Skip to main content
SpringerLink
Log in

Recent Advances in Genetic Epidemiology of Colorectal Cancer in Chinese Population

  • Chapter
  • First Online:
Progress in China Epidemiology

  • 419 Accesses

Abstract

Colorectal cancer is the most common malignant tumor in digestive system, ranks third in the incidence of cancers, but second in terms of cancer mortality all around the world. With the development of socioeconomic, the burden of colorectal cancer incidence and mortality is also rapidly growing in China. Moreover, the average age of patients developing colorectal cancer continues to decline, making it an urgent public health problem to be solved. Primary prevention remains the key strategy to reduce the increasing global burden of colorectal cancer. Tumorigenesis is a multistage and multistep process involving multiple factors, and the occurrence of colorectal cancer is closely related to gene–environment interactions. Environmental factors, such as diet patterns, physical activity, smoking, alcohol intake, overweight, occupational hazards, etc., could initiate the development of colorectal cancer. Additionally, genetic factors determine the individual susceptibility to cancer. Genome-wide association studies (GWASs) have identified about one hundred single nucleotide polymorphisms (SNPs) associated with colorectal cancer. Post-GWAS strategies integrate molecular, metabolic, computational together with statistical approaches to explain the underlying mechanism of association. These identified genetic susceptibility variants are of great importance in risk prediction, precision medicine, and drug development. Despite genetic epidemiology obtained notable successes, a substantial portion of colorectal cancer heritability remains unexplained. Besides, these statistical associations lack thorough investigation to clarify their mechanisms. Transforming research results to application in disease early screening, clinical diagnosis, and individualized treatment remains primary issue in future. Here, in addition to introducing the epidemiology and risk factors of colorectal cancer, we also summarized study strategies, potential benefits and challenges of genetic epidemiology to systematically characterize genetic epidemiology of colorectal cancer in Chinese population.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Rimbert H, Darrier B, Navarro J, et al. High throughput SNP discovery and genotyping in hexaploid wheat. PLoS One. 2018;13:e0186329.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Arnold M, Sierra MS, Laversanne M, et al. Global patterns and trends in colorectal cancer incidence and mortality. Gut. 2017;66:683–91.

    Article  PubMed  Google Scholar 

  3. Fidler MM, Soerjomataram I, Bray F. A global view on cancer incidence and national levels of the human development index. Int J Cancer. 2016;139:2436–46.

    Article  CAS  PubMed  Google Scholar 

  4. Zheng RS, Sun KX, Zhang SW, et al. Report of cancer epidemiology in China, 2015. Zhonghua Zhong Liu Za Zhi. 2019;41:19–28.

    CAS  PubMed  Google Scholar 

  5. Sun KXZRS, Zhang SW, et al. Report of cancer incidence and mortality in different areas of China. China Cancer. 2015;28:1–11.

    Google Scholar 

  6. Keum N, Giovannucci E. Global burden of colorectal cancer: emerging trends, risk factors and prevention strategies. Nat Rev Gastroenterol Hepatol. 2019;16:713–32.

    Article  PubMed  Google Scholar 

  7. Brenner H, Kloor M, Pox CP. Colorectal cancer. Lancet. 2014;383:1490–502.

    Article  PubMed  Google Scholar 

  8. Bardou M, Barkun AN, Martel M. Obesity and colorectal cancer. Gut. 2013;62:933–47.

    Article  CAS  PubMed  Google Scholar 

  9. Renehan AG, Tyson M, Egger M, et al. Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet. 2008;371:569–78.

    Article  PubMed  Google Scholar 

  10. Song M, Chan AT, Sun J. Influence of the gut microbiome, diet, and environment on risk of colorectal cancer. Gastroenterology. 2020;158:322–40.

    Article  CAS  PubMed  Google Scholar 

  11. Kerr J, Anderson C, Lippman SM. Physical activity, sedentary behaviour, diet, and cancer: an update and emerging new evidence. Lancet Oncol. 2017;18:e457–e71.

    Article  PubMed  Google Scholar 

  12. Hibler E. Epigenetics and colorectal neoplasia: the evidence for physical activity and sedentary behavior. Curr Colorectal Cancer Rep. 2015;11:388–96.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Robinson MM, Dasari S, Konopka AR, et al. Enhanced protein translation underlies improved metabolic and physical adaptations to different exercise training modes in young and old humans. Cell Metab. 2017;25:581–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Vieira AR, Abar L, Chan DSM, et al. Foods and beverages and colorectal cancer risk: a systematic review and meta-analysis of cohort studies, an update of the evidence of the WCRF-AICR continuous update project. Ann Oncol. 2017;28:1788–802.

