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

• Published in: PloS one Vol. 11; no. 5; p. e0154872
• Main Authors: Baurecht, Hansjörg, Hotze, Melanie, Rodríguez, Elke, Manz, Judith, Weidinger, Stephan, Cordell, Heather J, Augustin, Thomas, Strauch, Konstantin
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.05.2016; Public Library of Science (PLoS)
• Subjects: Biology and Life Sciences; Data Interpretation, Statistical; Dermatology; Distribution functions; Economic models; Environmental health; Epidemiology; Genetic Pleiotropy - genetics; Genome-wide association studies; Genome-Wide Association Study - methods; Genomes; Hospitals; Humans; Loci; Medicine and Health Sciences; Meta-analysis; Meta-Analysis as Topic; Models, Statistical; Null hypothesis; Phenotypes; Physical Sciences; Pleiotropy; Psoriasis; Research and Analysis Methods; Statistics; Germany
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0154872

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.