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Compare and Contrast Meta Analysis (CCMA): A Method for Identification of Pleiotropic Loci in Genome-Wide Association Studies

Lookup NU author(s): Professor Heather Cordell

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Abstract

In recent years, genome-wide association studies (GWAS) have identified many loci that are shared among common disorders and this has raised interest in pleiotropy. For performing appropriate analysis, several methods have been proposed, e.g. conducting a look-up in external sources or exploiting GWAS results by meta-analysis based methods. We recently proposed the Compare & Contrast Meta-Analysis (CCMA) approach where significance thresholds were obtained by simulation. Here we present analytical formulae for the density and cumulative distribution function of the CCMA test statistic under the null hypothesis of no pleiotropy and no association, which, conveniently for practical reasons, turns out to be exponentially distributed. This allows researchers to apply the CCMA method without having to rely on simulations. Finally, we show that CCMA demonstrates power to detect disease-specific, agonistic and antagonistic loci comparable to the frequently used Subset-Based Meta-Analysis approach, while better controlling the type I error rate.

Publication metadata

Author(s): Baurecht H, Hotze M, Rodriguez E, Manz J, Weidinger S, Cordell HJ, Augustin T, Strauch K

Publication type: Article

Publication status: Published

Journal: PLoS ONE

Year: 2016

Volume: 11

Issue: 5

Online publication date: 05/05/2016

Acceptance date: 20/04/2016

Date deposited: 27/07/2016

ISSN (electronic): 1932-6203

Publisher: Public Library of Science

URL: http://dx.doi.org/10.1371/journal.pone.0154872

DOI: 10.1371/journal.pone.0154872

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