Rare diseases load through the study of a regional population
Bibliographic record
Abstract
Rare genetic diseases impact many people worldwide and are challenging to diagnose. In this study, we introduce a novel regional population cohort approach to identify pathogenic variants causing Mendelian diseases that occur more frequently within specific populations and are of clinical interest for carrier testing. We utilized a cohort from Quebec, including the Saguenay-Lac-Saint-Jean region, which is known for its founder effect followed by a rapid expansion and higher frequency of certain pathogenic variants. By analyzing both their frequency and origin through shared identical-by-descent segments, we identified founder variants. We calculated and compared their frequency in individuals originating from the Saguenay-Lac-Saint-Jean and from other urban Quebec regions. We validated 38 previously reported variants as being more common due to the founder effect and population expansion. Additionally, we identified 42 unreported founder variants in Quebec or Saguenay-Lac-Saint-Jean, some with carrier rates estimates as high as 1/22. We also observed a greater deleterious mutational load for the studied variants in individuals from the Saguenay-Lac-Saint-Jean compared to other urban Quebec regions. These findings were brought to the clinic, where 12 pathogenic variants were detected in diagnosed patients. Five variants found in this study are responsible for very severe diseases and could be considered for inclusion in a carrier test for the Saguenay-Lac-Saint-Jean population. This study highlights the potential underestimation of rare disease prevalence and presents a population-based approach that could aid clinicians in their diagnostic efforts and patients' management.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".