Analysis of cross-population differentiation between Thoroughbred and Jeju horses
Bibliographic record
Abstract
OBJECTIVE: This study was intended to identify genes positively selected in Thoroughbred horses (THBs) that potentially contribute to their running performances. METHODS: The genomes of THB and Jeju horses (JH, Korean native horse) were compared to identify genes positively selected in THB. We performed cross-population extended haplotype homozygosity (XP-EHH) and cross-population composite likelihood ratio test (XP-CLR) statistical methods for our analysis using whole genome resequencing data of 14 THB and 6 JH. RESULTS: We identified 98 (XP-EHH) and 200 (XP-CLR) genes that are under positive selection in THB. Gene enrichment analysis identified 72 gene ontology biological process (GO BP) terms. The genes and GO BP terms explained some of THB's characteristics such as immunity, energy metabolism and eye size and function related to running performances. GO BP terms that play key roles in several cell signaling mechanisms, which affected ocular size and visual functions were identified. GO BP term Eye photoreceptor cell differentiation is among the terms annotated presumed to affect eye size. CONCLUSION: Our analysis revealed some positively selected candidate genes in THB related to their racing performances. The genes detected are related to the immunity, ocular size and function, and energy metabolism.
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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".