    Article  CAS  PubMed  Google Scholar 

  15. Song M, Garrett WS, Chan AT. Nutrients, foods, and colorectal cancer prevention. Gastroenterology. 2015;148:1244–60.e16.

    Article  CAS  PubMed  Google Scholar 

  16. Aune D, Lau R, Chan DS, et al. Nonlinear reduction in risk for colorectal cancer by fruit and vegetable intake based on meta-analysis of prospective studies. Gastroenterology. 2011;141:106–18.

    Article  PubMed  Google Scholar 

  17. Wiseman M. The second World Cancer Research Fund/American Institute for Cancer Research expert report. Food, nutrition, physical activity, and the prevention of cancer: a global perspective. Proc Nutr Soc. 2008;67:253–6.

    Article  PubMed  Google Scholar 

  18. Pollak M. The insulin and insulin-like growth factor receptor family in neoplasia: an update. Nat Rev Cancer. 2012;12:159–69.

    Article  CAS  PubMed  Google Scholar 

  19. Aune D, Lau R, Chan DSM, et al. Dairy products and colorectal cancer risk: a systematic review and meta-analysis of cohort studies. Ann Oncol. 2012;23:37–45.

    Article  CAS  PubMed  Google Scholar 

  20. Zhang D, Dai C, Zhou L, et al. Meta-analysis of the association between nut consumption and the risks of cancer incidence and cancer-specific mortality. Aging (Albany NY). 2020;12:10772–94.

    Article  PubMed  Google Scholar 

  21. Fadelu T, Zhang S, Niedzwiecki D, et al. Nut consumption and survival in patients with stage III colon cancer: results from CALGB 89803 (Alliance). J Clin Oncol. 2018;36:1112–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Feldman D, Krishnan AV, Swami S, et al. The role of vitamin D in reducing cancer risk and progression. Nat Rev Cancer. 2014;14:342–57.

    Article  CAS  PubMed  Google Scholar 

  23. Botteri E, Iodice S, Bagnardi V, et al. Smoking and colorectal cancer: a meta-analysis. JAMA. 2008;300:2765–78.

    Article  CAS  PubMed  Google Scholar 

  24. Lei Z, Xiaomin Y, He H, et al. Nicotine downregulates microRNA-200c to promote metastasis and the epithelial-mesenchymal transition in human colorectal cancer cells. J Cell Physiol. 2019;234:1369–79.

    Article  CAS  PubMed  Google Scholar 

  25. Park SY, Wilkens LR, Setiawan VW, et al. Alcohol intake and colorectal cancer risk in the multiethnic cohort study. Am J Epidemiol. 2019;188:67–76.

    Article  PubMed  Google Scholar 

  26. Yang T, Qiao Y, Xiang S, et al. Work stress and the risk of cancer: a meta-analysis of observational studies. Int J Cancer. 2019;144:2390–400.

    Article  CAS  PubMed  Google Scholar 

  27. Papantoniou K, Devore EE, Massa J, et al. Rotating night shift work and colorectal cancer risk in the nurses’ health studies. Int J Cancer. 2018;143:2709–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kwak K, Paek D, Zoh KE. Exposure to asbestos and the risk of colorectal cancer mortality: a systematic review and meta-analysis. Occup Environ Med. 2019;76:861–71.

    Article  PubMed  Google Scholar 

  29. Lichtenstein P, Holm NV, Verkasalo PK, 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. 2000;343:78–85.

    Article  CAS  PubMed  Google Scholar 

  30. Jiao S, Peters U, Berndt S, et al. Estimating the heritability of colorectal cancer. Hum Mol Genet. 2014;23:3898–905.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Dekker E, Tanis PJ, Vleugels JLA, et al. Colorectal cancer. Lancet. 2019;394:1467–80.

    Article  PubMed  Google Scholar 

  32. Ellis L, Canchola AJ, Spiegel D, et al. Racial and ethnic disparities in cancer survival: the contribution of tumor, sociodemographic, institutional, and neighborhood characteristics. J Clin Oncol. 2018;36:25–33.

