Genetic Analysis of Traits Affecting the Success of Embryo Transfer in Dairy Cattle
Bibliographic record
Abstract
The primary aim of this study was to estimate variance components for traits related to embryo transfer (ET) by applying generalized linear mixed models (GLMM) for different distributions of traits (normal, binomial, and Poisson) in a synergistic context. Synergistic models were originally developed for traits affected by several genotypes, denoted as maternal, paternal, and direct effects. In the case of ET, the number of flushed ova (FO) only depends on a donor's maternal genetic effect, whereas paternal fertility must be considered for other embryo survival traits, such as the number of transferable embryos (TE), the number of degenerated embryos (DE), the number of unfertilized oocytes (UO), and the percentage of transferable embryos (PTE). Data for these traits were obtained from 4,196 flushes of 2,489 Holstein cows within 4 regions of northwest Germany from January 1998 through October 2004. Estimates of maternal heritability were 0.231 for FO, 0.096 for TE, 0.021 for DE, 0.135 for UO, and 0.099 for PTE, whereas the relative genetic impact of the paternal component was near zero. Estimates of the genetic correlations between the maternal and the paternal component were slightly negative, indicating a genetic antagonism. For the analysis of pregnancy after ET, 8,239 transfers to 6,819 different Holstein-Friesian recipients were considered by applying threshold methodology. The direct heritability for pregnancy in the recipient after ET was 0.056. The relative genetic impact of maternal and paternal components on pregnancy of recipients describing a donor's and a sire's ability to produce viable embryos was below 1%. The genetic correlations of the direct effect of the recipient with the sire of embryos (paternal effect) and the donor cow (maternal effect) for pregnancy after ET were -0.32 and -0.14, respectively. With the exception of FO and PTE (-0.17), estimates of genetic correlations among traits for the maternal site were distinctly positive, especially between FO and TE (0.74). Based on this high genetic correlation and due to the higher heritability for FO, indirect selection on FO will increase selection response in TE by about 22% compared with direct selection on TE. The negative genetic correlation of -0.27 between TE and lactation milk yield indicates the need for development of an index for bull dams in multiple ovulation and embryo transfer (MOET) breeding schemes combining production as well as traits related to ET.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".