RT Journal
A1 Rabinowitz, Tom
A1 Polsky, Avital
A1 Golan, David
A1 Danilevsky, Artem
A1 Shapira, Guy
A1 Raff, Chen
A1 Basel-Salmon, Lina
A1 Matar, Reut Tomashov
A1 Shomron, Noam
T1 Bayesian-based noninvasive prenatal diagnosis of single-gene disorders
JF Genome Research 
JO Genome Research 
YR 2019 
FD March 01 
VO 29 
IS 3 
SP 428 
OP 438 
DO 10.1101/gr.235796.118 
UL http://genome.cshlp.org/content/29/3/428.abstract 
AB 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.