Comprehensive functional genomic resource and integrative model for the human brain
| dc.contributor.author | Wang, Daifeng | |
| dc.contributor.author | Mattei, Eugenio | |
| dc.contributor.author | Moore, Jill E. | |
| dc.contributor.author | Weng, Zhiping | |
| dc.contributor.author | Geschwind, Daniel H. | |
| dc.contributor.author | Knowles, James A. | |
| dc.contributor.author | Gerstein, Mark B. | |
| dc.date | 2022-08-11T08:07:59.000 | |
| dc.date.accessioned | 2022-08-23T15:38:03Z | |
| dc.date.available | 2022-08-23T15:38:03Z | |
| dc.date.issued | 2018-12-14 | |
| dc.date.submitted | 2019-01-09 | |
| 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.issn | 0036-8075 (Linking) | |
| dc.identifier.doi | 10.1126/science.aat8464 | |
| dc.identifier.pmid | 30545857 | |
| dc.identifier.uri | http://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.abstract | 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. | |
| dc.language.iso | en_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.url | https://doi.org/10.1126/science.aat8464 | |
| dc.subject | psychiatric disorders | |
| dc.subject | PsychENCODE Consortium | |
| dc.subject | genes | |
| dc.subject | genome | |
| dc.subject | Bioinformatics | |
| dc.subject | Computational Biology | |
| dc.subject | Computational Neuroscience | |
| dc.subject | Genetic Phenomena | |
| dc.subject | Genomics | |
| dc.subject | Integrative Biology | |
| dc.subject | Mental Disorders | |
| dc.title | Comprehensive functional genomic resource and integrative model for the human brain | |
| dc.type | Journal Article | |
| dc.source.journaltitle | Science (New York, N.Y.) | |
| dc.source.volume | 362 | |
| dc.source.issue | 6420 | |
| dc.identifier.legacycoverpage | https://escholarship.umassmed.edu/bioinformatics_pubs/146 | |
| dc.identifier.contextkey | 13591448 | |
| 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.submissionpath | bioinformatics_pubs/146 | |
| dc.contributor.department | Program in Bioinformatics and Integrative Biology | |
| dc.source.pages | eaat8464 |