Abstract
Epidemiology has made significant contribution to better understanding cancer etiology and improving public health. Recently, with increasingly available genetic and molecular data, methodology in cancer epidemiology has been greatly progressing through incorporation of those data. This chapter focuses on some topics in Genome -Wide Association Studies and also provides some discussion of investigating etiologic heterogeneity among molecular subtypes of cancer.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hindorff LA, MacArthur J (European Bioinformatics Institute), Morales J (European Bioinformatics Institute), et al. A catalog of published genome-wide association studies. www.genome.gov/gwastudies (2015).
Michailidou K, Hall P, Gonzalez-Neira A, et al. Large-scale genoty** identifies 41 new loci associated with breast cancer risk. Nat Genet. 2013;45(4):353–61, 361e351–352.
Venter JC, Adams MD, Myers EW, et al. The sequence of the human genome. Science. 2001;291(5507):1304–51.
Lander ES, Linton LM, Birren B, et al. Initial sequencing and analysis of the human genome. Nature. 2001;409(6822):860–921.
International HapMap, C. The international HapMap project. Nature. 2003;426(6968):789–96.
International HapMap, C. A haplotype map of the human genome. Nature. 2005;437(7063):1299–320.
International HapMap, C, Frazer KA, Ballinger DG, et al. A second generation human haplotype map of over 3.1 million SNPs. Nature. 2007;449(7164):851–61.
Balding DJ. A tutorial on statistical methods for population association studies. Nat Rev Genet. 2006;7(10):781–91.
Risch N, Merikangas K. The future of genetic studies of complex human diseases. Science. 1996;273(5281):1516–7.
Marchini J, Howie B, Myers S, McVean G, Donnelly P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet. 2007;39(7):906–13.
Amundadottir L, Kraft P, Stolzenberg-Solomon RZ, et al. Genome-wide association study identifies variants in the ABO locus associated with susceptibility to pancreatic cancer. Nat Genet. 2009;41(9):986–90.
Devlin B, Roeder K. Genomic control for association studies. Biometrics. 1999;55(4):997–1004.
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38(8):904–9.
Pe’er I, Yelensky R, Altshuler D, Daly MJ. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet Epidemiol. 2008;32(4):381–5.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995; 289–300.
Efron B, Tibshirani R. Empirical Bayes methods and false discovery rates for microarrays. Genet Epidemiol. 2002;23(1):70–86.
Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci USA. 2003;100(16):9440–5.
Storey JD. A direct approach to false discovery rates. J R Stat Soc Ser B Stat Methodol. 2002;64(3):479–98.
Panagiotou OA, Ioannidis JPA. What should the genome-wide significance threshold be? Empirical replication of borderline genetic associations. Int J Epidemiol. 2012;41(1):273–86.
Wacholder S, Chanock S, Garcia-Closas M, El Ghormli L, Rothman N. Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst. 2004;96(6):434–42.
Consortium, W.T.C.C. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447(7145):661–78.
Wakefield J. Bayes factors for genome-wide association studies: comparison with P-values. Genet Epidemiol. 2009;33(1):79–86.
Garner C. Upward bias in odds ratio estimates from genome-wide association studies. Genet Epidemiol. 2007;31(4):288–95.
Zollner S, Pritchard JK. Overcoming the winner’s curse: estimating penetrance parameters from case-control data. Am J Hum Genet. 2007;80(4):605–15.
Higgins JP, Thompson SG, Spiegelhalter DJ. A re-evaluation of random-effects meta-analysis. J R Stat Soc Ser A. 2009;172(1):137–59.
Pfeiffer RM, Gail MH, Pee D. On combining data from genome-wide association studies to discover disease-associated SNPs. Stat Sci. 2009;24(4):547–60.
He Q, Cai T, Liu Y, et al. Prioritizing individual genetic variants after kernel machine testing using variable selection. Genet Epidemiol. 2016;40(8):722–31.
Yu K, Li Q, Bergen AW, et al. Pathway analysis by adaptive combination of P-values. Genet Epidemiol. 2009;33(8):700–9.
Schork AJ, Thompson WK, Pham P, et al. All SNPs are not created equal: genome-wide association studies reveal a consistent pattern of enrichment among functionally annotated SNPs. PLoS Genet. 2013;9(4):e1003449.
