Frontiers in Genetics (Jul 2022)
Non-Invasive Prenatal Diagnosis of Monogenic Disorders Through Bayesian- and Haplotype-Based Prediction of Fetal Genotype
- Jia Li,
- Jia Li,
- Jiaqi Lu,
- Fengxia Su,
- Fengxia Su,
- Jiexia Yang,
- Jiexia Yang,
- Jia Ju,
- Yu Lin,
- Jinjin Xu,
- Yiming Qi,
- Yiming Qi,
- Yaping Hou,
- Yaping Hou,
- Jing Wu,
- Jing Wu,
- Wei He,
- Wei He,
- Zhengtao Yang,
- Zhengtao Yang,
- Yujing Wu,
- Yujing Wu,
- Zhuangyuan Tang,
- Zhuangyuan Tang,
- Yingping Huang,
- Yingping Huang,
- Guohong Zhang,
- Guohong Zhang,
- Ying Yang,
- Ying Yang,
- Zhou Long,
- Xiaofang Cheng,
- Ping Liu,
- Jun Xia,
- Yanyan Zhang,
- Yicong Wang,
- Fang Chen,
- Jianguo Zhang,
- Jianguo Zhang,
- Lijian Zhao,
- Lijian Zhao,
- Lijian Zhao,
- Xin Jin,
- Ya Gao,
- Ya Gao,
- Aihua Yin,
- Aihua Yin
Affiliations
- Jia Li
- BGI Genomics, BGI-Shenzhen, Shenzhen, China
- Jia Li
- Hebei Industrial Technology Research Institute of Genomics in Maternal and Child Health, Shijiazhuang BGI Genomics, Shijiazhuang, China
- Jiaqi Lu
- Medical Genetics Centre, Guangdong Women and Children’s Hospital, Guangzhou Medical University, Guangzhou, China
- Fengxia Su
- BGI-Shenzhen, Shenzhen, China
- Fengxia Su
- Shenzhen Engineering Laboratory for Birth Defects Screening, Shenzhen, China
- Jiexia Yang
- Prenatal Diagnosis Centre, Guangdong Women and Children’s Hospital, Guangzhou, China
- Jiexia Yang
- Maternal and Children Metabolic-Genetic Key Laboratory, Guangdong Women and Children’s Hospital, Guangzhou, China
- Jia Ju
- BGI-Shenzhen, Shenzhen, China
- Yu Lin
- BGI-Shenzhen, Shenzhen, China
- Jinjin Xu
- BGI-Shenzhen, Shenzhen, China
- Yiming Qi
- Prenatal Diagnosis Centre, Guangdong Women and Children’s Hospital, Guangzhou, China
- Yiming Qi
- Maternal and Children Metabolic-Genetic Key Laboratory, Guangdong Women and Children’s Hospital, Guangzhou, China
- Yaping Hou
- Prenatal Diagnosis Centre, Guangdong Women and Children’s Hospital, Guangzhou, China
- Yaping Hou
- Maternal and Children Metabolic-Genetic Key Laboratory, Guangdong Women and Children’s Hospital, Guangzhou, China
- Jing Wu
- Prenatal Diagnosis Centre, Guangdong Women and Children’s Hospital, Guangzhou, China
- Jing Wu
- Maternal and Children Metabolic-Genetic Key Laboratory, Guangdong Women and Children’s Hospital, Guangzhou, China
- Wei He
- Prenatal Diagnosis Centre, Guangdong Women and Children’s Hospital, Guangzhou, China
- Wei He
- Maternal and Children Metabolic-Genetic Key Laboratory, Guangdong Women and Children’s Hospital, Guangzhou, China
- Zhengtao Yang
- BGI-Shenzhen, Shenzhen, China
- Zhengtao Yang
- College of Life Sciences, University of the Chinese Academy of Sciences, Beijing, China
- Yujing Wu
- BGI-Shenzhen, Shenzhen, China
- Yujing Wu
- Shenzhen Engineering Laboratory for Birth Defects Screening, Shenzhen, China
- Zhuangyuan Tang
- BGI-Shenzhen, Shenzhen, China
- Zhuangyuan Tang
- Shenzhen Engineering Laboratory for Birth Defects Screening, Shenzhen, China
- Yingping Huang
- BGI-Shenzhen, Shenzhen, China
- Yingping Huang
- Shenzhen Engineering Laboratory for Birth Defects Screening, Shenzhen, China
- Guohong Zhang
- BGI-Shenzhen, Shenzhen, China
- Guohong Zhang
- Shenzhen Engineering Laboratory for Birth Defects Screening, Shenzhen, China
- Ying Yang
- BGI-Shenzhen, Shenzhen, China
- Ying Yang
- Shenzhen Engineering Laboratory for Birth Defects Screening, Shenzhen, China
