QTL Mapping of Seed Fe Concentration in an Interspecific RIL Population Derived from <i>Lens culinaris</i> × <i>Lens ervoides</i>
Bibliographic record
Abstract
Abstract Biofortification of lentil ( Lens culinaris Medik.) was investigated to potentially increase bioavailable iron (Fe) in the human diet. This study assessed the genetic variation for seed Fe concentration (SFeC) and identified the genomic regions associated with SFeC in an interspecific mapping population derived from crossing between L. culinaris cv. ‘Eston’ and L. ervoides accession IG 72815. A total of 134 RILs were evaluated in three environments. The SFeC data for individual environments and best linear unbiased prediction (BLUP) of the SFeC across environments were used for QTL analysis. The seeds of the RILs exhibited variation for SFeC from 47.0 to 102.9 mg kg -1 and several RILs showed transgressive segregation for SFeC. QTL analysis identified two QTLs on chromosomes 2 and 6 that accounted for 11.9-14.0% and 12.5-20.5%, respectively, of the total phenotypic variation for SFeC. The SNP markers linked to the identified QTLs may prove useful for increasing SFeC via marker-assisted selection. RILs with high SFeC can be incorporated into the lentil breeding program to broaden the genetic base of the breeding pool and/or used for the development of genetic resources for future genomic studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".