Lewinger JP, Conti DV, Baurley JW, Triche TJ, Thomas DC. Hierarchical Bayes prioritization of marker associations from a genome-wide association scan for further investigation. Genet Epidemiol. 2007;31(8):871–82.
Pickrell JK. Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am J Hum Genet. 2014;94(4):559–73.
Mechanic LE, Chen HS, Amos CI, et al. Next generation analytic tools for large scale genetic epidemiology studies of complex diseases. Genet Epidemiol. 2012;36(1):22–35.
Khoury MJ, Wacholder S. Invited commentary: from genome-wide association studies to gene-environment-wide interaction studies–challenges and opportunities. Am J Epidemiol. 2009;169(2):227–30 discussion 234–225.
Aschard H, Chen J, Cornelis MC, Chibnik LB, Karlson EW, Kraft P. Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases. Am J Hum Genet. 2012;90(6):962–72.
Hein R, Flesch-Janys D, Dahmen N, et al. A genome-wide association study to identify genetic susceptibility loci that modify ductal and lobular postmenopausal breast cancer risk associated with menopausal hormone therapy use: a two-stage design with replication. Breast Cancer Res Treat. 2013;138(2):529–42.
Kraft P, Yen YC, Stram DO, Morrison J, Gauderman WJ. Exploiting gene-environment interaction to detect genetic associations. Hum Hered. 2007;63(2):111–9.
Cornelis MC, Tchetgen EJ, Liang L, et al. Gene-environment interactions in genome-wide association studies: a comparative study of tests applied to empirical studies of type 2 diabetes. Am J Epidemiol. 2012;175(3):191–202.
Aschard H, Lutz S, Maus B, et al. Challenges and opportunities in genome-wide environmental interaction (GWEI) studies. Hum Genet. 2012;131(10):1591–613.
Aschard H. A perspective on interaction effects in genetic association studies. Genet Epidemiol. 2016;40(8):678–88.
Chen S, Parmigiani G. Meta-analysis of BRCA1 and BRCA2 penetrance. J Clin Oncol (Off J Am Soc Clin Oncol). 2007;25(11):1329–33.
Gail MH, Brinton LA, Byar DP, et al. Projecting individualized probabilities of develo** breast cancer for white females who are being examined annually. J Natl Cancer Inst. 1989;81(24):1879–86.
Fisher B, Costantino JP, Wickerham DL, et al. Tamoxifen for prevention of breast cancer: report of the National Surgical Adjuvant Breast and Bowel Project P-1 Study. J Natl Cancer Inst. 1998;90(18):1371–88.
Wacholder S, Hartge P, Prentice R, et al. Performance of common genetic variants in breast-cancer risk models. N Engl J Med. 2010;362(11):986–93.
Chatterjee N, Shi J, Garcia-Closas M. Develo** and evaluating polygenic risk prediction models for stratified disease prevention. Nat Rev Genet. 2016;17(7):392–406.
Sampson JN, Wheeler WA, Yeager M, et al. Analysis of heritability and shared heritability based on genome-wide association studies for thirteen cancer types. J Natl Cancer Inst. 2015;107(12):djv279.
Lee SH, Wray NR, Goddard ME, Visscher PM. Estimating missing heritability for disease from genome-wide association studies. Am J Hum Genet. 2011;88(3):294–305.
Mavaddat N, Pharoah PD, Michailidou K, et al. Prediction of breast cancer risk based on profiling with common genetic variants. J Natl Cancer Inst. 2015;107(5).
Machiela MJ, Chen CY, Chen C, Chanock SJ, Hunter DJ, Kraft P. Evaluation of polygenic risk scores for predicting breast and prostate cancer risk. Genet Epidemiol. 2011;35(6):506–14.
Gail MH. Personalized estimates of breast cancer risk in clinical practice and public health. Stat Med. 2011;30(10):1090–104.
Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol. 2004;159(9):882–90.
Kuchiba A, Morikawa T, Yamauchi M, et al. Body mass index and risk of colorectal cancer according to fatty acid synthase expression in the nurses’ health study. J Natl Cancer Inst. 2012;104(5):415–20.
