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

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.

SUBMITTER: Burgess S

PROVIDER: S-EPMC3648673 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

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

Burgess Stephen S Thompson Simon G SG Burgess S S Thompson S G SG Andrews G G Samani N J NJ Hall A A Whincup P P Morris R R Lawlor D A DA Davey Smith G G Timpson N N Ebrahim S S Ben-Shlomo Y Y Davey Smith G G Timpson N N Brown M M Ricketts S S Sandhu M M Reiner A A Psaty B B Lange L L Cushman M M Hung J J Thompson P P Beilby J J Warrington N N Palmer L J LJ Nordestgaard B G BG Tybjaerg-Hansen A A Zacho J J Wu C C Lowe G G Tzoulaki I I Kumari M M Sandhu M M Yamamoto J F JF Chiodini B B Franzosi M M Hankey G J GJ Jamrozik K K Palmer L L Rimm E E Pai J J Psaty B B Heckbert S S Bis J J Anand S S Engert J J Collins R R Clarke R R Melander O O Berglund G G Ladenvall P P Johansson L L Jansson J-H JH Hallmans G G Hingorani A A Humphries S S Rimm E E Manson J J Pai J J Watkins H H Clarke R R Hopewell J J Saleheen D D Frossard R R Danesh J J Sattar N N Robertson M M Shepherd J J Schaefer E E Hofman A A Witteman J C M JC Kardys I I Ben-Shlomo Y Y Davey Smith G G Timpson N N de Faire U U Bennet A A Sattar N N Ford I I Packard C C Kumari M M Manson J J Lawlor Debbie A DA Davey Smith George G Anand S S Collins R R Casas J P JP Danesh J J Davey Smith G G Franzosi M M Hingorani A A Lawlor D A DA Manson J J Nordestgaard B G BG Samani N J NJ Sandhu M M Smeeth L L Wensley F F Anand S S Bowden J J Burgess S S Casas J P JP Di Angelantonio E E Engert J J Gao P P Shah T T Smeeth L L Thompson S G SG Verzilli C C Walker M M Whittaker J J Hingorani A A Danesh J J

Statistics in medicine 20100501 12

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 coefficient ...[more]

PMID: 20209660

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Project description:BackgroundThis article demonstrates a means of assessing long-term intervention cost-effectiveness in the absence of data from randomized controlled trials and without recourse to Markov simulation or similar types of cohort simulation.MethodsUsing a Mendelian randomization study design, we developed causal estimates of the genetically predicted effect of bladder, breast, colorectal, lung, multiple myeloma, ovarian, prostate, and thyroid cancers on health care costs and quality-adjusted life-years (QALYs) using outcome data drawn from the UK Biobank cohort. We then used these estimates in a simulation model to estimate the cost-effectiveness of a hypothetical population-wide preventative intervention based on a repurposed class of antidiabetic drugs known as sodium-glucose cotransporter-2 (SGLT2) inhibitors very recently shown to reduce the odds of incident prostate cancer.ResultsGenetic liability to prostate cancer and breast cancer had material causal impacts on either or both health care costs and QALYs. Mendelian randomization results for the less common cancers were associated with considerable uncertainty. SGLT2 inhibition was unlikely to be a cost-effective preventative intervention for prostate cancer, although this conclusion depended on the price at which these drugs would be offered for a novel anticancer indication.ImplicationsOur new causal estimates of cancer exposures on health economic outcomes may be used as inputs into decision-analytic models of cancer interventions such as screening programs or simulations of longer-term outcomes associated with therapies investigated in randomized controlled trials with short follow-ups. Our method allowed us to rapidly and efficiently estimate the cost-effectiveness of a hypothetical population-scale anticancer intervention to inform and complement other means of assessing long-term intervention value.HighlightsThe article demonstrates a novel method of assessing long-term intervention cost-effectiveness without relying on randomized controlled trials or cohort simulations.Mendelian randomization was used to estimate the causal effects of certain cancers on health care costs and quality-adjusted life-years (QALYs) using data from the UK Biobank cohort.Given causal data on the association of different cancer exposures on costs and QALYs, it was possible to simulate the cost-effectiveness of an anticancer intervention.Genetic liability to prostate cancer and breast cancer significantly affected health care costs and QALYs, but the hypothetical intervention using SGLT2 inhibitors for prostate cancer may not be cost-effective, depending on the drug's price for the new anticancer indication. The methods we propose and implement can be used to efficiently estimate intervention cost-effectiveness and to inform decision making in all manner of preventative and therapeutic contexts.

Project description:Multivariate Mendelian randomization (MVMR) is a statistical technique that uses sets of genetic instruments to estimate the direct causal effects of multiple exposures on an outcome of interest. At genomic loci with pleiotropic gene regulatory effects, that is, loci where the same genetic variants are associated to multiple nearby genes, MVMR can potentially be used to predict candidate causal genes. However, consensus in the field dictates that the genetic instruments in MVMR must be independent (not in linkage disequilibrium), which is usually not possible when considering a group of candidate genes from the same locus. Here we used causal inference theory to show that MVMR with correlated instruments satisfies the instrumental set condition. This is a classical result by Brito and Pearl (2002) for structural equation models that guarantees the identifiability of individual causal effects in situations where multiple exposures collectively, but not individually, separate a set of instrumental variables from an outcome variable. Extensive simulations confirmed the validity and usefulness of these theoretical results even at modest sample sizes (n≳500 -1000). Importantly, the causal effect estimates remain unbiased and their variance small when instruments are highly correlated. We applied MVMR with correlated instrumental variable sets at genome-wide significant loci for coronary artery disease (CAD) risk using expression Quantitative Trait Loci (eQTL) data from seven vascular and metabolic tissues in the STARNET study. Our method predicts causal genes at twelve loci, each associated with multiple colocated genes in multiple tissues. We confirm causal roles for PHACTR1 and ADAMTS7 in arterial tissues, among others. However, the extensive degree of regulatory pleiotropy across tissues and the limited number of causal variants in each locus still require that MVMR is run on a tissue-by-tissue basis, and testing all gene-tissue pairs with cis-eQTL associations at a given locus in a single model to predict causal gene-tissue combinations remains infeasible. Our results show that within tissues, MVMR with dependent, as opposed to independent, sets of instrumental variables significantly expands the scope for predicting causal genes in disease risk loci with pleiotropic regulatory effects. However, considering risk loci with regulatory pleiotropy that also spans across tissues remains an unsolved problem.

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

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

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