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Record W7126555662

Potential of Anogenital Distance as a Genetic Selection Trait in Canadian Holstein

2025· article· W7126555662 on OpenAlex
G.R. Dodd, F.S. Schenkel, F. Miglior, T.C. Bruinjé, M. Gobikrushanth, J.E. Carrelli, M. Oba, C.F. III Baes

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOpen PRAIRIE (South Dakota State University) · 2025
Typearticle
Language
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
Fundersnot available
KeywordsHeritabilityBest linear unbiased predictionTraitGenetic gainSelection (genetic algorithm)FertilityHerdDairy cattleGenetic correlation
DOInot available

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.211
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it