Validation of a Targeted Region-Based Sliding Window (TRSW) Approach for Copy Number Variation Detection in a Large Cohort of Patients with ADPKD
Bibliographic record
Abstract
Background: With advances in next-generation sequencing(NGS), targeted NGS(tNGS) is widely used in ADPKD, showing excellent diagnostic yield for single-nucleotide variants and indels. However, copy number variations(CNVs) are often missed but can be detected by multiplex ligation-dependent probe amplification(MLPA) as an additional follow-up test. Recently, sequence-based CNV detection from tNGS has emerged as a potential approach for CNV detection, but has not been systematically evaluated large PKD cohorts. Here, we report our results on the diagnostic accuracy of a TRSW-based method for CNV detection in a large PKD cohort. Methods: We retrospectively analyzed patients with exome-based gene panels from the Toronto Genetic Epidemiology Study of PKD(TGSEP) using TRSW. In this computational method, average read depths from tNGS are compared to those of a normal pool to generate a potential CNV call. After filtering(Figure 1), patients with potential CNVs in PKD1/2 exons were reviewed in Integrative Genomics Viewer(IGV) to classify calls as likely positive(LP) or negative(LN) and compared with MLPA when available. Results: Among 2365 patients from TGESP, 934 had tNGS data(Figure 1). After filtering, review of 448 potential CNVs in PKD1/2 on the IGV identified 44 LP and 404 LN cases. In the LP group, MLPA confirmed 40/43 CNVs; 3 were missed by MLPA but confirmed by other methods. In the LN group, MLPA was also negative in 156/166 cases; 9 had false positives by MLPA, and 1 CNV was false negative by TRSW. Conclusion: TRSW provides a sensitive and accurate method for CNV detection from NGS data with high concordance to MLPA, although it requires extensive filtering and manual review. A combined TRSW-MLPA approach may enhance CNV diagnostic yield in ADPKD.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".