Layered founders : a novel approach to investigate the ancestral transmission of complex traits
Bibliographic record
Abstract
ABSTRACT: A novel approach based on graph theory is presented to reason about the genetic contribution of ancestors at different genealogical distances from today's individuals (different definitions of layers and distances are propos ed and discussed). It allows the maximum likelihood classification of specific founders who predominantly contribute to one class of individuals and the analysis of separability of specific founders with respect to two classes of individuals that have been selected based on LOD (logarithm of odds) score determined by a total genome scan and on ScaI marker genotype of a candidate gene of hypertension, ANP. Several experiments have been performed on a genealogy comprising more than 40,000 people and spanning 17 generations from the Saguenay-Lac-Saint-Jean population. We have computed: the founders obtained by using different definitions of layers and distances, the contribution of specific and unique founders, and the separability of specific founders. The results indicate that most defini tions of layers of founders show a similar trend over layers for size and content, that specific and unique genetic contributions are very high for recent generations and, as expected, decrease for older generations, and, also, that separability is higher for recent generations than for older ones. The presented approach allows a much finer grain analysis of genetic contribution of founders than previously-reported approaches.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".