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Brain. 2014 Oct;137(Pt 10):2680-9. doi: 10.1093/brain/awu206. Epub 2014 Jul 26.

Describing the genetic architecture of epilepsy through heritability analysis.

Author information

1
1 UCL Genetics Institute, University College London, London WC1E 6BT, UK doug.speed@ucl.ac.uk m.johnson@imperial.ac.uk.
2
2 The Departments of Medicine and Neurology, The Royal Melbourne Hospital, The University of Melbourne, Australia.
3
3 Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland 4 The Broad Institute of MIT and Harvard, Cambridge, USA 5 Department of Medical Genetics, University of Helsinki, Finland 6 University Central Hospital, Helsinki, Finland.
4
7 Division of Brain Sciences, Imperial College London, London W6 8RF, UK 8 Medical Research Council (MRC) Clinical Sciences Centre, Faculty of Medicine, Imperial College London, UK.
5
9 Department of Molecular and Clinical Pharmacology, University of Liverpool, UK.
6
1 UCL Genetics Institute, University College London, London WC1E 6BT, UK.
7
7 Division of Brain Sciences, Imperial College London, London W6 8RF, UK doug.speed@ucl.ac.uk m.johnson@imperial.ac.uk.

Abstract

Epilepsy is a disease with substantial missing heritability; despite its high genetic component, genetic association studies have had limited success detecting common variants which influence susceptibility. In this paper, we reassess the role of common variants on epilepsy using extensions of heritability analysis. Our data set consists of 1258 UK patients with epilepsy, of which 958 have focal epilepsy, and 5129 population control subjects, with genotypes recorded for over 4 million common single nucleotide polymorphisms. Firstly, we show that on the liability scale, common variants collectively explain at least 26% (standard deviation 5%) of phenotypic variation for all epilepsy and 27% (standard deviation 5%) for focal epilepsy. Secondly we provide a new method for estimating the number of causal variants for complex traits; when applied to epilepsy, our most optimistic estimate suggests that at least 400 variants influence disease susceptibility, with potentially many thousands. Thirdly, we use bivariate analysis to assess how similar the genetic architecture of focal epilepsy is to that of non-focal epilepsy; we demonstrate both significant differences (P = 0.004) and significant similarities (P = 0.01) between the two subtypes, indicating that although the clinical definition of focal epilepsy does identify a genetically distinct epilepsy subtype, there is also scope to improve the classification of epilepsy by incorporating genotypic information. Lastly, we investigate the potential value in using genetic data to diagnose epilepsy following a single epileptic seizure; we find that a prediction model explaining 10% of phenotypic variation could have clinical utility for deciding which single-seizure individuals are likely to benefit from immediate anti-epileptic drug therapy.

KEYWORDS:

association studies; complex trait prediction; epilepsy; heritability analysis

PMID:
25063994
PMCID:
PMC4163034
DOI:
10.1093/brain/awu206
[Indexed for MEDLINE]
Free PMC Article

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