Non‐invasive prediction of fetal growth restriction by whole‐genome promoter profiling of maternal plasma DNA: a nested case–control study. (2nd June 2020)

• Record Type: Journal Article
• Authors: Xu, C; Guo, Z; Zhang, J; Lu, Q; Tian, Q; Liu, S; Li, K; Wang, K; Tao, Z; Li, C; Lv, Z; Zhang, Z; Yang, X; Yang, F
• Is Part Of: BJOG. Volume 128:Number 2(2021)
• Page Start: 458
• Page End: 466
• Publication Date: 2020-06-02
• Subjects: Cell‐free DNA -- classifier -- fetal growth restriction -- low‐coverage whole‐genome sequencing -- non‐invasive prediction; Obstetrics -- Periodicals; Gynecology -- Periodicals; 618
• DOI: 10.1111/1471-0528.16292
• Languages: English
• ISSNs: 1470-0328

Abstract

Abstract : Objective: To predict fetal growth restriction (FGR) by whole‐genome promoter profiling of maternal plasma. Design: Nested case–control study. Setting: Hospital‐based. Population or Sample: 810 pregnancies: 162 FGR cases and 648 controls. Methods: 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. Main outcome measures: 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. Results: 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' (CFGR 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. Conclusions: 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. Tweetable abstract: A promising FGR prediction method was successfully developed, based on maternal plasma cfDNA nucleosome profiling. Tweetable abstract: A promising FGR prediction method was successfully developed, based on maternal plasma cfDNA nucleosome profiling.