Genome-wide linkage meta-analysis identifies susceptibility loci at 2q34 and 13q31.3 for genetic generalized epilepsies

Authors

  • EPICURE Consortium,

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  • Costin Leu,

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    2. Cologne Center for Genomics, University of Cologne, Cologne, Germany
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  • Carolien G.F. de Kovel,

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    2. Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
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  • Federico Zara,

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    2. Muscular and Neurodegenerative Disease Unit, G. Gaslini Institute, University of Genoa, Genoa, Italy
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  • Pasquale Striano,

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    2. Muscular and Neurodegenerative Disease Unit, G. Gaslini Institute, University of Genoa, Genoa, Italy
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  • Marianna Pezzella,

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    2. Muscular and Neurodegenerative Disease Unit, G. Gaslini Institute, University of Genoa, Genoa, Italy
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  • Angela Robbiano,

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    2. Muscular and Neurodegenerative Disease Unit, G. Gaslini Institute, University of Genoa, Genoa, Italy
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  • Amedeo Bianchi,

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    2. Epilepsy Center, Unit of Neurology, Hospital “San Donato,” Arezzo, Italy
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  • Francesca Bisulli,

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    2. Department of Neurological Sciences, University of Bologna, Bologna, Italy
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  • Antonietta Coppola,

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    2. Epilepsy Center, Federico II University, Napoli, Italy
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  • Anna Teresa Giallonardo,

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    2. Department of Neurological Sciences, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
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  • Francesca Beccaria,

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    2. Department of Child Neuropsychiatry, Ospedale C. Poma, Mantua, Italy
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  • Dorothée Kasteleijn-Nolst Trenité,

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    2. Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
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  • Dick Lindhout,

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    2. Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
    3. SEIN Epilepsy Institute in the Netherlands, Hoofddorp, The Netherlands
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  • Verena Gaus,

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    2. Department of Neurology, Charité University Medicine, Campus Virchow Clinic, Humboldt University of Berlin, Berlin, Germany
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  • Bettina Schmitz,

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    2. Department of Neurology, Charité University Medicine, Campus Virchow Clinic, Humboldt University of Berlin, Berlin, Germany
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  • Dieter Janz,

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    2. Department of Neurology, Charité University Medicine, Campus Virchow Clinic, Humboldt University of Berlin, Berlin, Germany
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  • Yvonne G. Weber,

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    2. Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research, University of Tübingen, Tübingen, Germany
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  • Felicitas Becker,

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    2. Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research, University of Tübingen, Tübingen, Germany
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  • Holger Lerche,

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    2. Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research, University of Tübingen, Tübingen, Germany
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  • Ailing A. Kleefuß-Lie,

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    2. Department of Epileptology, University Clinics Bonn, Bonn, Germany
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  • Kerstin Hallman,

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    2. Department of Epileptology, University Clinics Bonn, Bonn, Germany
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  • Wolfram S. Kunz,

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    2. Department of Epileptology, University Clinics Bonn, Bonn, Germany
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  • Christian E. Elger,

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    2. Department of Epileptology, University Clinics Bonn, Bonn, Germany
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  • Hiltrud Muhle,

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    2. Department of Neuropediatrics, University Medical Center Schleswig-Holstein (Kiel Campus), Kiel, Germany
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  • Ulrich Stephani,

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    2. Department of Neuropediatrics, University Medical Center Schleswig-Holstein (Kiel Campus), Kiel, Germany
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  • Rikke S. Møller,

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    2. Department of Neurology, Danish Epilepsy Center, Dianalund, Denmark
    3. Wilhelm Johannsen Center for Functional Genome Research, University of Copenhagen, Copenhagen, Denmark
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  • Helle Hjalgrim,

    1. EPICURE Consortium, Participating Groups are Listed in the Appendix
    2. Department of Neurology, Danish Epilepsy Center, Dianalund, Denmark
    3. Institute for Regional Health Services Research, University of Southern Denmark, Odense, Denmark
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  • Saul Mullen,

    1. Epilepsy Research Centre and Department of Medicine, University of Melbourne, Austin Health, Heidelberg, Victoria, Australia
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  • Ingrid E. Scheffer,

    1. Epilepsy Research Centre and Department of Medicine, University of Melbourne, Austin Health, Heidelberg, Victoria, Australia
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  • Samuel F. Berkovic,

    1. Epilepsy Research Centre and Department of Medicine, University of Melbourne, Austin Health, Heidelberg, Victoria, Australia
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  • Kate V. Everett,

    1. Human Genetics Research Centre, St George′s University of London, London, United Kingdom
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  • Mark R. Gardiner,

    1. UCL Institute of Child Health, University College London, London, United Kingdom
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  • Carla Marini,

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    2. Child Neurology Unit, Children’s Hospital A. Meyer, University of Florence, Florence, Italy
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  • Renzo Guerrini,

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    2. Child Neurology Unit, Children’s Hospital A. Meyer, University of Florence, Florence, Italy
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  • Anna-Elina Lehesjoki,

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    2. Neuroscience Center, University of Helsinki and Folkhälsan Institute of Genetics, Helsinki, Finland
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  • Auli Siren,

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    2. Neuroscience Center, University of Helsinki and Folkhälsan Institute of Genetics, Helsinki, Finland
    3. Department of Pediatrics, Tampere University Hospital, Tampere, Finland
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  • Rima Nabbout,

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    2. Department of Neuropediatrics, Hôpital Necker-Enfants Malades, AP-HP and INSERM U663, Paris-Descartes University, Paris, France
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  • Stephanie Baulac,

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    2. CRICM, UPMC/Inserm UMR-S 975, CNRS UMR 7225, Brain and Spine Institute, Pitie-Salpetriere Hospital, Paris, France
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  • Eric Leguern,

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    2. CRICM, UPMC/Inserm UMR-S 975, CNRS UMR 7225, Brain and Spine Institute, Pitie-Salpetriere Hospital, Paris, France
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  • Jose M. Serratosa,

