La génétique des populations à effet fondateur ; un miroir de la démographie et de l’histoire
Bibliographic record
Abstract
Les populations à effet fondateur ont été très utiles afin d’identifier des variants liés à des maladies rares, mais également afin de mieux comprendre l’impact des phénomènes démographiques sur la génétique de la population. Nous croyons que l’investigation approfondie de la structure fine présente au sein de ce type de population est cruciale pour l’étude et l’identification de nouveaux variants rares. En effet, une cohorte de plus petite taille, possédant une structure fine, permet de concentrer ce type de variant. Ainsi, leur fréquence est augmentée, ce qui faciliterait l’identification de nouveaux variants. Néanmoins, comprendre d’où provient cette structure aide également à bâtir de meilleures connaissances pour mieux étudier les maladies associées aux populations à effet fondateur. Avec l’aide de données généalogiques, il est possible de suivre la structure de la population québécoise qui est apparue dès 1750 jusqu’à aujourd’hui. De plus, ces mêmes données aident à comprendre l’impact de certains processus démographiques sur l’apparition de ces structures fines au Québec. Ce mémoire dévoile donc l’utilité d’étudier les populations à effet fondateur et les structures fines de population. Nous croyons que la recherche sur des maladies débute par une bonne compréhension de la population à l’étude. Autant pour une population à effet fondateur ou non, cela commence par une investigation de la structure fine de cette population afin de tirer profit de cette structure unique. De plus, au Québec, nous sommes très choyés d’avoir accès à des données généalogiques complètes sur plus de 400 ans qui révèlent les processus démographiques vécus par la population, mais aussi pour suivre la transmission et comprendre la répartition des variants génétiques et leur impact sur la santé populationnelle. The populations with a founder effect have been extremely useful in identifying variants associated with rare diseases, as well as in better understanding the impact of demographic phenomena on population genetics. We believe that thorough investigation into the fine structure within this type of populations is crucial for studying and identifying new rare variants. Indeed, a smaller cohort with a fine structure allows the concentration of this type of variant. Thus, this increases their frequency, which would facilitate the identification of new variants. However, understanding the origin of this structure also contributes to build better knowledge for studying diseases associated with populations exhibiting a founder effect. With the help of genealogical data, it is possible to track the structure of the Quebec population from as early as 1750 to the present day. Furthermore, this same data helps understand the impact of certain demographic processes on the appearance of these fine structures in Quebec. This thesis thus reveals the utility of studying populations with a founder effect and population fine structures. We believe that research on diseases begins with a thorough comprehension of the population under study. Whether for a population with a founder effect or not, this begins with an investigation into the fine structure of the population to take advantage of this unique structure. Moreover, in Quebec, we are privileged to have access to comprehensive genealogical data spanning over 400 years, revealing the demographic processes experienced by the population, as well as for tracking transmission and understanding the distribution of genetic variants and their impact on populational health.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".