- Zhou Long
- BGI-Shenzhen, Shenzhen, China
- Xiaofang Cheng
- BGI-Shenzhen, Shenzhen, China
- Ping Liu
- BGI-Shenzhen, Shenzhen, China
- Jun Xia
- BGI-Shenzhen, Shenzhen, China
- Yanyan Zhang
- BGI-Shenzhen, Shenzhen, China
- Yicong Wang
- BGI-Shenzhen, Shenzhen, China
- Fang Chen
- BGI-Shenzhen, Shenzhen, China
- Jianguo Zhang
- BGI Genomics, BGI-Shenzhen, Shenzhen, China
- Jianguo Zhang
- Hebei Industrial Technology Research Institute of Genomics in Maternal and Child Health, Shijiazhuang BGI Genomics, Shijiazhuang, China
- Lijian Zhao
- BGI Genomics, BGI-Shenzhen, Shenzhen, China
- Lijian Zhao
- Hebei Industrial Technology Research Institute of Genomics in Maternal and Child Health, Shijiazhuang BGI Genomics, Shijiazhuang, China
- Lijian Zhao
- College of Medical Technology, Hebei Medical University, Shijiazhuang, China
- Xin Jin
- BGI-Shenzhen, Shenzhen, China
- Ya Gao
- BGI-Shenzhen, Shenzhen, China
- Ya Gao
- Shenzhen Engineering Laboratory for Birth Defects Screening, Shenzhen, China
- Aihua Yin
- Prenatal Diagnosis Centre, Guangdong Women and Children’s Hospital, Guangzhou, China
- Aihua Yin
- Maternal and Children Metabolic-Genetic Key Laboratory, Guangdong Women and Children’s Hospital, Guangzhou, China
- DOI
- https://doi.org/10.3389/fgene.2022.911369
- Journal volume & issue
-
Vol. 13
Abstract
Background: Non-invasive prenatal diagnosis (NIPD) can identify monogenic diseases early during pregnancy with negligible risk to fetus or mother, but the haplotyping methods involved sometimes cannot infer parental inheritance at heterozygous maternal or paternal loci or at loci for which haplotype or genome phasing data are missing. This study was performed to establish a method that can effectively recover the whole fetal genome using maternal plasma cell-free DNA (cfDNA) and parental genomic DNA sequencing data, and validate the method’s effectiveness in noninvasively detecting single nucleotide variations (SNVs), insertions and deletions (indels).Methods: A Bayesian model was developed to determine fetal genotypes using the plasma cfDNA and parental genomic DNA from five couples of healthy pregnancy. The Bayesian model was further integrated with a haplotype-based method to improve the inference accuracy of fetal genome and prediction outcomes of fetal genotypes. Five pregnancies with high risks of monogenic diseases were used to validate the effectiveness of this haplotype-assisted Bayesian approach for noninvasively detecting indels and pathogenic SNVs in fetus.Results: Analysis of healthy fetuses led to the following accuracies of prediction: maternal homozygous and paternal heterozygous loci, 96.2 ± 5.8%; maternal heterozygous and paternal homozygous loci, 96.2 ± 1.4%; and maternal heterozygous and paternal heterozygous loci, 87.2 ± 4.7%. The respective accuracies of predicting insertions and deletions at these types of loci were 94.6 ± 1.9%, 80.2 ± 4.3%, and 79.3 ± 3.3%. This approach detected pathogenic single nucleotide variations and deletions with an accuracy of 87.5% in five fetuses with monogenic diseases.Conclusions: This approach was more accurate than methods based only on Bayesian inference. Our method may pave the way to accurate and reliable NIPD.
Keywords
- non-invasive prenatal diagnosis
- massively parallel sequencing
- fetal genome
- single nucleotide variations
- monogenic disease