Dominant variants in major spliceosome U4 and U5 small nuclear RNA genes cause neurodevelopmental disorders through splicing disruption
Bibliographic record
Abstract
Abstract Variants in RNU4-2 , encoding the small nuclear RNA (snRNA) U4, were recently identified as a major cause of neurodevelopmental disorders (ReNU syndrome). Here, we investigated de novo variants in 50 snRNAs in a French cohort of 23,649 individuals with rare disorders and collected data of additional patients through an international collaboration. Altogether, we identified 133 probands with pathogenic or likely pathogenic variants in RNU4-2 and 15 individuals with de novo and/or recurrent variants in constrained regions of RNU5B-1 , one of five genes encoding U5. These variants cluster in evolutionarily conserved regions of U4 and U5 essential for splicing. RNU4-2 variants affecting stem III are associated with milder phenotypes than those in the T-loop (quasi-pseudoknot). Phaseable variants associated with severe phenotypes occurred on the maternal allele. Individuals with RNU4-2 variants show specific defects in alternative 5’ splice site usage, correlating with variant location and clinical severity. Additionally, we report an episignature associated with severe ReNU syndrome. This study further highlights the importance of de novo variants in snRNAs and establishes RNU5B-1 as a new neurodevelopmental disorder gene.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".