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

PLoS Genetics

Volumn 12, Issue 10, 2016, Pages

  Gong, Jian ^a   Hutter, Carolyn M ^b   Newcomb, Polly A ^a   Ulrich, Cornelia M ^a,c   Bien, Stephanie A ^a   Campbell, Peter T ^d   Baron, John A ^e   Berndt, Sonja I ^f   Bezieau, Stephane ^g   Brenner, Hermann ^h   Casey, Graham ⁱ   Chan, Andrew T ^j,m   Chang Claude, Jenny ^h   Du, Mengmeng ^a   Duggan, David ^k   Figueiredo, Jane C ⁱ   Gallinger, Steven ^l   Giovannucci, Edward L ^m   Haile, Robert W ⁱ   Harrison, Tabitha A ^a   Hayes, Richard B ⁿ   Hoffmeister, Michael ^h   Hopper, John L ^o   Hudson, Thomas J ^p   Jeon, Jihyoun ^a   Jenkins, Mark A ^o   Kocarnik, Jonathan ^a   Küry, Sébastien ^g   Le Marchand, Loic ^q   Lin, Yi ^a   Lindor, Noralane M ^r   Nishihara, Reiko ^s   Ogino, Shuji ^t   Potter, John D ^a,u   Rudolph, Anja ^h   Schoen, Robert E ^v   Schrotz King, Petra ^h   Seminara, Daniela ^f   Slattery, Martha L ^w   Thibodeau, Stephen N ^x   Thornquist, Mark ^a   Toth, Reka ^h   Wallace, Robert ^y   White, Emily ^a   Jiao, Shuo ^a   Lemire, Mathieu ^p   Hsu, Li ^a   Peters, Ulrike ^a   more..

^a USA   (United States)

^b NATIONAL HUMAN GENOME RESEARCH INSTITUTE   (United States)

^c UNIVERSITY OF UTAH   (United States)

^d AMERICAN CANCER SOCIETY   (United States)

^e UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^f NATIONAL CANCER INSTITUTE   (United States)

^g NANTES UNIVERSITY HOSPITAL   (France)

^h GERMAN CANCER RESEARCH CENTER DKFZ   (Germany)

ⁱ UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

^j MASSACHUSETTS GENERAL HOSPITAL   (United States)

^k TRANSLATIONAL GENOMICS RESEARCH INSTITUTE   (United States)

^l TORONTO GENERAL HOSPITAL   (Canada)

^m BRIGHAM AND WOMEN S HOSPITAL   (United States)

ⁿ NEW YORK UNIVERSITY SCHOOL OF MEDICINE   (United States)

^o UNIVERSITY OF MELBOURNE   (Australia)

^p ONTARIO INSTITUTE FOR CANCER RESEARCH   (Canada)

^q UNIVERSITY OF HAWAII CANCER CENTER   (United States)

^r MAYO CLINIC   (United States)

^s HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^t HARVARD MEDICAL SCHOOL   (United States)

^u MASSEY UNIVERSITY   (New Zealand)

^v UNIVERSITY OF PITTSBURGH MEDICAL CENTER   (United States)

^w UNIVERSITY OF UTAH HEALTH SCIENCES CENTER   (United States)

^x MAYO CLINIC   (United States)

^y UNIVERSITY OF IOWA   (United States)

more..

Author keywords

[No Author keywords available]

Indexed keywords

ALCOHOL CONSUMPTION; ARTICLE; CANCER RISK; CANCER SUSCEPTIBILITY; COLON CARCINOGENESIS; COLORECTAL CANCER; CONTROLLED STUDY; FEMALE; GENETIC VARIABILITY; GENOME-WIDE ASSOCIATION STUDY; GENOTYPE; HUMAN; MAJOR CLINICAL STUDY; MALE; SMOKING; AGED; COLORECTAL NEOPLASMS; DRINKING BEHAVIOR; GENETIC PREDISPOSITION; GENETICS; GENOTYPE ENVIRONMENT INTERACTION; MIDDLE AGED; PATHOLOGY; RISK FACTOR; SINGLE NUCLEOTIDE POLYMORPHISM;

CARRIER PROTEIN; HIATL1 PROTEIN, HUMAN; TUMOR SUPPRESSOR PROTEIN;

AGED; ALCOHOL DRINKING; COLORECTAL NEOPLASMS; FEMALE; GENE-ENVIRONMENT INTERACTION; GENETIC PREDISPOSITION TO DISEASE; GENOME-WIDE ASSOCIATION STUDY; GENOTYPE; HUMANS; MALE; MEMBRANE TRANSPORT PROTEINS; MIDDLE AGED; POLYMORPHISM, SINGLE NUCLEOTIDE; RISK FACTORS; SMOKING; TUMOR SUPPRESSOR PROTEINS;

EID: 84994218031     PISSN: 15537390     EISSN: 15537404     Source Type: Journal    
DOI: 10.1371/journal.pgen.1006296     Document Type: Article

References (90)