    Article  CAS  PubMed  Google Scholar 

  33. Nadeem MS, Kumar V, Al-Abbasi FA, et al. Risk of colorectal cancer in inflammatory bowel diseases. Semin Cancer Biol. 2020;64:51–60.

    Article  PubMed  Google Scholar 

  34. Nan H, Hutter CM, Lin Y, et al. Association of aspirin and NSAID use with risk of colorectal cancer according to genetic variants. JAMA. 2015;313:1133–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Shastry BS. SNPs: impact on gene function and phenotype. Methods Mol Biol. 2009;578:3–22.

    Article  CAS  PubMed  Google Scholar 

  36. Zhu M, Zhao S. Candidate gene identification approach: progress and challenges. Int J Biol Sci. 2007;3:420–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Te Paske I, Ligtenberg MJL, Hoogerbrugge N, et al. Candidate gene discovery in hereditary colorectal cancer and polyposis syndromes-considerations for future studies. Int J Mol Sci. 2020;21:8757.

    Article  Google Scholar 

  38. Miao XP, Yang S, Tan W, et al. Association between genetic variations in methylenetetrahydrofolate reductase and risk of colorectal cancer in a Chinese population. Zhonghua Yu Fang Yi Xue Za Zhi. 2005;39:409–11.

    CAS  PubMed  Google Scholar 

  39. Yang S, Miao XP, Zhang XM, et al. Genetic polymorphisms of apoptosis-associated genes FAS and FASL and risk of colorectal cancer. Zhonghua Yi Xue Za Zhi. 2005;85:2132–5.

    CAS  PubMed  Google Scholar 

  40. Gong J, Shen N, Zhang HM, et al. A genetic variant in microRNA target site of TGF-beta signaling pathway increases the risk of colorectal cancer in a Chinese population. Tumour Biol. 2014;35:4301–6.

    Article  CAS  PubMed  Google Scholar 

  41. Manolio TA, Collins FS, Cox NJ, et al. Finding the missing heritability of complex diseases. Nature. 2009;461:747–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Sud A, Kinnersley B, Houlston RS. Genome-wide association studies of cancer: current insights and future perspectives. Nat Rev Cancer. 2017;17:692–704.

    Article  CAS  PubMed  Google Scholar 

  43. Stadler ZK, Gallagher DJ, Thom P, et al. Genome-wide association studies of cancer: principles and potential utility. Oncology (Williston Park). 2010;24:629–37.

    PubMed  Google Scholar 

  44. Huyghe JR, Bien SA, Harrison TA, et al. Discovery of common and rare genetic risk variants for colorectal cancer. Nat Genet. 2019;51:76–87.

    Article  CAS  PubMed  Google Scholar 

  45. Ho JW, Choi SC, Lee YF, et al. Replication study of SNP associations for colorectal cancer in Hong Kong Chinese. Br J Cancer. 2011;104:369–75.

    Article  CAS  PubMed  Google Scholar 

  46. Xiong F, Wu C, Bi X, et al. Risk of genome-wide association study-identified genetic variants for colorectal cancer in a Chinese population. Cancer Epidemiol Biomarkers Prev. 2010;19:1855–61.

    Article  CAS  PubMed  Google Scholar 

  47. Zeng C, Matsuda K, Jia WH, et al. Identification of susceptibility loci and genes for colorectal cancer risk. Gastroenterology. 2016;150:1633–45.

    Article  CAS  PubMed  Google Scholar 

  48. Wang M, Gu D, Du M, et al. Common genetic variation in ETV6 is associated with colorectal cancer susceptibility. Nat Commun. 2016;7:11478.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Tam V, Patel N, Turcotte M, et al. Benefits and limitations of genome-wide association studies. Nat Rev Genet. 2019;20:467–84.

    Article  CAS  PubMed  Google Scholar 

  50. Hirschhorn JN, Lohmueller K, Byrne E, et al. A comprehensive review of genetic association studies. Genet Med. 2002;4:45–61.

    Article  CAS  PubMed  Google Scholar 

  51. Edwards SL, Beesley J, French JD, et al. Beyond GWASs: illuminating the dark road from association to function. Am J Hum Genet. 2013;93:779–97.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Spain SL, Barrett JC. Strategies for fine-mapping complex traits. Hum Mol Genet. 2015;24:R111–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Altshuler DM, Gibbs RA, Peltonen L, et al. Integrating common and rare genetic variation in diverse human populations. Nature. 2010;467:52–8.

