Imperial College London


Faculty of MedicineDepartment of Brain Sciences

Professor of Neurology and Genomic Medicine



m.johnson Website




E419Burlington DanesHammersmith Campus






BibTex format

author = {Speed, D and O'Brien, TJ and Palotie, A and Shkura, K and Marson, AG and Balding, DJ and Johnson, MR},
doi = {brain/awu206},
journal = {Brain},
pages = {2680--2689},
title = {Describing the genetic architecture of epilepsy through heritability analysis},
url = {},
volume = {137},
year = {2014}

RIS format (EndNote, RefMan)

AB - 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.
AU - Speed,D
AU - O'Brien,TJ
AU - Palotie,A
AU - Shkura,K
AU - Marson,AG
AU - Balding,DJ
AU - Johnson,MR
DO - brain/awu206
EP - 2689
PY - 2014///
SN - 1460-2156
SP - 2680
TI - Describing the genetic architecture of epilepsy through heritability analysis
T2 - Brain
UR -
UR -
VL - 137
ER -