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Article: Enhancing Psychosis-Spectrum Nosology Through an International Data Sharing Initiative

TitleEnhancing Psychosis-Spectrum Nosology Through an International Data Sharing Initiative
Authors
Keywordsdata sharing
genetic
HiTOP
ICSR
phenotype
psychosis
schizophrenia
schizotypal
schizotypy
Issue Date2018
Citation
Schizophrenia Bulletin, 2018, v. 44, p. S460-S467 How to Cite?
AbstractThe latent structure of schizotypy and psychosis-spectrum symptoms remains poorly understood. Furthermore, molecular genetic substrates are poorly defined, largely due to the substantial resources required to collect rich phenotypic data across diverse populations. Sample sizes of phenotypic studies are often insufficient for advanced structural equation modeling approaches. In the last 50 years, efforts in both psychiatry and psychological science have moved toward (1) a dimensional model of psychopathology (eg, the current Hierarchical Taxonomy of Psychopathology [HiTOP] initiative), (2) an integration of methods and measures across traits and units of analysis (eg, the RDoC initiative), and (3) powerful, impactful study designs maximizing sample size to detect subtle genomic variation relating to complex traits (the Psychiatric Genomics Consortium [PGC]). These movements are important to the future study of the psychosis spectrum, and to resolving heterogeneity with respect to instrument and population. The International Consortium of Schizotypy Research is composed of over 40 laboratories in 12 countries, and to date, members have compiled a body of schizotypy-And psychosis-related phenotype data from more than 30000 individuals. It has become apparent that compiling data into a protected, relational database and crowdsourcing analytic and data science expertise will result in significant enhancement of current research on the structure and biological substrates of the psychosis spectrum. The authors present a data-sharing infrastructure similar to that of the PGC, and a resource-sharing infrastructure similar to that of HiTOP. This report details the rationale and benefits of the phenotypic data collective and presents an open invitation for participation.
Persistent Identifierhttp://hdl.handle.net/10722/367532
ISSN
2023 Impact Factor: 5.3
2023 SCImago Journal Rankings: 2.249

 

DC FieldValueLanguage
dc.contributor.authorDocherty, Anna R.-
dc.contributor.authorFonseca-Pedrero, Eduardo-
dc.contributor.authorDebbané, Martin-
dc.contributor.authorChan, Raymond C.K.-
dc.contributor.authorLinscott, Richard J.-
dc.contributor.authorJonas, Katherine G.-
dc.contributor.authorCicero, David C.-
dc.contributor.authorGreen, Melissa J.-
dc.contributor.authorSimms, Leonard J.-
dc.contributor.authorMason, Oliver-
dc.contributor.authorWatson, David-
dc.contributor.authorEttinger, Ulrich-
dc.contributor.authorWaszczuk, Monika-
dc.contributor.authorRapp, Alexander-
dc.contributor.authorGrant, Phillip-
dc.contributor.authorKotov, Roman-
dc.contributor.authorDeYoung, Colin G.-
dc.contributor.authorRuggero, Camilo J.-
dc.contributor.authorEaton, Nicolas R.-
dc.contributor.authorKrueger, Robert F.-
dc.contributor.authorPatrick, Christopher-
dc.contributor.authorHopwood, Christopher-
dc.contributor.authorO'Neill, F. Anthony-
dc.contributor.authorZald, David H.-
dc.contributor.authorConway, Christopher C.-
dc.contributor.authorAdkins, Daniel E.-
dc.contributor.authorWaldman, Irwin D.-
dc.contributor.authorVan Os, Jim-
dc.contributor.authorSullivan, Patrick F.-
dc.contributor.authorAnderson, John S.-
dc.contributor.authorShabalin, Andrey A.-
dc.contributor.authorSponheim, Scott R.-
dc.contributor.authorTaylor, Stephan F.-
dc.contributor.authorGrazioplene, Rachel G.-
dc.contributor.authorBacanu, Silviu A.-
dc.contributor.authorBigdeli, Tim B.-
dc.contributor.authorHaenschel, Corinna-
dc.contributor.authorMalaspina, Dolores-
dc.contributor.authorGooding, Diane C.-
dc.contributor.authorNicodemus, Kristin-
dc.contributor.authorSchultze-Lutter, Frauke-
dc.contributor.authorBarrantes-Vidal, Neus-
dc.contributor.authorMohr, Christine-
dc.contributor.authorCarpenter, William T.-
dc.contributor.authorCohen, Alex S.-
dc.date.accessioned2025-12-19T07:57:16Z-
dc.date.available2025-12-19T07:57:16Z-
dc.date.issued2018-
dc.identifier.citationSchizophrenia Bulletin, 2018, v. 44, p. S460-S467-
dc.identifier.issn0586-7614-
dc.identifier.urihttp://hdl.handle.net/10722/367532-
dc.description.abstractThe latent structure of schizotypy and psychosis-spectrum symptoms remains poorly understood. Furthermore, molecular genetic substrates are poorly defined, largely due to the substantial resources required to collect rich phenotypic data across diverse populations. Sample sizes of phenotypic studies are often insufficient for advanced structural equation modeling approaches. In the last 50 years, efforts in both psychiatry and psychological science have moved toward (1) a dimensional model of psychopathology (eg, the current Hierarchical Taxonomy of Psychopathology [HiTOP] initiative), (2) an integration of methods and measures across traits and units of analysis (eg, the RDoC initiative), and (3) powerful, impactful study designs maximizing sample size to detect subtle genomic variation relating to complex traits (the Psychiatric Genomics Consortium [PGC]). These movements are important to the future study of the psychosis spectrum, and to resolving heterogeneity with respect to instrument and population. The International Consortium of Schizotypy Research is composed of over 40 laboratories in 12 countries, and to date, members have compiled a body of schizotypy-And psychosis-related phenotype data from more than 30000 individuals. It has become apparent that compiling data into a protected, relational database and crowdsourcing analytic and data science expertise will result in significant enhancement of current research on the structure and biological substrates of the psychosis spectrum. The authors present a data-sharing infrastructure similar to that of the PGC, and a resource-sharing infrastructure similar to that of HiTOP. This report details the rationale and benefits of the phenotypic data collective and presents an open invitation for participation.-
dc.languageeng-
dc.relation.ispartofSchizophrenia Bulletin-
dc.subjectdata sharing-
dc.subjectgenetic-
dc.subjectHiTOP-
dc.subjectICSR-
dc.subjectphenotype-
dc.subjectpsychosis-
dc.subjectschizophrenia-
dc.subjectschizotypal-
dc.subjectschizotypy-
dc.titleEnhancing Psychosis-Spectrum Nosology Through an International Data Sharing Initiative-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1093/schbul/sby059-
dc.identifier.pmid29788473-
dc.identifier.scopuseid_2-s2.0-85055005258-
dc.identifier.volume44-
dc.identifier.spageS460-
dc.identifier.epageS467-
dc.identifier.eissn1745-1701-

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