Genome-Wide Interaction Analyses between Genetic Variants and Alcohol Consumption and Smoking for Risk of Colorectal Cancer


Alcohol consumption and smoking are associated with CRC risk. We performed a genome-wide analysis for interaction between genetic variants and alcohol consumption and cigarette smoking to identify potential new genetic regions associated with CRC. About 8,000 CRC cases and 8,800 controls were included in alcohol-related analysis and over 11,000 cases and 11,000 controls were involved in smoking-related analysis. We identified interaction between variants at 9q22.32/HIATL1 and alcohol consumption in relation to CRC risk (Pinteraction = 1.76×10−8). If replicated our suggested finding of the interaction between genetic variants and alcohol consumption might contribute to understanding colorectal cancer etiology and identifying subpopulations with differential susceptible to the effect of alcohol on CRC risk.


Vyšlo v časopise: Genome-Wide Interaction Analyses between Genetic Variants and Alcohol Consumption and Smoking for Risk of Colorectal Cancer. PLoS Genet 12(10): e32767. doi:10.1371/journal.pgen.1006296
Kategorie: Research Article

Souhrn

Alcohol consumption and smoking are associated with CRC risk. We performed a genome-wide analysis for interaction between genetic variants and alcohol consumption and cigarette smoking to identify potential new genetic regions associated with CRC. About 8,000 CRC cases and 8,800 controls were included in alcohol-related analysis and over 11,000 cases and 11,000 controls were involved in smoking-related analysis. We identified interaction between variants at 9q22.32/HIATL1 and alcohol consumption in relation to CRC risk (Pinteraction = 1.76×10−8). If replicated our suggested finding of the interaction between genetic variants and alcohol consumption might contribute to understanding colorectal cancer etiology and identifying subpopulations with differential susceptible to the effect of alcohol on CRC risk.


Zdroje

1. Ferlay J, S.H., Bray F, Forman D, Mathers C and Parkin DM, GLOBOCAN 2008 v1.2. Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 10 [Internet], 2008(Lyon, France: International Agency for Research on Cancer; 2010. Available from: http://globocan.iarc.fr).

2. Peters U, et al., Meta-analysis of new genome-wide association studies of colorectal cancer risk. Hum Genet, 2012. 131(2): p. 217–34. doi: 10.1007/s00439-011-1055-0 21761138

3. Peters U, et al., Identification of Genetic Susceptibility Loci for Colorectal Tumors in a Genome-Wide Meta-analysis. Gastroenterology, 2013. 144(4): p. 799–807 e24. doi: 10.1053/j.gastro.2012.12.020 23266556

4. 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, 2000. 343(2): p. 78–85. doi: 10.1056/NEJM200007133430201 10891514

5. Tenesa A and Dunlop MG, New insights into the aetiology of colorectal cancer from genome-wide association studies. Nat Rev Genet, 2009. 10(6): p. 353–8. doi: 10.1038/nrg2574 19434079

6. Cunningham D, et al., Colorectal cancer. Lancet, 2010. 375(9719): p. 1030–47. doi: 10.1016/S0140-6736(10)60353-4 20304247

7. Brenner H, Kloor M, and Pox CP, Colorectal cancer. Lancet, 2014. 383(9927): p. 1490–502. doi: 10.1016/S0140-6736(13)61649-9 24225001

8. Peters U, Bien S, and Zubair N, Genetic architecture of colorectal cancer. Gut, 2015. doi: 10.1136/gutjnl-2013-306705 26187503

9. Al-Tassan NA, et al., Erratum: A new GWAS and meta-analysis with 1000Genomes imputation identifies novel risk variants for colorectal cancer. Sci Rep, 2015. 5: p. 12372. doi: 10.1038/srep12372 26237130

10. Lemire M, et al., A genome-wide association study for colorectal cancer identifies a risk locus in 14q23.1. Hum Genet, 2015. 134(11–12): p. 1249–1262. doi: 10.1007/s00439-015-1598-6 26404086

11. Zeng C, et al., Identification of Susceptibility Loci and Genes for Colorectal Cancer Risk. Gastroenterology, 2016. doi: 10.1053/j.gastro.2016.02.076 26965516

12. Thomas D, Gene—environment-wide association studies: emerging approaches. Nat Rev Genet, 2010. 11(4): p. 259–72. doi: 10.1038/nrg2764 20212493

