Integrative Omics reveals genetic basis and TaMYB7-A1’s function in wheat WUE and drought resilience
Bibliographic record
Abstract
Abstract Improving water use efficiency (WUE) and drought resistance in wheat is critical for ensuring global food security under changing climate conditions. Here, we integrated multi-omic data, including population-scale phenotyping, transcriptomics, and genomics, to dissect the genetic and molecular mechanisms underlying WUE and drought resilience in wheat. Genome-wide association studies (GWAS) revealed 8,135 SNPs associated with WUE-related traits, identifying 258 conditional and non-conditional QTLs, many of which co-localized with known drought-resistance genes. Pan-transcriptome analysis uncovered tissue-specific expression patterns, core and unique gene functions, and dynamic sub-genomic biases in response to drought. eQTL mapping pinpointed 146,966 regulatory loci, including condition-specific hotspots enriched for genes involved in water regulation, osmoregulation, and photosynthesis. Integration of Weighted gene co-expression network analysis (WGCNA), Summary-data-based Mendelian Randomization (SMR) and GWAS, eQTLs identified 207 candidate causal genes as key regulators for WUE-related traits in wheat, such as TaMYB7-A1. Functional analyses found that TaMYB7-A1 enhances drought tolerance by promoting root growth, reducing oxidative stress, and improving osmotic regulation, enabling better water access and survival under stress. It also increases photosynthesis efficiency and WUE, boosting yield under drought without compromising performance in well-watered conditions, making it ideal target for breeding. Our findings provide a comprehensive omic framework for understanding the genetic architecture of WUE and drought resistance, offering valuable targets for breeding resilient wheat varieties.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".