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Study Description

In the last decade, non-invasive 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 used today 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 fragment length distribution of fetal- and maternal-derived cfDNA 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.

(Reprinted from Bayesian-based noninvasive prenatal diagnosis of single-gene disorders, with permission from Genome Research)

Authorized Access
Publicly Available Data (Public ftp)
Study Inclusion/Exclusion Criteria

Inclusion: parent-offspring trios with 11 weeks gestation pregnant women

Molecular Data
TypeSourcePlatformNumber of Oligos/SNPsSNP Batch IdComment
Whole Exome Sequencing Illumina HiSeq 4000 N/A N/A
Whole Exome Sequencing Illumina NovaSeq N/A N/A
Whole Genome Sequencing Illumina HiSeq X Ten N/A N/A
Whole Genome Sequencing Illumina NovaSeq N/A N/A
Selected publications
Diseases/Traits Related to Study (MeSH terms)
Links to Related Resources
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See research articles citing use of the data from this study
Study Attribution
  • Principal Investigator
    • Noam Shomron, PhD. Tel Aviv University, Tel Aviv, Israel.
  • Founding Source
    • Adelis Foundation. Liechtenstein.