Whole-Exome Sequencing and Targeted Copy Number Analysis in Primary Ciliary Dyskinesia
Bibliographic record
Abstract
Primary ciliary dyskinesia (PCD) is an autosomal-recessive disorder resulting from loss of normal ciliary function. Symptoms include neonatal respiratory distress, chronic sinusitis, bronchiectasis, situs inversus, and infertility. Clinical features may be subtle and highly variable, making the diagnosis of PCD challenging. The diagnosis can be confirmed with ciliary ultrastructure analysis and/or molecular genetic testing of 32 PCD-associated genes. However, because of this genetic heterogeneity, comprehensive molecular genetic testing is not considered the standard of care, and the most efficient molecular approach has yet to be elucidated. Here, we propose a cost-effective and time-efficient molecular genetic algorithm to solve cases of PCD. We conducted targeted copy number variation (CNV) analysis and/or whole-exome sequencing on 20 families (22 patients) from a subset of 45 families (52 patients) with a clinical diagnosis of PCD who did not have a molecular genetic diagnosis after Sanger sequencing of 12 PCD-associated genes. This combined molecular genetic approach led to the identification of 4 of 20 (20%) families with clinically significant CNVs and 7 of 20 (35%) families with biallelic pathogenic mutations in recently identified PCD genes, resulting in an increased molecular genetic diagnostic rate of 55% (11/20). In patients with a clinical diagnosis of PCD, whole-exome sequencing followed by targeted CNV analysis results in an overall molecular genetic yield of 76% (34/45).
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".