Browse > Article
http://dx.doi.org/10.5808/GI.2012.10.3.175

Prediction of Colorectal Cancer Risk Using a Genetic Risk Score: The Korean Cancer Prevention Study-II (KCPS-II)  

Jo, Jae-Seong (Institute for Health Promotion and Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University)
Nam, Chung-Mo (Department of Public Health, Graduate School of Yonsei University)
Sull, Jae-Woong (Metabolic Syndrome Research Initiatives)
Yun, Ji-Eun (Institute for Health Promotion and Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University)
Kim, Sang-Yeun (Department of Biomedical Laboratory Science, College of Health Sciences, Eulji University)
Lee, Sun-Ju (Department of Biomedical Laboratory Science, College of Health Sciences, Eulji University)
Kim, Yoon-Nam (Department of Public Health, Graduate School of Yonsei University)
Park, Eun-Jung (Department of Public Health, Graduate School of Yonsei University)
Kimm, Hee-Jin (Institute for Health Promotion and Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University)
Jee, Sun-Ha (Institute for Health Promotion and Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University)
Publication Information
Genomics & Informatics / v.10, no.3, 2012 , pp. 175-183 More about this Journal
Abstract
Colorectal cancer (CRC) is among the leading causes of cancer deaths and can be caused by environmental factors as well as genetic factors. Therefore, we developed a prediction model of CRC using genetic risk scores (GRS) and evaluated the effects of conventional risk factors, including family history of CRC, in combination with GRS on the risk of CRC in Koreans. This study included 187 cases (men, 133; women, 54) and 976 controls (men, 554; women, 422). GRS were calculated with most significantly associated single-nucleotide polymorphism with CRC through a genomewide association study. The area under the curve (AUC) increased by 0.5% to 5.2% when either counted or weighted GRS was added to a prediction model consisting of age alone (AUC 0.687 for men, 0.598 for women) or age and family history of CRC (AUC 0.692 for men, 0.603 for women) for both men and women. Furthermore, the risk of CRC significantly increased for individuals with a family history of CRC in the highest quartile of GRS when compared to subjects without a family history of CRC in the lowest quartile of GRS (counted GRS odds ratio [OR], 47.9; 95% confidence interval [CI], 4.9 to 471.8 for men; OR, 22.3; 95% CI, 1.4 to 344.2 for women) (weighted GRS OR, 35.9; 95% CI, 5.9 to 218.2 for men; OR, 18.1, 95% CI, 3.7 to 88.1 for women). Our findings suggest that in Koreans, especially in Korean men, GRS improve the prediction of CRC when considered in conjunction with age and family history of CRC.
Keywords
area under curve; colorectal neoplasms; genetic risk score (GRS); prediction;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Slattery ML, Kerber RA. Family history of cancer and colon cancer risk: the Utah Population Database. J Natl Cancer Inst 1994;86:1618-1626.   DOI   ScienceOn
2 Jasperson KW, Tuohy TM, Neklason DW, Burt RW. Hereditary and familial colon cancer. Gastroenterology 2010;138:2044-2058.   DOI   ScienceOn
3 McCashland TM, Brand R, Lyden E, de Garmo P; CORI Research Project. Gender differences in colorectal polyps and tumors. Am J Gastroenterol 2001;96:882-886.   DOI   ScienceOn
4 Balding DJ. A tutorial on statistical methods for population association studies. Nat Rev Genet 2006;7:781-791.   DOI   ScienceOn
5 Cornelis MC, Qi L, Zhang C, Kraft P, Manson J, Cai T, et al. Joint effects of common genetic variants on the risk for type 2 diabetes in U.S. men and women of European ancestry. Ann Intern Med 2009;150:541-550.   DOI   ScienceOn
6 Ripatti S, Tikkanen E, Orho-Melander M, Havulinna AS, Silander K, Sharma A, et al. A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet 2010;376:1393-1400.   DOI   ScienceOn
7 Jee SH, Yun JE, Park EJ, Cho ER, Park IS, Sull JW, et al. Body mass index and cancer risk in Korean men and women. Int J Cancer 2008;123:1892-1896.   DOI   ScienceOn
8 World Health Organization. International Statistical Classification of Diseases and Related Health Problems. 10th Rev. Geneva: World Health Organization, 1992.
