The quantitative genetics of a complex trait under continuous directional selection
Bibliographic record
Abstract
We analyzed data from a long‐term artificial selection experiment that includes 4 lines of mice bred for high voluntary wheel running (HR) and 4 non‐selected control (C) lines. The HR lines reached a selection limit at generation ~16, running ~3‐fold more revolutions/day than C lines. In addition, wheel running varied across generations in an apparently cyclical fashion in both HR and C. We used the first 25 generations to estimate quantitative genetic parameters before, during, and after the selection limit was reached. We used ASReml‐R to apply the “animal model”, a linear mixed‐model that uses all the information on the coefficients of co‐ancestry among individuals in a pedigree. Our preliminary results indicate additive genetic variance ( V A ) was not eliminated in HR lines after the limit was reached. However, the selection regime led to a negative covariance between V A and maternal genetic variance ( V M ), which could maintain V A in the selected trait and potentially explain the presence of a cycle. We also found that the genetic correlation between mean running speed and duration of wheel running tended to be lower in females than in males, which may explain why the response to selection was achieved differently in females (mainly speed) and males (both speed and duration). Supported by NSF grants IOS‐1121273 to TG and EF0328594 to PAC, and a NSERC postdoctoral fellowship to VC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".