Kalbfleisch JD, Prentice RL. The statistical analysis of failure time data, vol. 360. New York: Wiley; 2011.
Lunn M, McNeil D. Applying Cox regression to competing risks. Biometrics. 1995;51(2):524–32.
Begg CB, Zhang ZF: Statistical analysis of molecular epidemiology studies employing case-series. Cancer Epidemiol Biomark Prev: A Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology. 1994;3(2):173–5.
Wang M, Spiegelman D, Kuchiba A, et al. Statistical methods for studying disease subtype heterogeneity. Stat Med. 2016;35(5):782–800.
Hughes LA, Khalid-de Bakker CA, Smits KM, et al. The CpG island methylator phenotype in colorectal cancer: progress and problems. Biochim Biophys Acta. 2012;1825(1):77–85.
Tanaka N, Huttenhower C, Nosho K, et al. Novel application of structural equation modeling to correlation structure analysis of CpG island methylation in colorectal cancer. Am J Pathol. 2010;177(6):2731–40.
Chia VM, Newcomb PA, Bigler J, Morimoto LM, Thibodeau SN, Potter JD. Risk of microsatellite-unstable colorectal cancer is associated jointly with smoking and nonsteroidal anti-inflammatory drug use. Cancer Res. 2006;66(13):6877–83.
Limsui D, Vierkant RA, Tillmans LS, et al. Cigarette smoking and colorectal cancer risk by molecularly defined subtypes. J Natl Cancer Inst. 2010;102(14):1012–22.
Samowitz WS, Albertsen H, Sweeney C, et al. Association of smoking, CpG island methylator phenotype, and V600E BRAF mutations in colon cancer. J Natl Cancer Inst. 2006;98(23):1731–8.
Poynter JN, Haile RW, Siegmund KD, et al. Associations between smoking, alcohol consumption, and colorectal cancer, overall and by tumor microsatellite instability status. Cancer Epidemiol Biomark Prev: A Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology. 2009;18(10):2745–50.
Rozek LS, Herron CM, Greenson JK, et al. Smoking, gender, and ethnicity predict somatic BRAF mutations in colorectal cancer. Cancer Epidemiol Biomark Prev: A Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology. 2010;19(3):838–43.
Wang M, Kuchiba A, Ogino S. A meta-regression method for studying etiological heterogeneity across disease subtypes classified by multiple biomarkers. Am J Epidemiol. 2015;182(3):263–70.
Chatterjee N, Sinha S, Diver WR, Feigelson HS. Analysis of cohort studies with multivariate and partially observed disease classification data. Biometrika. 2010;97(3):683–98.
Chatterjee N. A two-stage regression model for epidemiological studies with multivariate disease classification data. J Am Stat Assoc. 2004;99(465):127–38.
Rosner B, Glynn RJ, Tamimi RM, et al. Breast cancer risk prediction with heterogeneous risk profiles according to breast cancer tumor markers. Am J Epidemiol. 2013;178(2):296–308.
Begg CB, Zabor EC. Detecting and exploiting etiologic heterogeneity in epidemiologic studies. Am J Epidemiol. 2012;176(6):512–8.
Bhattacharjee S, Rajaraman P, Jacobs KB, et al. A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits. Am J Hum Genet. 2012;90(5):821–35.
Begg CB, Zabor EC, Bernstein JL, Bernstein L, Press MF, Seshan VE. A conceptual and methodological framework for investigating etiologic heterogeneity. Stat Med. 2013;32(29):5039–52.
Schork NJ, Murray SS, Frazer KA, Topol EJ. Common versus rare allele hypotheses for complex diseases. Curr Opin Genet Dev. 2009;19(3):212–9.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Kuchiba, A. (2017). Evaluation of Cancer Risk in Epidemiologic Studies with Genetic and Molecular Data. In: Matsui, S., Crowley, J. (eds) Frontiers of Biostatistical Methods and Applications in Clinical Oncology. Springer, Singapore. https://doi.org/10.1007/978-981-10-0126-0_18
Download citation
DOI: https://doi.org/10.1007/978-981-10-0126-0_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0124-6
Online ISBN: 978-981-10-0126-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)