Case-only genome-wide interaction study of disease risk, prognosis and treatment

Genetic Epidemiology

Volumn 34, Issue 1, 2010, Pages 7-15

  Pierce, Brandon L ^a   Ahsan, Habibul ^a,b  

^a UNIVERSITY OF CHICAGO   (United States)

^b Columbia University ^*  (United States)

Author keywords

Case only study; Efficiency; Genome wide association; Pharmacogenetics

Indexed keywords

CASE CONTROL STUDY; CASE ONLY GENOME WIDE INTERACTION; CASE STUDY; CLINICAL STUDY; ENVIRONMENTAL EXPOSURE; GENE INTERACTION; GENE LOCUS; GENETIC SCREENING; GENETIC SUSCEPTIBILITY; GENETIC VARIABILITY; GENOME; HUMAN; METHODOLOGY; OBSERVATIONAL STUDY; PHARMACOGENETICS; POPULATION; PROGNOSIS; REVIEW; RISK; SAMPLE SIZE; TREATMENT RESPONSE;

ALLELES; DISEASE; EPIDEMIOLOGIC STUDIES; EPISTASIS, GENETIC; GENE FREQUENCY; GENETIC PREDISPOSITION TO DISEASE; GENOME-WIDE ASSOCIATION STUDY; HUMANS; PHARMACOGENETICS; PROGNOSIS; RISK FACTORS; THERAPEUTICS;

EID: 75649111227     PISSN: 07410395     EISSN: 10982272     Source Type: Journal    
DOI: 10.1002/gepi.20427     Document Type: Review

References (47)

