Assessing the impact of pedigree attributes on the validity of quantitative genetic parameter estimates
Bibliographic record
Abstract
Investigating the evolution of complex traits in nature requires accurate assessment of their genetic basis. Quantitative genetic (QG) modeling is frequently applied to estimate the additive genetic variance (VA) in traits, combining phenotypic and pedigree data from a sample of individuals. Whether reconstructed from social links or molecular markers, empirical pedigrees differ in completeness, genealogical error rates, and other attributes that can impact QG estimation. Here we investigate this impact using human genealogical data for 6 French-Canadian (FC) populations originating from the same genetic founding source but differing in their pedigrees' attributes. First, we simulated phenotypic values along pedigrees and under different trait architectures and "true" parameter values (e.g., VA). Then we fitted mixed effects "animal" models to these simulated data, to assess how QG estimation was impacted by pedigree attributes. Our results show that pedigree size and depth were important determinants of the precision, but not accuracy, of genetic parameter estimates. In contrast, pedigree completeness and entropy, 2 attributes related to the density of genealogical links, were not clearly associated with the performance of parameter estimation. Noticeably, a slight increase in the genealogical error rate was sufficient to cause a detectable underestimation of VA. Including maternal genetic effects in the simulations led to a slight underestimation of VA with pedigrees of smaller size and depth. Despite originating from the same genetic source, the 6 pedigrees yielded wide variations in QG estimates under identical conditions. These findings highlight the importance of sensitivity analyses in pedigree-based genetic studies on natural populations.
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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 | 0.046 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".