohorts of twins from Sweden, Denmark, and Finland. N. Engl. J. Med. 343, 78–85 (2000).

  3. 3.

    Czene, K., Lichtenstein, P. & Hemminki, K. Environmental and heritable causes of cancer among 9.6 million individuals in the Swedish Family-Cancer Database. Int. J. Cancer 99, 260–266 (2002).

  4. 4.

    Sud, A., Kinnersley, B. & Houlston, R. S. Genome-wide association studies of cancer: current insights and future perspectives. Nat. Rev. Cancer 17, 692–704 (2017).

  5. 5.

    Tomlinson, I. et al. A genome-wide association scan of tag SNPs identifies a susceptibility variant for colorectal cancer at 8q24.21. Nat. Genet. 39, 984–988 (2007).

  6. 6.

    Broderick, P. et al. A genome-wide association study shows that common alleles of SMAD7 influence colorectal cancer risk. Nat. Genet. 39, 1315–1317 (2007).

  7. 7.

    Tomlinson, I. P. M. et al. A genome-wide association study identifies colorectal cancer susceptibility loci on chromosomes 10p14 and 8q23.3. Nat. Genet. 40, 623–630 (2008).

  8. 8.

    Tenesa, A. et al. Genome-wide association scan identifies a colorectal cancer susceptibility locus on 11q23 and replicates risk loci at 8q24 and 18q21. Nat. Genet. 40, 631–637 (2008).

  9. 9.

    COGENT Study et al. Meta-analysis of genome-wide association data identifies four new susceptibility loci for colorectal cancer. Nat. Genet. 40, 1426–1435 (2008).

  10. 10.

    Houlston, R. S. et al. Meta-analysis of three genome-wide association studies identifies susceptibility loci for colorectal cancer at 1q41, 3q26.2, 12q13.13 and 20q13.33. Nat. Genet. 42, 973–977 (2010).

  11. 11.

    Tomlinson, I. P. M. et al. Multiple common susceptibility variants near BMP pathway loci GREM1, BMP4, and BMP2 explain part of the missing heritability of colorectal cancer. PLoS Genet. 7, e1002105 (2011).

  12. 12.

    Dunlop, M. G. et al. Common variation near CDKN1A, POLD3 and SHROOM2 influences colorectal cancer risk. Nat. Genet. 44, 770–776 (2012).

  13. 13.

    Peters, U. et al. Identification of genetic susceptibility loci for colorectal tumors in a genome-wide meta-analysis. Gastroenterology 144, 799–807.e24 (2013).

  14. 14.

    Jia, W.-H. et al. Genome-wide association analyses in East Asians identify new susceptibility loci for colorectal cancer. Nat. Genet. 45, 191–196 (2013).

  15. 15.

    Whiffin, N. et al. Identification of susceptibility loci for colorectal cancer in a genome-wide meta-analysis. Hum. Mol. Genet. 23, 4729–4737 (2014).

  16. 16.

    Wang, H. et al. Trans-ethnic genome-wide association study of colorectal cancer identifies a new susceptibility locus in VTI1A. Nat. Commun. 5, 4613 (2014).

  17. 17.

    Zhang, B. et al. Large-scale genetic study in East Asians identifies six new loci associated with colorectal cancer risk. Nat. Genet. 46, 533–542 (2014).

  18. 18.

    Schumacher, F. R. et al. Genome-wide association study of colorectal cancer identifies six new susceptibility loci. Nat. Commun. 6, 7138 (2015).

  19. 19.

    Al-Tassan, N. A. et al. A new GWAS and meta-analysis with 1000Genomes imputation identifies novel risk variants for colorectal cancer. Sci. Rep. 5, 10442 (2015).

  20. 20.

    Orlando, G. et al. Variation at 2q35 (PNKD and TMBIM1) influences colorectal cancer risk and identifies a pleiotropic effect with inflammatory bowel disease. Hum. Mol. Genet. 25, 2349–2359 (2016).

  21. 21.

    Zeng, C. et al. Identification of susceptibility loci and genes for colorectal cancer risk. Gastroenterology 150, 1633–1645 (2016).

  22. 22.

    Schmit, S. L. et al. Novel common genetic susceptibility loci for colorectal cancer. J. Natl. Cancer Inst. https://doi.org/10.1093/jnci/djy099 (2018).

  23. 23.

    Fuchsberger, C. et al. The genetic architecture of type 2 diabetes. Nature 536, 41–47 (2016).

  24. 24.

    1000 Genomes Project Consortium. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  25. 25.

    McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

  26. 26.

    Amos, C. I. et al. The Oncoarray Consortium: a network for understanding the genetic architecture of common cancers. Cancer Epidemiol. Biomarkers. Prev. 26, 126–135 (2017).

  27. 27.

    Zhao, D. & DePinho, R. A. Synthetic essentiality: Targeting tumor suppressor deficiencies in cancer. Bioessays 39, (2017).

  28. 28.

    Zhao, D. et al. Synthetic essentiality of chromatin remodelling factor CHD1 in PTEN-deficient cancer. Nature 542, 484–488 (2017).

  29. 29.

    Xiao, Y. et al. RGMb is a novel binding partner for PD-L2 and its engagement with PD-L2 promotes respiratory tolerance. J. Exp. Med. 211, 943–959 (2014).

  30. 30.

    Topalian, S. L. et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med. 366, 2443–2454 (2012).

  31. 31.

