Non‐invasive prediction of fetal growth restriction by whole‐genome promoter profiling of maternal plasma DNA: a nested case–control study
Chen Xu, Zhen Guo, Jinhua Zhang, Qing Lü, Qi Tian, Shiyuan Liu, K Li, Kaikuo Wang, Tao Zhang, C Li, Zhengbing Lv, Z. Zhang, Xuexi Yang, Fang Yang
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: 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.