title: Compare and Contrast Meta Analysis (CCMA): A Method for Identification of Pleiotropic Loci in Genome-Wide Association Studies.
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.
authors.affiliation: Department of Dermatology, Allergology and Venereology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.
authors.forename: Hansjörg
authors.initials: H
authors.lastname: Baurecht
authors.affiliation: Department of Dermatology, Allergology and Venereology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.
authors.forename: Melanie
authors.initials: M
authors.lastname: Hotze
authors.affiliation: Department of Dermatology, Allergology and Venereology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.
authors.forename: Elke
authors.initials: E
authors.lastname: Rodríguez
authors.affiliation: Research Unit of Molecular Epidemiology, Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
authors.forename: Judith
authors.initials: J
authors.lastname: Manz
authors.affiliation: Department of Dermatology, Allergology and Venereology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.
authors.forename: Stephan
authors.initials: S
authors.lastname: Weidinger
authors.affiliation: Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom.
authors.forename: Heather J
authors.initials: HJ
authors.lastname: Cordell
authors.affiliation: Department of Statistics, Ludwig-Maximilians-Universität Munich, Munich, Germany.
authors.forename: Thomas
authors.identifier: 0000-0002-1854-6226
authors.initials: T
authors.lastname: Augustin
authors.affiliation: Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.|Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany.
authors.forename: Konstantin
authors.initials: K
authors.lastname: Strauch
mesh_terms: D003627:Data Interpretation, Statistical; D058685:Genetic Pleiotropy / Q000235:genetics; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical