{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T03:41:04Z","timestamp":1790653264680,"version":"4.1.0"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T00:00:00Z","timestamp":1764288000000},"content-version":"vor","delay-in-days":52,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 GM148974"],"award-info":[{"award-number":["R01 GM148974"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R35 GM138342"],"award-info":[{"award-number":["R35 GM138342"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Mapping expression quantitative trait loci (eQTLs) is a powerful method to study how genetic variation influences gene expression. Traditional bulk eQTL methods rely on averaged gene expression across a possibly heterogeneous mixture of cells, which can obscure underlying regulatory heterogeneity. Single-cell eQTL methods circumvent the averaging artifacts, providing an immense opportunity to interrogate transcriptional regulation at a much finer resolution. Recent developments in metric space regression methods allow the use of full empirical distributions as response objects instead of simple summary statistics such as mean. Here, we leverage Fr\u00e9chet regression to identify distribution QTLs (distQTLs) using population-scale single-cell RNA-sequencing (scRNA-seq) data. We apply distQTL to the OneK1K cohort, consisting of scRNA-seq data of peripheral blood mononuclear cells from 982 donors, and compare results to various eQTL approaches based on summary statistics and mixed effects modeling. We demonstrate the superior performance of distQTL across different gene expression contexts compared to other methods and benchmark our results against findings from the Genotype-Tissue Expression Project. Finally, we orthogonally validate calls from distQTL using cell-type-specific epigenomic profiles.<\/jats:p>","DOI":"10.1093\/nargab\/lqaf155","type":"journal-article","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T11:46:50Z","timestamp":1761133610000},"source":"Crossref","is-referenced-by-count":2,"title":["distQTL: distribution quantitative trait loci identification by population-scale single-cell data"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9116-6588","authenticated-orcid":false,"given":"Alexander","family":"Coulter","sequence":"first","affiliation":[{"name":"Department of Statistics, College of Arts and Sciences, Texas A&M University , College Station, TX 77843 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8886-6463","authenticated-orcid":false,"given":"Chun Yip","family":"Tong","sequence":"additional","affiliation":[{"name":"Department of Biology, College of Arts and Sciences, Texas A&M University , College Station, TX 77843 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0636-2363","authenticated-orcid":false,"given":"Yang","family":"Ni","sequence":"additional","affiliation":[{"name":"Department of Statistics, College of Arts and Sciences, Texas A&M University , College Station, TX 77843 ,","place":["United States"]},{"name":"Department of Statistics and Data Sciences, College of Natural Sciences, The University of Texas at Austin , Austin, TX 78705 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6905-6377","authenticated-orcid":false,"given":"Yuchao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Statistics, College of Arts and Sciences, Texas A&M University , College Station, TX 77843 ,","place":["United States"]},{"name":"Department of Biology, College of Arts and Sciences, Texas A&M University , College Station, TX 77843 ,","place":["United States"]},{"name":"Department of Biomedical Engineering, College of Engineering, Texas A&M University , College Station, TX 77843 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,11,28]]},"reference":[{"key":"2025112805265458300_B1","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1038\/nrg2612","article-title":"The genetics of quantitative traits: challenges and prospects","volume":"10","author":"Mackay","year":"2009","journal-title":"Nat Rev Genet"},{"key":"2025112805265458300_B2","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1126\/science.aaz1776","article-title":"The GTEx Consortium atlas of genetic regulatory effects across human tissues","volume":"369","author":"The GTEx 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