Assessing the impact of pedigree quality on the validity of quantitative genetic parameter estimates
Bibliographic record
Abstract
Abstract Investigating the evolutionary dynamics of complex traits in nature requires the accurate assessment of their genetic architecture. Using a quantitative genetic (QG) modeling approach (e.g., animal model), relatedness information from a pedigree combined with phenotypic measurements can be used to infer the amount of additive genetic variance in traits. However, pedigree information from natural systems is not perfect and might contain errors or be of low quality. Published sensitivity analyses revealed a limited impact of expected error rates on parameter estimates. However, natural systems will differ in many respects (e.g., mating system, data availability, pedigree structure), thus it can be inappropriate to generalize outcomes from one system to another. French-Canadian (FC) genealogies are extensive and deep-rooted (up to 9 generations in this study) making them ideal to study how the quality and properties (e.g., errors, completeness) of pedigrees affect QG estimates. We conducted simulation analyses to infer the reliability of QG estimates using FC pedigrees and how it is impacted by genealogical errors and variation in pedigree structure. Broadly, results show that pedigree size and depth are important determinants of precision but not of accuracy. While the mean genealogical entropy (based on missing links) seems to be a good indicator of accuracy. Including a shared familial component into the simulations led to on average a 46% overestimation of the additive genetic variance. This has crucial implications for evolutionary studies aiming to estimate QG parameters given that many traits of interest, such as life history, exhibit important non-genetic sources of variation.
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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 | 0.082 | 0.354 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".