    Zhang, X. et al. Somatic superenhancer duplications and hotspot mutations lead to oncogenic activation of the KLF5 transcription factor. Cancer Discov. 8, 108–125 (2018).

  32. 32.

    Giannakis, M. et al. Genomic correlates of immune-cell infiltrates in colorectal carcinoma. Cell Rep. 15, 857–865 (2016).

  33. 33.

    Dekker, R. J. et al. KLF2 provokes a gene expression pattern that establishes functional quiescent differentiation of the endothelium. Blood 107, 4354–4363 (2006).

  34. 34.

    Boon, R. A. et al. KLF2 suppresses TGF-beta signaling in endothelium through induction of Smad7 and inhibition of AP-1. Arterioscler. Thromb. Vasc. Biol. 27, 532–539 (2007).

  35. 35.

    Chakroborty, D. et al. Dopamine stabilizes tumor blood vessels by up-regulating angiopoietin 1 expression in pericytes and Kruppel-like factor-2 expression in tumor endothelial cells. Proc. Natl Acad. Sci. USA 108, 20730–20735 (2011).

  36. 36.

    Lee, S.-J. et al. Regulation of hypoxia-inducible factor 1α (HIF-1α) by lysophosphatidic acid is dependent on interplay between p53 and Krüppel-like factor 5. J. Biol. Chem. 288, 25244–25253 (2013).

  37. 37.

    Zhang, H. et al. Lysophosphatidic acid facilitates proliferation of colon cancer cells via induction of Krüppel-like factor 5. J. Biol. Chem. 282, 15541–15549 (2007).

  38. 38.

    Ma, Z. et al. Long non-coding RNA SNHG15 inhibits P15 and KLF2 expression to promote pancreatic cancer proliferation through EZH2-mediated H3K27me3. Oncotarget 8, 84153–84167 (2017).

  39. 39.

    Evangelista, M., Tian, H. & de Sauvage, F. J. The hedgehog signaling pathway in cancer. Clin. Cancer Res. 12, 5924–5928 (2006).

  40. 40.

    Gerling, M. et al. Stromal Hedgehog signalling is downregulated in colon cancer and its restoration restrains tumour growth. Nat. Commun. 7, 12321 (2016).

  41. 41.

    Mille, F. et al. The Shh receptor Boc promotes progression of early medulloblastoma to advanced tumors. Dev. Cell. 31, 34–47 (2014).

  42. 42.

    Mathew, E. et al. Dosage-dependent regulation of pancreatic cancer growth and angiogenesis by hedgehog signaling. Cell Rep. 9, 484–494 (2014).

  43. 43.

    Zhao, B., Li, L., Lei, Q. & Guan, K.-L. The Hippo-YAP pathway in organ size control and tumorigenesis: an updated version. Genes Dev. 24, 862–874 (2010).

  44. 44.

    Camargo, F. D. et al. YAP1 increases organ size and expands undifferentiated progenitor cells. Curr. Biol. 17, 2054–2060 (2007).

  45. 45.

    Ma, X., Zhang, H., Xue, X. & Shah, Y. M. Hypoxia-inducible factor 2α (HIF-2α) promotes colon cancer growth by potentiating Yes-associated protein 1 (YAP1) activity. J. Biol. Chem. 292, 17046–17056 (2017).

  46. 46.

    MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896–D901 (2017).

  47. 47.

    Seshagiri, S. et al. Recurrent R-spondin fusions in colon cancer. Nature 488, 660–664 (2012).

  48. 48.

    Song, F. et al. Identification of a melanoma susceptibility locus and somatic mutation in TET2. Carcinogenesis 35, 2097–2101 (2014).

  49. 49.

    Eeles, R. A. et al. Identification of seven new prostate cancer susceptibility loci through a genome-wide association study. Nat. Genet. 41, 1116–1121 (2009).

  50. 50.

    Michailidou, K. et al. Association analysis identifies 65 new breast cancer risk loci. Nature 551, 92–94 (2017).

  51. 51.

    Schunkert, H. et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat. Genet. 43, 333–338 (2011).

  52. 52.

    Scott, L. J. et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316, 1341–1345 (2007).

  53. 53.

    Al Olama, A. A. et al. A meta-analysis of 87,040 individuals identifies 23 new susceptibility loci for prostate cancer. Nat. Genet. 46, 1103–1109 (2014).

  54. 54.

    Timofeeva, M. N. et al. Influence of common genetic variation on lung cancer risk: meta-analysis of 14 900 cases and 29 485 controls. Hum. Mol. Genet. 21, 4980–4995 (2012).

  55. 55.

    Shete, S. et al. Genome-wide association study identifies five susceptibility loci for glioma. Nat. Genet. 41, 899–904 (2009).

  56. 56.

    Bishop, D. T. et al. Genome-wide association study identifies three loci associated with melanoma risk. Nat. Genet. 41, 920–925 (2009).

  57. 57.

    Sapkota, Y. et al. Meta-analysis identifies five novel loci associated with endometriosis highlighting key genes involved in hormone metabolism. Nat. Commun. 8, 15539 (2017).

  58. 58.

    Cannon-Albright, L. A. et al. Assignment of a locus for familial melanoma, MLM, to chromosome 9p13-p22. Science 258, 1148–1152 (1992).

  59. 59.

    Hussussian, C. J. et al. Germline p16 mutations in familial melanoma. Nat. Genet. 8, 15–21 (1994).

  60. 60.

    Seoane, J. et al. TGFbeta influences Myc, Miz-1 and Smad to control the CDK inhibitor p15INK4b. Nat. Cell Biol. 3, 400–408 (2001).

  61. 61.

    Jung, B., Staudacher, J. J. & Beauchamp, D. Transforming growth factor β superfamily signaling in development of colorectal cancer. Gastroenterology 152, 36–52 (2017).

  62. 62.

    Guda, K. et al. Inactivating germ-line and somatic mutations in polypeptide N-acetylgalactosaminyltransferase 12 in human colon cancers. Proc. Natl Acad. Sci. USA 106, 12921–12925 (2009).

  63. 63.

    Groden, J. et al. Identification and characterization of the familial adenomatous polyposis coli gene. Cell 66, 589–600 (1991).

  64. 64.

    Saharia, A. et al. FEN1 ensures telomere stability by facilitating replication fork re-initiation. J. Biol. Chem. 285, 27057–27066 (2010).

  65. 65.

    Eeles, R. A. et al. Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping array. Nat. Genet. 45, 385–391 (2013).

  66. 66.

    Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

  67. 67.

    Paternoster, L. et al. Multi-ancestry genome-wide association study of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis. Nat. Genet. 47, 1449–1456 (2015).

  68. 68.

    Laken, S. J. et al. Familial colorectal cancer in Ashkenazim due to a hypermutable tract in APC. Nat. Genet. 17, 79–83 (1997).

  69. 69.

    Niell, B. L., Long, J. C., Rennert, G. & Gruber, S. B. Genetic anthropology of the colorectal cancer-susceptibility allele APC I1307K: evidence of genetic drift within the Ashkenazim. Am. J. Hum. Genet. 73, 1250–1260 (2003).

  70. 70.

    Karami, S. et al. Telomere structure and maintenance gene variants and risk of five cancer types. Int. J. Cancer 139, 2655–2670 (2016).

  71. 71.

    Congrains, A., Kamide, K., Ohishi, M. & Rakugi, H. ANRIL: molecular mechanisms and implications in human health. Int. J. Mol. Sci. 14, 1278–1292 (2013).

  72. 72.

    Zhang, X. et al. Identification of focally amplified lineage-specific super-enhancers in human epithelial cancers. Nat. Genet. 48, 176–182 (2016).

  73. 73.

    Rheinbay, E. et al. Discovery and characterization of coding and non-coding driver mutations in more than 2,500 whole cancer genomes. Preprint at https://www.biorxiv.org/content/early/2017/12/23/237313 (2017).

  74. 74.

    Iotchkova, V. et al. GARFIELD - GWAS analysis of regulatory or functional information enrichment with LD correction. Preprint at https://www.biorxiv.org/content/early/2016/11/07/085738 (2016).

  75. 75.

    Segrè, A. V. et al. Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet. 6, e1001058 (2010).

  76. 76.

    Yang, J. et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat. Genet. 47, 1114–1120 (2015).

  77. 77.

    Bhatia, G. et al. Subtle stratification confounds estimates of heritability from rare variants. Preprint at https://www.biorxiv.org/content/early/2016/04/12/048181 (2016).

  78. 78.

    Zhong, H. & Prentice, R. L. Bias-reduced estimators and confidence intervals for odds ratios in genome-wide association studies. Biostatistics 9, 621–634 (2008).

  79. 79.

    Cheetham, S. W., Gruhl, F., Mattick, J. S. & Dinger, M. E. Long noncoding RNAs and the genetics of cancer. Br. J. Cancer 108, 2419–2425 (2013).

  80. 80.

    Popejoy, A. B. & Fullerton, S. M. Genomics is failing on diversity. Nature 538, 161–164 (2016).

  81. 81.

    Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).

  82. 82.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  83. 83.

    Jun, G., Wing, M. K., Abecasis, G. R. & Kang, H. M. An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data. Genome Res. 25, 918–925 (2015).

  84. 84.

    Browning, B. L. & Yu, Z. Simultaneous genotype calling and haplotype phasing improves genotype accuracy and reduces false-positive associations for genome-wide association studies. Am. J. Hum. Genet. 85, 847–861 (2009).

  85. 85.

    Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).

  86. 86.

    1000 Genomes Project Consortium et al. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

  87. 87.

    Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

  88. 88.

    Laurie, C. C. et al. Quality control and quality assurance in genotypic data for genome-wide association studies. Genet. Epidemiol. 34, 591–602 (2010).

  89. 89.

    Bycroft, C. et al. Genome-wide genetic data on ~500,000 UK Biobank participants. Preprint at https://www.biorxiv.org/content/early/2017/07/20/166298 (2017).

  90. 90.

    Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

  91. 91.

    Price, A. L. et al. Long-range LD can confound genome scans in admixed populations. Am. J. Hum. Genet. 83, 132–135 (2008).

  92. 92.

    Weale, M. E. Quality control for genome-wide association studies. Methods Mol. Biol. 628, 341–372 (2010).

  93. 93.

    1000 Genomes Project Consortium. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

  94. 94.

    Delaneau, O., Howie, B., Cox, A. J., Zagury, J.-F. & Marchini, J. Haplotype estimation using sequencing reads. Am. J. Hum. Genet. 93, 687–696 (2013).

  95. 95.

    Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).

  96. 96.

    Sun, J., Zheng, Y. & Hsu, L. A unified mixed-effects model for rare-variant association in sequencing studies. Genet. Epidemiol. 37, 334–344 (2013).

  97. 97.

    Moutsianas, L. et al. The power of gene-based rare variant methods to detect disease-associated variation and test hypotheses about complex disease. PLoS Genet. 11, e1005165 (2015).