1. Albert PS, Ratnasinghe D, Tangrea J, Wacholder S. 2001. Limitations of the case-only design for identifying gene-environment interactions. Am J Epidemiol 154:687-693.
2. Ambrosone CB, Rebbeck TR, Morgan GJ, Albain KS, Calle EE, Evans WE, Hayes DF, Kushi LH, McLeod HL, Rowland JH, Ulrich CM. 2006. New developments in the epidemiology of cancer prognosis: traditional and molecular predictors of treatment response and survival. Cancer Epidemiol Biomarkers Prev 15:2042-2046.
3. Andrieu N, Goldstein AM. 1996. Use of relatives of cases as controls to identify risk factors when an interaction between environmental and genetic factors exists. Int J Epidemiol 25:649-657.
4. Andrieu N, Goldstein AM. 1998. Epidemiologic and genetic approaches in the study of gene-environment interaction: an overview of available methods. Epidemiol Rev 20:137-147.
5. Andrieu N, Goldstein AM. 2004. The case-combined-control design was efficient in detecting gene-environment interactions. J Clin Epidemiol 57:662-671.
6. Andrieu N, Dondon MG, Goldstein AM. 2005. Increased power to detect gene-environment interaction using siblings controls. Ann Epidemiol 15:705-711.
7. Ardlie KG, Kruglyak L, Seielstad M. 2002. Patterns of linkage disequilibrium in the human genome. Nat Rev Genet 3:299-309.
8. Begg CB, Zhang ZF. 1994. Statistical analysis of molecular epidemiology studies employing case-series. Cancer Epidemiol Biomarkers Prev 3:173-175.
9. Botto LD, Khoury MJ. 2001. Commentary: facing the challenge of gene-environment interaction: the two-by-four table and beyond. Am J Epidemiol 153:1016-1020.
10. Botto LD, Khoury MJ. 2004. Facing the challenge of complex genotypes and gene-environment interaction: the basic epidemiologic units in case-control and case-only designs. In: Khoury MJ, LittleJ, BurkeW, editors. Human Genome Epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent Disease. New York: Oxford University Press. p 111-126.
11. Carlson CS. 2006. Agnosticism and equity in genome-wide association studies. Nat Genet 38:605-606.
12. Demark-Wahnefried W, Aziz NM, Rowland JH, Pinto BM. 2005. Riding the crest of the teachable moment: promoting long-term health after the diagnosis of cancer. J Clin Oncol 23:5814-5830.
13. Evans DM, Marchini J, Morris AP, Cardon LR. 2006. Two-stage two-locus models in genome-wide association. PLoS Genet 2:e157.
14. Fagerholm R, Hofstetter B, Tommiska J, Aaltonen K, Vrtel R, Syrjakoski K, Kallioniemi A, Kilpivaara O, Mannermaa A, Kosma VM, Uusitupa M, Eskelinen M, Kataja V, Aittomaki K, von Smitten K, Heikkila P, Lukas J, Holli K, Bartkova J, Blomqvist C, Bartek J, Nevanlinna H. 2008. NAD(P)H:quinone oxidoreductase 1 NQO1*2 genotype (P187S) is a strong prognostic and predictive factor in breast cancer. Nat Genet 40:844-853.
15. Gatto NM, Campbell UB, Rundle AG, Ahsan H. 2004. Further development of the case-only design for assessing gene-environment interaction: evaluation of and adjustment for bias. Int J Epidemiol 33:1014-1024.
16. Gauderman WJ. 2002. Sample size requirements for association studies of gene-gene interaction. Am J Epidemiol 155:478-484.
17. Gauderman WJ, Morrison JM. 2006. QUANTO 1.1: a computer program for power and sample size calculations for genetic-epidemiology studies, http://hydra.usc.edu/gxe.
18. Goldstein AM, Falk RT, Korczak JF, Lubin JH. 1997. Detecting gene-environment interactions using a case-control design. Genet Epidemiol 14:1085-1089.
19. Goldstein AM, Dondon MG, Andrieu N. 2006. Unconditional analyses can increase efficiency in assessing gene-environment interaction of the case-combined-control design. Int J Epidemiol 35:1067-1073.
20. Hunter DJ. 2005. Gene-environment interactions in human diseases. Nat Rev Genet 6:287-298.
21. Hwang SJ, Beaty TH, Liang KY, Coresh J, Khoury MJ. 1994. Minimum sample size estimation to detect gene-environment interaction in case-control designs. Am J Epidemiol 140:1029-1037.
22. Kazma R, Dizier MH, Guilloud-Bataille M, Bonaiti-Pellie C, Genin E. 2007. Power comparison of different methods to detect genetic effects and gene-environment interactions. BMC Proc 1:S74.
23. Khoury MJ, Flanders WD. 1996. Nontraditional epidemiologic approaches in the analysis of gene-environment interaction: case-control studies with no controls! Am J Epidemiol 144:207-213.
24. Kraft P, Hunter D. 2005. Integrating epidemiology and genetic association: the challenge of gene-environment interaction. Philos Trans R Soc Lond B Biol Sci 360:1609-1616.
25. Lake SL, Laird NM. 2004. Tests of gene-environment interaction for case-parent triads with general environmental exposures. Ann Hum Genet 68:55-64.
26. Laupacis A,Wells G, Richardson WS, Tugwell P. 1994. Users' guides to the medical literature. V. How to use an article about prognosis. Evidence-Based Medicine Working Group. J Am Med Assoc 272:234-237.
27. Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G. 2008. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 27:1133-1163.
28. Link E, Parish S, Armitage J, Bowman L, Heath S, Matsuda F, Gut I, Lathrop M, Collins R. 2008. SLCO1B1 variants and statin-induced myopathy - a genomewide study. N Engl J Med 359:789-799.
29. Mak K, Kum CK. 2005. How to appraise a prognostic study. World J Surg 29:567-569.
30. Marchini J, Donnelly P, Cardon LR. 2005. Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat Genet 37:413-417.
31. Marchini J, Howie B, Myers S, McVean G, Donnelly P. 2007. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet 39:906-913.
32. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP, Hirschhorn JN. 2008. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 9:356-369.
33. Melzer D, Perry JR, Hernandez D, Corsi AM, Stevens K, Rafferty I, Lauretani F, Murray A, Gibbs JR, Paolisso G, Rafiq S, Simon-Sanchez J, Lango H, Scholz S, Weedon MN, Arepalli S, Rice N, Washecka N, Hurst A, Britton A, Henley W, van de Leemput J, Li R, Newman AB, Tranah G, Harris T, Panicker V, Dayan C, Bennett A, McCarthy MI, Ruokonen A, Jarvelin MR, Guralnik J, Bandinelli S, Frayling TM, Singleton A, Ferrucci L. 2008. A genome-wide association study identifies protein quantitative trait loci (pQTLs). PLoS Genet 4:e1000072.
34. Ottman R. 1996. Gene-environment interaction: definitions and study designs. Prev Med 25:764-770.
35. Piegorsch WW, Weinberg CR, Taylor JA. 1994. Non-hierarchical logistic models and case-only designs for assessing susceptibility in population-based case-control studies. Stat Med 13:153-162.
36. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. 2006. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904-909.
37. Pritchard JK, Stephens M, Donnelly P. 2000. Inference of population structure using multilocus genotype data. Genetics 155:945-959.
38. Roeder K, Devlin B, Wasserman L. 2007. Improving power in genome-wide association studies: weights tip the scale. Genet Epidemiol 31:741-747.
39. Rothman KJ, Greenland S, Lash TL. 2008. Modern Epidemiology. Philadelphia, PA: Lippincott Williams & Wilkins.
40. Schmidt S, Schaid DJ. 1999. Potential misinterpretation of the case-only study to assess gene-environment interaction. Am J Epidemiol 150:878-885.
41. Skol AD, Scott LJ, Abecasis GR, BoehnkeM. 2007. Optimal designs for two-stage genome-wide association studies. Genet Epidemiol 31:776-788.
42. Smith PG, Day NE. 1984. The design of case-control studies: the influence of confounding and interaction effects. Int J Epidemiol 13:356-365.
43. Umbach DM, Weinberg CR. 2000. The use of case-parent triads to study joint effects of genotype and exposure. Am J Hum Genet 66:251-261.
44. Vallbohmer D, Lenz HJ. 2006. Predictive and prognostic molecular markers in outcome of esophageal cancer. Dis Esophagus 19:425-432.
45. Wang LY, Lee WC. 2008. Population stratification bias in the case-only study for gene-environment interactions. Am J Epidemiol 168:197-201.
46. Yang Q, Khoury MJ, Flanders WD. 1997. Sample size requirements in case-only designs to detect gene-environment interaction. Am J Epidemiol 146:713-720.
47. Yang Q, Khoury MJ, Sun F, Flanders WD. 1999. Case-only design to measure gene-gene interaction. Epidemiology 10:167-170.