Quantitative trait loci analysis of seed coat color components for selective breeding in chickpea (<i>Cicer arietinum</i>L.)
Bibliographic record
Abstract
Hossain, S., Panozzo, J. F., Pittock, C. and Ford, R. 2011. Quantitative trait loci analysis of seed coat color components for selective breeding in chickpea (Cicer arietinumL.). Can. J. Plant Sci. 91: 49–55. Chickpea (Cicer arietinum L.) is an annual grain legume, grown worldwide for human consumption with the potential to attract premium prices in markets such as India, Bangladesh and southern Asia. The ability to accurately select for seed coat color, an important export quality trait, would greatly benefit chickpea breeding programs. In order to determine the major genomic loci governing the color trait, the color components of CIE L* (luminance), CIE a* (red/green color) and CIE b* (blue/yellow color), C* (chroma or saturation of the color) and h° (hue or purity of the color) were mapped, and associated molecular markers were identified. A linkage map was constructed with 80 SSR markers distributed over 10 linkage groups at an average marker density of 2.8 cM. Two major quantitative trait loci (QTL), which accounted for up to 36 and 49% of the genetic variance and several smaller genetic effects were determined to govern the color components. These were consistent across two differing environments. Once validated, the markers that are close to and flanking these QTL and significantly associated with the minor gene effects will be useful in future color selective breeding programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".