{
  "abstract": "The SNP-heritability of human diseases is extremely enriched in candidate regulatory elements (cREs) from disease-relevant cell types. Critical next steps are to understand whether these enrichments are driven by multiple causal cell types and whether individual variants impact disease risk via a single or multiple of cell types. Here, we propose CT-FM and CT-FM-SNP, 2 methods accounting for cREs shared across cell types to identify independent sets of causal cell types for a trait and its candidate causal variants, respectively. We applied CT-FM to 63 GWAS summary statistics (average N = 417K) using 924 cRE annotations, primarily from ENCODE4. CT-FM inferred 79 sets of causal cell types, with corresponding SNP-annotations explaining 39.0 ± 1.8% of trait SNP-heritability. It identified 14 traits with independent causal cell types, uncovering previously unexplored cellular mechanisms in height, schizophrenia and autoimmune diseases. We applied CT-FM-SNP to 39 UK Biobank traits and predicted high-confidence causal cell types for 3,091 candidate causal non-coding SNPs-trait pairs. Our results suggest that most SNPs affect a phenotype via a single set of cell types, whereas pleiotropic SNPs might target different cell types depending on the phenotype context. Altogether, CT-FM and CT-FM-SNP shed light on how genetic variants act collectively and individually at the cellular level to affect disease risk.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. Electronic address: artemkim@usc.edu",
    "Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA",
    "Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA",
    "Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA",
    "Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA. Electronic address: gazal@usc.edu"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Artem Kim",
      "orcid": "0000-0001-8824-2853"
    },
    {
      "affs": [
        1
      ],
      "name": "Zixuan Zhang",
      "orcid": "0000-0001-7193-8694"
    },
    {
      "affs": [
        1
      ],
      "name": "Come Legros"
    },
    {
      "affs": [
        1
      ],
      "name": "Hongbo Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Zeyun Lu",
      "orcid": "0000-0002-3511-6850"
    },
    {
      "affs": [
        1
      ],
      "name": "Adam J. de Smith",
      "orcid": "0000-0003-4880-7543"
    },
    {
      "affs": [
        2
      ],
      "name": "Jill E. Moore",
      "orcid": "0000-0002-3023-0806"
    },
    {
      "affs": [
        1
      ],
      "name": "Arun Durvasula",
      "orcid": "0000-0003-0631-3238"
    },
    {
      "affs": [
        3
      ],
      "name": "Nicholas Mancuso",
      "orcid": "0000-0002-9352-5927"
    },
    {
      "affs": [
        4
      ],
      "name": "Steven Gazal",
      "orcid": "0000-0003-4510-5730"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6yqgnek4aq1k4.bib",
  "cited_by": 14,
  "doi": "10.1016/j.xgen.2026.101325",
  "fulltext_url": null,
  "id": "6yqgnek4aq1k4",
  "issns": [
    "2666-979X"
  ],
  "landing_url": "https://doi.org/10.1016/j.xgen.2026.101325",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1311|Genetic Associations and Epidemiology"
    ]
  ],
  "openalex": "W4413215714",
  "pdf_url": "https://www.cell.com/cell-genomics/pdf/S2666-979X(26)00187-4.pdf",
  "pmcid": "PMC11118635",
  "pmid": "38798383",
  "published": "2026-08-14",
  "publisher": "Elsevier BV",
  "ris": "https://research.draconitic.com/paper/6yqgnek4aq1k4.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T13:21:05Z",
      "native_id": "W4413215714",
      "source": "openalex",
      "url": "https://doi.org/10.1016/j.xgen.2026.101325"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1311|Genetic Associations and Epidemiology",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Identifying independent causal cell types for human diseases and risk variants",
  "title_text": "Identifying independent causal cell types for human diseases and risk variants",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:05Z",
  "url": "https://research.draconitic.com/paper/6yqgnek4aq1k4",
  "venue": "Cell Genomics",
  "versions": []
}