DNA methylation profiles in bovine sperm are associated with daughter fertility
Bibliographic record
Abstract
The current decline in dairy cattle fertility has resulted in significant financial losses for dairy farmers. In the past, most efforts to improve dairy cattle fertility have been focused on either management or genetics, while epigenetics have received less attention. In this study, 12 bulls were selected from a provided 100 bull list and studied (High daughter fertility = 6, Low daughter fertility = 6) for Enzymatic methylation sequencing in the Illumina HiSeq platform according to the Canadian daughter fertility index (DFI), sires with high and low daughter fertility have average DFI of 92 and 112.6, respectively. And the bull list provided shows a mean DFI of 103.4. 252 CpGs with methylation differences greater than 20% (q < 0.01) were identified, as well as the top 10 promising DMRs with a 15% methylation difference (q < 1.1e−26). Interestingly, the DMCs and DMRs were found to be distributed more on the X chromosome than on the autosome, and they were covered by gene clusters linked to germ cell formation and development. In conclusion, these findings could enhance our ability to make informed decisions when deciding on superior bulls and advance our understanding of paternal epigenetic inheritance.
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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 | 0.000 | 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".