PSVII-11 Genetic parameter estimates for lamb survival in prolific and nonprolific ewes
Bibliographic record
Abstract
Abstract Given the increasing global demand for animal products, increasing lamb survival is a key breeding objective for sheep. However, high prolificacy can be associated with increases in fetal and lamb mortality. Therefore, the goal of this study was to estimate the variance components and genetic parameters for lamb survival (LS), using a: 1) single-trait model, where all ewes are analyzed together; and 2) two-trait model, where ewes are grouped in two groups based on birth type [i.e., Group 1 (G1) = single lamb/ewe, and Group 2 (G2) = multiple lambs/ewe]. In both models, LS was defined as the survival of lamb from birth to weaning and analyzed as a threshold trait. A total of 1,040,629 records (215,075 from single and 825,554 from multiple birth) from 319,467 ewes from several breeds/breed groups were available for the study. There were no females with two different types of births. Variance components were estimated through Bayesian inference via Gibbs sampling implemented in the GIBBSF90+ program, fitting a two-trait animal model with maternal effects, in which breed/breed groups were included as fixed effects. Average number of lambs/ewe in G2 was 2.57 (SD = 0.72). Heritability estimated (± SE) for LS on the underlying scale using the single-trait (0.04 ± 0.10) and two-trait model (0.010 ± 0.001 for LSG1 and 0.031 ± 0.004 for LSG2) were low. Maternal heritability estimated by the single-trait model was 0.007 ± 0.043, while by the two-trait model the estimates were 0.010 ± 0.006 and 0.040 ± 0.005 for LSG1 and LSG2, respectively. The genetic correlation estimated between LSG1 and LSG2 was weak and positive (0.25 ± 0.21). The low non-significant genetic correlation between single and multiple birth groups suggests that lamb survival is a different genetic trait in different birth types. Therefore, selecting for improving LS in nonprolific ewes may not necessarily translate to improving LS in prolific ewes. This underscores the need for careful consideration and targeted breeding strategies to achieve desired outcomes in nonprolific and prolific sheep simultaneously. Further studies to define the optimal statistical approach to genetically evaluate LS in prolific and nonprolific ewes are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".