Genetic correlations and causal effects of fighting ability on fitness traits in cattle reveal antagonistic trade-offs
Bibliographic record
Abstract
Complex genetic and phenotypic relationships are theorized to link different fitness components but revealing the correlations occurring among disparate traits requires large datasets of pedigreed populations. In particular, the association between traits beneficial to social dominance with health and fitness could be antagonistic, because of trade-offs, or positive, because of greater resource acquisition by dominant individuals. Studies investigating these relationships found some empirical evidence in support of both theories, mainly using multiple trait models (MTM). However, if a trait giving a social advantage is suspected to affect the expression of other traits, MTM could provide some bias, that structural equation models (SEM) could highlight. We used Aosta Chestnut-Black Pied cattle to investigate whether the fighting ability of cows (the capability of winning social dominance interactions) is genetically correlated with health and fitness traits. We ran both MTM and SEM using a Gibbs sampling algorithm to disentangle the possible causal effects of fighting ability from the genetic correlations that this trait shares with other traits: individual milk yield, somatic cells (representing mammary health), fertility, and longevity. We found antagonistic genetic correlations, similar under both approaches, for fighting ability vs. milk, somatic cells, and fertility, Accordingly, we found only a slight causal effects of fighting ability on these traits (–0.012 to 0.059 in standardized value). However, we found genetic correlations opposite in sign between fighting ability and longevity under MTM (0.237) and SEM (–0.183), suggesting a strong causal effect (0.386 standardized) of fighting ability on longevity. In other words, MTM found a positive correlation between longevity and fighting ability, while SEM found a negative correlation. The explanation could be that for economic reasons dominant cows are kept in this population for longer, thus attaining greater longevity: using MTM, the economic importance of competitions probably covers the true genetic correlation among traits. This artificially simulates a natural situation where an antagonistic genetic correlation between longevity and fighting ability appears positive under MTM due to a non-genetic advantage obtained by the best fighters. The use of SEM to properly assess the relationships among traits is suggested in both evolutionary studies and animal breeding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".