MG-132 Diagnostic utility of whole genome sequencing in paediatric medicine
Bibliographic record
Abstract
Background Chromosome microarray analysis (CMA) is the current first-tier clinical investigation for paediatric patients with congenital malformations and/or neurodevelopmental disorders. Whole genome sequencing (WGS) promises to capture all classes of genetic variation in a single experiment, but the diagnostic yield of WGS compared to CMA in the clinical setting has not been established. Objectives The purpose of the study was to compare the diagnostic yield of WGS with CMA and other targeted sequence analysis. Methods Through the SickKids genome clinic 100 consecutive patients undergoing CMA were recruited and WGS was performed to compare diagnostic yield. Results WGS identified variants meeting clinical diagnostic criteria in 32% of cases, representing a 4-fold increase in diagnostic rate over CMA (8%) alone and > 2-fold increase in CMA plus targeted sequence testing (15%). WGS identified all reportable rare CNVs that were detected by CMA. In an additional 27 patients, WGS revealed clinically significant SNV and indel mutations presenting in a dominant (72%) or a recessive (28%) manner. Four cases had variants in at least two genes involved in distinct genetic disorders, contributing to a more complex clinical phenotype. Conclusions Our data indicate that WGS is highly accurate and efficient, providing a diagnosis in 32% paediatric patients that meet clinical criteria for CMA. Clinical implementation of WGS as a single and primary molecular test will provide a higher diagnostic yield than conventional testing while decreasing the number of genetic tests and ultimately the time before reaching a genetic diagnosis.
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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 | 0.002 | 0.005 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".