Structural Variations Drive Phenotypic Divergence in Upland and Pima Cotton
Bibliographic record
Abstract
Structural variations (SVs) are major genomic alterations that contribute to species diversity and phenotypic differentiation. In this review, we explored how SVs drive the phenotypic divergence between Upland cotton ( Gossypium hirsutum ) and Pima cotton ( Gossypium barbadense ), two economically significant species with distinct fiber characteristics. We first summarized advances in sequencing technologies that facilitate the detection and characterization of SVs and analyzed their types, frequency, and lineage-specific patterns across cotton genomes. We then discussed the functional impact of SVs on gene expression, dosage, and regulation, emphasizing their role in modifying fiber traits, stress tolerance, yield, and plant architecture. Mechanistic insights revealed that transposable elements, homologous recombination, and epigenetic modifications are key forces shaping SV formation and genome plasticity. A case study on a major inversion on chromosome A07 further demonstrated how SVs influence fiber quality and provide new opportunities for marker-assisted selection. Finally, we highlighted the integration of SV data into breeding and genome-editing programs to enhance cotton improvement. This review underscores the central role of structural variations in cotton evolution and breeding innovation, offering a genomic foundation for future research on trait diversification and molecular breeding strategies.
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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) | 0.001 | 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".