Whole-genome resequencing of Ujumqin sheep to investigate the determinants of the multi-vertebral trait
Bibliographic record
Abstract
The Ujumqin sheep is one of the most profitable breeds in China, with unique multi-vertebral characteristics. We performed high-throughput genome resequencing of five multi-vertebral and three non-multi-vertebral sheep in an Ujumqin population. We identified the genomic regions that correlated with the germplasm characteristics to establish the cause of the "multi-vertebral" phenotype in this breed. Sequencing generated a total of 314 952 000 000 bp of raw data. The alignment rate of all the samples was between 98.53% and 99.11%, and the mean depth of coverage relative to the reference genome was between 11.58× and 14.92×. After comparing the differences between the two groups, we identified 21 homozygous single nucleotide polymorphisms (SNPs) in the mutant exons of 14 genes. Nineteen loci of 10 genes contained nonsynonymous mutations, while two loci contained synonymous mutations. Resequencing revealed homozygous mutations comprised of 44 indels located within exons of 19 genes. These indels included 37 frameshift mutations, 6 non-frameshift mutations, and 1 stopgain single nucleotide variation (SNV). Finally, comparisons of genotypic variations revealed 17 genes with homozygous mutations in their coding regions, 5 of which have previously been associated with vertebral development and the remaining 12 genes were newly identified in this study.
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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) | 0.000 | 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".