Genetic parameters for egg quality traits in Pekin ducks
Bibliographic record
Abstract
Pekin duck (Anas platyrhynchos domesticus) is the most widely consumed duck protein with nearly 35 million animals produced annually in the United States and exported worldwide. Pekin ducks are primarily utilized in meat production, so very little information is available about their heritability estimates and genetic correlations for traits related to egg quality. Genetically improving duck populations together with the implementation of more efficient nutritional and management strategies is paramount for the long-term sustainability of the US duck industry. There is a potential opportunity to increase meat duck productivity by improving hatching egg quality. The main objectives of this study were to estimate heritability and genetic correlations for various egg quality traits in a commercial population of Pekin ducks. Egg quality traits for 612 Pekin duck females were measured through 3 time points over 2 generations (GEN) [30, 32, and 35 wk of age (WOA)]. GEN 2 had an additional sampling occurring at 40 WOA. Genetic correlations and heritability estimates were calculated for all the traits using the BLUPF90 software, the Restricted Maximum Likelihood (REML) method, and a pedigree containing 9,418 individuals. All egg quality traits evaluated are moderately to highly heritable ranging from 0.20 for Haugh Unit (HU) and Vitelline Membrane Strength (VMS) to 0.71 for shell ratio (SR). Heritability estimates were calculated for each age of collection and in general heritability increased up to 35 WOA. Genetic correlations between egg quality traits showed a wide range of positive and negative relationships with correlation strengths ranging of -0.80 [yolk ratio (YR) and albumin ratio (AR)] to 0.99 [egg volume (EV) and egg weight (EW)]. The results of this study highlight the potential to improve hatching egg quality within Pekin ducks using a multi-trait selection scheme through direct genetic selection.
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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 | 0.001 | 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".