A selection signatures study among Middle Eastern and European sheep breeds
Bibliographic record
Abstract
Abstract Selection, both natural and artificial, leaves patterns on the genome during domestication of animals and leads to changes in allele frequencies among populations. Detecting genomic regions influenced by selection in livestock may assist in understanding the processes involved in genome evolution and discovering genomic regions related to traits of economic and ecological interests. In the current study, genetic diversity analyses were conducted on 34,206 quality‐filtered SNP positions from 450 individuals in 15 sheep breeds, including six indigenous breeds from the Middle East, namely Iranian Balouchi, Afshari, Moghani, Qezel, Karakas and Norduz, and nine breeds from Europe, namely East Friesian Sheep, Ile de France, Mourerous, Romane, Swiss Mirror, Spaelsau, Suffolk, Comisana and Engadine Red Sheep. The SNP genotype data generated by the Illumina OvineSNP50 Genotyping BeadChip array were used in this analysis. We applied two complementary statistical analyses, FST(fixation index) and xp‐EHH (cross‐population extended haplotype homozygosity), to detect selection signatures in Middle Eastern and European sheep populations. FSTand xp‐EHH detected 629 and 256 genes indicating signatures of selection, respectively. Genomic regions identified using FSTand xp‐EHH contained the CIDEA, HHATL, MGST1, FADS1, RTL1 and DGKG genes, which were reported earlier to influence a number of economic traits. Both FSTand xp‐EHH approaches identified 60 shared genes as the signatures of selection, including four candidate genes (NT5E, ADA2, C8A and C8B) that were enriched for two significant Gene Ontology (GO) terms associated with the adenosine metabolic procedure. Knowledge about the candidate genomic regions under selective pressure in sheep breeds may facilitate identification of the underlying genes and enhance our understanding on these genes role in local 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 | 0.001 | 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".