    1. EPICURE Consortium, Participating Groups are Listed in the Appendix
    2. Epilepsy Unit, Neurology Service and Institute for Medical Research, Fundación Jiménez Díaz, Madrid, Spain
    3. Unit 744, Center for Biomedical Network Research on Rare Diseases (CIBERER), Madrid, Spain
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  • Felix Rosenow,

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    2. Epilepsy-Center Hessen, Department of Neurology, University Hospitals and Philipps-University Marburg, Marburg, Germany
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  • Martha Feucht,

    1. Department of Pediatrics and Neonatology, Medical University of Vienna, Vienna, Austria
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  • Iris Unterberger,

    1. Department of Neurology, Medical University Innsbruck, Innsbruck, Austria
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  • Athanasios Covanis,

    1. Neurology Department, The Children Hospital “Agia Sophia,” Athens, Greece
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  • Arvid Suls,

    1. Neurogenetics Group, Department of Molecular Genetics, VIB, Antwerp, Belgium
    2. Laboratory of Neurogenetics, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
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  • Sarah Weckhuysen,

    1. Neurogenetics Group, Department of Molecular Genetics, VIB, Antwerp, Belgium
    2. Laboratory of Neurogenetics, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
    3. Epilepsy Center Kempenhaeghe, Hans Berger Kliniek, Oosterhout, The Netherlands
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  • Radka Kaneva,

    1. Department of Chemistry and Biochemistry, Molecular Medicine Center, Medical University-Sofia, Sofia, Bulgaria
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  • Hande Caglayan,

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    2. Department of Molecular Biology and Genetics, Bogazici University, Istanbul, Turkey
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  • Dilsad Turkdogan,

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    2. Institute of Neurological Sciences, Marmara University, Maltepe, Istanbul, Turkey
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  • Betul Baykan,

    1. EPICURE Consortium, Participating Groups are Listed in the Appendix
    2. Department of Neurology, Epilepsy Center (EPIMER), Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
    3. Department of Genetics, Experimental Medicine Research Institute (DETAE), Istanbul University, Istanbul, Turkey
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  • Nerses Bebek,

    1. EPICURE Consortium, Participating Groups are Listed in the Appendix
    2. Department of Neurology, Epilepsy Center (EPIMER), Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
    3. Department of Genetics, Experimental Medicine Research Institute (DETAE), Istanbul University, Istanbul, Turkey
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  • Ugur Ozbek,

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    2. Department of Genetics, Experimental Medicine Research Institute (DETAE), Istanbul University, Istanbul, Turkey
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  • Anne Hempelmann,

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    2. Department of Neurology, Charité University Medicine, Campus Virchow Clinic, Humboldt University of Berlin, Berlin, Germany
    3. Max-Delbrück-Centrum for Molecular Medicine, Berlin, Germany
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  • Herbert Schulz,

    1. Department of Neurology, Charité University Medicine, Campus Virchow Clinic, Humboldt University of Berlin, Berlin, Germany
    2. Max-Delbrück-Centrum for Molecular Medicine, Berlin, Germany
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  • Franz Rüschendorf,

    1. Max-Delbrück-Centrum for Molecular Medicine, Berlin, Germany
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  • Holger Trucks,

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    2. Cologne Center for Genomics, University of Cologne, Cologne, Germany
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  • Peter Nürnberg,

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    2. Cologne Center for Genomics, University of Cologne, Cologne, Germany
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  • Giuliano Avanzini,

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    2. Fondazione IRRCS, Carlo Besta Neurological Institute, Milan, Italy
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  • Bobby P.C. Koeleman,

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    2. Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
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  • Thomas Sander

    1. EPICURE Consortium, Participating Groups are Listed in the Appendix
    2. Cologne Center for Genomics, University of Cologne, Cologne, Germany
    3. Department of Neurology, Charité University Medicine, Campus Virchow Clinic, Humboldt University of Berlin, Berlin, Germany
    4. Max-Delbrück-Centrum for Molecular Medicine, Berlin, Germany
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Address correspondence to Thomas Sander, M.D., Cologne Center for Genomics, University of Cologne, Weyertal 115b, 50931 Cologne, Germany. E-mail: sandert@uni-koeln.de;
Bobby P.C. Koeleman, Ph.D., Department of Medical Genetics, University Medical Center Utrecht, Str. 0.308, PO Box 85060, 3508 AB Utrecht, The Netherlands. E-mail: B.P.C.Koeleman@umcutrecht.nl

Summary

Purpose:  Genetic generalized epilepsies (GGEs) have a lifetime prevalence of 0.3% with heritability estimates of 80%. A considerable proportion of families with siblings affected by GGEs presumably display an oligogenic inheritance. The present genome-wide linkage meta-analysis aimed to map: (1) susceptibility loci shared by a broad spectrum of GGEs, and (2) seizure type–related genetic factors preferentially predisposing to either typical absence or myoclonic seizures, respectively.

Methods:  Meta-analysis of three genome-wide linkage datasets was carried out in 379 GGE-multiplex families of European ancestry including 982 relatives with GGEs. To dissect out seizure type–related susceptibility genes, two family subgroups were stratified comprising 235 families with predominantly genetic absence epilepsies (GAEs) and 118 families with an aggregation of juvenile myoclonic epilepsy (JME). To map shared and seizure type–related susceptibility loci, both nonparametric loci (NPL) and parametric linkage analyses were performed for a broad trait model (GGEs) in the entire set of GGE-multiplex families and a narrow trait model (typical absence or myoclonic seizures) in the subgroups of JME and GAE families.

Key Findings:  For the entire set of 379 GGE-multiplex families, linkage analysis revealed six loci achieving suggestive evidence for linkage at 1p36.22, 3p14.2, 5q34, 13q12.12, 13q31.3, and 19q13.42. The linkage finding at 5q34 was consistently supported by both NPL and parametric linkage results across all three family groups. A genome-wide significant nonparametric logarithm of odds score of 3.43 was obtained at 2q34 in 118 JME families. Significant parametric linkage to 13q31.3 was found in 235 GAE families assuming recessive inheritance (heterogeneity logarithm of odds = 5.02).