    Article  CAS  PubMed  Google Scholar 

  54. Abecasis GR, Auton A, Brooks LD, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491:56–65.

    Article  PubMed  Google Scholar 

  55. Ayers KL, Cordell HJ. SNP selection in genome-wide and candidate gene studies via penalized logistic regression. Genet Epidemiol. 2010;34:879–91.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Breheny P, Huang J. Penalized methods for bi-level variable selection. Stat Interface. 2009;2:369–80.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Wang Q, Chen R, Cheng F, et al. A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data. Nat Neurosci. 2019;22:691–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Vandiedonck C. Genetic association of molecular traits: a help to identify causative variants in complex diseases. Clin Genet. 2018;93:520–32.

    Article  CAS  PubMed  Google Scholar 

  59. Westra HJ, Peters MJ, Esko T, et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet. 2013;45:1238–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Zhu Z, Zhang F, Hu H, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48:481–7.

    Article  CAS  PubMed  Google Scholar 

  61. Nica AC, Montgomery SB, Dimas AS, et al. Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations. PLoS Genet. 2010;6:e1000895.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Zou D, Lou J, Ke J, et al. Integrative expression quantitative trait locus-based analysis of colorectal cancer identified a functional polymorphism regulating SLC22A5 expression. Eur J Cancer. 2018;93:1–9.

    Article  CAS  PubMed  Google Scholar 

  63. Tian J, Lou J, Cai Y, et al. Risk SNP-mediated enhancer-promoter interaction drives colorectal cancer through both FADS2 and AP002754.2. Cancer Res. 2020;80:1804–18.

    Article  CAS  PubMed  Google Scholar 

  64. Gong J, Mei S, Liu C, et al. PancanQTL: systematic identification of cis-eQTLs and trans-eQTLs in 33 cancer types. Nucleic Acids Res. 2018;46:D971–d6.

    Article  CAS  PubMed  Google Scholar 

  65. Li J, Xue Y, Amin MT, et al. ncRNA-eQTL: a database to systematically evaluate the effects of SNPs on non-coding RNA expression across cancer types. Nucleic Acids Res. 2020;48:D956–D63.

    Article  CAS  PubMed  Google Scholar 

  66. Tian J, Wang Z, Mei S, et al. CancerSplicingQTL: a database for genome-wide identification of splicing QTLs in human cancer. Nucleic Acids Res. 2019;47:D909–D16.

    Article  CAS  PubMed  Google Scholar 

  67. Yang Y, Zhang Q, Miao YR, et al. SNP2APA: a database for evaluating effects of genetic variants on alternative polyadenylation in human cancers. Nucleic Acids Res. 2020;48:D226–d32.

    Article  CAS  PubMed  Google Scholar 

  68. Gong J, Wan H, Mei S, et al. Pancan-meQTL: a database to systematically evaluate the effects of genetic variants on methylation in human cancer. Nucleic Acids Res. 2019;47:D1066–D72.

    Article  CAS  PubMed  Google Scholar 

  69. Tian J, Cai Y, Li Y, et al. CancerImmunityQTL: a database to systematically evaluate the impact of genetic variants on immune infiltration in human cancer. Nucleic Acids Res. 2021;49:D1065–d73.

    Article  CAS  PubMed  Google Scholar 

  70. Pai AA, Cain CE, Mizrahi-Man O, et al. The contribution of RNA decay quantitative trait loci to inter-individual variation in steady-state gene expression levels. PLoS Genet. 2012;8:e1003000.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Mancuso N, Shi H, Goddard P, et al. Integrating gene expression with summary association statistics to identify genes associated with 30 complex traits. Am J Hum Genet. 2017;100:473–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Gusev A, Mancuso N, Won H, et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat Genet. 2018;50:538–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Wainberg M, Sinnott-Armstrong N, Mancuso N, et al. Opportunities and challenges for transcriptome-wide association studies. Nat Genet. 2019;51:592–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Guo X, Lin W, Wen W, et al. Identifying novel susceptibility genes for colorectal cancer risk from a transcriptome-wide association study of 125,478 subjects. Gastroenterology. 2020.

