Inferring phenotypic causal structures among feed efficiency traits in a commercial turkey population (Meleagris gallopavo)
Bibliographic record
Abstract
Feed costs are significant in animal production, consequently improving feed efficiency in animals has long been a selection goal. Variance component estimates for feed efficiency traits have been reported in the literature, however, in order to optimize breeding programs and management, potential functional relationships between traits should be examined. The objective of this study was to infer the phenotypic causal networks among feed intake (FI), body weight gain (WG), metabolic mid-weight (MMW), residual feed intake (RFI) and residual feed intake & body weight gain (RIG). Data from 5,619 toms with feed efficiency data was used for the analysis. As the causal links between these traits were not known a priori, the inductive causation (IC) algorithm was applied to search for them based on the joint distribution of genetic effects obtained from a standard Bayesian multi-trait model (MTM). Different highest posterior density (HPD) intervals were applied for the IC algorithm. Although the HPD interval 95% detected undirected links among the traits, lower HPD intervals (90%, 85%, 80% and 75%) uncovered identical fully directed graphs. Estimates of genetic variances and covariances for downstream traits were not similar between the two modelling approaches and the heritability estimates for those traits were higher under the structural equation model. The functional relationships (i.e., direct genetic effects) showed that hard interventions on WG would affect FI, but the reverse would not hold true. Similarly, hard intervention on FI would affect RIG, but not conversely. These causal effects suggest favorable conditions for the joint improvement and selection of MMW and RFI. Higher MMW and RFI would lead to higher WG. It is well known that WG is strongly associated with FI and there is a negative relationship between FI and RIG. The results indicate that the functional relationships between these traits should be carefully considered in designing optimized breeding programs that target improving feed efficiency in turkeys. In addition, adding behavior traits to the model may increase knowledge about causal effects for feed efficiency traits. Keywords: feed efficiency, causal inference, residual feed intake Acknowledgments The authors gratefully acknowledge support from Hybrid Turkeys (Kitchener, Canada), Genome Canada (Ottawa, Canada), Ontario Genomics (Toronto, Canada), and Hendrix Genetics (Boxmeer, Netherlands).
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".