Pan-Genome Analysis Reveals Genetic Diversity and Subgenome Dominance in Cotton
Bibliographic record
Abstract
The pan-genome concept has emerged as a powerful framework for understanding genome variability within a species, providing crucial insights into genetic diversity, adaptation, and evolution in plants. In this study, we review the landscape of cotton ( Gossypium spp.) genomes through the lens of pan-genomics, with a particular focus on the role of polyploidy and subgenome dynamics. We explore the structural evolution of diploid and polyploid cotton genomes, the composition of core and dispensable genes, and the presence of lineage-specific genes and structural variants across cultivars and wild relatives. Our analysis highlights how pan-genome studies have uncovered key agronomically relevant genes absent in reference genomes and revealed extensive gene presence/absence variation (PAV), SNPs, InDels, and CNVs that contribute to trait diversity. We also examine expression bias and subgenome dominance in allopolyploid cotton, revealing regulatory asymmetries that influence fiber development, stress responses, and reproductive traits. A focused case study on Gossypium hirsutum demonstrates the integration of genomic data from diverse accessions and the discovery of elite trait-associated genes. Finally, we discuss the implications of cotton pan-genomics for molecular breeding, biotechnology, and the development of high-yield, stress-tolerant varieties. This review underscores the transformative potential of pan-genome resources in shaping next-generation cotton improvement 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".