Significance:  Our linkage results support an oligogenic predisposition of familial GGE syndromes. The genetic risk factor at 5q34 confers risk to a broad spectrum of familial GGE syndromes, whereas susceptibility loci at 2q34 and 13q31.3 preferentially predispose to myoclonic seizures or absence seizures, respectively. Phenotype– genotype strategies applying narrow trait definitions in phenotypic homogeneous subgroups of families improve the prospects of disentangling the genetic basis of common familial GGE syndromes.

Genetic factors play a predominant role in about 40% of all epilepsies (ILAE Commission on Classification and Terminology, 1989). Genetic generalized epilepsies (GGEs, formerly called the idiopathic generalized epilepsies) represent the most common group of genetically determined epilepsies; they account for approximately 20–30% of all epilepsies (Jallon et al., 2001). The GGE syndromes are characterized by age-related recurrent unprovoked generalized seizures in the absence of detectable brain lesions or metabolic abnormalities (ILAE Commission on Classification and Terminology, 1989; Berg et al., 2010). The common classical GGE syndromes include childhood absence epilepsy (CAE), juvenile absence epilepsy (JAE), juvenile myoclonic epilepsy (JME), and epilepsy with generalized tonic–clonic seizures (EGTCS) alone (Nordli, 2005). The electroencephalographic signature is generalized spike-wave discharges (GSW-EEG), which reflect a synchronized hyperexcitability state of thalamocortical circuits (Blumenfeld, 2005).

Despite heritability estimates of >80% obtained by twin studies (Berkovic et al., 1998; Kjeldsen et al., 2003), the genetic factors predisposing to common GGE syndromes remain elusive (Gardiner, 2005). The genetic architecture of GGEs most likely represents a biologic continuum, in which a small fraction (1–2%) follows monogenic inheritance, whereas the majority of GGE patients presumably display an oligogenic/polygenic predisposition. Moreover, twin and family studies provide evidence for genetic determinants shared across common GGE syndromes, but also suggest that heterogeneous configurations of genetic risk factors specify the phenotypic expression of absence and myoclonic seizures (Berkovic et al., 1987; Beck-Mannagetta & Janz, 1991; Berkovic et al., 1988; Schmitz et al., 2000; Winawer et al., 2003; Marini et al., 2004; Winawar et al., 2005; Kinirons et al., 2008).

Linkage mapping and positional candidate gene analysis provide a suitable approach to identify major susceptibility genes in families showing a clustering of GGE syndromes. Most of the currently known genes for rare monogenic forms of genetic epilepsies encode voltage-gated or ligand-gated ion channels (e.g., SCN1A, GABRA1, KCNQ2, and CHRNA4) (Reid et al., 2009; Poduri & Lowenstein, 2011). Although the known epilepsy genes identified in rare monogenic forms of epilepsy explain only a small proportion of the genetic liability, the casual gene mutations have allowed important insights into key mechanisms of epileptogenesis (Noebels, 2003; Reid et al., 2009). However, none of these epilepsy genes seems to play a substantial role in the genetic predisposition of common GGE syndromes.

Most of the linkage claims reported in genetically complex GGE syndromes (2q34-q36, 3p23-p14, 3q26, 5p15, 5q22, 6p12, 6p23.1, 7q14, 8p12, 8q24, 11q13, 13q31, 14q23, 15q14, 18q21, 19q13) remain controversial, because replication studies have often failed to confirm initial linkage hints in independent sets of families (Greenberg et al., 1988; Zara et al., 1995; Liu et al., 1996; Elmslie et al., 1997; Fong et al., 1998; Greenberg et al., 2000; Sander et al., 2000; Durner et al., 2001; Tauer et al., 2005; Hempelmann et al., 2006; Chioza et al., 2009; Greenberg & Subaran, 2011). The failure to detect replicable susceptibility genes for common epilepsies most likely reflects the underestimated degree of genetic complexity and heterogeneity in human epilepsies. With regard to the drastic loss of power of a linkage scan by the extent of locus heterogeneity, >300 families would be necessary to achieve a reasonable power to map a genetic risk factor, which is present in at least 30% of the families (Table S5). Given that most of the published linkage studies on common GGE syndromes included relatively small samples of <100 GGE-multiplex families, these studies provided low power to detect a major susceptibility locus when <50% of the families were linked to the same locus. Therefore, it is not surprising that the linkage studies reported so far did not reveal replicable susceptibility loci.

To achieve adequate power, the present genome-wide linkage analysis combined three linkage datasets, including 379 clinically well-characterized GGE-multiplex families of European ancestry. Our linkage meta-analysis aimed: (1) to map susceptibility loci shared by a broad spectrum of common GGEs and (2) to dissect out seizure type–related susceptibility genes contributing to the familial clustering of the same seizure type in familial GGEs.

Subjects and Methods

Family ascertainment and clinical assessment

The family sample comprised 379 GGE-multiplex families of European descent, including at least two siblings with GGEs. All families were ascertained under the following inclusion criteria: (1) proband with either genetic absence epilepsy (GAE: CAE/JAE), juvenile myoclonic epilepsy (JME), or EGTCS alone starting before the age of 26 years and exhibiting a GSW-EEG during the course of the epilepsy; and (2) one or more siblings with GGE. In the presence of multigenerational inheritance, family ascertainment was extended toward additional affected first-degree relatives. Phenotyping and diagnostic classification of GGE syndromes was carried out according to standardized protocols available at: http://portal.ccg.uni-koeln.de/ccg/research/epilepsy-genetics/sampling-procedure (ILAE Commission on Classification and Terminology, 1989; Berg et al., 2010). The status of affectedness of patients with GGE and with a history of severe major psychiatric disorders (autism, schizophrenia, affective disorder: recurrent episodes requiring pharmacotherapy or treatment in a hospital), or severe and profound intellectual disability (no basic education, permanently requiring professional support in their daily life) was classified as “unknown” in the linkage analyses.