13. van Ijzendoorn MH, et al., Gene-by-environment experiments: a new approach to finding the missing heritability. Nat Rev Genet, 2011. 12(12): p. 881; author reply 881. doi: 10.1038/nrg2764-c1 22094952

14. Gauderman WJ, et al., Finding novel genes by testing G x E interactions in a genome-wide association study. Genet Epidemiol, 2013. 37(6): p. 603–13. doi: 10.1002/gepi.21748 23873611

15. Hutter CM, et al., Gene-environment interactions in cancer epidemiology: a National Cancer Institute Think Tank report. Genet Epidemiol, 2013. 37(7): p. 643–57. doi: 10.1002/gepi.21756 24123198

16. Cho E, et al., Alcohol intake and colorectal cancer: a pooled analysis of 8 cohort studies. Ann Intern Med, 2004. 140(8): p. 603–13. doi: 10.7326/0003-4819-140-8-200404200-00007 15096331

17. Fedirko V, et al., Alcohol drinking and colorectal cancer risk: an overall and dose-response meta-analysis of published studies. Ann Oncol, 2011. 22(9): p. 1958–72. doi: 10.1093/annonc/mdq653 21307158

18. Wei EK, et al., Comparison of risk factors for colon and rectal cancer. Int J Cancer, 2004. 108(3): p. 433–42. doi: 10.1002/ijc.11540 14648711

19. Longnecker MP, et al., A meta-analysis of alcoholic beverage consumption in relation to risk of colorectal cancer. Cancer Causes Control, 1990. 1(1): p. 59–68. doi: 10.1007/BF00053184 2151680

20. Fekjaer HO, Alcohol-a universal preventive agent? A critical analysis. Addiction, 2013. 108(12): p. 2051–7. doi: 10.1111/add.12104 23297738

21. Bergmann MM, et al., The association of pattern of lifetime alcohol use and cause of death in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. International Journal of Epidemiology, 2013. 42(6): p. 1772–1790. doi: 10.1093/ije/dyt154 24415611

22. Kontou N, et al., Alcohol consumption and colorectal cancer in a Mediterranean population: a case-control study. Dis Colon Rectum, 2012. 55(6): p. 703–10. doi: 10.1097/DCR.0b013e31824e612a 22595851

23. Gong J, et al., A pooled analysis of smoking and colorectal cancer: timing of exposure and interactions with environmental factors. Cancer Epidemiol Biomarkers Prev, 2012. 21(11): p. 1974–85. doi: 10.1158/1055-9965.EPI-12-0692 23001243

24. Botteri E, et al., Smoking and colorectal cancer: a meta-analysis. JAMA, 2008. 300(23): p. 2765–78. doi: 10.1001/jama.2008.839 19088354

25. Liang PS, Chen TY, and Giovannucci E, Cigarette smoking and colorectal cancer incidence and mortality: systematic review and meta-analysis. Int J Cancer, 2009. 124(10): p. 2406–15. doi: 10.1002/ijc.24191 19142968

26. Varela-Rey M, et al., Alcohol, DNA methylation, and cancer. Alcohol Res, 2013. 35(1): p. 25–35. 24313162

27. Oyesanmi O, et al., Alcohol consumption and cancer risk: understanding possible causal mechanisms for breast and colorectal cancers. Evid Rep Technol Assess (Full Rep), 2010(197): p. 1–151. 23126574

28. Cleary SP, et al., Cigarette smoking, genetic variants in carcinogen-metabolizing enzymes, and colorectal cancer risk. Am J Epidemiol, 2010. 172(9): p. 1000–14. doi: 10.1093/aje/kwq245 20937634

29. Hutter CM, et al., Characterization of gene-environment interactions for colorectal cancer susceptibility loci. Cancer Res, 2012. 72(8): p. 2036–44. doi: 10.1158/0008-5472.CAN-11-4067 22367214

30. Newcomb PA, et al., Colon Cancer Family Registry: an international resource for studies of the genetic epidemiology of colon cancer. Cancer Epidemiol Biomarkers Prev, 2007. 16(11): p. 2331–43. doi: 10.1158/1055-9965.EPI-07-0648 17982118

31. Research, W.C.R.F.A.I.f.C., Continuous Update Project Report. Food, Nutrition, Physical Activity, and the Prevention of Colorectal Cancer. 2011, Washington, DC: AICR.

