Routine Karyotyping Reveals Frequent Mosaic Reciprocal Chromosome Translocations in Swine: Prevalence, Pedigree, and Litter Size
Bibliographic record
Abstract
In the routine commercial karyotype analysis on 5,481 boars, we identified 32 carriers of mosaic reciprocal translocations, half of which were carrying a specific recurrent translocation, mos t(7;9). An additional 7 mosaic translocations were identified through lymphocyte karyotype analysis from parents and relatives of mosaic carriers (n = 45), a control group of non-carrier boars (n = 73), and a mitogen assessment study (n = 20), bringing the total number of mosaic carriers to 39 cases. Mosaic translocations in all carriers were recognized to be confined to hematopoietic cells as no translocations were identified in fibroblasts cells of the carriers. In addition, negative impact on reproduction was not observed as the fertility of the carriers and their relatives were comparable to breed averages, and cryptic mosaicism was not detected in the family tree. This paper presents the first study of mosaic reciprocal translocations identified in swine through routine screening practices on reproductively unproven breeding boars while presenting evidence that these type of chromosome abnormalities are not associated with any affected phenotype on the carrier animals. In addition, the detection of recurrent mosaic translocations in this study may emphasize the non-random nature of mosaic rearrangements in swine and the potential role of genomic elements in their formation.
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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) | 0.001 | 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".