Meta-analysis of family-based and case-control genetic association studies that use the same cases

Abstract

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.