Diagnostic yield of clinical exome sequencing in adulthood in medical genetics clinics
Bibliographic record
Abstract
Clinical exome sequencing (ES) is the most comprehensive genomic test to identify underlying genetic diseases in Canada. We performed this retrospective cohort study to investigate the diagnostic yield of clinical ES in adulthood. Inclusion criteria were: (1) Adult patients ≥18 years old; (2) Patients underwent clinical ES between January 1 and December 31, 2021; (3) Patients were seen in the Department of Medical Genetics. We reviewed patient charts. We applied American College of Medical Genetics and Genomics and the Association for Molecular Pathology variant classification guidelines for interpretation of variants. Non-parametric Fisher's exact statistical test was used. Seventy-seven patients underwent clinical ES. Fourteen different genetic diseases were confirmed in 15 patients: FBXO11, MYH7, MED13L, NSD2, ANKRD11 (n = 2), SHANK3, RHOBTB2, CDKL5, TRIO, TCF4, SCN1, SMAD3, POGZ, and EIF2B3 diseases. The diagnostic yield of clinical ES was 19.5%. Patients with a genetic diagnosis had a significantly higher frequency of neurodevelopmental disorders than those with no genetic diagnosis (p = 0.00339). The diagnostic yield of clinical ES was the highest in patients with seizures (35.7%), and with progressive neurodegenerative diseases (33.3%). Clinical ES is a helpful genomic test to provide genetic diagnoses to the patients who are referred to medical genetic clinics due to suspected genetic diseases in adulthood to end their diagnostic odyssey. Targeted next generation sequencing panels for specific phenotypes may decrease the cost of genomic test in adulthood.
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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 | 0.011 |
| 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 | 0.000 | 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".