Debunking Occam's razor: Diagnosing multiple genetic diseases in families by whole‐exome sequencing
Bibliographic record
Abstract
BACKGROUND: Recent clinical whole exome sequencing (WES) cohorts have identified unanticipated multiple genetic diagnoses in single patients. However, the frequency of multiple genetic diagnoses in families is largely unknown. AIMS: We set out to identify the rate of multiple genetic diagnoses in probands and their families referred for analysis in two national research programs in Canada. MATERIALS & METHODS: We retrospectively analyzed WES results for 802 undiagnosed probands referred over the past 5 years in either the FORGE or Care4Rare Canada WES initiatives. RESULTS: Of the 802 probands, 226 (28.2%) were diagnosed based on mutations in known disease genes. Eight (3.5%) had two or more genetic diagnoses explaining their clinical phenotype, a rate in keeping with the large published studies (average 4.3%; 1.4 - 7.2%). Seven of the 8 probands had family members with one or more of the molecularly diagnosed diseases. Consanguinity and multisystem disease appeared to increase the likelihood of multiple genetic diagnoses in a family. CONCLUSION: Our findings highlight the importance of comprehensive clinical phenotyping of family members to ultimately provide accurate genetic counseling.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".