Mutation Spectrum of the Extended Toronto Genetic Epidemiologic Study of Polycystic Kidney Disease (eTGESP)
Bibliographic record
Abstract
Background: Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disease worldwide. Mutations in PKD1 and PKD2, respectively, account for 85% and 15% of the genetically resolved cases in clinical series enriched with high-risk patients. Here, we report the ADPKD mutation spectrum of a large cohort of relatively unselected patients from a single geographical region. Methods: We performed mutation screening in 2,171 patients from 1,606 different families from the Greater Toronto Area (population 6.8 million) using NGS targeted sequencing and multiplex ligation-dependent probe amplification of PKD1 and PKD2, as well as NGS of a panel of 50 cystic disease genes. Standard algorithms for sequence alignment, base calling, and QC filtering were applied to identify rare (MAF ≤1%) deleterious variants as predicted by multiple algorithms. Results: We detected PKD1 and PKD2 mutations in 1205 (75%) families, non-PKD1 and PKD2 (i.e. ALG8, ALG9, PKHD1, GANAB, PRKCSH, SEC63, LRP5, WFS1, TSC1-2, COL4A1, and COL4A3-5) rare putative pathogenic variants in 120 (10%) families, with no mutations detected in 281 (15%) families. Among the PKD1 and PKD2 genetically resolved families, 916 (76%) and 289 (24%) were due to mutations in PKD1 and PKD2, respectively. Adjusted for exon size across all 46 exons in PKD1, we found an enrichment of truncating mutations in exon 44. We also found over 100 recurrent mutations in ≥ 2 different families (haplotype analysis is in progress). Conclusions: We found extensive genic and allelic heterogeneity in ADPKD with a higher prevalence of PKD2 mutations than reported in the clinical series. We also found non-PKD1 and non-PKD2 cystic disease mutations in 10% of families, while 15% of the families remained genetically unresolved. Recurrent PKD1 and PKD2 Mutations
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".