The entire sample of 379 European GGE-multiplex families comprised three sets of families collected since 1995 by EPICURE partners and collaborating international groups (Table S1) (Sander et al., 2000; Hempelmann et al., 2006; EPICURE Integrated Project). The 379 GGE-multiplex families included 1,920 family members, of whom 982 were affected by GGEs (Tables 1 and S2; 596 female/386 male; syndrome classification: CAE/JAE [n = 504] JME [n = 258], EGTCS alone (GSW-EEG, age of onset <26 years) n = 205, unclassified GGE syndromes [n = 15]; origins by country: Austria [n = 3], Australia/United Kingdom [n = 32], Belgium [n = 1], Bosnia [n = 1], Bulgaria [n = 4], Denmark [n = 10], Finland [n = 7], France [n = 45], Germany [n = 93], Greece [n = 8], Italy [n = 93], Poland [n = 2], Russia [n = 4], Spain [n = 6], Sweden [n = 1], The Netherlands [n = 23], Turkey [n = 21], United Kingdom [n = 25]).

Table 1.   Clinical characterization of the groups of GGE-multiplex families
SampleTraitFam. NInd. NTyped Ind.Affected Ind.GAEGAE & JMEJMEEGTCS aloneUnclass GGEGSW-EEG onlyOther epilepsiesFS only
  1. Fam., family; Ind., individual; GGE, genetic generalized epilepsy; GAE, genetic absence epilepsy; JME, juvenile myoclonic epilepsy; EGTCS, epilepsy with generalized tonic–clonic seizures; unclass GGE, unclassified GGE; GSW-EEG, generalized spike-wave EEG discharges; FS, febrile seizure; BM, broad trait model (all GGE syndromes); NM, narrow trait model (typical absence or myoclonic seizures).

GGEBM3791,9201,728982504601982051521618
GAENM2351,2131,09556744254716071839
JMENM1186245602897454161375937

Family groups and trait models

The genome-wide linkage scan for susceptibility loci shared by a wide spectrum of common GGE syndromes was carried out under the broad trait model in the entire group of 379 GGE-multiplex families. The broad trait model classified family members with any GGE as “affected.”

To dissect out seizure type–related susceptibility loci, two family subgroups were ascertained through a family member affected either by JME-related myoclonic seizures (118 JME families) or typical absence seizures (235 GAE families) and the occurrence of at least two siblings affected by either typical absence seizures or JME-related myoclonic seizures (Tables 1, S3, and S4). Linkage analysis was performed under the narrow trait model, which considered individuals with either typical absence seizures or JME-related myoclonic seizures as “affected.” Together, our ascertainment scheme and the application of the narrow trait model result in a familial clustering of the target seizure type in the affected individuals of both family subgroups (Table 1): (1) 235 GAE families included 567 affected relatives, of whom 87.5% exhibited typical absence seizures; (2) 118 JME families comprised 289 affected relatives, of whom 74.4% exhibited JME-related myoclonic seizures.

The “affection” status of family members with forms of seizures or epilepsies (e.g., febrile seizures, focal epilepsies) other than those specified in the trait model, or known GSW-EEG discharges without seizures, or missing clinical information were classified as “unknown.” All remaining individuals were considered as “unaffected.”

Genome scan marker panels

The present genome-wide linkage meta-analysis consists of three datasets (Table S1): (1) 107 GGE-multiplex families genotyped by a genome-wide panel including 383 autosomal short tandem repeat polymorphisms (STRs) (Sander et al., 2000); (2) 95 GGE-multiplex families genotyped by 639 STRs (Hempelmann et al., 2006); and (3) 177 GGE multiplex-families collected by the EPICURE Integrated Project and genotyped by the Illumina HumanLinkage-12 BeadChip consisting of 6,090 single nucleotide polymorphisms (SNPs). Rutgers sex-averaged combined linkage-physical map of the human genome (Rutgers map v.2) was used for integrating the genetic map positions of STR and SNP marker panels (Matise et al., 2007). The map positions of new STRs were obtained from Rutgers Map Interpolator software (http://compgen.rutgers.edu/old/map-interpolator). STR markers and SNPs had to achieve a genotyping call rate >95% and >98%, respectively. Mendelian errors were assessed by the program Pedcheck (O’Connell & Weeks, 1998) and in case of errors of a marker, the marker genotypes were set to missing for all family members. The pedigree relationship was validated by the graphical representation of relationship errors (GRR) program (http://bioinformatics.well.ox.ac.uk/GRR; Abecasis et al., 2001). The linkage program MERLIN (http://www.sph.umich.edu/csg/abecasis/merlin/index.html; Abecasis et al., 2002) was applied to detect unlikely recombination events and unlikely genotypes were set to missing.

Statistical linkage analyses

Multipoint linkage analysis was performed for nonparametric (NPL) and parametric inheritance models using the ALLEGRO v2 software program (Gudbjartsson et al., 2005). NPL analysis applied the linear model of the Sall scoring statistics, which measures identical-by-descent (IBD) allele sharing among all affected family members in a pedigree (Whittemore & Halpern, 1994). For any marker polymorphism linked to an epilepsy gene, we expect an excess in IBD allele-sharing by the affected family members, relative to expectations of a random Mendelian allele segregation. NPL analyses were chosen as screening method, because this linkage statistic does not require the specification of an inheritance model and allows the simultaneous detection of oligogenic linkage signals.

For parametric multipoint linkage analyses, we analyzed models of dominant (risk allele frequency 0.01) and recessive (risk allele frequency 0.1) inheritance with reduced penetrance (narrow trait model [NM]: 50%, broad trait model (BM): 70%; phenocopy rate: 0.5%) with allowance for locus heterogeneity. This approach covers a wide range of oligogenic inheritance models and can be more powerful than NPL statistics, as long as the parameters of the inheritance model are correctly specified (Hodge et al., 1997; Abreu et al., 2002). Notably, power simulations demonstrate that the entire sample of 379 GGE-multiplex families has a power of nearly 100% to achieve genome-wide significance (heterogeneity logarithm (base 10) of odds [HLOD] = 3.5) under the broad trait model and a recessive mode of inheritance, when we presume that 30% of the families are linked to the same susceptibility locus. For the dominant approximation model, the power is about 80% for this scenario. Power simulations for the family groups are shown in Table S5.

