{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T10:46:29Z","timestamp":1794048389654,"version":"4.8.0"},"posted":{"date-parts":[[2025,5,25]]},"group-title":"Genetic and Genomic Medicine","reference-count":58,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T00:00:00Z","timestamp":1748131200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,9,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Genome-wide association studies (GWAS) have discovered numerous trait-associated variants, but their biological context remains unclear. Integrating GWAS summary statistics with single-cell RNA-sequencing (scRNA-seq) data enables prioritization of cell types in which these variants influence traits. Existing methods broadly follow two strategies: \u201csingle cell to GWAS\u201d, which identifies cell-type-specific genes and tests their enrichment in GWAS signals, and \u201cGWAS to single cell\u201d, which begins with GWAS-prioritized genes and scores cells or cell types according to their expression profiles. Here, we developed a literature-informed benchmark by integrating PubMed evidence with large language model-assisted literature synthesis to evaluate 20 trait-cell type mapping methods. We identify CATCH, a Cauchy combination of complementary methods, as the most robust overall approach, consistently achieving high statistical power while maintaining effective false-positive control across simulations and real-data benchmarks. We further identify key determinants of performance, including cell-type specificity metrics, GWAS statistical power, and the diversity of scRNA-seq reference datasets, providing practical guidance for the development and application of trait-cell type mapping methods.<\/jats:p>","DOI":"10.1101\/2025.05.24.25328275","type":"posted-content","created":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T12:30:20Z","timestamp":1748176220000},"source":"Crossref","is-referenced-by-count":8,"title":["Benchmarking methods integrating GWAS and single-cell transcriptomic data for mapping trait-cell type associations"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1186-3448","authenticated-orcid":false,"given":"Ang","family":"Li","sequence":"first","affiliation":[{"name":"Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia"},{"name":"Department of Psychiatry, University of Oxford, Oxford, 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