32. Hilbe JM, Negative Binomial Regression. 2nd ed. 2011: Cambridge University Press. doi: 10.1017/CBO9780511811852

33. Vanderweele TJ, Ko YA, and Mukherjee B, Environmental confounding in gene-environment interaction studies. Am J Epidemiol, 2013. 178(1): p. 144–52. doi: 10.1093/aje/kws439 23821317

34. Consortium GT, The Genotype-Tissue Expression (GTEx) project. Nat Genet, 2013. 45(6): p. 580–5. doi: 10.1038/ng.2653 23715323

35. Uhlen M, et al., Towards a knowledge-based Human Protein Atlas. Nat Biotechnol, 2010. 28(12): p. 1248–50. doi: 10.1038/nbt1210-1248 21139605

36. Kaiser S, et al., Transcriptional recapitulation and subversion of embryonic colon development by mouse colon tumor models and human colon cancer. Genome Biology, 2007. 8(7). doi: 10.1186/gb-2007-8-7-r131 17615082

37. Goldman M, et al., The UCSC Cancer Genomics Browser: update 2013. Nucleic Acids Res, 2013. 41(Database issue): p. D949–54. doi: 10.1093/nar/gks1008 23109555

38. Sanborn JZ, et al., The UCSC Cancer Genomics Browser: update 2011. Nucleic Acids Res, 2011. 39(Database issue): p. D951–9. doi: 10.1093/nar/gkq1113 21059681

39. Zhu J, et al., The UCSC Cancer Genomics Browser. Nat Methods, 2009. 6(4): p. 239–40. doi: 10.1038/nmeth0409-239 19333237

40. Zeller T, et al., Genetics and beyond—the transcriptome of human monocytes and disease susceptibility. PLoS One, 2010. 5(5): p. e10693. doi: 10.1371/journal.pone.0010693 20502693

41. Veyrieras JB, et al., High-Resolution Mapping of Expression-QTLs Yields Insight into Human Gene Regulation. Plos Genetics, 2008. 4(10). doi: 10.1371/journal.pgen.1000214 18846210

42. Yang TP, et al., Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies. Bioinformatics, 2010. 26(19): p. 2474–6. doi: 10.1093/bioinformatics/btq452 20702402

43. Akhtar-Zaidi B, et al., Epigenomic enhancer profiling defines a signature of colon cancer. Science, 2012. 336(6082): p. 736–739. doi: 10.1126/science.1217277 22499810

44. Chadwick LH, The NIH Roadmap Epigenomics Program data resource. Epigenomics, 2012. 4(3): p. 317–324. doi: 10.2217/epi.12.18 22690667

45. Hoffman MM, et al., Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nat Methods, 2012. 9(5): p. 473–U88. doi: 10.1038/nmeth.1937 22426492

46. Schlessinger A, et al., Comparison of human solute carriers. Protein Science, 2010. 19(3): p. 412–428. doi: 10.1002/pro.320 20052679

47. Hoglund PJ, et al., The Solute Carrier Families Have a Remarkably Long Evolutionary History with the Majority of the Human Families Present before Divergence of Bilaterian Species. Molecular Biology and Evolution, 2011. 28(4): p. 1531–1541. doi: 10.1093/molbev/msq350 21186191

48. Sreedharan S, et al., Long evolutionary conservation and considerable tissue specificity of several atypical solute carrier transporters. Gene, 2011. 478(1–2): p. 11–18. doi: 10.1016/j.gene.2010.10.011 21044875

49. Nakanishi T and Tamai I, Solute Carrier Transporters as Targets for Drug Delivery and Pharmacological Intervention for Chemotherapy. Journal of Pharmaceutical Sciences, 2011. 100(9): p. 3731–3750. doi: 10.1002/jps.22576 21630275

50. Okudaira H, et al., Putative Transport Mechanism and Intracellular Fate of Trans-1-Amino-3-F-18-Fluorocyclobutanecarboxylic Acid in Human Prostate Cancer. Journal of Nuclear Medicine, 2011. 52(5): p. 822–829. doi: 10.2967/jnumed.110.086074 21536930

51. Fan XT, et al., Impact of system L amino acid transporter 1 (LAT1) on proliferation of human ovarian cancer cells: A possible target for combination therapy with anti-proliferative aminopeptidase inhibitors. Biochemical Pharmacology, 2010. 80(6): p. 811–818. doi: 10.1016/j.bcp.2010.05.021 20510678