Thresholds for suggestive and significant linkage for each analysis were assessed empirically by simulation analyses implemented in the simulate option of the MERLIN software package (Abecasis et al., 2002). The gene-dropping approach allowed us to account for incomplete extraction of inheritance information and for the diversity of pedigree structure. This empiric approach is more appropriate than theoretically derived significance thresholds as proposed by Lander and Kruglyak (1995), which are often conservative, because of their assumption of fully informative inheritance (Wiltshire et al., 2002). The empirically derived threshold for suggestive linkage refers to the probability that a linkage score greater than this threshold occurs only once at random in a single genome scan, and once in 20 linkage scans for significant linkage. These critical significance thresholds based on 5,000 simulations of the real data sets were the following: (1) NPL analyses (Table S6); suggestive: nonparametric logarithm of odds (LODnpl) >1.80, significant: LODnpl >3.15; and (2) parametric linkage analysis (Table S7); suggestive: HLOD >2.13, significant: HLOD >3.49 including a correction of HLOD = 0.3 for testing two inheritance models (Hodge et al., 1997; Abreu et al., 2002). We did not include a correction for performing parametric as well as NPL analyses, because of the strong correlation of both linkage statistics, and we did not adjust for multiple testing of three family groups, because these tests evaluate specific phenotype–genotype relationships.

Results

To search for genetic risk factors shared by a wide spectrum of familial GGE syndromes, we performed a genome-wide NPL scan under the broad trait model (all GGEs). The genome-wide NPL results are presented in Fig. 1A. None of the NPL results met genome-wide significance (LODnpl >3.15), but multipoint NPL analysis revealed four loci achieving suggestive evidence for linkage (LODnpl >1.80) at 3p14.2 (LODnpl = 2.96 at rs624755, chromosomal position: chr3:61709002 according to NCBI build 36.3), 5q34 (LODnpl = 1.95 at rs1432881, chr5:166865098), 13q12.12 (LODnpl = 2.42 at rs1008812, chr13:22864145), and 19q13.42 (LODnpl = 2.86 at rs9788, chr19:58411062) (Table 2; Fig. 1A). Consistent with the NPL results, parametric HLOD analyses revealed suggestive evidence for linkage at 3p14 for both inheritance models (dominant: HLOD = 2.84 for α = 0.20 at rs782728, chr3:66408992; recessive: HLOD = 3.21 for α = 0.13 at rs1374679, chr3:63050307) (Table 3; Fig. 1B,C). In addition, suggestive evidence for linkage was obtained at 1p36.22 assuming dominant inheritance (HLOD = 2.50 for α = 0.17 at rs1216213, chr1:9969047), and at 13q31.3 assuming recessive inheritance (HLOD = 2.67 for α = 0.11 at D13S1230, chr13:88834621) (Table 3; Fig. 1C).

Figure 1.


Genome-wide linkage scan in 379 GGE families assuming a broad trait definition. Linkage results are shown for: (A) nonparametric linkage analysis, (B) parametric linkage analysis applying a dominant inheritance model with 70% penetrance, and (C) a recessive inheritance model with 70% penetrance. The chromosomes are arranged in linear scale from pter to qter on the upper x-axis. Empirically derived genome-wide significance thresholds are indicated in the plots as horizontal lines. Broad trait definition: all GGEs.

Table 2.   Suggestive and significant nonparametric linkage results
SampleTraitLODnplChrom.MarkerChrom. pos.
  1. GGE, genetic generalized epilepsy; GAE, genetic absence epilepsy; JME, juvenile myoclonic epilepsy; BM, broad trait model (all GGE syndromes); NM, narrow trait model (typical absence or myoclonic seizures); Chrom., chromosome; Chrom. pos., physical chromosomal nucleotide position; significant linkage scores are highlighted in bold scores.

GGEBM2.963p14.2rs6247553:61709002
GGEBM1.955q34rs14328815:166865098
GGEBM2.4213q12.12rs100881213:22864145
GGEBM2.8619q13.42rs978819:58411062
GAENM2.315q34rs2449035:167846088
JMENM3.432q34D2S1432:214624639
JMENM2.815q34rs10254825:166825835
Table 3.   Suggestive and significant parametric linkage results
SampleTraitMOIHLODαChrom.MarkerChrom. pos.
  1. GGE, genetic generalized epilepsy; GAE, genetic absence epilepsy; JME, juvenile myoclonic epilepsy; BM, broad trait model (all GGE syndromes); NM, narrow trait model (typical absence or myoclonic seizures); MOI, mode of inheritance; AD70, autosomal dominant inheritance with 70% penetrance; AR70, autosomal recessive inheritance with 70% penetrance; AD50, autosomal dominant inheritance with 50% penetrance; AR50, autosomal recessive inheritance with 50% penetrance; HLOD, heterogeneity LOD score; α, proportion of linked families; Chrom., chromosome; Chrom. pos., physical chromosomal nucleotide position; significant linkage scores are highlighted in bold scores.

GGEBMAD702.500.171p36.22rs121362131:9969047
GGEBMAD702.840.203p14.1rs7827283:66408992
GGEBMAR703.210.133p14.2rs13746793:63050307
GGEBMAR702.670.1113q31.3D13S123013:88834621
GAENMAD503.230.315q33.1rs3576085:150820573
GAENMAR505.020.2213q31.3rs133247013:90215191
JMENMAD502.500.392q34D2S1432:214624639
JMENMAD502.960.405q34rs20693475:162799773
JMENMAD502.570.3921q22.3rs283937721:46902240
JMENMAR502.590.252q34D2S1432:214624639

To dissect out seizure type–related susceptibility genes, linkage analyses under the narrow trait model were carried out in two family subgroups exhibiting a clustering of the target seizure type. The genome-wide parametric and non-parametric linkage results in 235 GAE families are shown in Fig. 2; Tables 2 and 3. NPL analysis revealed suggestive evidence for linkage in the chromosomal region 5q34 (Fig. 2A; LODnpl = 2.31 at rs244903, chr5:167846088). Corresponding to the NPL results, parametric HLOD score analysis showed suggestive evidence for linkage at 5q34, assuming dominant inheritance (Fig. 2B; HLOD = 3.23 for α = 0.31 at rs357608, chr5:150820573). Significant parametric linkage was found in the chromosomal region 13q31.3, assuming recessive inheritance (Fig. 2C; HLOD = 5.02 for α = 0.22 at rs1332470, chr13:90215191).

