- 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
- PsychENCODE Consortium
- Roussos, Panos
- Akbarian, Schahram
- Jaffe, Andrew E
- White, Kevin P
- Weng, Zhiping
- Sestan, Nenad
- Geschwind, Daniel H
- Knowles, James A
- Gerstein, Mark B
- et al.
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.