Genetic diversity and selection signatures in sheep breeds
Bibliographic record
Abstract
Natural and artificial selection in domesticated animals can cause specific changes in genomic regions known as selection signatures. Our study used the integrated haplotype score (iHS) and Tajima's D tests within non-overlapping windows of 100 kb to identify selection signatures, in addition to genetic diversity and linkage disequilibrium estimates in 9498 sheep from breeds in Ireland (Belclare, Charollais, Suffolk, Texel, and Vendeen). The mean observed and expected heterozygosity for all the sheep breeds were 0.353 and 0.355, respectively. Suffolk had the least genetic variation and, along with Texel, had slower linkage disequilibrium decay. iHS and Tajima's D detected selection signatures for all breeds, with some regions overlapping, thus forming longer segments of selection signatures. Common selection signatures were identified across iHS and Tajima's D methods for all breeds, with Belclare and Texel having several common regions under positive selection. Several genes were detected within the selection signature regions, including ITGA4, TLR3, and TGFB2 related to the immune system against endoparasites; DLG1, ROBO2, MXI1, MTMR2, CEP57, and FAM78B related to reproductive traits; WDR70 related to milk traits; SCHM1 and MYH15 related to meat traits; and TAS2R4, TAS2R39, and TAS2R40 related to adaptive traits. In conclusion, our results demonstrated moderate genetic diversity in the sheep breeds and detected and characterized selection signatures harboring genes associated with reproductive traits, milk production, meat production, and adaptive traits such as endoparasite resistance.
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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) | 0.000 | 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".