Genotype‐phenotype relationships in mucopolysaccharidosis type I (MPS I): Insights from the International MPS I Registry
Bibliographic record
Abstract
Mucopolysaccharidosis type I (MPS I) is a rare autosomal recessive disorder resulting from pathogenic variants in the α-L-iduronidase (IDUA) gene. Clinical phenotypes range from severe (Hurler syndrome) to attenuated (Hurler-Scheie and Scheie syndromes) and vary in age of onset, severity, and rate of progression. Defining the phenotype at diagnosis is essential for disease management. To date, no systematic analysis of genotype-phenotype correlation in large MPS I cohorts have been performed. Understanding genotype-phenotype is critical now that newborn screening for MPS I is being implemented. Data from 538 patients from the MPS I Registry (380 severe, 158 attenuated) who had 2 IDUA alleles identified were examined. In the 1076 alleles identified, 148 pathogenic variants were reported; of those, 75 were unique. Of the 538 genotypes, 147 (27%) were unique; 40% of patients with attenuated and 22% of patients with severe MPS I had unique genotypes. About 67.6% of severe patients had genotypes where both variants identified are predicted to severely disrupt protein/gene function and 96.1% of attenuated patients had at least one missense or intronic variant. This dataset illustrates a close genotype/phenotype correlation in MPS I but the presence of unique IDUA missense variants remains a challenge for disease prediction.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".