High-volume "-omics" technologies and the future of molecular epidemiology

Epidemiology

Volumn 17, Issue 5, 2006, Pages 490-491

  Thomas, Duncan C ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER;

ATM GENE; GENE; GENE EXPRESSION REGULATION; GENETIC ASSOCIATION; GENETIC CONSERVATION; GENETIC POLYMORPHISM; GENOME ANALYSIS; GENOMICS; METABOLOMICS; MOLECULAR EPIDEMIOLOGY; PRIORITY JOURNAL; PROTEOMICS; RISK FACTOR; SHORT SURVEY; SIGNAL TRANSDUCTION; SINGLE NUCLEOTIDE POLYMORPHISM; TECHNOLOGY; TRANSCRIPTOMICS;

EPIDEMIOLOGY, MOLECULAR; GENETIC PREDISPOSITION TO DISEASE; GENOMICS; HUMANS; MOLECULAR EPIDEMIOLOGY; POLYMORPHISM, GENETIC; PROTEOMICS; RISK FACTORS;

EID: 33747119698     PISSN: 10443983     EISSN: None     Source Type: Journal    
DOI: 10.1097/01.ede.0000229950.29674.68     Document Type: Short Survey

References (26)

1. Hunter DJ. Genomics and proteomics in epidemiology: treasure trove or "high-tech stamp collecting"? Epidemiology. 2006;17:xx.
2. Laird PW. The power and promise of DNA methylation markers. Nat Rev Cancer. 2003;3:253-266.
3. Goodman L. Hypothesis - limited research. Genome Res. 1999;9:673-674.
4. Hoh J, Ott J. Mathematical multi-locus approaches to localizing complex human trait genes. Nat Rev Genet. 2003;4:701-709.
5. Thomas DC. The need for a comprehensive approach to complex pathways in molecular epidemiology [Editorial]. Cancer Epidemiol Biomarkers Prev. 2005;14:557-559.
6. Rebbeck TR, Spitz M, Wu X. Assessing the function of genetic variants in candidate gene association studies. Nat Rev Genet. 2004;5:589-597.
7. Hung RJ, Brennan P, Malaveille C, et al. Using hierarchical modeling in genetic association studies with multiple markers: application to a case- control study of bladder cancer. Cancer Epidemiol Biomarkers Prev. 2004;13:1013-1021.
8. Conti DV, Cortessis V, Molitor J, et al. Bayesian modeling of complex metabolic pathways. Hum Hered. 2003;56:83-93.
9. Roeder K, Bacanu SA, Wasserman L, et al. Using linkage genome scans to improve power of association in genome scans. Am J Hum Genet. 2006;78:243-252.
10. Whittemore AS. Genetic association studies: time for a new paradigm? Cancer Epidemiol Biomarkers Prev. 2005;14:1359.
11. Cheung VG, Spielman RS, Ewens KG, et al. Mapping determinants of human gene expression by regional and genome-wide association. Nature. 2005;437:1365-1369.
12. Marchini J, Donnelly P, Cardon LR. Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat Genet. 2005;37:413-417.
13. Lin S, Chakravarti A, Cutler DJ. Exhaustive allelic transmission disequilibrium tests as a new approach to genome-wide association studies. Nat Genet. 2004;36:1181-1188.
14. Sellers TA, Yates JR. Review of proteomics with applications to genetic epidemiology. Genet Epidemiol. 2003;24:83-98.
15. Spiegelman D, Carroll RJ, Kipnis V. Efficient regression calibration for logistic regression in main study/internal validation study designs with an imperfect reference instrument. Stat Med. 2001;20:139-160.
16. Kraft P, Hunter D. Integrating epidemiology and genetic association: the challenge of gene- environment interaction. Philos Trans R Soc Lond B Biol Sci. 2005;360:1609-1616.
17. Davey Smith G, Ebrahim S. What can mendelian randomisation tell us about modifiable behavioural and environmental exposures? BMJ. 2005;330:1076-1079.
18. Yasui Y, Pepe M, Thompson ML, et al. A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection. Biostatistics. 2003;4:449-463.
19. Feng Z, Prentice R, Srivastava S. Research issues and strategies for genomic and proteomic biomarker discovery and validation: a statistical perspective. Pharmacogenomics. 2004;5:709-719.
20. Thomas DC. Are we ready for genome-wide association studies? Cancer Epidemiol Biomarkers Prev. In press.
21. Clayton DG, Walker NM, Smyth DJ, et al. Population structure, differential bias and genomic control in a large-scale, case- control association study. Nat Genet. 2005;37:1243-1246.
22. Rebbeck TR, Martinez ME, Sellers TA, et al. Genetic variation and cancer: Improving the environment for publication of association studies. Cancer Epidemiol Biomarkers Prev. 2004;13:1985-1986.
23. Skol AD, Scott LJ, Abecasis GR, et al. Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet. 2006;38:209-213.
24. Ulrich CM, Robien K, McLeod HL. Cancer pharmacogenetics: polymorphisms, pathways and beyond. Nat Rev Cancer. 2003;3:912-920.
25. Stoughton RB, Friend SH. How molecular profiling could revolutionize drug discovery. Nat Rev Drug Discov. 2005;4:345-350.
26. Little J, Sharp L, Khoury MJ, et al. The epidemiologic approach to pharmacogenomics. Am J Pharmacogenomics. 2005;5:1-20.