학술논문
Comprehensive functional genomic resource and integrative model for the human brain
Document Type
article
Author
Wang, Daifeng; Liu, Shuang; Warrell, Jonathan; Won, Hyejung; Shi, Xu; Navarro, Fabio CP; Clarke, Declan; Gu, Mengting; Emani, Prashant; Yang, Yucheng T; Xu, Min; Gandal, Michael J; Lou, Shaoke; Zhang, Jing; Park, Jonathan J; Yan, Chengfei; Rhie, Suhn Kyong; Manakongtreecheep, Kasidet; Zhou, Holly; Nathan, Aparna; Peters, Mette; Mattei, Eugenio; Fitzgerald, Dominic; Brunetti, Tonya; Moore, Jill; Jiang, Yan; Girdhar, Kiran; Hoffman, Gabriel E; Kalayci, Selim; Gümüş, Zeynep H; Crawford, Gregory E; Roussos, Panos; Akbarian, Schahram; Jaffe, Andrew E; White, Kevin P; Weng, Zhiping; Sestan, Nenad; Geschwind, Daniel H; Knowles, James A; Gerstein, Mark B; Ashley-Koch, Allison E; Garrett, Melanie E; Song, Lingyun; Safi, Alexias; Johnson, Graham D; Wray, Gregory A; Reddy, Timothy E; Goes, Fernando S; Zandi, Peter; Bryois, Julien; Price, Amanda J; Ivanov, Nikolay A; Collado-Torres, Leonardo; Hyde, Thomas M; Burke, Emily E; Kleiman, Joel E; Tao, Ran; Shin, Joo Heon; Kundakovic, Marija; Brown, Leanne; Kassim, Bibi S; Park, Royce B; Wiseman, Jennifer R; Zharovsky, Elizabeth; Jacobov, Rivka; Devillers, Olivia; Flatow, Elie; Lipska, Barbara K; Lewis, David A; Haroutunian, Vahram; Hahn, Chang-Gyu; Charney, Alexander W; Dracheva, Stella; Kozlenkov, Alexey; Belmont, Judson; DelValle, Diane; Francoeur, Nancy; Hadjimichael, Evi; Pinto, Dalila; van Bakel, Harm; Fullard, John F; Bendl, Jaroslav; Hauberg, Mads E; Mangravite, Lara M; Peters, Mette A; Chae, Yooree; Peng, Junmin; Niu, Mingming; Wang, Xusheng; Webster, Maree J; Beach, Thomas G; Chen, Chao; Jiang, Yi
Source
Science. 362(6420)
Subject
Language
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.