Genome-wide analysis of sperm DNA methylation from monozygotic twin bulls
Bibliographic record
Abstract
Monozygotic (MZ) twins are of great interest to elucidate the contributions of pre- and postnatal environmental factors on epigenetics in the expression of complex traits and diseases. Progeny testing recently revealed that MZ twin bulls do not necessarily lead to identical genetic merit estimates (i.e. breeding values). Therefore, to explain differences in offspring productivity of MZ twin bulls despite their identical genetic backgrounds, we hypothesised that paternal sperm epigenomes vary between MZ twin bulls. In the present study, semen characteristics and global sperm DNA methylome were profiled for four pairs of MZ twin bulls. Some MZ twin pairs had divergent semen quality (sperm morphology, motility and viability). Comparative genome-wide DNA methylome surveys were performed using methyl-sensitive enrichment and microarray identification. Between 2% and 10% of all probes (400000) were differentially methylated between MZ twin pairs. In addition, there were 580 loci differentially methylated across all pairs of MZ twins. Furthermore, enrichment analysis indicated a significant enrichment for fertility associated quantitative trait loci (P=0.033). In conclusion, differences in the sperm epigenome may contribute to incongruous diverging performances of daughters sired by bulls that are MZ twins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".