    Google Scholar 

  75. Maurano MT, Humbert R, Rynes E, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science. 2012;337:1190–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Broderick P, Carvajal-Carmona L, Pittman AM, et al. A genome-wide association study shows that common alleles of SMAD7 influence colorectal cancer risk. Nat Genet. 2007;39:1315–7.

    Article  CAS  PubMed  Google Scholar 

  77. Jia WH, Zhang B, Matsuo K, et al. Genome-wide association analyses in East Asians identify new susceptibility loci for colorectal cancer. Nat Genet. 2013;45:191–6.

    Article  CAS  PubMed  Google Scholar 

  78. Lu Y. et al. Large-scale genome-wide association study of east asians identifies loci associated with risk for colorectal cancer. Gastroenterology. 2019;156:1455–66. https://doi.org/10.1053/j.gastro.2018.11.066.

  79. Tehranchi A, Hie B, Dacre M, et al. Fine-mapping cis-regulatory variants in diverse human populations. Elife. 2019;8:e39595.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Gao L, Uzun Y, Gao P, et al. Identifying noncoding risk variants using disease-relevant gene regulatory networks. Nat Commun. 2018;9:702.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Schaid DJ, Chen W, Larson NB. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat Rev Genet. 2018;19:491–504.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Huang D, Yi X, Zhang S, et al. GWAS4D: multidimensional analysis of context-specific regulatory variant for human complex diseases and traits. Nucleic Acids Res. 2018;46:W114–W20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Boyle AP, Hong EL, Hariharan M, et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012;22:1790–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Hu H, Miao YR, Jia LH, et al. AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors. Nucleic Acids Res. 2019;47:D33–D8.

    Article  CAS  PubMed  Google Scholar 

  85. Mathelier A, Zhao X, Zhang AW, et al. JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles. Nucleic Acids Res. 2014;42:D142–7.

    Article  CAS  PubMed  Google Scholar 

  86. Kel AE, Gossling E, Reuter I, et al. MATCH: a tool for searching transcription factor binding sites in DNA sequences. Nucleic Acids Res. 2003;31:3576–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Lou J, Gong J, Ke J, et al. A functional polymorphism located at transcription factor binding sites, rs6695837 near LAMC1 gene, confers risk of colorectal cancer in Chinese populations. Carcinogenesis. 2017;38:177–83.

    CAS  PubMed  Google Scholar 

  88. Hnisz D, Abraham BJ, Lee TI, et al. Super-enhancers in the control of cell identity and disease. Cell. 2013;155:934–47.

    Article  CAS  PubMed  Google Scholar 

  89. Ke J, Tian J, Mei S, et al. Genetic predisposition to colon and rectal adenocarcinoma is mediated by a super-enhancer polymorphism coactivating CD9 and PLEKHG6. Cancer Epidemiol Biomarkers Prev. 2020;29:850–9.

    Article  PubMed  Google Scholar 

  90. Rinn JL, Chang HY. Genome regulation by long noncoding RNAs. Annu Rev Biochem. 2012;81:145–66.

    Article  CAS  PubMed  Google Scholar 

  91. Geisler S, Coller J. RNA in unexpected places: long non-coding RNA functions in diverse cellular contexts. Nat Rev Mol Cell Biol. 2013;14:699–712.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Sun QL, Zhao CP, Wang TY, et al. Expression profile analysis of long non-coding RNA associated with vincristine resistance in colon cancer cells by next-generation sequencing. Gene. 2015;572:79–86.

    Article  CAS  PubMed  Google Scholar 

  93. Yang X, Song JH, Cheng Y, et al. Long non-coding RNA HNF1A-AS1 regulates proliferation and migration in oesophageal adenocarcinoma cells. Gut. 2014;63:881–90.

    Article  CAS  PubMed  Google Scholar 

  94. Gong J, Tian J, Lou J, et al. A functional polymorphism in lnc-LAMC2–1:1 confers risk of colorectal cancer by affecting miRNA binding. Carcinogenesis. 2016;37:443–51.

    Article  CAS  PubMed  Google Scholar 

  95. Gong J, Liu W, Zhang J, et al. lncRNASNP: a database of SNPs in lncRNAs and their potential functions in human and mouse. Nucleic Acids Res. 2015;43:D181–6.

