Estimating a founder’s genomic proportion for each descendant in an outbred pedigree
Bibliographic record
Abstract
The question of how to estimate a founder's proportion of a single descendant's genome has renewed relevance for outbred pedigrees, given the abundant DNA sequence data for model and nonmodel eukaryotes alike. Here we show that a donor-recurrent shortcut method can provide a robust estimation of founder proportions. In addition, we define the theoretical variance and estimate confidence intervals using a nonparameteric bootstrap method. Using actual marker data from a highly heterozygous outbred Pinus taeda pedigree, it was found that each founder's genomic proportion varied widely for each descendant, ranging from 8.8% to 38.7%. In 1 case, skewed transmission of a founder's genome could be statistically detected. Its founder proportions ranged from 1.54% to 48.46%, and its mean value was 17.59%, well below the expected value of 25%. Two-thirds of its 91 descendants had 1 or the other founder haplotype, despite 2 successive meioses. The donor-recurrent method was robust; variation for estimated founder's proportions was also wide for simulated high-density datasets whether markers were dispersed or clustered. Estimating founder contributions using this computational shortcut has broad application for highly heterozygous outbred pedigrees characterized by large sibships, low population differentiation, and shallow physical mapping resources. The relevance of this computational shortcut for outbred populations used for conservation, domestication, and evolutionary biology research is discussed.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".