A method for meta-analysis of
case-control genetic association studies using random-effects
Pantelis G. Bagos and
Georgios Κ. Nikolopoulos
We propose here a simple and robust
approach for meta-analysis of molecular association studies.
Making use of the binary structure of the data, and by treating the
genotypes as independent variables in a logistic regression, we apply
a simple and commonly used in Epidemiology methodology that performs
quite well, being at the same time very flexible. We present
simple tests for detecting heterogeneity and we describe a random
effects extension of the method in order to allow for between studies
heterogeneity. We derive also simple tests for assessing the
most plausible genetic model of inheritance, and its between-studies
heterogeneity as well as adjusting for covariates. The methodology
introduced here is easily extended in cases with polytomous or
continuous outcomes as well as in cases with more than two alleles.
We apply the methodology in several already published meta-analyses
of genetic association studies with very encouraging results.
The main advantages of the proposed methodology is its flexibility
and the ease of use, while at the same time covers almost every
aspect of a meta-analysis providing overall estimates without the
need of multiple comparisons. We anticipate that this simple
method would be used in the future in meta-analyses of genetic
The Stata program for
fitting the models proposed in this work, is available here
(and a help file:
program was developed
in Stata 8.0 although it is
probably functional with older versions (6.0 and 7.0).
the gllamm module for fitting
the random-effects models.
The method implemented here is
Bagos PG, Nikolopoulos
GK. A method for meta-analysis of case-control genetic association
studies using logistic regression. 2007, Statistical Applications in
Genetics and Molecular Biology, 6(1): Article 17 [PDF] [Pubmed] [Google Scholar]
See also metagen at Econpapers and Repec
From within Stata issue the command:
net install metagen