| Literature DB >> 30545857 |
Daifeng Wang, Shuang Liu, Jonathan Warrell, Hyejung Won, Xu Shi, Fabio C P Navarro, Declan Clarke, Mengting Gu, Prashant Emani, Yucheng T Yang, Min Xu, Michael J Gandal, Shaoke Lou, Jing Zhang, Jonathan J Park, Chengfei Yan, Suhn Kyong Rhie, Kasidet Manakongtreecheep, Holly Zhou, Aparna Nathan, Mette Peters, Eugenio Mattei, Dominic Fitzgerald, Tonya Brunetti, Jill Moore, Yan Jiang, Kiran Girdhar, Gabriel E Hoffman, Selim Kalayci, Zeynep H Gümüş, Gregory E Crawford, Panos Roussos, Schahram Akbarian, Andrew E Jaffe, Kevin P White, Zhiping Weng, Nenad Sestan, Daniel H Geschwind, James A Knowles, Mark B Gerstein.
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.Entities:
Mesh:
Year: 2018 PMID: 30545857 PMCID: PMC6413328 DOI: 10.1126/science.aat8464
Source DB: PubMed Journal: Science ISSN: 0036-8075 Impact factor: 47.728