  98. 98.

    Kang, H. M. et al. Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42, 348–354 (2010).

  99. 99.

    Cook, J. P., Mahajan, A. & Morris, A. P. Guidance for the utility of linear models in meta-analysis of genetic association studies of binary phenotypes. Eur. J. Hum. Genet. 25, 240–245 (2017).

  100. 100.

    Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

  101. 101.

    Yang, J. et al. Genomic inflation factors under polygenic inheritance. Eur. J. Hum. Genet. 19, 807–812 (2011).

  102. 102.

    Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

  103. 103.

    Michailidou, K. et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat. Genet. 45, 353–361 (2013).

  104. 104.

    Wellcome Trust Case Control Consortium. et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. 44, 1294–1301 (2012).

  105. 105.

    Wakefield, J. A Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am. J. Hum. Genet. 81, 208–227 (2007).

  106. 106.

    Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

  107. 107.

    Adzhubei, I., Jordan, D. M. & Sunyaev, S. R. Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. Genet. Chapter 7, Unit7.20 (2013).

  108. 108.

    Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).

  109. 109.

    Quang, D., Chen, Y. & Xie, X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics 31, 761–763 (2015).

  110. 110.

    Ionita-Laza, I., McCallum, K., Xu, B. & Buxbaum, J. D. A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat. Genet. 48, 214–220 (2016).

  111. 111.

    Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  112. 112.

    Corradin, O. et al. Combinatorial effects of multiple enhancer variants in linkage disequilibrium dictate levels of gene expression to confer susceptibility to common traits. Genome Res. 24, 1–13 (2014).

  113. 113.

    Pruitt, K. D. et al. The consensus coding sequence (CCDS) project: Identifying a common protein-coding gene set for the human and mouse genomes. Genome Res. 19, 1316–1323 (2009).

  114. 114.

    Harmston, N. et al. Topologically associating domains are ancient features that coincide with Metazoan clusters of extreme noncoding conservation. Nat. Commun. 8, 441 (2017).

  115. 115.

    Berlivet, S. et al. Clustering of tissue-specific sub-TADs accompanies the regulation of HoxA genes in developing limbs. PLoS Genet. 9, e1004018 (2013).

  116. 116.

    Hu, Z. & Tee, W.-W. Enhancers and chromatin structures: regulatory hubs in gene expression and diseases. Biosci. Rep. 37, BSR20160183 (2017).

  117. 117.

    GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

  118. 118.

    Ward, L. D. & Kellis, M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–D934 (2012).

  119. 119.

    Landt, S. G. et al. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22, 1813–1831 (2012).

  120. 120.

    Witte, J. S., Visscher, P. M. & Wray, N. R. The contribution of genetic variants to disease depends on the ruler. Nat. Rev. Genet. 15, 765–776 (2014).

  121. 121.

    Cox, A. et al. A common coding variant in CASP8 is associated with breast cancer risk. Nat. Genet. 39, 352–358 (2007).

  122. 122.

    Johns, L. E. & Houlston, R. S. A systematic review and meta-analysis of familial colorectal cancer risk. Am. J. Gastroenterol. 96, 2992–3003 (2001).

  123. 123.

    Hsu, L. et al. A model to determine colorectal cancer risk using common genetic susceptibility loci. Gastroenterology 148, 1330–1339.e14 (2015).

  124. 124.

    Jeon, J. et al. Determining risk of colorectal cancer and starting age of screening based on lifestyle, environmental, and genetic factors. Gastroenterology 154, 2152–2164.e19 (2018).

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Acknowledgements

A full list of acknowledgements appears in the Supplementary Note.

Author information

Author notes

  1. These authors contributed equally: J. R. Huyghe, S. A. Bien, T. A. Harrison.

  2. These authors jointly supervised this work: D. A. Nickerson, S. B. Gruber, L. Hsu, U. Peters

Affiliations

  1. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    • Jeroen R. Huyghe
    • , Stephanie A. Bien
    • , Tabitha A. Harrison
    • , Conghui Qu
    • , Barbara L. Banbury
    • , Christopher S. Carlson
    • , Charles M. Connolly
    • , Keith R. Curtis
    • , Jian Gong
    • , Emiko Kobayashi
    • , Charles Kooperberg
    • , Christopher I. Li
    • , Yi Lin
    • , Polly A. Newcomb
    • , Ross L. Prentice
    • , Lori C. Sakoda
    • , Yu-Ru Su
    • , Sushma S. Thomas
    • , Emily White
    • , John D. Potter
    • , Li Hsu
    •  & Ulrike Peters
  2. Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA

    • Hyun Min Kang
    • , Sai Chen
    •  & Goncalo R. Abecasis
  3. Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

    • Stephanie L. Schmit
  4. Department of Preventive Medicine, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    • David V. Conti
    • , Christopher K. Edlund
    • , W. James Gauderman
    • , Gregory E. Idos
    • , Marilena Melas
    • , Duncan C. Thomas
    • , David J. Van Den Berg
    • , Anna H. Wu
    •  & Stephen B. Gruber
  5. Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA

    • Jihyoun Jeon
  6. Biomedical Informatics Program, Stanford University, Stanford, CA, USA

    • Peyton Greenside
  7. Department of Computer Science, Stanford University, Stanford, CA, USA

    • Michael Wainberg
    •  & Anshul Kundaje
  8. Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA

    • Fredrick R. Schumacher
  9. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    • Joshua D. Smith
    •  & Deborah A. Nickerson
  10. Department of Biostatistics, University of Washington, Seattle, WA, USA

    • David M. Levine
    • , Sarah C. Nelson
    • , Wan-Ling Hsu
    • , Cecelia A. Laurie
    • , Tin L. Louie
    •  & Li Hsu
  11. Department of Genetics, Stanford University, Stanford, CA, USA

    • Nasa A. Sinnott-Armstrong
    • , Michael C. Bassik
    •  & Anshul Kundaje
  12. Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    • Demetrius Albanes
    • , Sonja I. Berndt
    • , Stephen J. Chanock
    • , Wen-Yi Huang
    •  & Stephanie J. Weinstein
  13. Cancer Prevention and Control Program, Catalan Institute of Oncology-IDIBELL, L’Hospitalet de Llobregat, Barcelona, Spain

    • M. Henar Alonso
    • , Gemma Ibañez-Sanz
    •  & Victor Moreno
  14. CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain

    • M. Henar Alonso
    • , Maria-Dolores Chirlaque
    • , Vicente Martín
    • , Miguel Rodríguez-Barranco
    •  & Victor Moreno
  15. Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain

    • M. Henar Alonso
    •  & Victor Moreno
  16. Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA

    • Kristin Anderson
  17. Gastroenterology Department, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), University of Barcelona, Barcelona, Spain

    • Coral Arnau-Collell
    • , Sergi Castellví-Bel
    •  & Lorena Moreno
  18. Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany

    • Volker Arndt
    • , Hermann Brenner
    • , Katja Butterbach
    • , Katarina Cuk
    • , Michael Hoffmeister
    •  & Korbinian Weigl
  19. Hellenic Health Foundation, Athens, Greece

    • Christina Bamia
    •  & Antonia Trichopoulou
  20. WHO Collaborating Center for Nutrition and Health, Unit of Nutritional Epidemiology and Nutrition in Public Health, Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece

    • Christina Bamia
    •  & Antonia Trichopoulou
  21. Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC, USA

    • John A. Baron
  22. Service de Génétique Médicale, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France

    • Stéphane Bézieau
    •  & Sébastien Küry
  23. Leeds Institute of Medical Research at St James’s, University of Leeds, Leeds, UK

    • D. Timothy Bishop
    •  & Faye Elliott
  24. Huntsman Cancer Institute and Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA

    • Juergen Boehm
    •  & Cornelia M. Ulrich
  25. Department of Epidemiology, German Institute of Human Nutrition (DIfE), Potsdam-Rehbrücke, Germany

    • Heiner Boeing
  26. Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany

    • Hermann Brenner
  27. German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany

    • Hermann Brenner
    •  & Korbinian Weigl
  28. Institute of Cancer Research, Department of Medicine I, Medical University of Vienna, Vienna, Austria

    • Stefanie Brezina
    • , Andrea Gsur
    •  & Philipp Hofer
  29. Department of Medicine I, University Hospital Dresden, Technische Universität Dresden (TU Dresden), Dresden, Germany

    • Stephan Buch
    •  & Jochen Hampe
  30. Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia

    • Daniel D. Buchanan
  31. University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia

    • Daniel D. Buchanan
  32. Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, Victoria, Australia

    • Daniel D. Buchanan
    •  & Aung Ko Win
  33. Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, USA

    • Andrea Burnett-Hartman
  34. Division of Research, Kaiser Permanente Medical Care Program, Oakland, CA, USA

    • Bette J. Caan
  35. Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA

    • Peter T. Campbell
    •  & Eric J. Jacobs
  36. Department of Epidemiology, University of Washington, Seattle, WA, USA

    • Christopher S. Carlson
    • , Polly A. Newcomb
    •  & Ulrike Peters
  37. Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    • Andrew T. Chan
    •  & Manish Gala
  38. Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    • Andrew T. Chan
  39. Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    • Andrew T. Chan
    • , Manish Gala
    •  & Amit D. Joshi
  40. Broad Institute of Harvard and MIT, Cambridge, MA, USA

    • Andrew T. Chan
    •  & Shuji Ogino
  41. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA

    • Andrew T. Chan
    • , David J. Hunter
    • , Amit D. Joshi
    •  & Shuji Ogino
  42. Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA

    • Andrew T. Chan
  43. Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany

    • Jenny Chang-Claude
    •  & Tilman Kühn
  44. Cancer Epidemiology Group, University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany

    • Jenny Chang-Claude
  45. Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia University, Murcia, Spain

    • Maria-Dolores Chirlaque
  46. Department of Hematology-Oncology, Chonnam National University Hospital, Hwasun, South Korea

    • Sang Hee Cho
  47. Department of Epidemiology and Biostatistics, Imperial College London, London, UK

    • Amanda J. Cross
  48. Department of Surgery and Cancer, Imperial College London, London, UK

    • Amanda J. Cross
  49. Department of Cancer Biology and Genetics and the Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA

    • Albert de la Chapelle
  50. Center for Inherited Disease Research (CIDR), Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA

    • Kimberly F. Doheny
    • , Roxann Ingersoll
    • , Hua Ling
    • , Elizabeth Pugh
    • , Jane Romm
    •  & Tameka Shelford
  51. Translational Genomics Research Institute - An Affiliate of City of Hope, Phoenix, AZ, USA

    • David Duggan
  52. Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    • Douglas F. Easton
    •  & Paul D. P. Pharoah
  53. Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK

    • Douglas F. Easton
    •  & Mitul Shah
  54. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands

    • Sjoerd G. Elias
    •  & N. Charlotte Onland-Moret
  55. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia

    • Dallas R. English
    • , Graham G. Giles
    • , John L. Hopper
    • , Mark A. Jenkins
    • , Roger L. Milne
    •  & Aung Ko Win
  56. Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia

    • Dallas R. English
    • , Liesel M. FitzGerald
    • , Graham G. Giles
    •  & Roger L. Milne
  57. Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands

    • Edith J. M. Feskens
    •  & Franzel J. B. van Duijnhoven
  58. Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    • Jane C. Figueiredo
  59. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    • Jane C. Figueiredo
  60. University of Michigan Comprehensive Cancer Center, Ann Arbor, MI, USA

    • Rocky Fischer
  61. Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia

    • Liesel M. FitzGerald
  62. International Agency for Research on Cancer, World Health Organization, Lyon, France

    • David Forman
  63. Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada

    • Steven Gallinger
  64. Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA

    • Elizabeth Gillanders
    •  & Daniela Seminara
  65. SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    • Phyllis J. Goodman
    •  & Catherine M. Tangen
  66. Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    • William M. Grady
  67. University of Hawaii Cancer Research Center, Honolulu, HI, USA

    • John S. Grove
    •  & Loic Le Marchand
  68. Nutrition and Metabolism Section, International Agency for Research on Cancer, World Health Organization, Lyon, France

    • Marc J. Gunter
    •  & Neil Murphy
  69. Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA

    • Robert W. Haile
  70. Division of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA

    • Heather Hampel
    •  & Rachel Pearlman
  71. Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden

    • Sophia Harlid
    • , Robin Myte
    •  & Bethany Van Guelpen
  72. Division of Epidemiology, Department of Population Health, New York University School of Medicine, New York, NY, USA

    • Richard B. Hayes
  73. Department of Epidemiology, School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, South Korea

    • John L. Hopper
  74. Ontario Institute for Cancer Research, Toronto, Ontario, Canada

    • Thomas J. Hudson
    • , Mathieu Lemire
    •  & Syed H. Zaidi
  75. Nuffield Department of Population Health, University of Oxford, Oxford, UK

    • David J. Hunter
  76. Gastroenterology Department, Bellvitge University Hospital, L’Hospitalet de Llobregat, Barcelona, Spain

    • Gemma Ibañez-Sanz
  77. Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute-IDIBELL, Hospitalet de Llobregat, Barcelona, Spain

    • Gemma Ibañez-Sanz
    •  & Victor Moreno
  78. Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, The Ohio State University, Columbus, OH, USA

    • Rebecca D. Jackson
  79. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA

    • Corinne E. Joshu
    • , Elizabeth A. Platz
    •  & Kala Visvanathan
  80. Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC, USA

    • Temitope O. Keku
  81. Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK

    • Timothy J. Key
  82. Department of Surgery, Chonnam National University Hwasun Hospital and Medical School, Hwasun, Korea

    • Hyeong Rok Kim
  83. Office of Public Health Studies, University of Hawaii Manoa, Honolulu, HI, USA

    • Laurence N. Kolonel
  84. Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, Korea

    • Sun-Seog Kweon
    •  & Min-Ho Shin
  85. Jeonnam Regional Cancer Center, Chonnam National University Hwasun Hospital, Hwasun, Korea

    • Sun-Seog Kweon
  86. Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

    • Susanna C. Larsson
    •  & Alicja Wolk
  87. Center for Statistical Genetics, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA

    • Suzanne M. Leal
  88. Department of Haematology-Oncology, National University Cancer Institute, Singapore, Singapore

    • Soo Chin Lee
  89. Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore

    • Soo Chin Lee
  90. The Clalit Health Services, Personalized Genomic Service, Carmel, Haifa, Israel

    • Flavio Lejbkowicz
  91. Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel

    • Flavio Lejbkowicz
    • , Mila Pinchev
    • , Gad Rennert
    •  & Hedy S. Rennert
  92. Clalit National Cancer Control Center, Haifa, Israel

    • Flavio Lejbkowicz
    • , Gad Rennert
    •  & Hedy S. Rennert
  93. Center for Community Health Integration and Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA

    • Li Li
  94. Institute of Epidemiology, PopGen Biobank, Christian-Albrechts-University Kiel, Kiel, Germany

    • Wolfgang Lieb
  95. Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden

    • Annika Lindblom
  96. Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden

    • Annika Lindblom
  97. Department of Health Science Research, Mayo Clinic, Scottsdale, AZ, USA

    • Noralane M. Lindor
  98. Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland

    • Satu Männistö
  99. Departments of Medicine and Genetics, Case Comprehensive Cancer Center, Case Western Reserve University, and University Hospitals of Cleveland, Cleveland, OH, USA

    • Sanford D. Markowitz
  100. Biomedicine Institute (IBIOMED), University of León, León, Spain

    • Vicente Martín
  101. Cancer Risk Factors and Life-Style Epidemiology Unit, Institute of Cancer Research, Prevention and Clinical Network - ISPRO, Florence, Italy

    • Giovanna Masala
  102. USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA

    • Caroline E. McNeil
  103. Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic

    • Alessio Naccarati
    • , Pavel Vodicka
    • , Ludmila Vodickova
    •  & Veronika Vymetalkova
  104. Italian Institute for Genomic Medicine (IIGM), Turin, Italy

    • Alessio Naccarati
    •  & Barbara Pardini
  105. Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    • Kenneth Offit
  106. Department of Medicine, Weill Cornell Medical College, New York, NY, USA