    Article  CAS  PubMed  Google Scholar 

  96. Kircher M, Witten DM, Jain P, et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014;46:310–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Ioannidis NM, Rothstein JH, Pejaver V, et al. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am J Hum Genet. 2016;99:877–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Yang Y, Peng X, Ying P, et al. AWESOME: a database of SNPs that affect protein post-translational modifications. Nucleic Acids Res. 2019;47:D874–D80.

    Article  CAS  PubMed  Google Scholar 

  99. Cirulli ET, Goldstein DB. Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat Rev Genet. 2010;11:415–25.

    Article  CAS  PubMed  Google Scholar 

  100. Li J, Chang J, Tian J, et al. A rare variant P507L in TPP1 interrupts TPP1-TIN2 interaction, influences telomere length, and confers colorectal cancer risk in Chinese population. Cancer Epidemiol Biomarkers Prev. 2018;27:1029–35.

    Article  CAS  PubMed  Google Scholar 

  101. Chang J, Tian J, Yang Y, et al. A rare missense variant in TCF7L2 associates with colorectal cancer risk by interacting with a GWAS-identified regulatory variant in the MYC enhancer. Cancer Res. 2018;78:5164–72.

    Article  CAS  PubMed  Google Scholar 

  102. Farashi S, Kryza T, Clements J, et al. Post-GWAS in prostate cancer: from genetic association to biological contribution. Nat Rev Cancer. 2019;19:46–59.

    Article  CAS  PubMed  Google Scholar 

  103. Chen M, Wei L, Law CT, et al. RNA N6-methyladenosine methyltransferase-like 3 promotes liver cancer progression through YTHDF2-dependent posttranscriptional silencing of SOCS2. Hepatology. 2018;67:2254–70.

    Article  CAS  PubMed  Google Scholar 

  104. Meyer KD, Saletore Y, Zumbo P, et al. Comprehensive analysis of mRNA methylation reveals enrichment in 3’ UTRs and near stop codons. Cell. 2012;149:1635–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Wang X, Zhao BS, Roundtree IA, et al. N(6)-methyladenosine modulates messenger RNA translation efficiency. Cell. 2015;161:1388–99.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Zhang S, Zhao BS, Zhou A, et al. m(6)A demethylase ALKBH5 maintains tumorigenicity of glioblastoma stem-like cells by sustaining FOXM1 expression and cell proliferation program. Cancer Cell. 2017;31(591–606):e6.

    Article  Google Scholar 

  107. Tian J, Ying P, Ke J, et al. ANKLE1 N(6)-methyladenosine-related variant is associated with colorectal cancer risk by maintaining the genomic stability. Int J Cancer. 2020;146:3281–93.

    Article  CAS  PubMed  Google Scholar 

  108. Mercer TR, Edwards SL, Clark MB, et al. DNase I-hypersensitive exons colocalize with promoters and distal regulatory elements. Nat Genet. 2013;45:852–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Jia L, Landan G, Pomerantz M, et al. Functional enhancers at the gene-poor 8q24 cancer-linked locus. PLoS Genet. 2009;5:e1000597.

    Article  PubMed  PubMed Central  Google Scholar 

  110. Visel A, Blow MJ, Li Z, et al. ChIP-seq accurately predicts tissue-specific activity of enhancers. Nature. 2009;457:854–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Freedman ML, Monteiro AN, Gayther SA, et al. Principles for the post-GWAS functional characterization of cancer risk loci. Nat Genet. 2011;43:513–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Corradin O, Scacheri PC. Enhancer variants: evaluating functions in common disease. Genome Med. 2014;6:85.

    Article  PubMed  PubMed Central  Google Scholar 

  113. Kempfer R, Pombo A. Methods for mapping 3D chromosome architecture. Nat Rev Genet. 2020;21:207–26.

    Article  CAS  PubMed  Google Scholar 

  114. Bonjoch L, Franch-Exposito S, Garre P, et al. Germline mutations in FAF1 are associated with hereditary colorectal cancer. Gastroenterology. 2020;159(227–40):e7.

    Google Scholar 

  115. Mogno I, Kwasnieski JC, Cohen BA. Massively parallel synthetic promoter assays reveal the in vivo effects of binding site variants. Genome Res. 2013;23:1908–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Arnold CD, Gerlach D, Stelzer C, et al. Genome-wide quantitative enhancer activity maps identified by STARR-seq. Science. 2013;339:1074–7.