9 Chambless LE, Diao G. Estimation of time-dependent area under the ROC curve for long-term risk prediction. Stat Med 2006;25:3474-3486.   DOI   ScienceOn
10 Lim TS, Loh WY, Shih YS. A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Mach Learn 2000;40:203-228.   DOI   ScienceOn
11 Colditz GA, Atwood KA, Emmons K, Monson RR, Willett WC, Trichopoulos D, et al. Harvard report on cancer prevention volume 4: Harvard Cancer Risk Index. Risk Index Working Group, Harvard Center for Cancer Prevention. Cancer Causes Control 2000;11:477-488.   DOI   ScienceOn
12 Cappell MS. Pathophysiology, clinical presentation, and management of colon cancer. Gastroenterol Clin North Am 2008; 37:1-24.   DOI   ScienceOn
13 Yang ZR, Chou KC. Bio-support vector machines for computational proteomics. Bioinformatics 2004;20:735-741.   DOI   ScienceOn
14 Horne BD, Anderson JL, Carlquist JF, Muhlestein JB, Renlund DG, Bair TL, et al. Generating genetic risk scores from intermediate phenotypes for use in association studies of clinically significant endpoints. Ann Hum Genet 2005;69:176-186.   DOI   ScienceOn
15 Ortlepp JR, Lauscher J, Janssens U, Minkenberg R, Hanrath P, Hoffmann R. Analysis of several hundred genetic polymorphisms may improve assessment of the individual genetic burden for coronary artery disease. Eur J Intern Med 2002; 13:485-492.   DOI   ScienceOn
16 Aston CE, Ralph DA, Lalo DP, Manjeshwar S, Gramling BA, DeFreese DC, et al. Oligogenic combinations associated with breast cancer risk in women under 53 years of age. Hum Genet 2005;116:208-221.   DOI   ScienceOn
17 American Cancer Society. Cancer Facts and Figures 2006. Atlanta, GA: American Cancer Society, 2006.
18 Weitz J, Koch M, Debus J, Höhler T, Galle PR, Büchler MW. Colorectal cancer. Lancet 2005;365:153-165.   DOI   ScienceOn
19 Reeves GK, Travis RC, Green J, Bull D, Tipper S, Baker K, et al. Incidence of breast cancer and its subtypes in relation to individual and multiple low-penetrance genetic susceptibility loci. JAMA 2010;304:426-434.   DOI   ScienceOn
20 Steinthorsdottir V, Thorleifsson G, Reynisdottir I, Benediktsson R, Jonsdottir T, Walters GB, et al. A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet 2007;39:770-775.   DOI   ScienceOn
21 Freedman AN, Slattery ML, Ballard-Barbash R, Willis G, Cann BJ, Pee D, et al. Colorectal cancer risk prediction tool for white men and women without known susceptibility. J Clin Oncol 2009;27:686-693.   DOI   ScienceOn
22 Zanke BW, Greenwood CM, Rangrej J, Kustra R, Tenesa A, Farrington SM, et al. Genome-wide association scan identifies a colorectal cancer susceptibility locus on chromosome 8q24. Nat Genet 2007;39:989-994.   DOI   ScienceOn
23 Ma E, Sasazuki S, Iwasaki M, Sawada N, Inoue M; Shoichiro Tsugane, et al. 10-Year risk of colorectal cancer: development and validation of a prediction model in middle-aged Japanese men. Cancer Epidemiol 2010;34:534-541.   DOI   ScienceOn
24 Morrison AC, Bare LA, Chambless LE, Ellis SG, Malloy M, Kane JP, et al. Prediction of coronary heart disease risk using a genetic risk score: the Atherosclerosis Risk in Communities Study. Am J Epidemiol 2007;166:28-35.   DOI   ScienceOn
25 Lichtenstein P, Holm NV, Verkasalo PK, Iliadou A, Kaprio J, Koskenvuo M, 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.   DOI   ScienceOn
26 Xiong F, Wu C, Bi X, Yu D, Huang L, Xu J, et al. Risk of genome- wide association study-identified genetic variants for colorectal cancer in a Chinese population. Cancer Epidemiol Biomarkers Prev 2010;19:1855-1861.   DOI   ScienceOn
27 Tenesa A, Farrington SM, Prendergast JG, Porteous ME, Walker M, Haq N, et al. Genome-wide association scan identifies a colorectal cancer susceptibility locus on 11q23 and replicates risk loci at 8q24 and 18q21. Nat Genet 2008;40:631-637.   DOI   ScienceOn
28 Meigs JB, Shrader P, Sullivan LM, McAteer JB, Fox CS, Dupuis J, et al. Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Engl J Med 2008;359:2208-2219.   DOI   ScienceOn
29 Weedon MN, McCarthy MI, Hitman G, Walker M, Groves CJ, Zeggini E, et al. Combining information from common type 2 diabetes risk polymorphisms improves disease prediction. PLoS Med 2006;3:e374.   DOI
30 Wray NR, Goddard ME, Visscher PM. Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res 2007;17:1520-1528.   DOI   ScienceOn
31 Korean Ministry of Health and Welfare. The National Cancer Registry. Seoul: Korean Ministry of Health and Welfare, 2007.
