ArticleGenome research2019
Bayesian-based noninvasive prenatal diagnosis of single-gene disorders.
Article in Genome research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Universal noninvasive prenatal diagnosis for monogenic disorders using cell-free plasma DNA.Genome medicine · 2025Article
- Noninvasive fetal genotyping using deep neural networks.Briefings in bioinformatics · 2024Article
- Self-learning neural network as a prediction model in non-invasive prenatal testing to detect fetal SNVs.Journal of translational medicine · 2024Article
- Prospective prenatal cell-free DNA screening for genetic conditions of heterogenous etiologies.Nature medicine · 2024Observational
- Prediction of Potential Suitable Distribution Areas ofBiology · 2023Article
- DNA methylation analysis explores the molecular basis of plasma cell-free DNA fragmentation.Nature communications · 2023Article
- Article
- Noninvasive prenatal diagnosis targeting fetal nucleated red blood cells.Journal of nanobiotechnology · 2022Review
- Genetic deconvolution of fetal and maternal cell-free DNA in maternal plasma enables next-generation non-invasive prenatal screening.Cell discovery · 2022Article
- Noninvasive prenatal prediction of fetal haplotype with Spearman rank correlation analysis model.Molecular genetics & genomic medicine · 2022Article
- Noninvasive fetal genotyping of single nucleotide variants and linkage analysis for prenatal diagnosis of monogenic disorders.Human genomics · 2022Article
- Noninvasive prenatal diagnosis of genetic diseases induced by triplet repeat expansion by linked read haplotyping and Bayesian approach.Scientific reports · 2022Article
- Noninvasive prenatal exome sequencing diagnostic utility limited by sequencing depth and fetal fraction.Prenatal diagnosis · 2022Article
- Non-Invasive Prenatal Diagnosis of Monogenic Disorders Through Bayesian- and Haplotype-Based Prediction of Fetal Genotype.Frontiers in genetics · 2022Article
- Gut microbial response to host metabolic phenotypes.Frontiers in nutrition · 2022Review
- Liquid biopsy: state of reproductive medicine and beyond.Human reproduction (Oxford, England) · 2021Review
- Improved noninvasive fetal variant calling using standardized benchmarking approaches.Computational and structural biotechnology journal · 2021Article
- Genome-Wide Noninvasive Prenatal Diagnosis of De Novo Mutations.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Haplotype-Based Noninvasive Prenatal Diagnosis of 21 Families With Duchenne Muscular Dystrophy: Real-World Clinical Data in China.Frontiers in genetics · 2021Article
- Impact of Emerging Technologies in Prenatal Genetic Counseling.Cold Spring Harbor perspectives in medicine · 2020Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
In the last decade, noninvasive prenatal diagnosis (NIPD) has emerged as an effective procedure for early detection of inherited diseases during pregnancy. This technique is based on using cell-free DNA (cfDNA) and fetal cfDNA (cffDNA) in maternal blood, and hence, has minimal risk for the mother and fetus compared with invasive techniques. NIPD is currently used for identifying chromosomal abnormalities (in some instances) and for single-gene disorders (SGDs) of paternal origin. However, for SGDs of maternal origin, sensitivity poses a challenge that limits the testing to one genetic disorder at a time. Here, we present a Bayesian method for the NIPD of monogenic diseases that is independent of the mode of inheritance and parental origin. Furthermore, we show that accounting for differences in the length distribution of fetal- and maternal-derived cfDNA fragments results in increased accuracy. Our model is the first to predict inherited insertions-deletions (indels). The method described can serve as a general framework for the NIPD of SGDs; this will facilitate easy integration of further improvements. One such improvement that is presented in the current study is a machine learning model that corrects errors based on patterns found in previously processed data. Overall, we show that next-generation sequencing (NGS) can be used for the NIPD of a wide range of monogenic diseases, simultaneously. We believe that our study will lead to the achievement of a comprehensive NIPD for monogenic diseases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.