LAUSR

Noninvasive prenatal diagnosis (NIPD) has become a common, safe, and effective procedure for detection of inherited diseases early in pregnancy. It is based on the analysis of fetal cell-free DNA (cffDNA) derived from the placenta, circulating in the maternal plasma. De novo mutations, although rare, cause a considerable number of dominant genetic disorders. Due to the sparse representation of fetal-derived sequences in the blood, the challenge of detecting low frequency fetal de novo mutations becomes preponderant. Hence, this detection type requires deep genome-wide sequencing of cffDNA from maternal plasma and a unique analysis approach. Here we suggest and discuss a method for identifying de novo mutations based on whole genome sequencing (WGS) of cell-free DNA (cfDNA) from maternal plasma samples. Our method consists of an augmented pipeline for analysis of de novo mutation candidates. It begins with an enhanced noninvasive fetal variant calling step, followed by a candidate de novo mutation filtration, and then finally, a supervised machine learning approach is utilized for reduction of false positive rates. Overall, this study provides a basis for genome-wide de novo mutation analysis in NIPD procedures, which could be used in any procedure where rare de novo mutations should be carefully picked out of a sea of data.