Genotype-Phenotype Correlation in RBM10-Associated Syndromes – How Variant Function Shapes a Broad Phenotypic Landscape
Bibliographic record
Abstract
Abstract Severe loss of function variants in the splicing regulatory protein RBM10 are known to cause TARP syndrome, a rare X-linked recessive congenital syndrome. In recent years, individuals with milder phenotypes have been published, suggesting a broader phenotypic spectrum. We report 37 new individuals with RBM10 variants and compare to 34 published cases. We find that the phenotype can be described as an “RBM10-phenotypic spectrum” which can be further subdivided into two phenotypic groups, TARP syndrome (TARPS) and RBM10 Associated Intellectual Disability (RAID). Based on phenotype characterizations and functional studies, we describe a clear genotype-phenotype correlation. Splicing analysis of blood samples and CRISPR-edited cells representing different degrees of functional loss of RBM10 demonstrated a pattern of more exon inclusion in response to increased loss of RBM10 function. More inclusion was correlated with increasing phenotype severity. Functional studies of missense variants from the different phenotypic groups confirm this genotype-phenotype correlation and show that different molecular mechanisms can explain the underlying pathological alterations in RBM10 protein function. Interestingly, we show that some missense variants in the RNA binding, RRM2 domain of RBM10 alter RBM10 activity from splicing inhibition to stimulation, likely due to altered RNA binding characteristics. Graphical Abstract
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".