Genetic association and gene-environment interaction: A new method for overcoming the lack of exposure information in controls

American Journal of Epidemiology

Volumn 173, Issue 2, 2011, Pages 225-235

  Kazma, Rémi ^a,b   Babron, Marie Claude ^b,c   Génin, Emmanuelle ^b,c  

^a UNIVERSITÉ PARIS SACLAY   (France)

^b INSERM   (France)

^c UNIVERSITÉ PARIS DIDEROT   (France)

Author keywords

epidemiologic research design; genetic predisposition to disease; genotype environment interaction; logistic models; multinomial model; partial information; population control

Indexed keywords

COMPUTER SIMULATION; COST-BENEFIT ANALYSIS; DISEASE CONTROL; ENVIRONMENTAL FACTOR; EPIDEMIOLOGY; GENE EXPRESSION; GENETIC ANALYSIS; GENOME; LOGISTICS; PERFORMANCE ASSESSMENT; REGRESSION ANALYSIS;

ARTICLE; ENVIRONMENTAL EXPOSURE; GENETIC ASSOCIATION; GENOTYPE ENVIRONMENT INTERACTION; HEREDITY; HUMAN; LOGISTIC REGRESSION ANALYSIS; SIMULATION;

ENVIRONMENT; ENVIRONMENTAL EXPOSURE; EPIDEMIOLOGIC RESEARCH DESIGN; GENETIC ASSOCIATION STUDIES; GENETIC PREDISPOSITION TO DISEASE; HUMANS; LOGISTIC MODELS;

EID: 78650637042     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kwq352     Document Type: Article

References (26)

1. Lander ES, Schork NJ. Genetic dissection of complex traits. Science. 1994;265(5181):2037-2048.
2. Manolio TA, Brooks LD, Collins FS. A HapMap harvest of insights into the genetics of common disease. J Clin Invest. 2008;118(5):1590-1605.
3. Luca D, Ringquist S, Klei L, et al. On the use of general control samples for genome-wide association studies: genetic matching highlights causal variants. Am J Hum Genet. 2008; 82(2):453-463.
4. Nelson MR, Bryc K, King KS, et al. The Population Reference Sample, POPRES: a resource for population, disease, and pharmacological genetics research. Am J Hum Genet. 2008; 83(3):347-358.
5. GAIN Collaborative Research Group. New models of collaboration in genome-wide association studies: the Genetic Association Information Network. Nat Genet. 2007;39(9): 1045-1051.
6. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447(7145):661-678.
7. Wichmann HE, Gieger C, Illig T. KORA-gen-resource for population genetics, controls and a broad spectrum of disease phenotypes. Gesundheitswesen. 2005;67(suppl 1):S26-S30.
8. Selinger-Leneman H, Genin E, Norris JM, et al. Does accounting for gene-environment (G x E) interaction increase the power to detect the effect of a gene in a multifactorial disease? Genet Epidemiol. 2003;24(3):200-207.
9. Kraft P, Yen YC, Stram DO, et al. Exploiting gene-environment interaction to detect genetic associations. Hum Hered. 2007; 63(2):111-119.
10. Murcray CE, Lewinger JP, Gauderman WJ. Gene-environment interaction in genome-wide association studies. Am J Epidemiol. 2009;169(2):219-226.
11. Khoury MJ, Flanders WD. Nontraditional epidemiologic approaches in the analysis of gene-environment interaction: case-control studies with no controls! Am J Epidemiol. 1996; 144(3):207-213.
12. Piegorsch WW, Weinberg CR, Taylor JA. Non-hierarchical logistic models and case-only designs for assessing susceptibility in population-based case-control studies. Stat Med. 1994;13(2):153-162.
13. Lindström S, Yen YC, Spiegelman D, et al. The impact of gene-environment dependence and misclassification in genetic association studies incorporating gene-environment interactions. Hum Hered. 2009;68(3):171-181.
14. Dobson AJ. Nominal and ordinal logistic regression. In: An Introduction to Generalized Linear Models. Boca Raton, FL: Chapman & Hall, Inc; 2002:135-150.
15. Kleinbaum DG, Klein M. Polytomous logistic regression. In: Logistic Regression. New York, NY: Springer-Verlag; 2002: 267-292.
16. Morris AP, Lindgren CM, Zeggini E, et al. A powerful approach to sub-phenotype analysis in population-based genetic association studies. Genet Epidemiol. 2010;34(4):335-343.
17. R Development Core Team. R Version 2.9.1. Vienna, Austria: R Foundation for Statistical Computing; 2009.
18. Stata Corporation. Stata Statistical Software, Release 10. College Station, TX: StataCorp LP; 2007.
19. Albert PS, Ratnasinghe D, Tangrea J, et al. Limitations of the case-only design for identifying gene-environment interactions. Am J Epidemiol. 2001;154(8):687-693.
20. Gatto NM, Campbell UB, Rundle AG, et al. Further development of the case-only design for assessing gene-environment interaction: evaluation of and adjustment for bias. Int J Epidemiol. 2004;33(5):1014-1024.
21. Goldstein AM, Andrieu N. Detection of interaction involving identified genes: available study designs. J Natl Cancer Inst Monogr. 1999;(26):49-54.
22. Schmidt S, Schaid DJ. Potential misinterpretation of the case-only study to assess gene-environment interaction. Am J Epidemiol. 1999;150(8):878-885.
23. Guan W, Liang L, Boehnke M, et al. Genotype-based matching to correct for population stratification in large-scale case-control genetic association studies. Genet Epidemiol. 2009; 33(6):508-517.
24. Umbach DM, Weinberg CR. Designing and analysing case-control studies to exploit independence of genotype and exposure. Stat Med. 1997;16(15):1731-1743.
25. Gauderman WJ. Sample size requirements for matched case-control studies of gene-environment interaction. Stat Med. 2002;21(1):35-50.
26. Kazma R, Bonaïti-Pellié C, Norris JM, et al. On the use of sibling recurrence risks to select environmental factors liable to interact with genetic risk factors. Eur J Hum Genet. 2010; 18(1):88-94.