Kabuli chickpea seed quality diversity and preliminary genome‐wide association study identifies markers and potential candidate genes
Bibliographic record
Abstract
Abstract Malnutrition due to macro‐ and micro‐nutrient deficiencies is one of the major global health concerns, especially in developing countries. Using genomics‐assisted breeding to enhance the nutritional value of important crops such as chickpea (Cicer arietinum L.) can help to address the problem. In this study, we conducted genome‐wide association studies to identify genes associated with protein, starch, oil, and fiber in chickpea to create resources to speed the breeding process. The USDA kabuli chickpea mini‐core of 88 accessions was genotyped using genotyped‐by‐sequencing, and 36,645 single nucleotide polymorphisms (SNPs) were identified across the eight chromosomes of the chickpea genome. A genome‐wide marker‐trait analysis using the FarmCPU model was conducted to identify SNP markers that can enable marker‐assisted breeding for seed protein, fiber, oil, and starch concentrations. The most significantly associated markers for seed protein concentration (p = 8.82E‐12), starch (p = 2.79E‐12), fiber (p = 7.65E‐12), and oil (p = 1.37E‐08) were found on chromosomes 1, 2, 6, and 7, controlling 11%, 12%, 20%, and 16% of the phenotypic variation, respectively. Validation of the SNP markers in a broader set of plant genetic resources and environments will be needed to determine their usefulness in breeding for end‐use characteristics.
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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 | 0.001 | 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) | 0.002 | 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".