biostudies-literatureXu CScience and Technology Program of GuangzhouNational Natural Science Foundation of ChinaNatural Science Foundation of Guangdong ProvinceClinical Research Program of Nanfang Hospital458-466https://www.ebi.ac.uk/biostudies/studies/S-EPMC7818264biostudies-literatureUnknown128(2)<h4>Objective</h4>To predict fetal growth restriction (FGR) by whole-genome promoter profiling of maternal plasma.<h4>Design</h4>Nested case-control study.<h4>Setting</h4>Hospital-based.<h4>Population or sample</h4>810 pregnancies: 162 FGR cases and 648 controls.<h4>Methods</h4>We identified gene promoters with a nucleosome footprint that differed between FGR cases and controls based on maternal plasma cell-free DNA (cfDNA) nucleosome profiling. Optimal classifiers were developed using support vector machine (SVM) and logistic regression (LR) models.<h4>Main outcome measures</h4>Genes with differential coverages in promoter regions through the low-coverage whole-genome sequencing data analysis among FGR cases and controls. Receiver operating characteristic (ROC) analysis (area under the curve [AUC], accuracy, sensitivity and specificity) was used to evaluate the performance of classifiers.<h4>Results</h4>Through the low-coverage whole-genome sequencing data analysis of FGR cases and controls, genes with significantly differential DNA coverage at promoter regions (-1000 to +1000 bp of transcription start sites) were identified. The non-invasive 'FGR classifier 1' (C_FGR 1) had the highest classification performance (AUC, 0.803; 95% CI 0.767-0.839; accuracy, 83.2%) was developed based on 14 genes with differential promoter coverage using a support vector machine.<h4>Conclusions</h4>A promising FGR prediction method was successfully developed for assessing the risk of FGR at an early gestational age based on maternal plasma cfDNA nucleosome profiling.<h4>Tweetable abstract</h4>A promising FGR prediction method was successfully developed, based on maternal plasma cfDNA nucleosome profiling.BJOG : an international journal of obstetrics and gynaecologyNon-invasive prediction of fetal growth restriction by whole-genome promoter profiling of maternal plasma DNA: a nested case-control study.PMC78182642016040201042018A030313286818711772018030400092018CR039Lv ZGuo ZLi CZhang JXu CLiu SLi KWang KYang FYang XTao ZZhang ZLu QTian QfalseNon-invasive prediction of fetal growth restriction by whole-genome promoter profiling of maternal plasma DNA: a nested case-control study.<h4>Objective</h4>To predict fetal growth restriction (FGR) by whole-genome promoter profiling of maternal plasma.<h4>Design</h4>Nested case-control study.<h4>Setting</h4>Hospital-based.<h4>Population or sample</h4>810 pregnancies: 162 FGR cases and 648 controls.<h4>Methods</h4>We identified gene promoters with a nucleosome footprint that differed between FGR cases and controls based on maternal plasma cell-free DNA (cfDNA) nucleosome profiling. Optimal classifiers were developed using support vector machine (SVM) and logistic regression (LR) models.<h4>Main outcome measures</h4>Genes with differential coverages in promoter regions through the low-coverage whole-genome sequencing data analysis among FGR cases and controls. Receiver operating characteristic (ROC) analysis (area under the curve [AUC], accuracy, sensitivity and specificity) was used to evaluate the performance of classifiers.<h4>Results</h4>Through the low-coverage whole-genome sequencing data analysis of FGR cases and controls, genes with significantly differential DNA coverage at promoter regions (-1000 to +1000 bp of transcription start sites) were identified. The non-invasive 'FGR classifier 1' (C_FGR 1) had the highest classification performance (AUC, 0.803; 95% CI 0.767-0.839; accuracy, 83.2%) was developed based on 14 genes with differential promoter coverage using a support vector machine.<h4>Conclusions</h4>A promising FGR prediction method was successfully developed for assessing the risk of FGR at an early gestational age based on maternal plasma cfDNA nucleosome profiling.<h4>Tweetable abstract</h4>A promising FGR prediction method was successfully developed, based on maternal plasma cfDNA nucleosome profiling.2021-01-01T00:00:00Z2021 Jan2024-11-21T04:20:06.204Z2021-02-21T04:19:01ZS-EPMC78182643236431110.1111/1471-0528.16292