Benchmarking of univariate pleiotropy detection methods applied to epilepsy.

Autor: Adesoji OM; Cologne Center for Genomics, University of Cologne, Cologne, Germany.; University Hospital Cologne, Medical Faculty, University of Cologne, Cologne, Germany., Schulz H; Department of Microgravity and Translational Regenerative Medicine, Clinic of Plastic, Aesthetic and Hand Surgery, Otto von Guericke University, Magdeburg, Germany., May P; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg., Krause R; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg., Lerche H; Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany., Nothnagel M; Cologne Center for Genomics, University of Cologne, Cologne, Germany.; University Hospital Cologne, Medical Faculty, University of Cologne, Cologne, Germany.
Jazyk: angličtina
Zdroj: Human mutation [Hum Mutat] 2022 Sep; Vol. 43 (9), pp. 1314-1332. Date of Electronic Publication: 2022 Jul 08.
DOI: 10.1002/humu.24417
Abstrakt: Pleiotropy is a widespread phenomenon that may increase insight into the etiology of biological and disease traits. Since genome-wide association studies frequently provide information on a single trait only, only univariate pleiotropy detection methods are applicable, with yet unknown comparative performance. Here, we compared five such methods with respect to their ability to detect pleiotropy, including meta-analysis, ASSET, conditional false discovery rate (cFDR), cross-phenotype Bayes (CPBayes), and pleiotropic analysis under the composite null hypothesis (PLACO), by performing extended computer simulations that varied the underlying etiological model for pleiotropy for a pair of traits, including the number of causal variants, degree of traits' overlap, effect sizes as well as trait prevalence, and varying sample sizes. Our results indicate that ASSET provides the best trade-off between power and protection against false positives. We then applied ASSET to a previously published International League Against Epilepsy (ILAE) consortium data set on complex epilepsies, comprising genetic generalized epilepsy and focal epilepsy cases and corresponding controls. We identified a novel candidate locus at 17q21.32 and confirmed locus 2q24.3, previously identified to act pleiotropically on both epilepsy subtypes by a mega-analysis. Functional annotation, tissue-specific expression, and regulatory function analysis as well as Bayesian colocalization analysis corroborated this result, rendering 17q21.32 a worthwhile candidate for follow-up studies on pleiotropy in epilepsies.
(© 2022 The Authors. Human Mutation published by Wiley Periodicals LLC.)
Databáze: MEDLINE
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