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dc.contributor.authorWang, Daifeng
dc.contributor.authorMattei, Eugenio
dc.contributor.authorMoore, Jill E.
dc.contributor.authorWeng, Zhiping
dc.contributor.authorGeschwind, Daniel H.
dc.contributor.authorKnowles, James A.
dc.contributor.authorGerstein, Mark B.
dc.date2022-08-11T08:07:59.000
dc.date.accessioned2022-08-23T15:38:03Z
dc.date.available2022-08-23T15:38:03Z
dc.date.issued2018-12-14
dc.date.submitted2019-01-09
dc.identifier.citation<p>Science. 2018 Dec 14;362(6420). pii: eaat8464. doi: 10.1126/science.aat8464. <a href="https://doi.org/10.1126/science.aat8464">Link to article on publisher's site</a></p>
dc.identifier.issn0036-8075 (Linking)
dc.identifier.doi10.1126/science.aat8464
dc.identifier.pmid30545857
dc.identifier.urihttp://hdl.handle.net/20.500.14038/25854
dc.description<p>Full author list omitted for brevity. For the full list of authors, see article.</p>
dc.description.abstractDespite progress in defining genetic risk for psychiatric disorders, their molecular mechanisms remain elusive. Addressing this, the PsychENCODE Consortium has generated a comprehensive online resource for the adult brain across 1866 individuals. The PsychENCODE resource contains ~79,000 brain-active enhancers, sets of Hi-C linkages, and topologically associating domains; single-cell expression profiles for many cell types; expression quantitative-trait loci (QTLs); and further QTLs associated with chromatin, splicing, and cell-type proportions. Integration shows that varying cell-type proportions largely account for the cross-population variation in expression (with > 88% reconstruction accuracy). It also allows building of a gene regulatory network, linking genome-wide association study variants to genes (e.g., 321 for schizophrenia). We embed this network into an interpretable deep-learning model, which improves disease prediction by ~6-fold versus polygenic risk scores and identifies key genes and pathways in psychiatric disorders.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=30545857&dopt=Abstract">Link to Article in PubMed</a></p>
dc.relation.urlhttps://doi.org/10.1126/science.aat8464
dc.subjectpsychiatric disorders
dc.subjectPsychENCODE Consortium
dc.subjectgenes
dc.subjectgenome
dc.subjectBioinformatics
dc.subjectComputational Biology
dc.subjectComputational Neuroscience
dc.subjectGenetic Phenomena
dc.subjectGenomics
dc.subjectIntegrative Biology
dc.subjectMental Disorders
dc.titleComprehensive functional genomic resource and integrative model for the human brain
dc.typeJournal Article
dc.source.journaltitleScience (New York, N.Y.)
dc.source.volume362
dc.source.issue6420
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/bioinformatics_pubs/146
dc.identifier.contextkey13591448
html.description.abstract<p>Despite progress in defining genetic risk for psychiatric disorders, their molecular mechanisms remain elusive. Addressing this, the PsychENCODE Consortium has generated a comprehensive online resource for the adult brain across 1866 individuals. The PsychENCODE resource contains ~79,000 brain-active enhancers, sets of Hi-C linkages, and topologically associating domains; single-cell expression profiles for many cell types; expression quantitative-trait loci (QTLs); and further QTLs associated with chromatin, splicing, and cell-type proportions. Integration shows that varying cell-type proportions largely account for the cross-population variation in expression (with > 88% reconstruction accuracy). It also allows building of a gene regulatory network, linking genome-wide association study variants to genes (e.g., 321 for schizophrenia). We embed this network into an interpretable deep-learning model, which improves disease prediction by ~6-fold versus polygenic risk scores and identifies key genes and pathways in psychiatric disorders.</p>
dc.identifier.submissionpathbioinformatics_pubs/146
dc.contributor.departmentProgram in Bioinformatics and Integrative Biology
dc.source.pageseaat8464


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