Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables

Burgess, S., Thompson, S. G., Andrews, G., Samani, N. J., Hall, A., Whincup, P., Morris, R., Lawlor, D. A., Davey Smith, G., Timpson, N., Ebrahim, S., Ben-Shlomo, Y., Brown, M., Ricketts, S., Sandhu, M., Reiner, A., Psaty, B., Lange, L., Cushman, M., Hung, J., Thompson, P., Beilby, J., Warrington, N., Palmer, L. J., Nordestgaard, B. G., Tybjaerg-Hansen, A., Zacho, J., Wu, C., Lowe, G., Tzoulaki, I., Kumari, M., Yamamoto, J. F., Chiodini, B., Franzosi, M., Hankey, G. J., Jamrozik, K., Palmer, L., Rimm, E., Pai, J., Heckbert, S., Bis, J., Anand, S., Engert, J., Collins, R., Clarke, R., Melander, O., Berglund, G., Ladenvall, P., Johansson, L., Jansson, J.-H., Hallmans, G., Hingorani, A., Humphries, S., Manson, J., Watkins, H., Hopewell, J., Saleheen, D., Frossard, R., Danesh, J., Sattar, N., Robertson, M., Shepherd, J., Schaefer, E., Hofman, A., Witteman ,J. C. M., Kardys, I., De Faire, U., Bennet, A., Ford, I., Packard, C., Casas, J. P., Smeeth, L., Wensley, F., Bowden, J., Di Angelantonio, E., Gao, P., Shah, T., Verzilli, C., Walker, M. and Whittaker, J. (2010) Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables. Statistics in Medicine, 29 12: 1298-1311. doi:10.1002/sim.3843


Author Burgess, S.
Thompson, S. G.
Andrews, G.
Samani, N. J.
Hall, A.
Whincup, P.
Morris, R.
Lawlor, D. A.
Davey Smith, G.
Timpson, N.
Ebrahim, S.
Ben-Shlomo, Y.
Brown, M.
Ricketts, S.
Sandhu, M.
Reiner, A.
Psaty, B.
Lange, L.
Cushman, M.
Hung, J.
Thompson, P.
Beilby, J.
Warrington, N.
Palmer, L. J.
Nordestgaard, B. G.
Tybjaerg-Hansen, A.
Zacho, J.
Wu, C.
Lowe, G.
Tzoulaki, I.
Kumari, M.
Yamamoto, J. F.
Chiodini, B.
Franzosi, M.
Hankey, G. J.
Jamrozik, K.
Palmer, L.
Rimm, E.
Pai, J.
Heckbert, S.
Bis, J.
Anand, S.
Engert, J.
Collins, R.
Clarke, R.
Melander, O.
Berglund, G.
Ladenvall, P.
Johansson, L.
Jansson, J.-H.
Hallmans, G.
Hingorani, A.
Humphries, S.
Manson, J.
Watkins, H.
Hopewell, J.
Saleheen, D.
Frossard, R.
Danesh, J.
Sattar, N.
Robertson, M.
Shepherd, J.
Schaefer, E.
Hofman, A.
Witteman ,J. C. M.
Kardys, I.
De Faire, U.
Bennet, A.
Ford, I.
Packard, C.
Casas, J. P.
Smeeth, L.
Wensley, F.
Bowden, J.
Di Angelantonio, E.
Gao, P.
Shah, T.
Verzilli, C.
Walker, M.
Whittaker, J.
Title Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables
Journal name Statistics in Medicine   Check publisher's open access policy
ISSN 0277-6715
1097-0258
Publication date 2010-05-30
Year available 2010
Sub-type Article (original research)
DOI 10.1002/sim.3843
Open Access Status Not Open Access
Volume 29
Issue 12
Start page 1298
End page 1311
Total pages 14
Place of publication Chichester, West Sussex, United Kingdom
Publisher John Wiley and Sons Ltd.
Language eng
Subject 2713 Epidemiology
2613 Statistics and Probability
Abstract Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context of multiple genetic markers measured in multiple studies, based on the analysis of individual participant data. First, for a single genetic marker in one study, we show that the usual ratio of coefficients approach can be reformulated as a regression with heterogeneous error in the explanatory variable. This can be implemented using a Bayesian approach, which is next extended to include multiple genetic markers. We then propose a hierarchical model for undertaking a meta-analysis of multiple studies, in which it is not necessary that the same genetic markers are measured in each study. This provides an overall estimate of the causal relationship between the phenotype and the outcome, and an assessment of its heterogeneity across studies. As an example, we estimate the causal relationship of blood concentrations of C-reactive protein on fibrinogen levels using data from 11 studies. These methods provide a flexible framework for efficient estimation of causal relationships derived from multiple studies. Issues discussed include weak instrument bias, analysis of binary outcome data such as disease risk, missing genetic data, and the use of haplotypes. Copyright
Keyword Bayesian methods
Causal association
Instrumental variables
Mendelian randomization
Meta-analysis
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: UQ Diamantina Institute Publications
 
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