Comprehensive functional genomic resource and integrative model for the human brain

TLDR

Despite progress in defining genetic risk for psychiatric disorders, their molecular mechanisms remain elusive. The PsychENCODE Consortium created a comprehensive online resource for the adult brain, encompassing data from 1,866 individuals, to elucidate psychiatric disorder mechanisms. The resource compiles ~79,000 brain‑active enhancers, Hi‑C linkages, topologically associating domains, single‑cell expression profiles, and multiple QTLs, enabling construction of a gene‑regulatory network that links GWAS variants to genes. Integration reveals that cell‑type proportion differences explain most cross‑population expression variation (>88% accuracy) and that embedding the regulatory network in a deep‑learning model boosts disease prediction sixfold over polygenic risk scores while highlighting key genes and pathways.

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.