Machine learning-enhanced noninvasive prenatal testing of monogenic disorders.

OBJECTIVE: Single-nucleotide variants (SNVs) are of great significance in prenatal diagnosis as they are the leading cause of inherited single-gene disorders (SGDs). Identifying SNVs in a non-invasive prenatal screening (NIPS) scenario is particularly challenging for maternally inherited SNVs. We present an improved method to predict inherited SNVs from maternal or paternal origin in a genome-wide manner. METHODS: We performed SNV-NIPS based on the combination of fragments of cell free DNA (cfDNA) features, Bayesian inference and a machine-learning (ML) prediction refinement step using random forest (RF) classifiers trained on millions of non-pathogenic variants. We next evaluate the real-world performance of our refined method in a clinical setting by testing our models on 16 families with singleton pregnancies and varying fetal fraction (FF) levels, and validate the results over millions of inherited variants in each fetus. RESULTS: The average area under the ROC curve (AUC) values are 0.996 over all families for paternally inherited variants, 0.81 for the challenging maternally inherited variants, 0.86 for homozygous biallelic variants and 0.95 for compound heterozygous variants. Discriminative AUCs were achieved even in families with a low FF. We further investigate the performance of our method in correctly predicting SNVs in coding regions of clinically relevant genes and demonstrate significantly improved AUCs in these regions. Finally, we focus on the pathogenic variants in our cohort and show that our method correctly predicts if the fetus is unaffected or affected in all (10/10, 100%) of the families containing a pathogenic SNV. CONCLUSIONS: Overall, we demonstrate our ability to perform genome-wide NIPS for maternal and homozygous biallelic variants and showcase the utility of our method in a clinical setting.

Chromosomal microarray testing yield in 829 cases of microcephaly: a clinical characteristics-based analysis for prenatal and postnatal cases.

INTRODUCTION: Microcephaly, characterized by abnormal head growth, can often serve as an initial indicator of congenital, genetic, or acquired disorders. In this study, we sought to evaluate the effectiveness of chromosomal microarray (CMA) testing in detecting abnormalities in both prenatal and postnatal cases of microcephaly. MATERIALS AND METHODS: CMA Testing: We conducted CMA testing on 87 prenatally-detected microcephaly cases and 742 postnatal cases at a single laboratory. We evaluated the CMA yield in relation to specific clinical characteristics. RESULTS: In prenatal cases, pathogenic and likely pathogenic (LP) results were identified in 4.6% of cases, a significantly higher rate compared to low-risk pregnancies. The male-to-female ratio in this cohort was 3, and the CMA yield was not influenced by gender or other clinical parameters. For postnatal cases, the CMA yield was 15.0%, with a significantly higher detection rate associated with dysmorphism, hypotonia, epilepsy, congenital heart malformations (CHM), learning disabilities (LD), and a history of Fetal growth restriction (FGR). No specific recurrent copy number variations (CNVs) were observed, and the rate of variants of unknown significance was 3.9%. CONCLUSIONS: The yield of CMA testing in prenatal microcephaly is lower than in postnatal cases (4.6% vs. 15%). The presence of microcephaly, combined with dysmorphism, hypotonia, epilepsy, CHD, LD, and FGR, significantly increases the likelihood of an abnormal CMA result.

Bayesian-based noninvasive prenatal diagnosis of single-gene disorders.

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.