52. Laplante M and Sabatini DM, mTOR signaling at a glance. Journal of Cell Science, 2009. 122(20): p. 3589–3594. doi: 10.1242/jcs.051011 19812304

53. Hoeffer CA and Klann E, mTOR signaling: At the crossroads of plasticity, memory and disease. Trends in Neurosciences, 2010. 33(2): p. 67–75. doi: 10.1016/j.tins.2009.11.003 19963289

54. Zoncu R, Efeyan A, and Sabatini DM, mTOR: from growth signal integration to cancer, diabetes and ageing. Nature Reviews Molecular Cell Biology, 2011. 12(1): p. 21–35. doi: 10.1038/nrm3025 21157483

55. Hsu L, et al., Powerful cocktail methods for detecting genome-wide gene-environment interaction. Genet Epidemiol, 2012. 36(3): p. 183–94. doi: 10.1002/gepi.21610 22714933

56. Dudbridge F and Fletcher O, Gene-environment dependence creates spurious gene-environment interaction. Am J Hum Genet, 2014. 95(3): p. 301–7. doi: 10.1016/j.ajhg.2014.07.014 25152454

57. Mukherjee B and Chatterjee N, Exploiting gene-environment independence for analysis of case-control studies: an empirical Bayes-type shrinkage estimator to trade-off between bias and efficiency. biometrics, 2008. 64(3): p. 685–694. doi: 10.1111/j.1541-0420.2007.00953.x 18162111

58. Piegorsch WW, Weinberg CR, and Taylor JA, Non-hierarchical logistic models and case-only designs for assessing susceptibility in population-based case-control studies. Stat Med, 1994. 13(2): p. 153–162. doi: 10.1002/sim.4780130206 8122051

59. Schumacher FR, et al., Genome-wide association study of colorectal cancer identifies six new susceptibility loci. Nat Commun, 2015. 6: p. 7138. doi: 10.1038/ncomms8138 26151821

60. Smith PG and Day NE, The design of case-control studies: the influence of confounding and interaction effects. Int. J Epidemiol, 1984. 13(3): p. 356–365. doi: 10.1093/ije/13.3.356 6386716

61. Skol AD, et al., Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet, 2006. 38(2): p. 209–213. doi: 10.1038/ng1706 16415888

62. Garcia-Closas M, Thompson WD, and Robins JM, Differential misclassification and the assessment of gene-environment interactions in case-control studies. Am J Epidemiol, 1998. 147(5): p. 426–433. doi: 10.1093/oxfordjournals.aje.a009467 9525528

63. Society AC, Cancer Facts and Figures 2014. 2014: Altanta, GA.

64. Eriksson CJ, Genetic-epidemiological evidence for the role of acetaldehyde in cancers related to alcohol drinking. Adv Exp Med Biol, 2015. 815: p. 41–58. doi: 10.1007/978-3-319-09614-8_3 25427900

65. Guo XF, et al., Meta-analysis of the ADH1B and ALDH2 polymorphisms and the risk of colorectal cancer in East Asians. Intern Med, 2013. 52(24): p. 2693–9. doi: 10.2169/internalmedicine.52.1202 24334570

66. Chen B, et al., A critical analysis of the relationship between aldehyde dehydrogenases-2 Glu487Lys polymorphism and colorectal cancer susceptibility. Pathol Oncol Res, 2015. 21(3): p. 727–33. doi: 10.1007/s12253-014-9881-8 25573590

67. Houlston RS and Cogent, COGENT (COlorectal cancer GENeTics) revisited. Mutagenesis, 2012. 27(2): p. 143–151. doi: 10.1093/mutage/ger059 22294761

68. Ogino S, et al., Molecular pathological epidemiology of epigenetics: emerging integrative science to analyze environment, host, and disease. Mod Pathol, 2013. 26(4): p. 465–84. doi: 10.1038/modpathol.2012.214 23307060

69. Ogino S, et al., Molecular pathological epidemiology of colorectal neoplasia: an emerging transdisciplinary and interdisciplinary field. Gut, 2011. 60(3): p. 397–411. doi: 10.1136/gut.2010.217182 21036793

