Biology subjects

Yntema, H.

Publications and source records attributed to Yntema, H..

3 recordsLinked to original sources

bioRxiv2026

Genome sequencing reveals novel pathogenic deep-intronic PCDH15 variants, amenable to antisense oligonucleotide-based splice correction

Despite substantial advances in diagnostic testing, 10-15% of Usher syndrome patients remain without a genetic diagnosis, having significant implications for genetic counseling and potential future therapeutic interventions. In this study, genome sequencing data from probands clinically presenting with Usher syndrome were analyzed. Two novel deep-intronic variants were identified in PCDH15, c.3983+3635A>G and c.3123-1728A>G, in two independent patients. Both deep-intronic variants were classified as likely pathogenic and predicted to alter PCDH15 pre-mRNA splicing. Using a minigene splice assay and iPSC-derived photoreceptor precursor cells from patients, we confirmed that both variants lead to the inclusion of a pseudoexon in the PCDH15 transcript introducing a stop codon and subsequent premature termination of protein translation. We designed and evaluated antisense oligonucleotides (ASOs) with the purpose of redirecting aberrant pre-mRNA splicing caused by both deep-intronic variants. For both variants, designed ASOs were successful in restoring normal splicing patterns, highlighting their potential as a future therapeutic intervention strategy to halt the progression of retinitis pigmentosa caused by these novel variants. Overall, these findings contribute to the understanding of Usher syndrome caused by deep-intronic pathogenic variants in PCDH15 and describe for the first time the use of an ASO-mediated splice correction strategy for individuals diagnosed with these variants.

genetics↗

bioRxiv2021

DeNovoCNN: A deep learning approach to de novo variant calling in next generation sequencing data

De novo mutations (DNMs) are an important cause of genetic disorders. The accurate identification of DNMs from sequencing data is therefore fundamental to rare disease research and diagnostics. Unfortunately, identifying reliable DNMs remains a major challenge due to sequence errors, uneven coverage, and mapping artifacts. Here, we developed a deep convolutional neural network (CNN) DNM caller (DeNovoCNN), that encodes the alignment of sequence reads for a trio as 160x164 resolution images. DeNovoCNN was trained on DNMs of 5,616 whole exome sequencing (WES) trios achieving total 96.74% recall and 96.55% precision on the test dataset. We find that DeNovoCNN has increased recall/sensitivity and precision compared to existing DNM calling approaches (GATK, DeNovoGear, DeepTrio, Samtools) based on the Genome in a Bottle reference dataset and independent WES and WGS trios. Validations of DNMs based on Sanger and PacBio HiFi sequencing confirm that DeNovoCNN outperforms existing methods. Most importantly, our results suggest that DeNovoCNN is likely robust against different exome sequencing and analyses approaches, thereby allowing the application on other datasets. DeNovoCNN is freely available as a Docker container and can be run on existing alignment (BAM/CRAM) and variant calling (VCF) files from WES and WGS without a need for variant recalling.

bioinformatics↗

bioRxiv2019

Integrating healthcare and research genetic data empowers the discovery of 49 novel developmental disorders

De novo mutations (DNMs) in protein-coding genes are a well-established cause of developmental disorders (DD). However, known DD-associated genes only account for a minority of the observed excess of such DNMs. To identify novel DD-associated genes, we integrated healthcare and research exome sequences on 31,058 DD parent-offspring trios, and developed a simulation-based statistical test to identify gene-specific enrichments of DNMs. We identified 285 significantly DD-associated genes, including 28 not previously robustly associated with DDs. Despite detecting more DD-associated genes than in any previous study, much of the excess of DNMs of protein-coding genes remains unaccounted for. Modelling suggests that over 1,000 novel DD-associated genes await discovery, many of which are likely to be less penetrant than the currently known genes. Research access to clinical diagnostic datasets will be critical for completing the map of dominant DDs.

genomics↗