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Comprehensive functional genomic resource and integrative model for the human brain.

  • Author(s): 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.
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.

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