Figure 2.


Genome-wide linkage scan in 235 GAE families assuming a narrow trait definition. Linkage results are shown for: (A) nonparametric linkage analysis, (B) parametric linkage analysis applying a dominant inheritance model with 50% penetrance, and (C) a recessive inheritance model with 50% penetrance. The chromosomes are arranged in linear scale from pter to qter on the upper x-axis. Empirically derived genome-wide significance thresholds are indicated in the plots as horizontal lines. Narrow trait definition: typical absence or myoclonic seizures.

The genome-wide parametric and NPL results in 118 JME families are presented in Fig. 3; Tables 2 and 3. A genome-wide significant NPL score was obtained at 2q34 (LODnpl = 3.43 at D2S143, chr2:214624639) and a suggestive NPL score of LODnpl = 2.62 at 5q34 (rs1025482, chr5:166825835) (Fig. 3A). Consistently, both loci at 2q34 and 5q34 were supported by the parametric linkage results (Table 3; Fig. 3B,C). We observed suggestive evidence for linkage at 2q34 for both inheritance models (dominant: HLOD = 2.50 for α = 0.39 at D2S143; recessive: HLOD = 2.59, α = 0.25 at D2S143). Suggestive evidence for linkage was found at 5q34 (HLOD = 2.96 for α = 0.40 at rs2069347, chr5:162799773) and at 21q22.3 (HLOD = 2.57 for α = 0.39 at rs2839377, chr21:46902240), assuming dominant inheritance (Table 3; Fig. 3B,C).

Figure 3.


Genome-wide linkage scan in 118 JME families assuming a narrow trait definition. Linkage results are shown for: (A) nonparametric linkage analysis, (B) parametric linkage analysis applying a dominant inheritance model with 50% penetrance, and (C) a recessive inheritance model with 50% penetrance. The chromosomes are arranged in linear scale from pter to qter on the upper x-axis. Empirically derived genome-wide significance thresholds are indicated in the plots as horizontal lines. Narrow trait definition: typical absence or myoclonic seizures.

Discussion

The present linkage meta-analysis includes a sample of 379 European GGE-multiplex families, which is at least three times larger than any other study sample of GGE-multiplex families reported so far. This linkage study was designed to map genetic risk factors shared by a broad spectrum of common familial GGE syndromes and to dissect out seizure type–related susceptibility genes. To search for genetic risk factors shared by GGEs, we have carried out linkage analyses in the entire sample of 379 GGE-multiplex families under the broad trait model, considering all GGEs as “affected.” None of the linkage results for shared genetic risk factors met genome-wide significance. However, we found suggestive evidence for linkage to six chromosomal segments: 1p36.22, 3p14.2, 5q34, 13q12.12, 13q31.3, and 19q13.42. Given that a linkage finding that reaches the threshold of suggestive evidence for linkage is expected to occur once by chance in a single genome-wide linkage analysis, these linkage findings support an oligogenic predisposition of familial GGE syndromes. In particular, the linkage peak at 5q34 is consistently supported by both nonparametric and parametric linkage results across the entire set of families and both family subgroups. This linkage peak maps close to the gene cluster encoding the GABAAβ2-, α6-, α1-, and γ2 subunits (gene symbols: GABRB2, GABRA6, GABRA1, GABRG2). With respect to the important role of an impaired GABAergic inhibition in epileptogenesis (Noebels, 2003; Macdonald et al., 2010), the GABAA subunit genes at the 5q34 gene cluster represent plausible candidate genes. Specifically, the GABRA1 and GABRG2 genes are strong candidates, because most known GABAA-receptor mutations associated with GGEs have been found in the GABRA1 and GABRG2 genes (for review see Macdonald et al., 2010; Lachance-Touchette et al., 2011). Accordingly, mutations of the GABRB2, GABRA6, GABRA1, and GABRG2 genes may also play a causative role in some of the GGE-multiplex families investigated in the present study.

To dissect out seizure type-related genetic factors preferentially predisposing to either absence or myoclonic seizures, we stratified two subgroups of families that showed a strong clustering of the target seizure type, when linkage analysis was performed under the narrow trait definition (typical absence or myoclonic seizures). This ascertainment scheme allowed a partial overlap among both family subgroups (78 of 275 GGE-multiplex families), which reflects the common individual and familial co-occurrence of absence seizures and JME-related myoclonic seizures, but also takes into account the familial clustering of these target seizure types in both family subgroups (Berkovic et al., 1987; Beck-Mannagetta & Janz, 1991; Wirrell et al., 1996; Schmitz et al., 2000; Winawer et al., 2003; Marini et al., 2004; Winawer et al., 2005; Kinirons et al., 2008). Thereby, we aimed to reduce the genetic heterogeneity of these oligogenic traits and to accumulate single susceptibility factors that contribute to the familial clustering of either absence or myoclonic seizures. It is noteworthy that this approach focused on the dissection of seizure type–related but not seizure type– or syndrome-specific susceptibility factors.

To search for susceptibility loci involved in the genetic predisposition of absence seizures, linkage analysis was performed in 235 GAE families, in which 87.5% of the affected family members exhibited typical absence seizures. NPL analysis showed suggestive evidence for linkage in the 5q34 region. Parametric linkage analysis for a recessive genetic model met genome-wide significance at 13q31.3, a region previously implicated as a susceptibility locus for GGE and specifically for photosensitive GGEs (Tauer et al., 2005; Hempelmann et al., 2006). Because of the large overlap of these previous studies with the GGE-multiplex families included in the present linkage meta-analysis, these findings are not independent and cannot be considered as replicated linkage finding. Notably, the linkage evidence at 13q31.3 for parametric HLOD scores is substantially higher than that of the regional nonparametric LODnpl scores, suggesting that linkage evidence from unaffected individuals supports the linkage result. Post hoc exploratory analyses of inheritance parameters (e.g., affecteds-only analysis, trimming of large pedigrees to nuclear families, removal of markers with a pairwise linkage disequilibrium of r2 > 0.1) did not indicate that one of these parameters might have led to a spurious linkage finding (HLOD > 4.2 for all tests). Therefore, the significant parametric linkage finding at 13q31.3 appears to be robust and reliable, despite the lack of adequate support by NPL analysis. Among the most interesting candidate genes located in this region is the gene encoding glypican proteoglycan 5 (GPC5), which is expressed at the external surface of neuronal plasma membranes and has been implicated in brain patterning, synapse formation, axon regeneration, and guidance (Lee & Chien, 2004; Van Vactor et al., 2006; Luxardi et al., 2007). Of interest, genome-wide association analysis in multiple sclerosis (Baranzini et al., 2009; Lorentzen et al., 2010) and acquired nephrotic syndrome (Okamoto et al., 2011) revealed significant associations of SNPs in the genomic GPC5 gene region.

