Whole genome sequence analysis identifies a <scp>PAX2</scp> mutation to establish a correct diagnosis for a syndromic form of hyperuricemia
Bibliographic record
Abstract
Hereditary hyperuricemia may occur as part of a syndromic disorder or as an isolated nonsyndromic disease, and over 20 causative genes have been identified. Here, we report the use of whole genome sequencing (WGS) to establish a diagnosis in a family in which individuals were affected with gout, hyperuricemia associated with reduced fractional excretion of uric acid, chronic kidney disease (CKD), and secondary hyperparathyroidism, that are consistent with familial juvenile hyperuricemic nephropathy (FJHN). However, single gene testing had not detected mutations in the uromodulin (UMOD) or renin (REN) genes, which cause approximately 30-90% of FJHN. WGS was therefore undertaken, and this identified a heterozygous c.226G>C (p.Gly76Arg) missense variant in the paired box gene 2 (PAX2) gene, which co-segregated with renal tubulopathy in the family. PAX2 mutations are associated with renal coloboma syndrome (RCS), which is characterized by abnormalities in renal structure and function, and anomalies of the optic nerve. Ophthalmological examination in two adult brothers affected with hyperuricemia, gout, and CKD revealed the presence of optic disc pits, consistent with optic nerve coloboma, thereby revising the diagnosis from FJHN to RCS. Thus, our results demonstrate the utility of WGS analysis in establishing the correct diagnosis in disorders with multiple etiologies.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".