Meta-analysis of family-based and case-control genetic association studies that use the same cases
AuthorBagos, Pantelis G.
Dimou, Niki L.
Liakopoulos, Theodore D.
Nikolopoulos, Georgios K.
SourceStatistical Applications in Genetics and Molecular Biology
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In many cases in genetic epidemiology, the investigators in an effort to control for different sources of confounding and simultaneously to increase the power perform a family-based and a population-based case-control study within the same population, using the same or largely overlapping, set of cases. Various methods have been proposed for performing a combined analysis, but they all require access to individual data that are difficult to gather in a meta-analysis. Here, we propose a simple and efficient summary-based method for performing the meta-analysis. The key point, contrary to the methods presented earlier that need individual data, is the calculation of the covariance between the study estimates (log-Odds Ratios), using only data derived from the literature in the form of a 2x2 contingency table. Afterwards, the studies can easily be combined either in a two-step procedure using traditional methods for univariate meta-analysis or in a single-step approach using hierarchical models. In any case, the meta-analysis can be performed using standard software and because of the increased sample size the statistical power of the meta-analysis is increased whereas the procedure allows performing several diagnostics (publication bias, cumulative meta-analysis, sensitivity analysis). The method is evaluated on a dataset of 356 Single Nucleotide polymorphisms (SNPs) which were evaluated for their potential association with Respiratory Syncytial Virus Bronchiolitis (RSV) and subsequently is applied in a meta-analysis concerning the association of the 10-Repeat Allele of a VNTR Polymorphism in the 3'-UTR of Dopamine Transporter Gene with Attention Deficit Hyperactivity Disorder (ADHD), as well as in a genome-wide association study for Multiple Sclerosis. Implementation of the method is straightforward and in the Appendix, a Stata program is given for implementing the methods presented here. Copyright © 2011 The Berkeley Electronic Press. All rights reserved.
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