Meta-analysis of gene-disease association studies: a Bayesian genetic model-free approach and its implementation_Chinese Journal of Evidence-Based Medicine

Authors：

SONG Jiaqi ,  JIN Zhichao

Department of Health Statistics, Second Military Medical University, Shanghai, 200433, P.R.China;

Corresponding author：

JIN Zhichao, Email: jzc109@hotmail.com

Keywords：

Genetic-disease association studies; Genetic model; Meta-analysis; Bayesian

DOI：

10.7507/1672-2531.201703116

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The choice of genetic models was main difficulty in the meta-analysis of gene-disease association studies. In this study, we made a further discussion about the genetic model-free approach that proposed by Minelli et al. The program that coded by JAGS and R was carried out to perform the Bayesian procedure. In a real example, several kinds of prior distribution were used, including non-informative prior distribution and external clinical prior information. Especially, compared to Minelli’s study, we introduced clinical prior information. The results indicated that the pooled results were rather robust no matters the prior distribution were non-informative or informative, especially when the number of included studies were large.

Citation： SONG Jiaqi, JIN Zhichao. Meta-analysis of gene-disease association studies: a Bayesian genetic model-free approach and its implementation. Chinese Journal of Evidence-Based Medicine, 2017, 17(9): 1106-1110. doi: 10.7507/1672-2531.201703116 Copy

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12.	Spiegelhalter DJ, Abrams KR, Myles JP. Bayesian approaches to clinical trials and health-care evaluation. Hoboken: John Wiley and Sons, 2004: 1-67.

1. Yu W, Gwinn M, Clyne M, et al. A navigator for human genome epidemiology. Nat Genet, 2008, 40(2): 124-125.
2. Khoury MJ, Little J, Gwinn M, et al. On the synthesis and interpretation of consistent but weak gene-disease associations in the era of genome-wide association studies. Int J Epidemiol, 2007, 36(2): 439-445.
3. Kavvoura FK, Ioannidis JP. Methods for meta-analysis in genetic association studies: a review of their potential and pitfalls. Hum Genet, 2008, 123(1): 1-14.
4. Lohmueller KE, Pearce CL, Pike M, et al. Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nat Genet, 2003, 33(2): 177-182.
5. Minelli C, Thompson JR, Abrams KR, et al. The choice of a genetic model in the meta-analysis of molecular association studies. Int J Epidemiol, 2005, 34(6): 1319-1328.
6. Minelli C, Thompson JR, Abrams KR, et al. Bayesian implementation of a genetic model-free approach to the meta-analysis of genetic association studies. Stat Med, 2005, 24(24): 3845-3861.
7. Gelman A. Prior distributions for variance parameters in hierarchical models. Bayesian Analysis, 2004, 6(1): 515-533.
8. Fujisawa T, Ikegami H, Kawaguchi Y, et al. Meta-analysis of association of insertion/deletion polymorphism of angiotensin I-converting enzyme gene with diabetic nephropathy and retinopathy. Diabetologia, 1998, 41(1): 47-53.
9. Jin Z, Liu S, He J. Angiotensin converting enzyme (ACE) gene polymorphism (ACEI/D) in hypertensive adults with ACE inhibitor-induced cough (Protocol). Cochrane Database Syst Rev, 2013, CD010310.
10. Garthwaite PH, Dickey JM. Quantifying expert opinion in linear regression problems. J Royal Statist Societ, 1988, 50(3): 462-474.
11. Gokhale DV, James S. Assessment of a prior distribution for the correlation coefficient in a bivariate normal distribution. J Royal Statist Societ, 1982, 45(2): 237-249.
12. Spiegelhalter DJ, Abrams KR, Myles JP. Bayesian approaches to clinical trials and health-care evaluation. Hoboken: John Wiley and Sons, 2004: 1-67.

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Meta-analysis of gene-disease association studies: a Bayesian genetic model-free approach and its implementation

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