Analysis of spliceosome-related coding and noncoding genes and pseudogenes reveals novel candidates
Messaoud, O.; DiTroia, S.; Tarawneh, R.; Marten, D.; O'Heir, E.; O'Leary, M.; Pais, L.; Ganesh, V.; Singer-Berk, M.; Broad CMG and GREGoR consortium collaborators, ; Wojcik, M.; Samocha, K.; Rehm, H. L.; Austin-Tse, C.; O'Donnell-Luria, A.
Show abstract
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.
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