Selection signatures in Gir and Holstein cattle
Bibliographic record
Abstract
Natural and artificial selection leave footprints on the genome, known as selection signatures, that can indicate regions related to adaptive and economically important traits. The recurrent use of a limited number of sires and increased selection pressure may affect genetic diversity, potentially affecting long-term breeding programs. Among dairy cattle, the Holstein breed has been intensively selected to maximize productivity, particularly in Canada and the United States. In the dairy industry, the Gir breed plays an important role in milk production in tropical regions such as India and Latin America. Gir cattle were introduced into Brazil in the 19th and 20th centuries, and since 1985, this breed has been intensively selected for milk production. This study aimed to assess the genetic diversity and characterize the selection signatures in Holstein cattle from the United States and Canada (HOL), Gir cattle from India (GIR_IN), and Dairy Gir cattle from Brazil (GIR_BR). Genetic diversity was assessed by nucleotide diversity, single nucleotide variant density analysis, minor allele frequency, observed and expected heterozygosity, and the inbreeding coefficient. Selection signatures were identified via Tajima's D, the integrated haplotype score (iHS), the fixation index, and the cross-population extended haplotype homozygosity test for autosomes. Additionally, the analysis of selection signatures using Tajima's D and iHS was conducted for the X chromosome. Lower genetic diversity was observed in the HOL population, whereas the GIR_IN and GIR_BR populations presented greater diversity. Several genes previously related to economically important traits were identified as being under selection, including DNAJC18, FSHR, HELB, HMGA2, PLAG1, GAB3, and PTEN. In conclusion, the genes identified within the selection signatures were linked to several traits, including growth, reproduction, mastitis, milk production, heat tolerance, health, and adaptation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".