Evidence map›Paper›PMID 25063994›Full record

ArticleBrain : a journal of neurology2014

Describing the genetic architecture of epilepsy through heritability analysis.

Doug Speed, Terence J O'Brien, Aarno Palotie, Kirill Shkura, Anthony G Marson, David J Balding, Michael R Johnson

Open access · bronzeAbstract read
In one paragraph

Article in Brain : a journal of neurology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 4 pooled it
6.8field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

47 citing papers in PubMed, 4 syntheses or guidelines pooled it, 95 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Review
  17. Review
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 6 institutions in 4 countries.

Doug Speed1 UCL Genetics Institute, University College London, London WC1E 6BT, UK doug.speed@ucl.ac.uk m.johnson@imperial.ac.uk.
Terence J O'Brien2 The Departments of Medicine and Neurology, The Royal Melbourne Hospital, The University of Melbourne, Australia.
Aarno Palotie3 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.
Kirill Shkura7 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.
Anthony G Marson9 Department of Molecular and Clinical Pharmacology, University of Liverpool, UK.
David J Balding1 UCL Genetics Institute, University College London, London WC1E 6BT, UK.
Michael R Johnson7 Division of Brain Sciences, Imperial College London, London W6 8RF, UK doug.speed@ucl.ac.uk m.johnson@imperial.ac.uk.
University College London · GBHelsinki University Hospital · FIImperial College London · GBMedical Research Council · GBUniversity of Liverpool · GBUniversity of Melbourne · AU

Funding

Department of HealthMedical Research Council G0901388Medical Research Council L012561Medical Research Council MR/L012561/1Wellcome TrustWellcome Trust WT066056
6 · The paper itself

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.

Indexed as

Age of OnsetAlgorithmsArea Under CurveAsian PeopleEpilepsyGenetic LociGenetic Predisposition to DiseaseGenome-Wide Association StudyGenotypeGenotyping TechniquesHumansModels, StatisticalPolymorphism, Single NucleotidePopulationQuantitative Trait, HeritableROC Curveassociation studiescomplex trait predictionepilepsyheritability analysis

Identifiers

PMID25063994
PMCPMC4163034
OpenAlexW2162856568

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.