{
  "abstract": "Linking genetic variation to disease requires accurate prediction of its effects on gene expression in relevant cell types. Sequence-to-function models offer a way to predict these effects and, with training on personal genomes, have begun to capture inter-individual expression differences. However, effect sign accuracy remains limited, and predictive uncertainty is unquantified. Here we introduce ATTICUS, which combines personalised sequence representations with uncertainty-aware expression prediction. ATTICUS transfers regulatory information learned in bulk expression pretraining to cell-type-specific expression prediction without requiring genotypes paired with single-cell expression data. Across blood and brain cell types in OneK1K and ROSMAP, ATTICUS approximately doubles expression prediction correlations relative to scPrediXcan and substantially improves sign accuracy. Moreover, ATTICUS enables uncertainty-aware transcriptome-wide association studies (TWAS). Applied to systemic lupus erythematosus and Alzheimer's disease, ATTICUS identifies more associations, with greater cell-type specificity and stronger directional concordance, and reduced statistical inflation. Together, our results demonstrate that personalised training makes predictions more directionally consistent, and uncertainty propagation improves the calibration of TWAS and the prioritisation of disease genes. ATTICUS provides a framework for both, enabling the use of single-cell expression datasets without matched genotypes to inform uncertainty-aware TWAS, expanding their use in prioritising disease-associated genes and cell types.",
  "abstract_shown": true,
  "affiliations": [
    "ETH Zurich"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "P. Styll"
    },
    {
      "name": "Y. Di"
    },
    {
      "affs": [
        0
      ],
      "name": "N. Cai"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7ggsb3ammrp4q.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.06.764604",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.11.06.764604v1.full",
  "id": "7ggsb3ammrp4q",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.11.06.764604v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "genomics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.11.06.764604v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7ggsb3ammrp4q.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T00:24:51Z",
      "native_id": "10.64898/2026.11.06.764604",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.11.06.764604v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:genomics",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Uncertainty-Aware Personalised Gene Expression Prediction Improves Cell-Type-Specific Association Testing",
  "title_text": "Uncertainty-Aware Personalised Gene Expression Prediction Improves Cell-Type-Specific Association Testing",
  "type": "preprint",
  "updated_at": "2026-11-10T00:24:51Z",
  "url": "https://research.draconitic.com/paper/7ggsb3ammrp4q",
  "venue": "bioRxiv",
  "versions": []
}