Marker–Trait Association Analysis of Iron and Zinc Concentration in Lentil ( <i>Lens culinaris</i> Medik.) Seeds
Bibliographic record
Abstract
Lentil ( Medik.) seeds are relatively rich in iron (Fe) and zinc (Zn), making lentil a potential crop to aid in the global battle against human micronutrient deficiency. Understanding the genetic basis for uptake of seed Fe and Zn is required to increase sustainable concentrations of these minerals in seeds. The objectives of this study were to characterize genetic variation in seed Fe and Zn concentration and to identify molecular markers associated with these traits across diverse lentil accessions. A set of 138 cultivated lentil accessions from 34 countries were evaluated in four environments (2 sites × 2 yr) in Saskatchewan, Canada. The collection was genotyped using 1150 single-nucleotide polymorphism (SNP) markers that are distributed across the lentil genome. The germplasm tested exhibited a wide range of variation for seed Fe and Zn concentration. The marker-trait association analysis detected two SNP markers tightly linked to seed Fe and one linked to seed Zn concentration (-log10 ≥ 4.36). Additional markers were detected at -log10 ≥ 3.06. A number of putative candidate genes underlying detected loci encode Fe- and Zn-related functions. This study provides insight into the genetics of seed Fe and Zn concentration of lentil and opportunities for marker-assisted selection to improve micronutrient concentration as part of micronutrient biofortification programs.
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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 | 0.001 |
| 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".