{"title":"Epigenetic footprints in prenatal cell-free DNA reveal risk of preeclampsia","authors":"","doi":"10.1038/s41591-025-03536-7","DOIUrl":null,"url":null,"abstract":"We leveraged early-pregnancy prenatal cell-free DNA screening (PDNAS) sequencing data to determine tissue signatures associated with the development of preeclampsia. Next, we used these epigenetic signatures, as informed by nucleosome positioning, to build machine learning models to classify preeclampsia risk that we validated in distinct internal and external cohorts.","PeriodicalId":19037,"journal":{"name":"Nature Medicine","volume":"81 1","pages":""},"PeriodicalIF":58.7000,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1038/s41591-025-03536-7","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
We leveraged early-pregnancy prenatal cell-free DNA screening (PDNAS) sequencing data to determine tissue signatures associated with the development of preeclampsia. Next, we used these epigenetic signatures, as informed by nucleosome positioning, to build machine learning models to classify preeclampsia risk that we validated in distinct internal and external cohorts.
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