Linkage mapping of susceptibility loci contributing to the expression of myoclonic seizures was carried out in 118 JME families, in which 74.4% of the affected family members exhibited JME-related myoclonic seizures under the narrow trait model. NPL analysis provided suggestive evidence for linkage in the chromosomal regions 2q34 and again in 5q34. The linkage peak at 5q34 is in direct vicinity of the GABRB2/A6/A1/G2 gene cluster. This linkage finding is of particular interest, because a loss-of-function Ala322Asp mutation in the GABRA1 gene causes JME in a large multigeneration family including eight members with JME (Cossette et al., 2002). Furthermore, the present JME locus at 2q34 is supported by a recent significant linkage finding that maps JME to the region 2q33-q36 in a multigeneration family with seven JME members (Ratnapriya et al., 2010). An interesting positional candidate gene is the sodium-independent electroneutral anion exchanger 3 gene (AE3, gene symbol: SLC4A3), which regulates extracellular and intracellular pH in neurons and thereby may influence seizure susceptibility. For the SLC4A3 missense SNP rs635311 (c.2869A>C, p.A867D), we have previously demonstrated an allelic association of the p.867D allele with common GGE syndromes (Sander et al., 2002). Subsequently, it has been shown that the p.867D allele is a functional SLC4A3 mutation that causes changes in cell volume and abnormal intracellular pH in the brain, potentially leading to neuronal hyperexcitability (Vilas et al., 2009). In addition, mice with a targeted disruption of SLC4A3 display a reduced seizure threshold (Hentschke et al., 2006). These lines of evidence suggest that SLC4A3 modulates seizure susceptibility and represents a high-ranking candidate gene for JME.

Despite a relatively large sample of 379 European GGE-multiplex families, we found significant linkage only in two subgroups of families stratified for a more homogenous phenotypic spectrum of seizure types. This finding suggests that the outcome of linkage mapping is not only a question of large numbers of families, but more critically depends on an accurate dissection of more homogeneous epilepsy traits. Our present linkage results demonstrate that phenotype–genotype strategies that apply narrow trait definitions in phenotypic homogeneous subsets of families may improve the prospects to disentangle the genetic basis of common familial GGE syndromes. With regard to the considerable effort to collect large samples of multiplex families and the rapid advances in next-generation sequencing technologies, large-scale linkage studies of genetically complex traits will probably not be continued in the traditional approach. Future research strategies will apply linkage mapping in combination with exome and genome sequencing of familial GGE syndromes. Thereby, linkage information in single families will provide an efficient tool to prioritize epilepsy genes and to filter out causal mutations from the nearly comprehensive assembly of individual sequence variations (Ku et al., 2011). This integrative approach together with advances in genomics, technologies, and bioinformatics has a great potential to accelerate progress in finding the numerous susceptibility variants conferring risk to common GGE syndromes (Cooper & Shendure, 2011).

Acknowledgments

We thank all participants and their families for participating in this study. We gratefully acknowledge the excellent technical assistance of Sebastian Fey in high-throughput SNP genotyping. The Dutch NIGO program (Dutch IGE Genetics Research) and the Italian League Against Epilepsy thank all participants and contributing neurologists. This work was supported by grants from the European Community (FP6 Integrated Project EPICURE, grant LSHM-CT-2006-037315 to D.L., H.L., C.E.E. R.G., A-E.L., J.S., E.L., F.R., U.O., T.S., FP6 MEXCT visual sensitivity, 024224 to D.K.-N.T.); the German Research Foundation (grants SA434/4-2, SA434/5-1 to T.S., BE3828/4-1 to T.B.); the German Federal Ministry of Education and Research, National Genome Research Network (NGFN-2: NeuroNet to C.E.E., H.L., and T.S.; NGFNplus: EMINet, grants 01GS08120 to P.N. and T.S., and 01GS08123 to H.L.; IntenC, TUR 09/I10 to T.S.); the Belgian National Fund for Scientific Research (Flanders, grant 0399.08 to P.J.); the Research Fund of the University of Antwerp (IWS BOF UA 2008 to A.J and P.D.J.); the National Science Fund, Bulgarian Ministry of Education, Youth and Science (grant DTK02/67 to A.J.); Spanish Ministry of Education and Science (grant SAF2007-61003 to J.M.S); the Netherlands National Epilepsy Fund (grant 04-08 to B.P.C.K. and D.L.); the Netherlands Organization for Scientific Research (grant 917.66.315 to B.P.C.K. and C.G.F.d.K.); the Scientific and Technological Research Council of Turkey (TUBITAK grant 106S027 to H.C.) and Bogazici University Research Fund (grants 05HB104D, 06B107D & 08HB104D to H.C.); and the National Health and Medical Research Council (Australia) (grant to S.M., I.E.S. and S.F.B.).

