Exploiting sorghum genetic diversity for enhanced aluminum tolerance: Allele mining based on the AltSB locus
Bibliographic record
Abstract
Abstract Root damage due to aluminum (Al) toxicity restricts crop production on acidic soils, which are extensive in the tropics. The sorghum root Al-activated citrate transporter, SbMATE, underlies the Al tolerance locus,AltSB, and increases grain yield under Al toxicity. Here,AltSBloci associated with Al tolerance were converted intoAmplification Refractory Mutation System(ARMS) markers, which are cost effective and easy to use. A DNA pooling strategy allowed us to identify accessions harboring rare favorableAltSBalleles in three germplasm sets while greatly reducing genotyping needs. Population structure analysis revealed that favorableAltSBalleles are predominantly found in subpopulations enriched with guinea sorghums, supporting a possible Western African origin ofAltSB. The efficiency of allele mining in recovering Al tolerance accessions was the highest in the largest and highly diverse germplasm set, with a 10-fold reduction in the number of accessions that would need to be phenotyped in the absence of marker information. Finally, Al tolerant accessions were found to rely onSbMATEto exclude Al3+from sensitive sites in the root apex. This study emphasizes gene-specific markers as important tools for efficiently mining useful rare alleles in diverse germplasm, bridging genetic resource conservation efforts and pre-breeding for Al tolerance.
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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 | 0.000 | 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".