Dissecting Seed Shape in Lentil (Lens culinaris Medik.) with High-Throughput Phenotyping and Genome-Wide Association Studies
Bibliographic record
Abstract
Seed shape is an important trait for dehulling and splitting during the milling process of lentil. A high-throughput image-based phenotyping system was previously developed to provide direct metrics from single seeds. This study aimed to take this new, and more precise system, to phenotype and dissect lentil seed shape and identify new genetic markers for the lentil breeding program. To accomplish this, seed from 324 diverse genotypes that had been grown at six site-years in two major lentil growing environments (temperate and Mediterranean) were imaged. Significant differences were identified between seeds from temperate and Mediterranean macro-environments for four seed shape parameters: diameter, circularity (measure of uniformity of the seed edges), height (seed thickness), and plumpness. Each seed parameter had high heritability, suggesting a low environmental influence, which is also supported by an absence of significant correlations with phenological parameters or temperature. A stability analysis of the seed shape traits across the six site-years revealed that seed height has fewer stable lines when compared with circularity, diameter, and plumpness and that for each of these traits there is a range of stability, making some genotypes more stable than others across site-years. Genome-wide association studies were used to identify QTL for most seed shape traits, with diameter having the most consistent identification of significant QTL. The new phenotyping system used in this study coupled with multi-locus GWAS models achieved similar results when compared with previous studies, although it can help breeding programs identify new candidate genes with improved precision by extracting multiple traits like seed shape and seed color simultaneously.
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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 | 0.000 |
| 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) | 0.000 | 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 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".