Genetic dissection of transpiration efficiency and shoot traits in sorghum reveals genetic loci relevant to drought tolerance
Bibliographic record
Abstract
Sorghum serves as a critical crop for drought-prone regions. Enhancing its water use efficiency and biomass production is a key priority for sustainable agriculture, yet the genetic mechanisms underlying these traits remain poorly understood. Hence, we evaluated 284 sorghum lines under greenhouse conditions and performed SNP genotyping to identify genetic loci associated with key drought adaptive traits, including shoot transpiration efficiency (STE), shoot biomass, chlorophyll content and leaf characters. All traits showed significant variation and high heritability, indicating strong genetic control. Population structure analysis revealed five ancestral subpopulations with genetic admixture among the lines. A genome-wide association study (GWAS) using six multi-locus models identified 29 reliable markers, of which 19 were novel. Four markers were associated with shoot transpiration efficiency (STE), and genotypes ETSC17129–6–1 and ETSC17300–4–1 exhibited high STE while carrying favorable alleles at all four loci. From several pleiotropic markers identified, marker S9_57480917, linked to a GATA transcription factor implicated in stomatal regulation, showed pleiotropic effects on both transpiration efficiency and shoot dry weight, underscoring its potential role in coordinating drought-responsive traits. Additionally, shoot biomass-related markers co-localized with genes involved in energy metabolism (Phosphofructokinase) and stress signaling (SnRK2 kinase). Similarly, markers associated with chlorophyll content and leaf traits were linked to genes implicated in stress signaling and developmental pathways, including PDCD4, LEA proteins, and Cytochrome P450. These findings advance our understanding of the genetic architecture underlying drought-adaptive traits in sorghum and provide both molecular markers and promising genotypes to support genomics-assisted breeding for improved water-use efficiency and biomass productivity.
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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".