Bayesian-based noninvasive prenatal diagnosis of single-gene disorders

Genome Research
Tom RabinowitzNoam Shomron

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 pattern...Continue Reading

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Citations

Feb 26, 2020·Journal of Perinatology : Official Journal of the California Perinatal Association·Monica H WojcikPankaj B Agrawal
Oct 2, 2019·Cold Spring Harbor Perspectives in Medicine·Blair Stevens
Oct 3, 2020·Computational and Structural Biotechnology Journal·Tom Rabinowitz, Noam Shomron
Jan 30, 2021·Computational and Structural Biotechnology Journal·Tom RabinowitzNoam Shomron
Sep 26, 2021·Human Reproduction·Gaby SchobersMasoud Zamani Esteki

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