Investigating Interspecific Lentil Germplasm: Near-Infrared Spectroscopy for Protein and Amino Acid Contents and Quantitative Trait Loci Analyses
Bibliographic record
Abstract
Cultivated lentil (Lens culinaris Medik.) has a relatively narrow genetic base which poses many challenges to the improvement of the crop. Introgressing traits like biotic and abiotic stress resistance from wild relatives is often hindered by linkage drag of undesirable traits. Lentil is a nutritious staple crop, rich in protein, complex carbohydrates, vitamins, and minerals. This study aimed to investigate the effects of crossing with wild lentil on seed protein and amino acid contents. An interspecific recombinant inbred line (RIL) population, LR-68, generated from a cross between Lens culinaris and Lens orientalis was grown in four site-years in Saskatchewan, Canada. LR-68 was evaluated for protein and 18 amino acid contents using near-infrared (NIR) spectroscopy. Quantitative trait loci (QTL) analyses were then conducted to identify regions of the genome associated with protein and amino acid contents. LR-68 exhibited significant variation for protein and amino acid content, with protein content ranging from 24.9 to 34.9% on a dry basis. Protein and amino acid contents were highly correlated with each other. The correlations between protein content and days to flower, days to maturity, seed size, and seed shape were minor. Protein and amino acid contents were significantly affected by genotype, environment, and genotype by environment interaction. Genetic analyses revealed three QTL dense regions, with QTL for protein and multiple amino acids present at the same loci. At two of the three QTL dense regions, genotypes containing the allele from the wild parent, L. orientalis, had significantly higher protein and amino acid contents. Selecting for increased protein content when crossing with L. orientalis will also increase individual amino acid contents.
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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 | 0.000 | 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".