Copy Number Deletion Has Little Impact on Gene Expression Levels in Racehorses
Bibliographic record
Abstract
Copy number variations (CNVs), important genetic factors for study of human diseases, may have as large of an effect on phenotype as do single nucleotide polymorphisms. Indeed, it is widely accepted that CNVs are associated with differential disease susceptibility. However, the relationships between CNVs and gene expression have not been characterized in the horse. In this study, we investigated the effects of copy number deletion in the blood and muscle transcriptomes of Thoroughbred racing horses. We identified a total of 1,246 CNVs of deletion polymorphisms using DNA re-sequencing data from 18 Thoroughbred racing horses. To discover the tendencies between CNV status and gene expression levels, we extracted CNVs of four Thoroughbred racing horses of which RNA sequencing was available. We found that 252 pairs of CNVs and genes were associated in the four horse samples. We did not observe a clear and consistent relationship between the deletion status of CNVs and gene expression levels before and after exercise in blood and muscle. However, we found some pairs of CNVs and associated genes that indicated relationships with gene expression levels: a positive relationship with genes responsible for membrane structure or cytoskeleton and a negative relationship with genes involved in disease. This study will lead to conceptual advances in understanding the relationship between CNVs and global gene expression in the horse.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".