{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T18:04:06Z","timestamp":1785434646205,"version":"3.56.0"},"reference-count":38,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Gestational diabetes mellitus (GDM) is a common pregnancy complication with long-term metabolic consequences for both mother and offspring. While genome-wide association studies (GWAS) have identified risk loci, the cell-type-specific genetic architecture remains poorly characterized due to the limitations of bulk-tissue analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We integrated GWAS summary statistics from FinnGen (12,332 cases, 131,109 controls) with single-cell expression quantitative trait loci (sc-eQTL) data from 12 immune cell types in the OneK1K cohort. Using the OTTERS framework and ACAT-O omnibus testing, we performed single-cell transcriptome-wide association studies (scTWAS) to identify GDM-associated genes. We performed bulk-level TWAS as a sensitivity analysis. To investigate the function of significant genes, we performed functional enrichment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We detected 14 unique genes significantly associated with GDM across immune cell types (FDR\u2009&lt;\u20090.05), with the strongest signals in CD4\n                      <jats:sup>+<\/jats:sup>\n                      T cells and monocytes. Notably,\n                      <jats:italic>ERAP1<\/jats:italic>\n                      and\n                      <jats:italic>ERAP2<\/jats:italic>\n                      showed associations in 10 of 12 cell types (ACAT.\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20092.17\u2009\u00d7\u200910\n                      <jats:sup>\u22125<\/jats:sup>\n                      ), and\n                      <jats:italic>RIOK2<\/jats:italic>\n                      emerged as a shared regulator. Gene Ontology analysis consistently highlighted \u201cantigen processing and presentation via MHC class I\u201d as the top enriched pathway (FDR\u2009&lt;\u200910\n                      <jats:sup>\u22125<\/jats:sup>\n                      ). In contrast, traditional bulk TWAS using GTEx whole blood identified only two genes with nominal associations.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      Our sc-TWAS identifies cell-type-specific GDM-associated genes that are not detected in bulk tissue, highlighting dysregulated antigen presentation in peripheral immune cells. Colocalization prioritizes monocytic\n                      <jats:italic>LNPEP<\/jats:italic>\n                      as the primary causal candidate, while associations for\n                      <jats:italic>ERAP1<\/jats:italic>\n                      ,\n                      <jats:italic>ERAP2<\/jats:italic>\n                      , and\n                      <jats:italic>RIOK2<\/jats:italic>\n                      likely reflect complex LD or tissue-specific effects.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0355339","type":"journal-article","created":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T17:44:09Z","timestamp":1785433449000},"page":"e0355339","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging expression quantitative trait loci information in single-cell resolution to identify cell-specific genes for gestational diabetes"],"prefix":"10.1371","volume":"21","author":[{"given":"Wenyan","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaowei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4969-4966","authenticated-orcid":true,"given":"Rongkui","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,7,30]]},"reference":[{"key":"pone.0355339.ref001","article-title":"2. 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Management of diabetes in pregnancy: standards of care in diabetes-2023","volume":"46","author":"NA ElSayed","year":"2023","journal-title":"Diabetes Care"},{"issue":"1","key":"pone.0355339.ref006","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1186\/s12933-024-02131-3","article-title":"Gestational diabetes mellitus and risk of long-term all-cause and cardiac mortality: a prospective cohort study","volume":"23","author":"Q Ying","year":"2024","journal-title":"Cardiovasc Diabetol"},{"key":"pone.0355339.ref007","doi-asserted-by":"crossref","first-page":"109050","DOI":"10.1016\/j.diabres.2021.109050","article-title":"IDF diabetes atlas: estimation of global and regional gestational diabetes mellitus prevalence for 2021 by international association of diabetes in pregnancy study group\u2019s criteria","volume":"183","author":"H Wang","year":"2022","journal-title":"Diabetes Res Clin 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