    • Kenneth Offit
  107. Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    • Shuji Ogino
  108. Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, MA, USA

    • Shuji Ogino
  109. Department of Medical Sciences, University of Turin, Turin, Italy

    • Barbara Pardini
  110. The Clinical Epidemiology Unit, Memorial University Medical School, Newfoundland, Canada

    • Patrick S. Parfrey
  111. Laboratoire de Mathématiques Appliquées MAP5 (UMR CNRS 8145), Université Paris Descartes, Paris, France

    • Vittorio Perduca
  112. CESP (Inserm U1018), Facultés de Medicine Université Paris-Sud, UVSQ, Université Paris-Saclay, Gustave Roussy, Villejuif, France

    • Vittorio Perduca
  113. Division of Epidemiology, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, TN, USA

    • Leon Raskin
  114. Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel

    • Gad Rennert
    •  & Hedy S. Rennert
  115. School of Public Health, Imperial College London, London, UK

    • Elio Riboli
  116. Escuela Andaluza de Salud Pública. Instituto de Investigación Biosanitaria ibs.GRANADA, Hospitales Universitarios de Granada, Universidad de Granada, Granada, Spain

    • Miguel Rodríguez-Barranco
  117. Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA

    • Lori C. Sakoda
  118. Department of General and Thoracic Surgery, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany

    • Clemens Schafmayer
  119. Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA

    • Robert E. Schoen
  120. Oncology Unit, Hillel Yaffe Medical Center, Hadera, Israel

    • Katerina Shulman
  121. Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy

    • Sabina Sieri
  122. Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA

    • Martha L. Slattery
  123. Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Melbourne, Australia

    • Melissa C. Southey
  124. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    • Zsofia K. Stadler
    •  & Joseph Vijai
  125. Saarland Cancer Registry, Saarbrücken, Germany

    • Christa Stegmaier
  126. Division of Laboratory Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA

    • Stephen N. Thibodeau
  127. Departments of Cancer Biology and Genetics and Internal Medicine, Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA

    • Amanda E. Toland
  128. National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands

    • Henk van Kranen
  129. Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic

    • Pavel Vodicka
    • , Ludmila Vodickova
    •  & Veronika Vymetalkova
  130. Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic

    • Pavel Vodicka
    • , Ludmila Vodickova
    •  & Veronika Vymetalkova
  131. Medical Faculty, University of Heidelberg, Heidelberg, Germany

    • Korbinian Weigl
  132. School of Medicine, University of Dundee, Dundee, Scotland, UK

    • C. Roland Wolf
  133. Department of Surgical Sciences, Uppsala University, Uppsala, Sweden

    • Alicja Wolk
  134. Memorial University of Newfoundland, Discipline of Genetics, St. John’s, Newfoundland, Canada

    • Michael O. Woods
  135. Division of Hematology, University of Toronto, Toronto, Ontario, Canada

    • Brent W. Zanke
  136. Genomics Shared Resource, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    • Qing Zhang
  137. Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, TN, USA

    • Wei Zheng
  138. Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Case Comprehensive Cancer Center, Cleveland, OH, USA

