16 Identification of Structural Variants Associated with Mastitis in Holstein Dairy Cows Using Whole Genome Sequencing and RNA-sequencing
Bibliographic record
Abstract
Abstract For the dairy industry, animal welfare and economic viability are key factors in determining the long-term sustainability of the industry. One challenge in lactating dairy cows is mastitis, as there are significant costs associated with each mastitis case due to treatment, milk loss, and potential cow culling. Differentially expressed (DE) genes, DE mRNA isoforms and DE long non-coding RNA (lncRNA) candidates were previously identified by our group between healthy and mastitic samples from six Holstein dairy cows. In total, 7 candidate genes, 5 mRNA isoforms and 4 lncRNA were targeted as they play an important role due to their association with mastitis or the immune system. The aim of the current research is to identify new structural variants in the transcriptome of these previously identified potential candidates using RNA-Sequencing. These structural variants could occur in both coding and intergenic regions of the genome and potentially impact the amino acid that is transcribed, creating a new functional or non-functional protein. In addition, whole genome sequencing (WGS) analysis was performed using hair samples collected from ten Holstein dairy cows (first lactation). Five of these animals had no previous reports of mastitis and five animals had at least one report of mastitis in her lifespan. The WGS results will be used to identify additional structural variants, especially within introns, of the previously identified candidates that could cause a genetic variation in individuals and an association to their susceptibility to mastitis disease.
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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 | 0.000 |
| 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".