Prevalence of rare diseases in the Quebec French Canadian population
Bibliographic record
Abstract
For many rare diseases, incomplete data on prevalence limits targeted genetic screening efforts.While rare disease prevalence is typically estimated from patient cohorts or newborn screening, population biobanks offer an alternative.Prevalence estimates are particularly important in founder populations, such as the French Canadian (FC) population, who may be at increased risk for rare diseases due to the founder effect.In this thesis, I use a genotype-first approach to estimate the prevalence of rare Mendelian diseases in the FC founder population.To achieve this, I developed a systematic and scalable approach to estimate rare disease prevalence from population-based genomic datasets.This approach was applied at scale in the CARTaGENE FC sample, assessing disease prevalence on both provincial and regional scales.My prevalence estimation approach used disease and variant databases to apply variant filtering while maintaining scalability.I estimated the province-wide prevalence for 93 rare Mendelian diseases in the FC population and regional prevalence estimates for 11 of the 93 diseases.Within the 93 diseases, I identified highly prevalent conditions with early age of onset, high severity, and available treatments that are not currently included in Quebec's screening programs, highlighting candidates for expanding screening panels.These results provide essential information for genetic screening programs. RÉSUMÉPour de nombreuses maladies rares, l'absence de données sur la prévalence constitue un frein au développement de stratégies de dépistage génétique ciblé.Alors que la prévalence des maladies rares est généralement estimée à partir de cohortes de patients ou de programmes de dépistage néonatal, les biobanques populationnelles, en pleine expansion, offrent une alternative prometteuse.Les estimations de prévalence sont particulièrement importantes dans les populations dites fondatrices, telles que la population canadiennefrançaise (CF), qui peuvent présenter une fréquence accrue de certaines maladies rares en raison de l'effet fondateur.Cette thèse s'appuie sur une approche "genotype-first" afin d'estimer la prévalence des maladies mendéliennes rares dans la population fondatrice canadienne-française. Pour ce faire, j'ai développé une approche systématique et adaptée à l'analyse à grande échelle afin d'estimer la prévalence des maladies rares à partir d'ensembles de données génomiques de biobanques populationnelles.Mon approche a été déployée dans l'échantillon CF de CARTaGENE, permettant l'estimation de la prévalence des maladies à l'échelle provinciale et régionale.Mon approche s'appuie sur des bases de données de maladies et de variants génétiques pour effectuer un filtrage rigoureux tout en maintenant une scalabilité.J'ai estimé la prévalence, à l'échelle provinciale, de 93 maladies mendéliennes rares présentes dans la population CF ainsi que la prévalence régionale pour 11 de ces 93 maladies.Parmi ces maladies analysées, j'ai identifié plusieurs pathologies présentant simultanément une
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".