70. Brenner H, et al., Risk of progression of advanced adenomas to colorectal cancer by age and sex: estimates based on 840,149 screening colonoscopies. Gut, 2007. 56(11): p. 1585–1589. doi: 10.1136/gut.2007.122739 17591622

71. Kinzler KW and Vogelstein B, Lessons from hereditary colorectal cancer. Cell, 1996. 87(2): p. 159–70. doi: 10.1016/S0092-8674(00)81333-1 8861899

72. Price AL, et al., Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet, 2006. 38(8): p. 904–9. doi: 10.1038/ng1847 16862161

73. Alavanja MC, Brownson RC, and Benichou J, Estimating the effect of dietary fat on the risk of lung cancer in nonsmoking women. Lung Cancer, 1996. 14 Suppl 1: p. S63–S74. doi: 10.1016/S0169-5002(96)90211-1 8785668

74. Jiao S, et al., The Use of Imputed Values in the Meta-Analysis of Genome-Wide Association Studies. Genet Epidemiol, 2011. 35(7): p. 597–605. doi: 10.1002/gepi.20608 21769935

75. Woolf B, On estimating the relation between blood group and disease. Ann Hum Genet, 1955. 19(4): p. 251–3. doi: 10.1111/j.1469-1809.1955.tb01348.x 14388528

76. Lieber CS, in Gender differences in alcohol metabolism and susceptibility. In Wilsnack RW, Wilsnack SC (eds). Gender and alcohol. New Brunswick, NJ: Rutgers Center of Alcohol Studies.

77. Frezza M, et al., High blood alcohol levels in women. The role of decreased gastric alcohol dehydrogenase activity and first-pass metabolism. N Engl J Med, 1990. 322(2): p. 95–9. doi: 10.1056/NEJM199001113220205 2248624

78. International HapMap, C., A haplotype map of the human genome. Nature, 2005. 437(7063): p. 1299–320. doi: 10.1038/nature04226 16255080

79. Wellcome Trust Case Control, C., Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 2007. 447(7145): p. 661–78. doi: 10.1038/nature05911 17554300

80. Risch N and Merikangas K, The future of genetic studies of complex human diseases. Science, 1996. 273(5281): p. 1516–1517. doi: 10.1126/science.273.5281.1516 8801636

81. Hoggart CJ, et al., Genome-wide significance for dense SNP and resequencing data. Genet Epidemiol, 2008. 32(2): p. 179–85. doi: 10.1002/gepi.20292 18200594

82. Pe'er I, et al., Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet Epidemiol, 2008. 32(4): p. 381–5. doi: 10.1002/gepi.20303 18348202

83. Dudbridge F and Gusnanto A, Estimation of significance thresholds for genomewide association scans. Genet Epidemiol, 2008. 32(3): p. 227–234. doi: 10.1002/gepi.20297 18300295

84. Kooperberg C and LeBlanc M, Increasing the power of identifying gene x gene interactions in genome-wide association studies. Genet Epidemiol, 2008. 32(3): p. 255–263. doi: 10.1002/gepi.20300 18200600

85. Murcray CE, Lewinger JP, and Gauderman WJ, Gene-environment interaction in genome-wide association studies. Am J Epidemiol, 2009. 169(2): p. 219–26. doi: 10.1093/aje/kwn353 19022827

86. Roeder K and Wasserman L, Genome-Wide Significance Levels and Weighted Hypothesis Testing. Stat Sci, 2009. 24(4): p. 398–413. doi: 10.1214/09-STS289 20711421

87. Ionita-Laza I, et al., Genomewide weighted hypothesis testing in family-based association studies, with an application to a 100K scan. Am J Hum Genet, 2007. 81(3): p. 607–14. doi: 10.1086/519748 17701906

88. Efron B, 1977 Rietz Lecture—Bootstrap Methods—Another Look at the Jackknife. Annals of Statistics, 1979. 7(1): p. 1–26.

89. Edgar R, Domrachev M, and Lash AE, Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res, 2002. 30(1): p. 207–10. doi: 10.1093/nar/30.1.207 11752295

90. Barrett T, et al., NCBI GEO: archive for functional genomics data sets—10 years on. Nucleic Acids Res, 2011. 39(Database issue): p. D1005–10. doi: 10.1093/nar/gkq1184 21097893

Štítky
Genetika Reprodukčná medicína
Prihlásenie
Zabudnuté heslo

Nemáte účet?  Registrujte sa

Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa