SpliceAI is an open-source deep learning splicing prediction algorithm that has demonstrated in the past few years its high ability to predict splicing defects caused by DNA variations. However, its outputs present several drawbacks: (1) although the numerical values are very convenient for batch filtering, their precise interpretation can be difficult, (2) the outputs are delta scores which can sometimes mask a severe consequence, and (3) complex delins are most often not handled. We present here SpliceAI-visual, a free online tool based on the SpliceAI algorithm, and show how it complements the traditional SpliceAI analysis. First, SpliceAI-visual manipulates raw scores and not delta scores, as the latter can be misleading in certain circumstances. Second, the outcome of SpliceAI-visual is user-friendly thanks to the graphical presentation. Third, SpliceAI-visual is currently one of the only SpliceAI-derived implementations able to annotate complex variants (e.g., complex delins). We report here the benefits of using SpliceAI-visual and demonstrate its relevance in the assessment/modulation of the PVS1 classification criteria. We also show how SpliceAI-visual can elucidate several complex splicing defects taken from the literature but also from unpublished cases. SpliceAI-visual is available as a Google Colab notebook and has also been fully integrated in a free online variant interpretation tool, MobiDetails (https://mobidetails.iurc.montp.inserm.fr/MD). Graphical abstract

Objectives: Cell-free fetal DNA (cffDNA) analysis is performed routinely for aneuploidy screening, RhD genotyping or sex determination. Although applications to single gene disorders (SGD) are being rapidly developed worldwide, only a few laboratories offer cffDNA testing routinely as a diagnosis service for this indication. In a previous report, we described a standardised protocol for non-invasive exclusion of paternal variant in SGD. Three years later, we now report our clinical experience with the protocol.Design: Descriptive study.Setting: Multi-centre French.Population: Indications for referral included pregnancies at risk of 25% or 50% of paternally inherited SGD, and pregnancies associated with an increased risk of SGD due to a de novo variant, either from strongly suggestive ultrasound findings or from a possible parental germinal mosaicism in the context of a previously affected child.Methods: Non-invasive prenatal diagnosis was performed using custom assays for droplet digital PCR. Feasibility, diagnostic performance and turn-around time were evaluated. Results: Mean time for a new assay design and validation was evaluated at 14 days, and mean result reporting time was 6 days. All referred pathogenic variants could be targeted except one located in a complex genomic region. A result was obtained for every 198 referrals except two. Conclusion:This service was successfully implemented as a routine laboratory practice. It has been widely adopted by French clinicians and patients for paternal variant exclusion in various disorders.

Non-invasive prenatal diagnosis for single-gene disorders (SGD-NIPD) has been widely adopted by patients, but is mostly limited to the exclusion of paternal or de novo mutations. Indeed, it is still difficult to infer the inheritance of maternal allele from cell free DNA (cfDNA) analysis. Based on the study of maternal haplotypes imbalance in cfDNA, relative haplotype dosage (RHDO) was developed to address this challenge. Although RHDO has proven to be reliable, robust control of statistical error and explicit delineation of critical parameters for assessing the quality of analysis have not been fully considered yet. Here we propose a universal and adaptable enhanced-RHDO procedure (eRHDO) through an automated bioinformatics pipeline with a didactical visualization of results that aims to be applied for any SGD-NIPD in routine care. A training cohort of 43 families carriers for CFTR, NF1,  DMD, or F8 mutations allowed the characterization and optimal setting of several adjustable data variables, such as minimal sequencing depth and type 1 and type 2 statistical errors, as well as the quality assessment for intermediate steps and final result through block score and concordance score. Validation was successfully carried out on 56 pregnancies of the test cohort. Finally, computer simulations were used to estimate the effect of fetal-fraction, sequencing depth and number of informative SNPs on the quality of results. Our workflow proved to be robust, as we obtained 94.9% conclusive and correctly inferred fetal genotypes, without any false negative or false positive result. By standardizing data generation and analysis, we fully describe a turnkey protocol for laboratories wishing to offer eRHDO-based non-invasive prenatal diagnosis for single-gene disorders as an alternative to conventional prenatal diagnosis.

Emery-Dreifuss Muscular Dystrophy (EDMD) is an early-onset, slowly-progressive group of myopathies, presenting with joint contractures, muscle weakness and cardiac abnormalities. Variants in the EMD gene cause an X-linked recessive form (EDMD1). The scarce EDMD1 muscle MRI accounts in the literature describe fatty replacement of posterior thigh and leg muscles. We report a 22-year-old patient with early-onset bilateral joint contractures, slowly progressive muscle weakness and minor cardiac rhythm abnormalities. A novel loss-of-function variant of EMD was identified and deemed probably pathogenic in the absence of emerin detection by immunofluorescence and Western Blot. MRI revealed fatty replacement of the lumbar spinal erectors and the posterior compartment of lower limbs. Interestingly, Short Tau Inversion Recovery (STIR) sequences showed a heterogenous hyper signal on the vasti, hamstrings and left lateral gastrocnemius muscles. Oedema-like abnormalities were previously reported in early stages of other muscular dystrophies, preceding fatty replacement and muscle atrophy, but not in EDMD1 patients. We hypothesize that these oedema-like changes may be a marker of early muscle pathology in EDMD1. Further studies focusing on these abnormalities in the early phase of EDMD1 are required to test our hypothesis.

Non-invasive prenatal diagnosis of single-gene disorders (SGD-NIPD) has been widely accepted, but is mostly limited to the exclusion of either paternal or de novo mutations. Indeed, it is still difficult to infer the inheritance of the maternal allele from cell-free DNA (cfDNA) analysis. Based on the study of maternal haplotype imbalance in cfDNA, relative haplotype dosage (RHDO) was developed to address this challenge. Although RHDO has been shown to be reliable, robust control of statistical error and explicit delineation of critical parameters for assessing the quality of the analysis have not been fully addressed. We present here a universal and adaptable enhanced-RHDO (eRHDO) procedure through an automated bioinformatics pipeline with a didactic visualization of the results, aiming to be applied for any SGD-NIPD in routine care. A training cohort of 43 families carrying CFTR, NF1, DMD, or F8 mutations allowed the characterization and optimal setting of several adjustable data variables, such as minimum sequencing depth, type 1 and type 2 statistical errors, as well as the quality assessment of intermediate steps and final results by block score and concordance score. Validation was successfully performed on a test cohort of 56 pregnancies. Finally, computer simulations were used to estimate the effect of fetal-fraction, sequencing depth and number of informative SNPs on the quality of results. Our workflow proved to be robust, as we obtained conclusive and correctly inferred fetal genotypes in 94.9% of cases, with no false-negative or false-positive results. By standardizing data generation and analysis, we fully describe a turnkey protocol for laboratories wishing to offer eRHDO-based non-invasive prenatal diagnosis for single-gene disorders as an alternative to conventional prenatal diagnosis.

No abstract

No abstract

Until recently, fetal genetic testing was possible only via invasive sampling (amniocentesis or chorionic villus sampling). Although such testing allows for prenatal diagnosis (PND) of inherited monogenetic disorders, it is also associated with a risk of miscarriage. Identification of cell-free fetal DNA (cfDNA) from maternal circulation allows for noninvasive fetal sex determination and detection of the common aneuploidies. In fact, the entire fetal genome is represented within the circulating