Exploration of gene presence/absence variations in <i>Oncorhynchus mykiss</i> and their differentiation between wild and selection populations
Bibliographic record
Abstract
Gene presence/absence variations (PAVs) have been considered as the important determinants of genome evolution and phenotypic diversity. However, studies on gene PAVs have been poorly documented, especially in fishes. In the present study, the pan-genome of rainbow trout was constructed based on 268 whole-genome re-sequencing accessions (4.38 Tb data). It recovered an additional 62 Mb sequences and 1288 protein-coding genes. Then, 9831 (22.77%) gene PAVs were genotyped across the 268 individuals. PAV-based PCA analysis, together with phylogenetic topology and STRUCTURE, revealed the clear separation among the different wild and selection populations. Additionally, a PAV-based genome-wide association study (GWAS) identified three candidate PAVs significantly associated with artificial selection. Meanwhile, fixation index analysis revealed 35 PAVs with significant frequency differences between wild and selection populations in Canada, while 15 candidate PAVs were detected between the populations in America. Their biological functions have been reported to participate in the regulation of growth performance and stress response. The present study deepens our understanding of widespread gene PAVs and facilitates the identification of key candidates that contribute to important traits.
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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".