File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Leveraging genome-wide association and clinical data in revealing schizophrenia subgroups

TitleLeveraging genome-wide association and clinical data in revealing schizophrenia subgroups
Authors
Issue Date2018
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/jpsychires
Citation
Journal of Psychiatric Research, 2018, v. 106, p. 106-117 How to Cite?
AbstractSchizophrenia (SCZ) has long been recognized as a highly heterogeneous disorder. Patients differed in their clinical manifestations, prognosis, and underlying pathophysiologies. Here we presented and applied a framework for finding subtypes of SCZ utilizing genome-wide association study (GWAS) and clinical data. We postulated that genetic information may help stratify patient into useful subgroups, and incorporation of other clinical information and cognitive profiles will further improve patient subtyping. We conducted cluster analysis in 387 Hong Kong Chinese with SCZ. First we performed 'single-view' clustering using genetic or clinical data alone, then proceeded to 'multi-view' clustering (MVC) accounting for both types of information. We validated clustering results by assessing subgroup differences in various outcomes. We found significant differences in outcomes including treatment response, disease course and symptom severity (Simes overall p-value using MVC = 1.64E-9). Overall speaking, we identified three subgroups with good, intermediate and poor prognosis respectively. MVC generally out-performed single-view methods. The analysis was repeated for different sets of input SNPs, and stratified analysis of male and female patients, and the results remained largely robust. We also found significant enrichment for SCZ loci among the SNPs selected by the cluster algorithm. Numerous selected genes (e.g. NRG1, ERBB4, NRXN1, ANK3) and pathways (e.g. neuregulin-ErbB4 and calcium signaling) were implicated in SCZ or related pathophysiological processes. This is first study to combine both genetic and clinical data for subtyping SCZ, and to employ genome-wide SNP data in cluster analysis of a complex disease. This work points to a new way of GWAS analysis of translational potential.
Persistent Identifierhttp://hdl.handle.net/10722/274016
ISSN
2017 Impact Factor: 4.0
2015 SCImago Journal Rankings: 2.265
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorCheung, EFC-
dc.contributor.authorChen, RYL-
dc.contributor.authorWong, HM-
dc.contributor.authorWong, HM-
dc.contributor.authorSham, PC-
dc.contributor.authorSo, HC-
dc.date.accessioned2019-08-18T14:53:20Z-
dc.date.available2019-08-18T14:53:20Z-
dc.date.issued2018-
dc.identifier.citationJournal of Psychiatric Research, 2018, v. 106, p. 106-117-
dc.identifier.issn0022-3956-
dc.identifier.urihttp://hdl.handle.net/10722/274016-
dc.description.abstractSchizophrenia (SCZ) has long been recognized as a highly heterogeneous disorder. Patients differed in their clinical manifestations, prognosis, and underlying pathophysiologies. Here we presented and applied a framework for finding subtypes of SCZ utilizing genome-wide association study (GWAS) and clinical data. We postulated that genetic information may help stratify patient into useful subgroups, and incorporation of other clinical information and cognitive profiles will further improve patient subtyping. We conducted cluster analysis in 387 Hong Kong Chinese with SCZ. First we performed 'single-view' clustering using genetic or clinical data alone, then proceeded to 'multi-view' clustering (MVC) accounting for both types of information. We validated clustering results by assessing subgroup differences in various outcomes. We found significant differences in outcomes including treatment response, disease course and symptom severity (Simes overall p-value using MVC = 1.64E-9). Overall speaking, we identified three subgroups with good, intermediate and poor prognosis respectively. MVC generally out-performed single-view methods. The analysis was repeated for different sets of input SNPs, and stratified analysis of male and female patients, and the results remained largely robust. We also found significant enrichment for SCZ loci among the SNPs selected by the cluster algorithm. Numerous selected genes (e.g. NRG1, ERBB4, NRXN1, ANK3) and pathways (e.g. neuregulin-ErbB4 and calcium signaling) were implicated in SCZ or related pathophysiological processes. This is first study to combine both genetic and clinical data for subtyping SCZ, and to employ genome-wide SNP data in cluster analysis of a complex disease. This work points to a new way of GWAS analysis of translational potential.-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/jpsychires-
dc.relation.ispartofJournal of Psychiatric Research-
dc.titleLeveraging genome-wide association and clinical data in revealing schizophrenia subgroups-
dc.typeArticle-
dc.identifier.emailChen, RYL: rylchen@hkucc.hku.hk-
dc.identifier.emailSham, PC: pcsham@hku.hk-
dc.identifier.authoritySham, PC=rp00459-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.jpsychires.2018.09.010-
dc.identifier.pmid30312963-
dc.identifier.scopuseid_2-s2.0-85055632564-
dc.identifier.hkuros300997-
dc.identifier.volume106-
dc.identifier.spage106-
dc.identifier.epage117-
dc.identifier.isiWOS:000450134000015-
dc.publisher.placeUnited Kingdom-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats