BMC Psychiatry (Nov 2010)

The cross-sectional GRAS sample: <it>A comprehensive phenotypical data collection of schizophrenic patients</it>

  • Oestereich Cornelia,
  • Müller-Isberner Rüdiger,
  • Mielke Andreas,
  • Maier Wolfgang,
  • Löhrer Frank,
  • Franz Michael,
  • Kunze Heinrich,
  • Kruse Gunther,
  • Hesse Dirk,
  • Herpertz Sabine,
  • Günther Rolf,
  • Freese Roland,
  • Folkerts Here,
  • Dose Matthias,
  • Czernik Adelheid,
  • Becker Thomas,
  • Becker-Emner Marianne,
  • Aldenhoff Josef B,
  • Adler Lothar,
  • Flögel Marlene,
  • Treitz Annika,
  • Tarami Asieh,
  • Ackermann Verena,
  • Gerchen Martin F,
  • Kästner Anne,
  • Papiol Sergi,
  • Grube Sabrina,
  • Begemann Martin,
  • Friedrichs Heidi,
  • Ribbe Katja,
  • Pajonk Frank-Gerald,
  • Pollmächer Thomas,
  • Schneider Udo,
  • Schwarz Hans-Joachim,
  • Kröner-Herwig Birgit,
  • Havemann-Reinecke Ursula,
  • Frahm Jens,
  • Stühmer Walter,
  • Falkai Peter,
  • Brose Nils,
  • Nave Klaus-Armin,
  • Ehrenreich Hannelore

DOI
https://doi.org/10.1186/1471-244X-10-91
Journal volume & issue
Vol. 10, no. 1
p. 91

Abstract

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Abstract Background Schizophrenia is the collective term for an exclusively clinically diagnosed, heterogeneous group of mental disorders with still obscure biological roots. Based on the assumption that valuable information about relevant genetic and environmental disease mechanisms can be obtained by association studies on patient cohorts of ≥ 1000 patients, if performed on detailed clinical datasets and quantifiable biological readouts, we generated a new schizophrenia data base, the GRAS (Göttingen Research Association for Schizophrenia) data collection. GRAS is the necessary ground to study genetic causes of the schizophrenic phenotype in a 'phenotype-based genetic association study' (PGAS). This approach is different from and complementary to the genome-wide association studies (GWAS) on schizophrenia. Methods For this purpose, 1085 patients were recruited between 2005 and 2010 by an invariable team of traveling investigators in a cross-sectional field study that comprised 23 German psychiatric hospitals. Additionally, chart records and discharge letters of all patients were collected. Results The corresponding dataset extracted and presented in form of an overview here, comprises biographic information, disease history, medication including side effects, and results of comprehensive cross-sectional psychopathological, neuropsychological, and neurological examinations. With >3000 data points per schizophrenic subject, this data base of living patients, who are also accessible for follow-up studies, provides a wide-ranging and standardized phenotype characterization of as yet unprecedented detail. Conclusions The GRAS data base will serve as prerequisite for PGAS, a novel approach to better understanding 'the schizophrenias' through exploring the contribution of genetic variation to the schizophrenic phenotypes.