Rare disorders have many faces: in silico characterization of rare disorder spectrum
Bibliographic record
Abstract
BACKGROUND: The diagnostic journey for many rare disease patients remains challenging despite use of latest genetic technological advancements. We hypothesize that some patients remain undiagnosed due to more complex diagnostic scenarios that are currently not considered in genome analysis pipelines. To better understand this, we characterized the rare disorder (RD) spectrum using various bioinformatics resources (e.g., Orphanet/Orphadata, Human Phenotype Ontology, Reactome pathways) combined with custom-made R scripts. RESULTS: Our in silico characterization led to identification of 145 borderline-common, 412 rare and 2967 ultra-rare disorders. Based on these findings and point prevalence, we would expect that approximately 6.53%, 0.34%, and 0.30% of individuals in a randomly selected population have a borderline-common, rare, and ultra-rare disorder, respectively (equaling to 1 RD patient in 14 people). Importantly, our analyses revealed that (1) a higher proportion of borderline-common disorders were caused by multiple gene defects and/or other factors compared with the rare and ultra-rare disorders, (2) the phenotypic expressivity was more variable for the borderline-common disorders than for the rarer disorders, and (3) unique clinical characteristics were observed across the disorder categories forming the spectrum. CONCLUSIONS: Recognizing that RD patients who remain unsolved even after genome sequencing might belong to the more common end of the RD spectrum support the usage of computational pipelines that account for more complex genetic and phenotypic scenarios.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".