PKD1 and PKD2 Copy Number Variations (CNVs) in Extended Toronto Genetic Epidemiologic Study of Polycystic Kidney Disease (eTGESP)
Bibliographic record
Abstract
Background: Autosomal dominant polycystic kidney disease (ADPKD) is genetically heterogeneous and primarily due to mutations in PKD1 or PKD2. Although CNVs including genomic deletion/duplication in PKD1 and PKD2 are uncommon, they explain a subset of cases with no mutations detected by NGS studies. Here, we report our results of a CNV screen in a cohort of 1,811 patients from 1,271 different families from eTGESP. Methods: We screened all study patients for PKD1 and PKD2 mutations by targeted NGS and multiplex ligation-dependent probe amplification (MLPA) in NGS mutationnegative cases. Standard algorithms for sequence alignment, base calling, and QC filtering were applied to identify rare (MAF ≤1%) deleterious variants of high and moderate impact as predicted by multiple predictive algorithms. MLPA was performed using two kits (Probemix P351-D1 and P352-E1) from MRC Holland; all detected CNVs were validated by Sanger sequencing and droplet digital PCR (ddPCR) whenever possible. Results: NGS-based screen failed to detect any definitive PKD1 or PKD2 mutations in 253/1,271 (20%) of families. Follow-up testing with MLPA identified CNVs in 29 of 253 (11.5%) NGS screen-negative families, including 15 with heterozygous PKD1 deletions, 12 with heterozygous PKD2 deletions, and 2 with heterozygous PKD1 duplications; one PKD1 CNV mosiac was also identified. Conclusions: In this large cohort study from a single geographical region, we found PKD1 and PKD2 CNVs are rare causes of ADPKD, but account for 2.3% (29/1,271) of the entire study cohort and ˜10% of our NGS screen-negative families. MPLA provides an important follow-up test for NGS-based PKD1 and PKD2 screen.
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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 | 0.002 |
| 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".