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Existing approaches generally aggregate expression across cells, use Gaussian models poorly suited to read counts or do not scale. Here we show that SAIGE-QTL, a Poisson mixed model that directly models single-cell read counts, enables calibrated and computationally scalable eQTL mapping while accounting for donor-level and cell-level covariates and unequal numbers of cells per donor. In simulations and three single-cell datasets, SAIGE-QTL improved statistical efficiency and discovery of genes with significant eQTLs (eGenes) relative to pseudobulk methods and produced results consistent with existing single-cell models at substantially lower computational cost. In 1.2 million immune cells, SAIGE-QTL identified 48.8% more eGenes than pseudobulk analysis. SAIGE-QTL further enabled genome-wide\n                    <jats:italic>trans<\/jats:italic>\n                    -eQTL mapping and rare variant set-based testing, identifying rare variant associations independent of common eQTL signals. SAIGE-QTL therefore expands single-cell eQTL discovery across common, rare and\n                    <jats:italic>trans<\/jats:italic>\n                    -acting genetic variation.\n                  <\/jats:p>","DOI":"10.1038\/s41588-026-02798-2","type":"journal-article","created":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T10:02:15Z","timestamp":1793959335000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient and accurate mixed model association tool for single-cell eQTL analysis"],"prefix":"10.1038","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7719-0859","authenticated-orcid":false,"given":"Wei","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5168-6979","authenticated-orcid":false,"given":"Anna S. 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B.M.N. is a member of the scientific advisory board at Deep Genomics, Camp4 Therapeutics, Vesalius Therapeutics and Aluco BioSciences Inc. M.J.D. is a founder of Maze Therapeutics. C.J.Y. is the founder of and holds equity in DropPrint Genomics (now ImmunAI) and Survey Genomics. He serves as a Scientific Advisory Board member for and holds equity in Related Sciences and ImmunAI, and is a consultant or has consulted for Maze Therapeutics, TReX Bio, HiBio, ImYoo, Kiragen and Santa Ana Bio. Additionally, C.J.Y. is an Innovation Investigator at the Arc Institute. He has received research support from the Parker Institute for Cancer Immunotherapy, Chan Zuckerberg Initiative, Chan Zuckerberg Biohub, Genentech, BioLegend, ScaleBio and Illumina. D.G.M is a member of the Scientific Advisory Board of Insitro. The other authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Ethics"}}]}}