Splicing is a complex molecular mechanism in eukaryotic cells essential to gene expression and regulation, involving more than 300 protein-coding genes (PCGs) and 43 small nuclear RNA (snRNA) genes. However, fewer than 30 gene-disease relationships have been described as spliceosomopathies to date. This discrepancy suggests the splicing machinery as an underexplored area for human disease gene discovery. For snRNA currently classified as pseudogenes, we prioritized candidates with similar epigenomic, genomic, and hypermutability features as functional snRNA genes. Population-variant-depletion analysis was performed to identify regions under negative selection. We analyzed rare variants in PCGs and snRNA genes and prioritized snRNA pseudogenes across a large heterogeneous rare disease cohort. There was high concordance for prioritizing genes annotated as pseudogenes by the variant-depleted region analysis (9) and by random forest models of hypermutation, genomic and epigenomic features (6). We identified 26 variants of interest across six PCGs with established gene-disease relationships (GDRs) and 14 genes not yet disease-associated, including one pseudogene across 30 individuals. For snRNAs genes, we identified 49 variants of interest located in seven genes with established GDR and 11 genes not yet disease-associated, including two pseudogenes across 80 individuals. This study highlights the importance of splicing-related PCG and snRNA in the genetic etiology of rare diseases. By leveraging specialized approaches for prioritizing pseudogenes, combined with the PCG and snRNA analysis, the genes and variants expand the variant pathogenicity spectrum of spliceosomopathies and suggest variants for follow-up case series and future functional validation.
O. Messaoud, S. DiTroia, R. Tarawneh et al.· medRxiv· 0 citations
Face2Gene is a clinical tool that leverages facial features to aid genetic diagnosis. The DeepGestalt application suggests potential diagnoses based on facial similarity, while the D-Score evaluates likelihood of an individual having dysmorphic features suggestive of a possible genetic diagnosis. Given performance variability across populations and limited data from South Africa, this study assessed clinical utility in South African children with neurodevelopmental disorders (NDDs). Facial photographs from 301 children were analysed. The cohort comprised three groups: 36 children with NDDs with confirmed molecular diagnoses, 176 with NDDs without molecular diagnoses and 89 unaffected children. Diagnostic (recognition) accuracy was measured by whether the confirmed diagnosis appeared in the top-1 or top-10 ranked algorithm-generated suggestions (DeepGestalt). D-Scores were extracted to calculate group differences. Among children with confirmed molecular diagnoses, accuracy was 19% (95% CI: 9-35%) (top-1) and 34% (95% CI: 20-52%) (top-10), improving to 33% (95% CI: 16-56%) and 61% (95% CI: 39-80%) when limited to conditions included in the DeepGestalt training set. One-way ANOVA revealed differences between participants with and without significant dysmorphic features, as assessed by clinicians. The D-Score demonstrated moderate sensitivity (78%, [95% CI: 0.64, 0.88]) and low specificity (42%, [95% CI: 0.38, 0.50]), but high negative predictive value (91%, [95% CI: 0.84, 0.95]), suggesting it may be more useful for ruling out dysmorphism; however, the low specificity indicates a high rate of false positives, even among clinically non-dysmorphic children. These findings suggest that under-representation of African populations may limit clinical performance and equity of AI-based facial phenotyping tools.
Z. Bruwer, Hendrike Mc Donald, Michal R. Zieff et al.· European Journal of Human Ge...· 0 citations
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