Comprehensive functional genomic resource and integrative model for the human brain.
Daifeng WangShuang LiuJonathan WarrellHyejung WonXu ShiFábio C P NavarroDeclan ClarkeMengting GuPrashant S EmaniYucheng T YangMin XuMichael J GandalShaoke LouJing ZhangJonathan J ParkChengfei YanSuhn Kyong RhieKasidet ManakongtreecheepHolly ZhouAparna NathanMette PetersEugenio MatteiDominic FitzgeraldTonya BrunettiJill E MooreYan JiangKiran GirdharGabriel E HoffmanSelim KalayciZeynep Hülya GümüşGregory E Crawfordnull nullPanagiotis RoussosSchahram AkbarianAndrew E JaffeKevin P WhiteZhiping WengNenad SestanDaniel H GeschwindJames A KnowlesMark B GersteinPublished in: Science (New York, N.Y.) (2019)
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.
Keyphrases
- genome wide
- copy number
- single cell
- genome wide association study
- dna methylation
- poor prognosis
- deep learning
- white matter
- rna seq
- resting state
- bipolar disorder
- gene expression
- social media
- binding protein
- high throughput
- high resolution
- transcription factor
- multiple sclerosis
- functional connectivity
- healthcare
- network analysis
- long non coding rna
- stem cells
- artificial intelligence
- cerebral ischemia
- blood brain barrier
- young adults
- mesenchymal stem cells
- brain injury
- oxidative stress