In this work we present a simple, yet powerful approach for performing
multivariate meta-analysis of genetic association studies when multiple
outcomes are assessed. The key element of our approach is the analytical
calculation of the within-studies covariances. We propose a model based on
summary data, uniformly defined for both discrete and continuous outcomes
(using log odds-ratios or mean differences). The within-studies covariances can
be calculated using the cross-classification of the genotypes in both outcomes,
which are retrieved using a log-linear model using the iterative proportional
fitting algorithm under the assumption of no three-way interaction. As an
example, we examine the association of GNB3 C825T polymorphism with two
non-exclusive dichotomous outcomes (Type 2 Diabetes
Mellitus and Essential Hypertension). We also present an application using
continuous outcomes (diastolic and systolic blood pressure). We show the
applicability and the generality of the method performing the analysis assuming
the genetic model beforehand or following a genetic model-free approach. The
method is simple and fast, it can be extended for several outcomes and can be
fitted in nearly all statistical packages. There is no need for individual
patient data or the simultaneous evaluation of both outcomes in all studies. We
conclude that the proposed method constitute a useful framework for performing
meta-analysis for multiple outcomes within the context of genetic association
studies. Connections to other similar models presented in the literature, are
discussed, as well as potential extensions to future work. International Journal Publications
Presentations in International Conferences
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