Genetic diversity and population structure of Pisum sativum accessions for marker-trait association of lipid content
Bibliographic record
Abstract
Field pea ( Pisum sativum L.) is an important protein-rich pulse crop produced globally. Increasing the lipid content of Pisum seeds through conventional and contemporary molecular breeding tools may bring added value to the crop. However, knowledge about genetic diversity and lipid content in field pea is limited. An understanding of genetic diversity and population structure in diverse germplasm is important and a prerequisite for genetic dissection of complex characteristics and marker-trait associations. Fifty polymorphic microsatellite markers detecting a total of 207 alleles were used to obtain information on genetic diversity, population structure and marker-trait associations. Cluster analysis was performed using UPGMA to construct a dendrogram from a pairwise similarity matrix. Pea genotypes were divided into five major clusters. A model-based population structure analysis divided the pea accessions into four groups. Percentage lipid content in 35 diverse pea accessions was used to find potential associations with the SSR markers. Markers AD73, D21, and AA5 were significantly associated with lipid content using a mixed linear model (MLM) taking population structure (Q) and relative kinship ( K ) into account. The results of this preliminary study suggested that the population could be used for marker-trait association mapping 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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".