Genomic structure and selection history across Angus populations worldwide: insights from ROH, selection mapping, and functional analyses
Bibliographic record
Abstract
Angus cattle, originally from Scotland, have been selectively bred for over 400 years, making them one of the most prominent beef breeds globally. Known for their adaptability, natural polled traits, and high-quality beef, Angus cattle have been intensively selected for growth, body size, and feed efficiency. This study investigates the genetic diversity, selection history, and key genomic regions across five Angus populations from the USA, Canada, Australia, Brazil, and Red Angus of America. Genomic data from 71,283 animals born between 1961 and 2024 were analyzed using Principal Component Analysis (PCA), phylogenetic tree construction, and Runs of Homozygosity (ROH), with the Generation Proxy Selection Mapping (GPSM) approach used to assess selection history. Functional annotation identified candidate genes and pathways related to selection. Our analysis revealed both similarities and differences across populations. The PCA and FST metrics showed minimal differentiation between the American, Canadian, Australian, and Brazilian populations, with greater differentiation observed in the Red Angus population. The ROH analysis revealed that the Brazilian population had the highest number of ROHs. The ROH islands identified on BTA8 and BTA13 in the American and Australian populations were linked to traits like body weight, marbling, and tenderness. The GPSM identified significant markers associated with body weight and growth in all populations, reflecting ongoing selection pressures. This study highlights the potential of genomics to improve our understanding of Angus cattle's genetic architecture and selection history. It underscores the feasibility of integrating global populations for more accurate genomic evaluations, enhancing genetic predictions, and supporting sustainable beef production worldwide.
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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 | 0.000 | 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".