Disclosure

None of the authors has any conflict of interest to disclose. We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

Appendix

EPICURE Integrated Project (participating centers listed by country): Department of Clinical Neurology (Fritz Zimprich) and Department of Pediatrics and Neonatology (Martina Mörzinger, Martha Feucht), Medical University of Vienna, Währinger Gürtel 18-20, A-1090 Vienna, Austria. VIB Department of Molecular Genetics, University of Antwerp, Universiteitsplein 1, 2610 Antwerpen, Belgium (Arvid Suls, Sarah Weckhuysen, Lieve Claes, Liesbet Deprez, Katrien Smets, Tine Van Dyck, Tine Deconinck, Peter De Jonghe). Department of Neurology, Medical University-Sofia, Georgi Sofijski Str 1, 1431 Sofia, Bulgaria (Reana Velizarova, Petya Dimova, Melania Radionova, Ivaylo Tournev). Department of Chemistry and Biochemistry, Molecular Medicine Center, Medical University-Sofia, Zdrave Str 2, 1431 Sofia, Bulgaria (Dahlia Kancheva, Radka Kaneva, Albena Jordanova). Research Department, Danish Epilepsy Centre, Dianalund Kolonivej 7, 4293 Dianalund, Denmark (Rikke S Møller, Helle Hjalgrim). Wilhelm Johannsen Centre for Functional Genome Research, University of Copenhagen, Blegdamsvej 3, 2200 Copenhagen N, Denmark (Rikke S Møller). Neuroscience Center, University of Helsinki, Biomedicum Helsinki, Haartmaninkatu 8, 00290 Helsinki, Finland (Anna-Elina Lehesjoki, Auli Siren). Epileptology unit, Hôpital de la Pitié-Salpêtrière, 75013 Paris, France (Michel Baulac, Stephanie Baulac, Isabelle Gourfinkel-An). Department of Neuropediatrics, University Medical Center Schleswig-Holstein (Kiel Campus), Schwanenweg 20, 24105 Kiel, Germany (Ingo Helbig, Hiltrud Muhle, Sarah von Spiczak, Philipp Ostertag, Ulrich Stephani). Cologne Center for Genomics, University of Cologne, Weyertal 115b, 50931 Cologne, Germany (Markus Leber, Costin Leu, Thomas Sander, Mohammad R. Toliat, Holger Trucks, Peter Nürnberg). Max-Delbrück-Center for Molecular Medicine, Robert-Rössle-Strasse 10, 13125 Berlin, Germany (Anne Hempelmann, Franz Rüschendorf, Thomas Sander). Department of Epileptology, University Clinics Bonn, Sigmund Freud Strasse 1, 53105 Bonn, Germany (Christian E. Elger, Kerstin Hallmann, Ailing A. Kleefuß-Lie, Wolfram S. Kunz). Department of Neurology, Charité University Medicine, Campus Virchow Clinic, Humboldt University of Berlin, Augustenburger Platz 1, 13353 Berlin, Germany (Verena Gaus, Dieter Janz, Thomas Sander, Bettina Schmitz). Epilepsy-Center Hessen, Department of Neurology, University Hospitals and Philipps-University, 35043 Marburg, Germany (Karl Martin Klein, Philipp S. Reif, Wolfgang H. Oertel, Hajo M. Hamer, Felix Rosenow). Abteilung Neurologie mit Schwerpunkt Epileptologie, Hertie Institut für klinische Hirnforschung, Universität Tübingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Germany (Felicitas Becker, Yvonne Weber, Holger Lerche). Child Neurology Unit, Children’s Hospital A. Meyer, University of Florence, Florence, Italy (Carla Marini, Reno Guerrini, Davide Mei, Vanessa Norci). Department of Neuroscience, Institute G. Gaslini (Federico Zara, Pasquale Striano, Angela Robbiano, Marianna Pezzella), Italian League Against Epilepsy (Amedeo Bianchi, Antonio Gambardella, Paolo Tinuper, Angela La Neve, Giuseppe Capovilla, Piernanda Vigliano, Giovanni Crichiutti, Francesca Vanadia, Aglaia Vignoli, Antonietta Coppola, Salvatore Striano, Gabriella Egeo, Anna Teresa Giallonardo, Silvana Franceschetti, Vincenzo Belcastro, Paolo Benna, Giangennaro Coppola, Alessia De Palo, Edoardo Ferlazzo, Marilena Vecchi, Vittorio Martinelli, Francesca Bisulli, Francesca Beccaria, Ennio Del Giudice, Margherita Mancardi, Giuseppe Stranci, Aldo Scabar, Giuseppe Gobbi, Ivan Giordano). Epilepsy Unit, Neurology Service and Instituto de Investigaciones Sanitarias, Fundación Jiménez Díaz, and Centro de Investigación Biomédica en Red en Enfermedades Raras (CIBERER) Madrid, Spain (Rosa Guerero, Beatriz G Giraldez, Jose M Serratosa). Netherlands Section Complex Genetics, Department of Medical Genetics, University Medical Center Utrecht, Str. 2.112 Universiteitsweg 100, 3584 CG Utrecht, the Netherlands (Bobby P.C. Koeleman, Carolien de Kovel, Dick Lindhout). SEIN Epilepsy Institute in the Netherlands, P.O. Box 540, 2130AM Hoofddorp, Netherlands (Gerrit-Jan de Haan). Department of Genetics, Experimental Medicine Research Institute (DETAE) and Epilepsy Center (EPIMER), Istanbul University, Millet Cad, Capa 34390, Istanbul, Turkey (Ugur Ozbek, Nerses Bebek, Betul Baykan, Ozkan Ozdemir, Sibel Ugur, Elif Kocasoy-Orhan, Emrah Yücesan, Naci Cine, Aysen Gokyigit, Candan Gurses, Gunay Gul, Semih Ayta, Zuhal Yapici, Cigdem Ozkara). Department of Molecular Biology and Genetics, Bogazici University, Istanbul, Turkey (Hande Caglayan, Ozlem Yalcin). Department of Neurology, Umraniye Education Hospital, Istanbul, Turkey (Destina Yalcin). Institute of Neurological Sciences, Marmara University, Maltepe, Istanbul, Turkey (Dilsad Turkdogan). Department of Pediatric Neurology, Faculty of Medicine, Cukurova University, Adana, Turkey (Kezban Arslan, Hacer Bozdemir). Dr. Behcet Uz Child Disease and Pediatric Surgery Training and Research Hospital, Izmir, Turkey (Gulsen Dizdarer). Cerrahpasa Medical School, Istanbul University, Istanbul, Turkey (Derya Uluduz).

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