{"title":"克服数量性状位点分析中的协作障碍。","authors":"Wen Zhang, Xiaohong Wu, Jing Gong","doi":"10.1016/j.xgen.2025.100773","DOIUrl":null,"url":null,"abstract":"<p><p>In this issue of Cell Genomics, Choi et al.<sup>1</sup> report a novel approach, privateQTL, which leverages secure multiparty computation (MPC) to enable federated expression quantitative trait loci (eQTL) mapping across institutions without compromising data privacy. Zhang et al. preview their approach and discuss its application in future genetic analyses.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":"5 2","pages":"100773"},"PeriodicalIF":11.1000,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11872532/pdf/","citationCount":"0","resultStr":"{\"title\":\"Overcoming collaboration barriers in quantitative trait loci analysis.\",\"authors\":\"Wen Zhang, Xiaohong Wu, Jing Gong\",\"doi\":\"10.1016/j.xgen.2025.100773\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In this issue of Cell Genomics, Choi et al.<sup>1</sup> report a novel approach, privateQTL, which leverages secure multiparty computation (MPC) to enable federated expression quantitative trait loci (eQTL) mapping across institutions without compromising data privacy. Zhang et al. preview their approach and discuss its application in future genetic analyses.</p>\",\"PeriodicalId\":72539,\"journal\":{\"name\":\"Cell genomics\",\"volume\":\"5 2\",\"pages\":\"100773\"},\"PeriodicalIF\":11.1000,\"publicationDate\":\"2025-02-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11872532/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cell genomics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.xgen.2025.100773\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell genomics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.xgen.2025.100773","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
Overcoming collaboration barriers in quantitative trait loci analysis.
In this issue of Cell Genomics, Choi et al.1 report a novel approach, privateQTL, which leverages secure multiparty computation (MPC) to enable federated expression quantitative trait loci (eQTL) mapping across institutions without compromising data privacy. Zhang et al. preview their approach and discuss its application in future genetic analyses.
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