Development of a Nested Association Mapping (NAM) population for untangling complex traits in lentil ( <i>Lens culinaris</i> Medik.)
Bibliographic record
Abstract
Abstract Understanding the genetic basis of complex traits remains a key challenge in crop improvement. This study aimed to develop a structured, multi-parental mapping population to enhance the resolution of quantitative trait dissection, based on the hypothesis that a Nested Association Mapping (NAM) design would enable the detection of minor-effect loci often overlooked by traditional biparental or diversity panel-based approaches. A lentil ( Lens culinaris Medik.) NAM population was developed by crossing the Canadian cultivar CDC Redberry with 32 diverse genotypes sourced globally from three major lentil-growing macro-environments: Northern temperate, Mediterranean, and South Asia. The resulting recombinant inbred lines were phenotyped for key phenological traits, days to emergence (DTE), flowering (DTF), and maturity (DTM), under field conditions, and genotyped using exome capture sequencing. Genome-wide association studies for DTF identified 14 significant loci across six chromosomes, including the known FTb locus and novel associations near AP3a, HUB2a , and PIF6 genes. These results demonstrate the utility of the NAM design in detecting both major and minor-effect loci that underlie complex trait variation. To our knowledge, this is the first publicly available NAM population in lentil. It provides a high-resolution, globally representative platform for trait discovery, pre-breeding, and collaborative genetic improvement of this nutritionally and agronomically important legume.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".