    • Peter C. Scacheri
  139. Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA

    • Graham Casey

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Contributions

J.R.H., S.A.B., T.A.H., H.M.K., D.V.C., M.W., F.R.S., J.D.S., D.A., M.H.A., K.A., C.A.-C., V.A., C.B., J.A.B., S.I.B., S.B., D.T.B., J.B., H.Boeing, H.Brenner, S.Brezina, S.Buch, D.D.B., A.B.-H., K.B., B.J.C., P.T.C., S.C.-B., A.T.C., J.C.-C., S.J.C., M.-D.C., S.H.C., A.J.C., K.C., A.d.l.C., D.F.E., S.G.E., F.E., D.R.E., E.J.M.F., J.C.F., D.F., S.G., G.G.G., E.G., P.J.G., J.S.G., A.G., M.J.G., R.W.H., J.H., H.H., R.B.H., P.H., M.H., J.L.H., W.-Y.H., T.J.H., D.J.H., R.J., E.J.J., M.A.J., T.O.K., T.J.K., H.R.K., L.N.K., C.K., S.K., S.-S.K., L.L.M., S.C.L., C.I.L., L.L., A.L., N.M.L., S.M., S.D.M., V.M., G.M., M.M., R.L.M., L.M., R.M., A.N., P.A.N., K.O., N.C.O.-M., B.P., P.S.P., R.P., V.P., P.D.P.P., E.A.P., R.L.P., G.R., H.S.R., E.R., M.R.-B., C.S., R.E.S., D.S., M.-H.S., S.S., M.L.S., C.M.T., S.N.T., A.T., C.M.U., F.J.B.v.D., B.V.G., H.v.K., J.V., K.V., P.V., L.V., V.V., E.W., C.R.W., A.W., M.O.W., A.H.W., B.W.Z., W.Z., P.C.S., J.D.P., M.C.B., G.C., V.M., G.R.A., D.A.N., S.B.G., L.H. and U.P. conceived and designed the experiments. T.A.H., M.W., J.D.S., K.F.D., D.D., R.I., E.K., H.L., C.E.M., E.P., J.R., T.S., S.S.T., D.J.V.D.B., M.C.B. and D.A.N. performed the experiments. J.R.H., H.M.K., S.C., S.L.S., D.V.C., C.Q., J.J., C.K.E., P.G., F.R.S., D.M.L., S.C.N., N.A.S.-A., C.A.L., M.L., T.L.L., Y.-R.S., A.K., G.R.A. and L.H. performed statistical analysis. J.R.H., S.A.B., T.A.H., H.M.K., S.C., S.L.S., D.V.C., C.Q., J.J., C.K.E., P.G., M.W., F.R.S., D.M.L., S.C.N., N.A.S.-A., B.L.B., C.S.C., C.M.C., K.R.C., J.G., W.-L.H., C.A.L., S.M.L., M.L., Y.L., T.L.L., M.S., Y.-R.S., A.K., G.R.A., L.H. and U.P. analyzed the data. H.M.K., C.K.E., D.A., M.H.A., K.A., C.A.-C., V.A., C.B., J.A.B., S.I.B., S.B., D.T.B., J.B., H.Boeing, H.Brenner, S.Brezina, S.Buch, D.D.B., A.B.-H., K.B., B.J.C., P.T.C., S.C.-B., A.T.C., J.C.-C., S.J.C., M.-D.C., S.H.C., A.J.C., K.C., A.d.l.C., D.F.E., S.G.E., F.E., D.R.E., E.J.M.F., J.C.F., R.F., L.M.F., D.F., M.G., S.G., W.J.G., G.G.G., P.J.G., W.M.G., J.S.G., A.G., M.J.G., R.W.H., J.H., H.H., S.H., R.B.H., P.H., M.H., J.L.H., W.-Y.H., T.J.H., D.J.H., G.I.-S., G.E.I., R.J., E.J.J., M.A.J., A.D.J., C.E.J., T.O.K., T.J.K., H.R.K., L.N.K., C.K., T.K., S.K., S.-S.K., S.C.L., L.L.M., S.C.L., F.L., C.I.L., L.L., W.L., A.L., N.M.L., S.M., S.D.M., V.M., G.M., M.M., R.L.M., L.M., N.M., R.M., A.N., P.A.N., K.O., S.O, N.C.O.-M., B.P., P.S.P., R.P., V.P., P.D.P.P., M.P., E.A.P., R.L.P., L.R., G.R., H.S.R., E.R., M.R.-B., L.C.S., C.S., R.E.S., M.S., M.-H.S., K.S., S.S., M.L.S., M.C.S., Z.K.S., C.S., C.M.T., S.N.T., D.C.T., A.E.T., A.T., C.M.U., F.J.B.v.D., B.V.G., H.v.K., J.V., K.V., P.V., L.V., V.V., K.W., S.J.W., E.W., A.K.W., C.R.W., A.W., M.O.W., A.H.W., S.H.Z., B.W.Z., Q.Z., W.Z., P.C.S., J.D.P., M.C.B., A.K., G.C., V.M., G.R.A., S.B.G. and U.P. contributed reagents, materials and analysis tools. J.R.H., S.A.B., T.A.H., J.J., L.H. and U.P. wrote the paper.

Competing interests

G.R.A. has received compensation from 23andMe and Helix. He is currently an employee of Regeneron Pharmaceuticals. H.H. performs collaborative research with Ambry Genetics, InVitae Genetics, and Myriad Genetic Laboratories, is on the scientific advisory board for InVitae Genetics and Genome Medical, and has stock in Genome Medical. R.P. has participated in collaborative funded research with Myriad Genetics Laboratories and Invitae Genetics but has no financial competitive interest.

Corresponding author

Correspondence to Ulrike Peters.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–8 and Supplementary Note

  2. Reporting Summary

  3. Supplementary Table 1

    Characteristics of studies and study participants contributing to the whole-genome sequencing analysis and GWAS meta-analysis

  4. Supplementary Table 2

    Association results broken down by sample set together with imputation qualities, and heterogeneity statistics for new loci reported in Table 1

  5. Supplementary Table 3

    Colorectal cancer risk signals previously associated at genome-wide significance

  6. Supplementary Table 4

    Conditional association results broken down by sample set together with imputation qualities, and heterogeneity statistics for new conditionally independent association signals reported in Table 2

  7. Supplementary Table 5

    Known and newly identified CRC risk loci with multiple conditionally independent association signals that reach a significance threshold of P < 1 × 10–5 in the combined meta-analysis of up to 125,478 individuals

  8. Supplementary Table 6

    Reported associations of colorectal cancer risk variants with (non-colorectal cancer) diseases and traits in the NHGRI-EBI GWAS catalog

  9. Supplementary Table 7

    Summary of 99% credible sets for the 40 new association signals for colorectal cancer risk

  10. Supplementary Table 8

    CRC relevant annotations, bioinformatic follow-up of newly identified loci, and bioinformatic follow-up of secondary signals

  11. Supplementary Table 9

    Enrichment of CRC risk associations in 1,005 genomic annotations from the ENCODE, Roadmap Epigenomics and GENCODE projects at the 1 × 10–5 and 1 × 10–8 significance thresholds

  12. Supplementary Table 10

    MAGENTA pathway enrichment results

  13. Supplementary Table 11

    Risk allele frequencies (RAFs) across populations for the 95 variants used in the polygenic risk score analyses

  14. Supplementary Table 12

    Covariates included in the association analysis

  15. Supplementary Table 13

    CRC relevant regulatory genomic datasets

  16. Supplementary Table 14

    Results from ATAC-QC

  17. Supplementary Table 15

    Colorectal cancer risk variants and effect size estimates used in the familial risk explained and genetic risk score analyses

About this article

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DOI

https://doi.org/10.1038/s41588-018-0286-6