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/s00439-011-1055-021761138
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.2012.12.02023266556
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/NEJM20000713343020110891514
5. Tenesa A, 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/nrg257419434079
6. Cunningham D, et al., Colorectal cancer. Lancet, 2010. 375(9719): p. 1030–47. doi: 10.1016/S0140-6736(10)60353-420304247
7. Brenner H, Kloor M, Pox CP, Colorectal cancer. Lancet, 2014. 383(9927): p. 1490–502. doi: 10.1016/S0140-6736(13)61649-924225001
8. Peters U, Bien S, Zubair N, Genetic architecture of colorectal cancer. Gut, 2015. doi: 10.1136/gutjnl-2013-30670526187503
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/srep1237226237130
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/s00439-015-1598-626404086
11. Zeng C, et al., Identification of Susceptibility Loci and Genes for Colorectal Cancer Risk. Gastroenterology, 2016. doi: 10.1053/j.gastro.2016.02.07626965516
12. Thomas D, Gene—environment-wide association studies: emerging approaches. Nat Rev Genet, 2010. 11(4): p. 259–72. doi: 10.1038/nrg276420212493
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-c122094952
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.2174823873611
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.2175624123198
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: 10.7326/0003-4819-140-8-200404200-0000715096331
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/mdq65321307158
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.1154014648711
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/BF000531842151680
20. Fekjaer HO, Alcohol-a universal preventive agent? A critical analysis. Addiction, 2013. 108(12): p. 2051–7. doi: 10.1111/add.1210423297738
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/dyt15424415611
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.0b013e31824e612a22595851
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-12-069223001243
24. Botteri E, et al., Smoking and colorectal cancer: a meta-analysis. JAMA, 2008. 300(23): p. 2765–78. doi: 10.1001/jama.2008.83919088354
25. Liang PS, Chen TY, 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.2419119142968
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/kwq24520937634
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-11-406722367214
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-07-064817982118
31. Research, W.C.R.F.A.I.f.CContinuous 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, Mukherjee B, Environmental confounding in gene-environment interaction studies. Am J Epidemiol, 2013. 178(1): p. 144–52. doi: 10.1093/aje/kws43923821317
34. Consortium GT, The Genotype-Tissue Expression (GTEx) project. Nat Genet, 2013. 45(6): p. 580–5. doi: 10.1038/ng.265323715323
35. Uhlen M, et al., Towards a knowledge-based Human Protein Atlas. Nat Biotechnol, 2010. 28(12): p. 1248–50. doi: 10.1038/nbt1210-124821139605
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-r13117615082
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/gks100823109555
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/gkq111321059681
39. Zhu J, et al., The UCSC Cancer Genomics Browser. Nat Methods, 2009. 6(4): p. 239–40. doi: 10.1038/nmeth0409-23919333237
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.001069320502693
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.100021418846210
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/btq45220702402
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.121727722499810
44. Chadwick LH, The NIH Roadmap Epigenomics Program data resource. Epigenomics, 2012. 4(3): p. 317–324. doi: 10.2217/epi.12.1822690667
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.193722426492
46. Schlessinger A, et al., Comparison of human solute carriers. Protein Science, 2010. 19(3): p. 412–428. doi: 10.1002/pro.32020052679
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/msq35021186191
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.2010.10.01121044875
49. Nakanishi T, 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.2257621630275
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.110.08607421536930
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.2010.05.02120510678
52. Laplante M, Sabatini DM, mTOR signaling at a glance. Journal of Cell Science, 2009. 122(20): p. 3589–3594. doi: 10.1242/jcs.05101119812304
53. Hoeffer CA, 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.2009.11.00319963289
54. Zoncu R, Efeyan A, 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/nrm302521157483
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.2161022714933
56. Dudbridge F, 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.2014.07.01425152454
57. Mukherjee B, 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.x18162111
58. Piegorsch WW, Weinberg CR, 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.47801302068122051
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/ncomms813826151821
60. Smith PG, 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/13.3.3566386716
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/ng170616415888
62. Garcia-Closas M, Thompson WD, 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.a0094679525528
63. Society ACCancer 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_325427900
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.52.120224334570
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/s12253-014-9881-825573590
67. Houlston RS, CogentCOGENT (COlorectal cancer GENeTics) revisited. Mutagenesis, 2012. 27(2): p. 143–151. doi: 10.1093/mutage/ger05922294761
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.2012.21423307060
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.2010.21718221036793
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.2007.12273917591622
71. Kinzler KW, Vogelstein B, Lessons from hereditary colorectal cancer. Cell, 1996. 87(2): p. 159–70. doi: 10.1016/S0092-8674(00)81333-18861899
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/ng184716862161
73. Alavanja MC, Brownson RC, Benichou J, Estimating the effect of dietary fat on the risk of lung cancer in nonsmoking women. Lung Cancer, 1996. 14Suppl 1: p. S63–S74. doi: 10.1016/S0169-5002(96)90211-18785668
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.2060821769935
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.x14388528
76. Lieber CS, Wilsnack RW, Wilsnack SC, Gender differences in alcohol metabolism and susceptibility. In (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/NEJM1990011132202052248624
78. International HapMap, C.A haplotype map of the human genome. Nature, 2005. 437(7063): p. 1299–320. doi: 10.1038/nature0422616255080
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/nature0591117554300
80. Risch N, Merikangas K, The future of genetic studies of complex human diseases. Science, 1996. 273(5281): p. 1516–1517. doi: 10.1126/science.273.5281.15168801636
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.2029218200594
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.2030318348202
83. Dudbridge F, Gusnanto A, Estimation of significance thresholds for genomewide association scans. Genet Epidemiol, 2008. 32(3): p. 227–234. doi: 10.1002/gepi.2029718300295
84. Kooperberg C, 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.2030018200600
85. Murcray CE, Lewinger JP, Gauderman WJ, Gene-environment interaction in genome-wide association studies. Am J Epidemiol, 2009. 169(2): p. 219–26. doi: 10.1093/aje/kwn35319022827
86. Roeder K, Wasserman L, Genome-Wide Significance Levels and Weighted Hypothesis Testing. Stat Sci, 2009. 24(4): p. 398–413. doi: 10.1214/09-STS28920711421
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: 10.1086/51974817701906
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, 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/30.1.20711752295
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/gkq118421097893