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{
  "pmid": "38781369",
  "doi": "10.1126/science.adi5199",
  "abstract": "Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.",
  "journal": "Science",
  "year": 2024,
  "authors": "Emani, Liu, Liu, Clarke, Jensen et al.",
  "url": "https://escholarship.org/content/qt3cs7f3r3/qt3cs7f3r3.pdf",
  "external_ids": {
    "doi": "10.1126/science.adi5199",
    "pmid": "38781369",
    "pmcid": "",
    "openalex": "W4398250269",
    "orcid_author": "0000-0001-9012-6552"
  },
  "citation_count": 126
}