{
  "abstract": "Integrating single-cell RNA sequencing(scRNA-seq) with the phenotypic depth of population-scale biobanks is essential for elucidating the cellular basis of complex diseases. However, this is hindered by limited scRNA-seq sample size and the lack of cellular resolution in biobanks. We present CausalCellInfer, a scalable computational framework bringing cellular resolution to bulk and genotype-imputed transcriptomes. CausalCellInfer employs an invariant causal prediction-inspired method(scRNA-seq-based invariant generalized covariance measure, scI-GCM) to select environment-stable marker genes, a parsimonious deep neural network for cell-fraction deconvolution, and regularized matrix completion to reconstruct cell-type-specific (CTS) expression profiles. This design is optimized for biobank-scale data with technical heterogeneity and limited gene overlap. Validated across simulated data, pseudo-bulk and real peripheral blood mononuclear cell (PBMC) datasets, CausalCellInfer demonstrated superior accuracy and efficiency over existing methods. Applied to ∼500 000 UK Biobank (UKBB) participants across 29 traits, it captured known pathological shifts, such as reduced pancreatic β-cell proportions in type 2 diabetes, and identified novel biological signals, including disrupted excitatory neuron-oligodendrocyte interactions in depression. Inferred CTS differential expression patterns showed significant concordance with independent single-cell studies and were enriched for Open Targets disease genes. Overall, CausalCellInfer bridges single-cell insights and population-scale genomics, enabling systematic discovery of putative disease mechanisms at cellular resolution.",
  "abstract_shown": true,
  "affiliations": [
    "School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.",
    "Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.",
    "Department of Psychiatry, The University of Hong Kong, Hong Kong Island, Hong Kong SAR, China.",
    "KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Institute of Zoology and The Chinese University of Hong Kong, Kunming, China.",
    "Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China.",
    "CUHK Shenzhen Research Institute, Shenzhen, China.",
    "Margaret K.L. Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Hong Kong SAR, China.",
    "Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China.",
    "Hong Kong Branch of the Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Liangying Yin",
      "orcid": "0000-0002-1029-7317"
    },
    {
      "affs": [
        0
      ],
      "name": "Yujia Shi"
    },
    {
      "affs": [
        0
      ],
      "name": "Ruoyu Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Yong Xiang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jinghong Qiu"
    },
    {
      "affs": [
        2
      ],
      "name": "Pak-Chung Sham"
    },
    {
      "affs": [
        0,
        3,
        4,
        5,
        6,
        7,
        8
      ],
      "name": "Hon-Cheong So",
      "orcid": "0000-0002-7102-833X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c4sebafacsqx9.bib",
  "cited_by": 0,
  "doi": "10.1002/advs.78197",
  "fulltext_url": "https://europepmc.org/articles/PMC13669230",
  "id": "c4sebafacsqx9",
  "issns": [
    "2198-3844"
  ],
  "landing_url": "https://europepmc.org/article/MED/42875726",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7221795523",
  "pdf_url": "https://europepmc.org/articles/PMC13669230?pdf=render",
  "pmcid": "PMC13669230",
  "pmid": "42875726",
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/c4sebafacsqx9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:46Z",
      "native_id": "MED:42875726",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42875726"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Single-cell and spatial transcriptomics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "CausalCellInfer: Prioritizing Cell-Type-Specific Disease Associations From Genetically Predicted Transcriptomes in Biobank Cohorts.",
  "title_text": "CausalCellInfer: Prioritizing Cell-Type-Specific Disease Associations From Genetically Predicted Transcriptomes in Biobank Cohorts.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:46Z",
  "url": "https://research.draconitic.com/paper/c4sebafacsqx9",
  "venue": "Advanced science (Weinheim, Baden-Wurttemberg, Germany)",
  "versions": []
}