Genetic diversity and positive signatures of selection in indigenous cattle breeds of Iran
Bibliographic record
Abstract
Animal domestication, climate changes over time, and artificial selection have played significant roles in shaping the genome structure of various animal species, including cattle. These processes have led to the emergence of several indigenous cattle breeds with distinct genetic characteristics. This study focused on unraveling the genetic diversity and identifying candidate genomic regions in eight indigenous cattle breeds of Iran. The data consisted of ∼777 962 single nucleotide polymorphisms (SNPs) of 89 animals from Iranian indigenous cattle scattered throughout the country. We employed various methods, including integrated haplotype score, FST, and cross-population composite likelihood ratio, to conduct a genome scan for detecting selection signals within and between cattle populations. Average observed heterozygosity across the populations was 0.36, with a range of 0.32–0.40. In addition, negative and low rates of inbreeding (FIS) in the populations were observed. The genome-wide analysis revealed several genomic regions that harbored candidate genes associated with production traits (e.g., MFSD1, TYW5, ADRB2, BLK, and CRTC3), adaptation to local environmental constraints (CACNA2D1, CXCL3, and GRO1), and coat color (DYM). Finally, the study of the reported quantitative trait loci (QTL) regions in the cattle genome demonstrated that the identified regions were associated with QTL related to important traits such as milk composition, body weight, daily gain, feed conversion, and residual feed intake. Overall, this study contributes to a better understanding of the genetic diversity and potential candidate genes underlying important traits in Iranian indigenous cattle breeds, which can inform future breeding and conservation efforts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".