<scp>HLA</scp>‐A, B and <scp>DRB</scp>1 genetic heterogeneity in <scp>Q</scp>uebec
Bibliographic record
Abstract
Recent studies have shown that under specific conditions such as high sample sizes and Hardy-Weinberg equilibrium, bone marrow donor registry data can be used to describe HLA molecular variation across a specific geographic area, thus providing excellent data sets to infer human migrations history. The province of Quebec is known to have experienced a complex history of settlement, characterized by multiple migrations and demographic changes. We thus analysed the data of more than 13 000 unrelated individuals acting as volunteer bone marrow donors who were molecularly typed for HLA-A, B and DRB1 polymorphisms in the Héma-Quebec registry. HLA allelic and haplotypic frequencies were estimated and compared among regions. The results indicate that, despite an overall low genetic diversity in Quebec, genetic variation is correlated with geography, compatible with isolation-by-distance across the province. However, some localities also harbour contrasting genetic profiles, that is a highly diversified genetic pool in the two main urban centres (Montréal and Laval) and a more pronounced genetic divergence of two specific regions characterized by a peculiar peopling history (Saguenay-Lac-St-Jean and Gaspésie-Îles-De-La-Madeleine). In agreement with other independent molecular markers, the observations based on HLA data thus account for the main demographic mechanisms that shaped the genetic structure of the present day Quebecer population. In addition, the detailed analysis of the Héma-Quebec registry provides key genetic information on which an efficient bone marrow transplantation recruitment strategy can be settled.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".