When to think outside the autozygome: Best practices for exome sequencing in “consanguineous” families
Bibliographic record
Abstract
Exome sequencing (ES) is an effective diagnostic tool with a high yield in consanguineous families. However, how diagnostic yield and mode of inheritance relate to family structure has not been well delineated. We reviewed ES results from families enrolled in the Care4Rare Canada research consortium with various degrees of consanguinity. We contrasted the diagnostic yield in families with parents who are second cousins or closer ("close" consanguinity) vs those more distantly related or from isolated populations ("presumed" consanguinity). We further stratified by number of affected individuals (multiple affected ["multiplex"] vs single affected [simplex]). The overall yield in 116 families was 45.7% (n = 53) with no significant difference between subgroups. Homozygous variants accounted for 100% and 75% of diagnoses in close and presumed consanguineous multiplex families, respectively. In simplex presumed consanguineous families, a striking 46.2% of diagnoses were due to de novo variants, vs only 11.8% in simplex closely consanguineous families (88.2% homozygous). Our data underscores the high yield of ES in consanguineous families and highlights that while a singleton approach may frequently be reasonable and a responsible use of resources, trio sequencing should be strongly considered in simplex families in the absence of confirmed consanguinity given the proportion of de novo variants.
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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 | 0.072 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".