Effects of environment, genetics and data analysis pitfalls in an esophageal cancer genome-wide association study

PLoS ONE

Volumn 2, Issue 9, 2007, Pages

  Statnikov, Alexander ^a   Li, Chun ^a,b   Aliferis, Constantin F ^a,c  

^a VANDERBILT UNIVERSITY   (United States)

^b VANDERBILT UNIVERSITY MEDICAL CENTER   (United States)

^c VANDERBILT UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CANCER PATIENT; CANCER RISK; CLINICAL ARTICLE; CONTROLLED STUDY; DATA ANALYSIS; ENVIRONMENTAL FACTOR; ESOPHAGUS CANCER; FAMILY HISTORY; FEMALE; GENETIC ASSOCIATION; GENETIC DATABASE; GENOME ANALYSIS; HEREDITY; HUMAN; MALE; SINGLE NUCLEOTIDE POLYMORPHISM; SYSTEMATIC ERROR; AGE; DNA MICROARRAY; DRINKING BEHAVIOR; ENVIRONMENT; ESOPHAGUS TUMOR; FAMILY HEALTH; GENETIC PREDISPOSITION; GENETICS; GENOME; GENOTYPE; PRINCIPAL COMPONENT ANALYSIS; RISK FACTOR; SMOKING;

AGE FACTORS; ALCOHOL DRINKING; ENVIRONMENT; ESOPHAGEAL NEOPLASMS; FAMILY HEALTH; FEMALE; GENETIC PREDISPOSITION TO DISEASE; GENOME, HUMAN; GENOTYPE; HUMANS; MALE; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; POLYMORPHISM, SINGLE NUCLEOTIDE; PRINCIPAL COMPONENT ANALYSIS; RISK FACTORS; SMOKING;

EID: 41549136980     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0000958     Document Type: Article

References (30)

1. Engle LJ, Simpson CL, Landers JE (2006) Using high-throughput SNP technologies to study cancer. Oncogene 25: 1594-1601.
2. Hirschhorn JN, Daly MJ (2005) Genome-wide association studies for common disease and complex traits. Nat Rev Genet 6: 95-108.
3. Wang WY, Barratt BJ, Clayton DG, Todd JA (2005) Genome-wide association studies: theoretical and practical concerns. Nat Rev Genet 6: 109-118.
4. Waddell M, Page D, Zhan F, Barlogie B, Shaughnessy J (2005) Predicting cancer susceptibility from sigle-nucleotide polymorphism data: a case study in multiple myeloma. Proceedings of the Fifth ACM SIGKDD Workshop on Data Mining in Bioinformatics (BIOKDD).
5. Mitra N, Ye TZ, Smith A, Chuai S, Kirchhoff T, et al. (2004) Localization of cancer susceptibility genes by genome-wide single-nucleotide polymorphism linkage-disequilibrium mapping. Cancer Res 64: 8116-8125.
6. Rudd MF, Webb EL, Matakidou A, Sellick GS, Williams RD, et al. (2006) Variants in the GH-IGF axis confer susceptibility to lung cancer. Genome Res 16: 693-701.
7. Ellis NA, Kirchhoff T, Mitra N, Ye TZ, Chuai S, et al. (2006) Localization of breast cancer susceptibility loci by genome-wide SNP linkage disequilibrium mapping. Genet Epidemiol 30: 48-61.
8. Hu N, Wang C, Hu Y, Yang HH, Giffen C, et al. (2005) Genome-wide association study in esophageal cancer using GeneChip mapping 10K array. Cancer Res 65: 2542-2546.
9. Czene K, Lichtenstein P, Hemminki K (2002) Environmental and heritable causes of cancer among 9.6 million individuals in the Swedish Family-Cancer Database. Int J Cancer 99: 260-266.
10. Lichtenstein P, Holm NV, Verkasalo PK, Iliadou A, Kaprio J, et al. (2000) Environmental and heritable factors in the causation of cancer-analyses of cohorts of twins from Sweden, Denmark and Finland. N Engl J Med 343: 78-85.
11. Hosking L, Lumsden S, Lewis K, Yeo A, McCarthy L, et al. (2004) Detection of genotyping errors by Hardy-Weinberg equilibrium testing. Eur J Hum Genet 12: 395-399.
12. Little RJA, Rubin DB (2002) Stastistical analysis with missing data. Hoboken, NJ: Wiley.
13. Batista GEAPA, Monard MC (2003) An Analysis of Four Missing Data Treatment Methods for Supervised Learning. Applied Artificial Intelligence 17: 519-533.
14. Benjamini Y, Hochberg Y (1995) Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological) 57: 289-300.
15. Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Ann Statist 29: 1165-1188.
16. Storey JD, Tibshirani R (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci U S A 100: 9440-9445.
17. Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Machine Learning 46: 389-422.
18. Vapnik VN (1998) Statistical learning theory. New York: Wiley.
19. Furey TS, Cristianini N, Duffy, N, Bednarski DW, Schummer M, et al. (2000) Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16: 906-914.
20. Statnikov A, Aliferis CF, Tsamardinos I, Hardin D, Levy S (2005) A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics 21: 631-643.
21. Wagner M, Naik DN, Pothen A, Kasukurti S, Devineni RR, et al. (2004) Computational protein biomarker prediction: a case study for prostate cancer. BMC Bioinformatics 5: 26.
22. Aliferis CF, Statnikov A, Tsamardinos I (2006) Challenges in the analysis of mass-throughput data: a technical commentary from the statistical machine learning perspective. Cancer Informatics 2: 133-162.
23. Ling CX, Huang J, Zhang H (2003) AUC: a statistically consistent and more discriminating measure than accuracy. Proceedings of the Eighteenth International Joint Conference of Artificial Intelligence (IJCAI).
24. Harrell FE Jr, Lee KL, Mark DB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15: 361-387.
25. Fawcett T (2003) ROC Graphs: Notes and Practical Considerations for Researchers. Technical Report, HPL-2003-4, HP Laboratories.
26. Braga-Neto UM, Dougherty ER (2004) Is cross-validation valid for small-sample microarray classification? Bioinformatics 20: 374-380.
27. Simon R, Radmacher MD, Dobbin K, McShane LM (2003) Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J Natl Cancer Inst 95: 14-18.
28. Dupuy A, Simon RM (2007) Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst 99: 147-157.
29. Barrett JC, Cardon LR (2006) Evaluating coverage of genome-wide association studies. Nat Genet 38: 659-662.
30. Nicolae DL, Wen X, Voight BF, Cox NJ (2006) Coverage and characteristics of the Affymetrix GeneChip Human Mapping 100K SNP set. PLoS Genet 2: e67.