    Article  CAS  PubMed  Google Scholar 

  117. Van Arensbergen J, Pagie L, FitzPatrick VD, et al. High-throughput identification of human SNPs affecting regulatory element activity. Nat Genet. 2019;51:1160–9.

    Article  PubMed  PubMed Central  Google Scholar 

  118. McDonald ER 3rd, de Weck A, Schlabach MR, et al. Project DRIVE: a compendium of cancer dependencies and synthetic lethal relationships uncovered by large-scale, deep RNAi screening. Cell. 2017;170(577–92):e10.

    Google Scholar 

  119. Fulco CP, Munschauer M, Anyoha R, et al. Systematic mapping of functional enhancer-promoter connections with CRISPR interference. Science. 2016;354:769–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Tian J, Chang J, Gong J, et al. Systematic functional interrogation of genes in GWAS loci identified ATF1 as a key driver in colorectal cancer modulated by a promoter-enhancer interaction. Am J Hum Genet. 2019;105:29–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Aschard H, Chen J, Cornelis MC, et al. Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases. Am J Hum Genet. 2012;90:962–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Corradin O, Saiakhova A, Akhtar-Zaidi B, et al. Combinatorial effects of multiple enhancer variants in linkage disequilibrium dictate levels of gene expression to confer susceptibility to common traits. Genome Res. 2014;24:1–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Gong J, Tian J, Lou J, et al. A polymorphic MYC response element in KBTBD11 influences colorectal cancer risk, especially in interaction with an MYC-regulated SNP rs6983267. Ann Oncol. 2018;29:632–9.

    Article  CAS  PubMed  Google Scholar 

  124. Zhong R, Liu L, Zou L, et al. Genetic variations in the TGFbeta signaling pathway, smoking and risk of colorectal cancer in a Chinese population. Carcinogenesis. 2013;34:936–42.

    Article  CAS  PubMed  Google Scholar 

  125. Wang F, Zhu J, Yao P, et al. Cohort profile: the Dongfeng-Tongji cohort study of retired workers. Int J Epidemiol. 2013;42:731–40.

    Article  PubMed  Google Scholar 

  126. Moayyeri A, Hammond CJ, Hart DJ, et al. The UK adult twin registry (TwinsUK resource). Twin Res Hum Genet. 2013;16:144–9.

    Article  PubMed  Google Scholar 

  127. Bycroft C, Freeman C, Petkova D, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562:203–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Power C, Elliott J. Cohort profile: 1958 British birth cohort (National Child Development Study). Int J Epidemiol. 2006;35:34–41.

    Article  PubMed  Google Scholar 

  129. Volzke H, Alte D, Schmidt CO, et al. Cohort profile: the study of health in Pomerania. Int J Epidemiol. 2011;40:294–307.

    Article  PubMed  Google Scholar 

  130. Colditz GA, Philpott SE, Hankinson SE. The impact of the nurses’ health study on population health: prevention, translation, and control. Am J Public Health. 2016;106:1540–5.

    Article  PubMed  PubMed Central  Google Scholar 

  131. Sabatti C, Service SK, Hartikainen AL, et al. Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nat Genet. 2009;41:35–46.

    Article  CAS  PubMed  Google Scholar 

  132. Scholtens S, Smidt N, Swertz MA, et al. Cohort profile: LifeLines, a three-generation cohort study and biobank. Int J Epidemiol. 2015;44:1172–80.

    Article  PubMed  Google Scholar 

  133. Tigchelaar EF, Zhernakova A, Dekens JA, et al. Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the Northern Netherlands: study design and baseline characteristics. BMJ Open. 2015;5:e006772.

    Article  PubMed  PubMed Central  Google Scholar 

  134. Prorok PC, Andriole GL, Bresalier RS, et al. Design of the prostate, lung, colorectal and ovarian (PLCO) cancer screening trial. Control Clin Trials. 2000;21:273S–309S.

    Article  CAS  PubMed  Google Scholar 

  135. Wray NR, Goddard ME, Visscher PM. Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res. 2007;17:1520–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Zhang YD, Hurson AN, Zhang H, et al. Assessment of polygenic architecture and risk prediction based on common variants across fourteen cancers. Nat Commun. 2020;11:3353.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Li J, Chang J, Zhu Y, et al. Risk prediction of colorectal cancer with common genetic variants and conventional non-genetic factors in a Chinese Han population. Zhonghua Liu Xing Bing Xue Za Zhi. 2015;36:1053–7.

