Potential of Anogenital Distance as a Genetic Selection Trait in Canadian Holstein
Bibliographic record
Abstract
Maintaining optimal fertility in dairy cattle herds is a global challenge that is typically addressed through the genetic selection of fertility indicator traits. However, many of the traits currently implemented in breeding programs are heavily influenced by environmental factors, resulting in a slow rate of genetic improvement. Anogenital distance (AGD) has recently emerged as a promising fertility indicator trait due to its association with favorable reproductive outcomes and its higher heritability estimates compared with currently evaluated traits. This study aimed to enhance the understanding of AGD's genetic potential by estimating its genetic parameters in Canadian Holsteins, assessing the reliability of breeding values, comparing pedigree BLUP to single-step genomic BLUP approaches, and estimating the correlation between AGD breeding values and those of currently evaluated traits. The dataset used in this study comprised 5,541 Canadian Holstein cows and heifers from 20 herds, collected between 2015 and 2020. The final dataset consisted of 4,988 animals with AGD phenotypes after filtering. The pedigree-based heritability estimate for AGD was 0.39 ± 0.04, whereas the incorporation of genomics resulted in a lower estimate of 0.37 ± 0.03. The reliability of estimated breeding values ranged from 0.49 ± 0.03 for phenotyped animals to 0.81 ± 0.05 for proven sires with at least 30 phenotyped daughters. The integration of genomic information improved the reliability of breeding values, with gains ranging from 0.01 gain for proven sires to 0.14 relative gain for unproven sires. High gain in observed reliability for females without records was demonstrated when genomic information was included, using both split forward validation (0.26) and 5-fold cross-validation (0.14). The AGD breeding values showed moderate unfavorable correlations with relative breeding values of age at first service and production traits including milk yield, fat yield, and protein yield. This suggests that AGD may influence reproductive maturity in heifers but could also have an unfavorable association with production traits, highlighting the need for balanced breeding strategies that consider both fertility and production outcomes. Future studies should aim to expand phenotype data across lifetimes and breeds and estimate genetic correlations with traditional reproduction and production traits using multitrait models.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".