32 Jee SH, Sull JW, Lee JE, Shin C, Park J, Kimm H, et al. Adiponectin concentrations: a genome-wide association study. Am J Hum Genet 2010;87:545-552.   DOI   ScienceOn
33 Purcell S, Neale K, Todd-Brown L, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559-575.   DOI   ScienceOn
34 Rabbee N, Speed TP. A genotype calling algorithm for affymetrix SNP arrays. Bioinformatics 2006;22:7-12.   DOI   ScienceOn
35 Cancer. Geneva: World Health Organization, 2011. Accessed 2012 Jul 5. Available from: http:www.who.int/mediacentre/ factsheets/fs297/en.
36 Cancer of the Colon and Rectum. Bethesda: National Cancer Institute, 2011. Accessed 2012 Jul 5. Available from: http://www.cancer.gov/cancertopics/wyntk/colon-andrectal.
37 Lee YS, Choi HB, Lee IK, Kim TG, Oh ST. Association between interleukin-4R and TGF-beta1 gene polymorphisms and the risk of colorectal cancer in a Korean population. Colorectal Dis 2010;12:1208-1212.   DOI   ScienceOn
38 Korean Ministry of Health and Welfare. The National Cancer Registry. Seoul: Korean Ministry of Health and Welfare, 2003.
39 Korean Ministry of Health and Welfare. The Korea National Health and Nutrition Examination Survey (KNHANES). Seoul: Korean Ministry of Health and Welfare, 2007.
40 Freedman AN, Seminara D, Gail MH, Hartge P, Colditz GA, Ballard-Barbash R, et al. Cancer risk prediction models: a workshop on development, evaluation, and application. J Natl Cancer Inst 2005;97:715-723.   DOI   ScienceOn
41 Fuchs CS, Giovannucci EL, Colditz GA, Hunter DJ, Speizer FE, Willett WC. A prospective study of family history and the risk of colorectal cancer. N Engl J Med 1994;331:1669-1674.   DOI   ScienceOn
42 Park Y, Freedman AN, Gail MH, Pee D, Hollenbeck A, Schatzkin A, et al. Validation of a colorectal cancer risk prediction model among white patients age 50 years and older. J Clin Oncol 2009;27:694-698.   DOI   ScienceOn
43 Otani T, Iwasaki M, Inoue M; Shoichiro Tsugane for the Japan Public Health Center-based Prospective Study Group. Body mass index, body height, and subsequent risk of colorectal cancer in middle-aged and elderly Japanese men and women: Japan public health center-based prospective study. Cancer Causes Control 2005;16:839-850.   DOI   ScienceOn
44 Harriss DJ, Atkinson G, Batterham A, George K, Cable NT, Reilly T, et al. Lifestyle factors and colorectal cancer risk (2): a systematic review and meta-analysis of associations with leisure- time physical activity. Colorectal Dis 2009;11:689-701.   DOI   ScienceOn
45 Tamimi RM, Rosner B, Colditz GA. Evaluation of a breast cancer risk prediction model expanded to include category of prior benign breast disease lesion. Cancer 2010;116:4944-4953.   DOI   ScienceOn
46 Kivimäki M, Nyberg ST, Batty GD, Shipley MJ, Ferrie JE, Virtanen M, et al. Does adding information on job strain improve risk prediction for coronary heart disease beyond the standard Framingham risk score? The Whitehall II study. Int J Epidemiol 2011;40:1577-1584.   DOI   ScienceOn
47 Cauchi S, El Achhab Y, Choquet H, Dina C, Krempler F, Weitgasser R, et al. TCF7L2 is reproducibly associated with type 2 diabetes in various ethnic groups: a global metaanalysis. J Mol Med (Berl) 2007;85:777-782.   DOI   ScienceOn
48 Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 2007;445:881-885.   DOI   ScienceOn