    CAS  PubMed  Google Scholar 

  138. Yau TO. Precision treatment in colorectal cancer: now and the future. JGH Open. 2019;3:361–9.

    Article  PubMed  PubMed Central  Google Scholar 

  139. Wilkinson NW, Yothers G, Lopa S, et al. Long-term survival results of surgery alone versus surgery plus 5-fluorouracil and leucovorin for stage II and stage III colon cancer: pooled analysis of NSABP C-01 through C-05. A baseline from which to compare modern adjuvant trials. Ann Surg Oncol. 2010;17:959–66.

    Article  PubMed  PubMed Central  Google Scholar 

  140. Sheridan C. Milestone approval lifts illumina’s NGS from research into clinic. Nat Biotechnol. 2014;32:111–2.

    Article  CAS  PubMed  Google Scholar 

  141. Diaz LA Jr, Bardelli A. Liquid biopsies: genotyping circulating tumor DNA. J Clin Oncol. 2014;32:579–86.

    Article  PubMed  PubMed Central  Google Scholar 

  142. Misale S, Yaeger R, Hobor S, et al. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature. 2012;486:532–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Hingorani AD, Kuan V, Finan C, et al. Improving the odds of drug development success through human genomics: modelling study. Sci Rep. 2019;9:18911.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Sham PC, Purcell SM. Statistical power and significance testing in large-scale genetic studies. Nat Rev Genet. 2014;15:335–46.

    Article  CAS  PubMed  Google Scholar 

  145. McCarthy MI, Abecasis GR, Cardon LR, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet. 2008;9:356–69.

    Article  CAS  PubMed  Google Scholar 

  146. Chubb D, Broderick P, Dobbins SE, et al. Rare disruptive mutations and their contribution to the heritable risk of colorectal cancer. Nat Commun. 2016;7:11883.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Livingstone KM, Celis-Morales C, Papandonatos GD, et al. FTO genotype and weight loss: systematic review and meta-analysis of 9563 individual participant data from eight randomised controlled trials. BMJ. 2016;354:i4707.

    Article  PubMed  PubMed Central  Google Scholar 

  148. Dienstmann R, Vermeulen L, Guinney J, et al. Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer. Nat Rev Cancer. 2017;17:268.

    Article  CAS  PubMed  Google Scholar 

  149. Angelova M, Mlecnik B, Vasaturo A, et al. Evolution of metastases in space and time under immune selection. Cell. 2018;175:751–65.

    Article  CAS  PubMed  Google Scholar 

  150. Fearon ER. Molecular genetics of colorectal cancer. Annu Rev Pathol. 2011;6:479–507.

    Article  CAS  PubMed  Google Scholar 

  151. Ocvirk S, O’Keefe SJD. Dietary fat, bile acid metabolism and colorectal cancer. Semin Cancer Biol. 2020.

    Google Scholar 

  152. Murphy N, Carreras-Torres R, Song M, et al. Circulating levels of insulin-like growth factor 1 and insulin-like growth factor binding protein 3 associate with risk of colorectal cancer based on serologic and Mendelian randomization analyses. Gastroenterology. 2020;158(1300–12):e20.

    Google Scholar 

  153. Thrift AP, Gong J, Peters U, et al. Mendelian randomization study of height and risk of colorectal cancer. Int J Epidemiol. 2015;44:662–72.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China

    Jianbo Tian & Xiaoping Miao

  2. Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Dongxin Lin

Authors
  1. Jianbo Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoping Miao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dongxin Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoping Miao .

Editor information

Editors and Affiliations

  1. Epidemiology and Biostatistics, Anhui Medical University, Hefei, China

    Dong-Qing Ye

Rights and permissions

Reprints and permissions

Copyright information

© 2022 People's Medical Publishing House, PR of China

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Tian, J., Miao, X., Lin, D. (2022). Recent Advances in Genetic Epidemiology of Colorectal Cancer in Chinese Population. In: Ye, DQ. (eds) Progress in China Epidemiology. Springer, Singapore. https://doi.org/10.1007/978-981-19-2199-5_9

Download citation

Publish with us

Policies and ethics

Search

Navigation