TITLE

The cross-sectional GRAS sample: A comprehensive phenotypical data collection of schizophrenic patients

AUTHOR(S)
Ribbe, Katja; Friedrichs, Heidi; Begemann, Martin; Grube, Sabrina; Papiol, Sergi; Kästner, Anne; Gerchen, Martin F.; Ackermann, Verena; Tarami, Asieh; Treitz, Annika; Flögel, Marlene; Adler, Lothar; Aldenhoff, Josef B.; Becker-Emner, Marianne; Becker, Thomas; Czernik, Adelheid; Dose, Matthias; Folkerts, Here; Freese, Roland; Günther, Rolf
PUB. DATE
January 2010
SOURCE
BMC Psychiatry;2010, Vol. 10, p91
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
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.
ACCESSION #
57221011

 

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