Fragmentation of the Qu�bec population genetic pool (Canada): Evidence from the genetic contribution of founders per region in the 17th and 18th centuries
Bibliographic record
Abstract
The 6 million French-Canadians of Québec derive from a relatively small number of founders. Consequently, some hereditary diseases, which may or may not present a worldwide distribution, have been detected in high frequency in this population. Several studies, however, indicate a nonuniform distribution of these diseases through the population, suggesting that the French-Canadian founder effect has been geographically stratified. Here we explore this stratification by using a demographic database, the Population Register of Early Québec, that contains almost all birth, marriage, and death certificates (>712,000) recorded in parish registers between 1608-1800. In this database, every genealogical link has been traced back to the founders of the population, so that we can compute the genetic contribution of founder per region, and then account for the early events that have shaped the distribution of diseases. Ten regions, comprising varying numbers of parishes, have been selected. We first describe each region in terms of homogeneity and concentration of its gene pool. For this purpose, a new concept is introduced, the founders' uniform contribution number (FUN), i.e., the number of founders a population would have if all its founders had an equal contribution. Second, we estimate genetic similarity between regions on the basis of differential genetic contribution. To classify the regions, we use principal component and cluster analysis. Our results show a tripartite clustering of the population, and invite us to reconsider the results obtained from biomolecular and clinical studies, which show a bipartite clustering.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".