4.5 Article

Benchmarking of univariate pleiotropy detection methods applied to epilepsy

期刊

HUMAN MUTATION
卷 43, 期 9, 页码 1314-1332

出版社

WILEY-HINDAWI
DOI: 10.1002/humu.24417

关键词

association; epilepsies; meta-analysis; pleiotropy; SNPs

资金

  1. Deutsche Forschungsgemeinschaft [NO755/6-1, NO755/13-1]
  2. Fonds National de la Recherche Luxembourg [INTER/DFG/17/11583046, NCER-PD/FNR11264123]

向作者/读者索取更多资源

Pleiotropy is a widespread phenomenon that can provide insight into the etiology of biological and disease traits. This study compared five methods for detecting pleiotropy and found that ASSET offers the best balance between power and protection against false positives. Applying ASSET to a dataset on complex epilepsies identified a novel candidate locus and confirmed a previously identified pleiotropic locus.
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.

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