Multivariate Pattern Analysis of Genotype–Phenotype Relationships in Schizophrenia.
Genetic risk variants for schizophrenia have been linked to many related clinical and biological phenotypes with the hopes of delineating how individual variation across thousands of variants corresponds to the clinical and etiologic heterogeneity within schizophrenia. This has primarily been done u...
| Publicado en: | Schizophrenia Bulletin Vol. 44; no. 5; pp. 1045 - 1053 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Sep2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131371055&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131371055 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 05867614 2CZ jtl: Schizophrenia Bulletin issn: 05867614 maglogo: N pubinfo: dt: Sep2018 vid: 44 iid: 5 pid: 622 pub: Oxford University Press / USA artinfo: ui: 131371055 131371055 131371055 10.1093/schbul/sby005 131371055 ppf: 1045 ppct: 8 formats: tig: atl: Multivariate Pattern Analysis of Genotype–Phenotype Relationships in Schizophrenia. aug: au: Zheutlin, Amanda B Chekroud, Adam M Polimanti, Renato Gelernter, Joel Sabb, Fred W Bilder, Robert M Freimer, Nelson London, Edythe D Hultman, Christina M Cannon, Tyrone D affil: Department of Psychology, Yale University, New Haven, CT sug: subj: Schizophrenia Familial and Genetic Genotype Phenotype Polymorphism, Genetic Human Multivariate Analysis Algorithms Cognition Psychiatric Patients Linear Regression Schizophrenia Risk Factors Task Performance and Analysis Memory, Short Term ab: Genetic risk variants for schizophrenia have been linked to many related clinical and biological phenotypes with the hopes of delineating how individual variation across thousands of variants corresponds to the clinical and etiologic heterogeneity within schizophrenia. This has primarily been done using risk score profiling, which aggregates effects across all variants into a single predictor. While effective, this method lacks flexibility in certain domains: risk scores cannot capture nonlinear effects and do not employ any variable selection. We used random forest, an algorithm with this flexibility designed to maximize predictive power, to predict 6 cognitive endophenotypes in a combined sample of psychiatric patients and controls (N = 739) using 77 genetic variants strongly associated with schizophrenia. Tenfold cross-validation was applied to the discovery sample and models were externally validated in an independent sample of similar ancestry (N = 336). Linear approaches, including linear regression and task-specific polygenic risk scores, were employed for comparison. Random forest models for processing speed (P =.019) and visual memory (P =.036) and risk scores developed for verbal (P =.042) and working memory (P =.037) successfully generalized to an independent sample with similar predictive strength and error. As such, we suggest that both methods may be useful for mapping a limited set of predetermined, disease-associated SNPs to related phenotypes. Incorporating random forest and other more flexible algorithms into genotype–phenotype mapping inquiries could contribute to parsing heterogeneity within schizophrenia; such algorithms can perform as well as standard methods and can capture a more comprehensive set of potential relationships. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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