Multi-Trait GWAS for Fiber Quality and Disease Resistance in Cotton
Bibliographic record
Abstract
Cotton is a globally important dual-purpose crop valued for its fiber yield, but both its yield and quality are severely impacted by a variety of pathogens. This study reviews the genetic architecture of fiber quality traits (such as strength, length, and fineness) and resistance to major diseases such as Verticillium wilt, Fusarium wilt, and bacterial wilt, focusing on potential genetic overlap and independence. We explore the methodological framework for multi-trait genome-wide association studies (MT-GWAS), highlighting statistical models such as multivariate linear mixed models and Bayesian methods, which outperform single-trait analyses by capturing pleiotropic loci and genetic correlations. We present key findings from cotton MT-GWAS, including the identification of co-localized QTLs, novel candidate genes, and genotype-by-environment interactions across multiple environmental datasets. We also highlight the integration of MT-GWAS with transcriptomic, metabolomic, epigenomic, and proteomic data, and the validation of functional genes using CRISPR, RNAi, and overexpression technologies. A case study demonstrates the practical application of MT-GWAS in a breeding program targeting fiber quality and disease resistance, enabling genetic validation and germplasm improvement. While MT-GWAS faces challenges such as population structure, statistical complexity, and translational gaps, advances in high-resolution phenotyping, pan-genomics, and predictive breeding strategies hold promise for broader application. This study highlights the potential of MT-GWAS to accelerate cotton improvement by revealing complex trait architecture and informing integrated breeding processes.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".