Genetic diversity and population structure analysis in Asparagus officinalis
Bibliographic record
Abstract
Asparagus cultivars grown worldwide are thought to have originated from a limited genetic base, however, selection has resulted in variation for climatic adaptation and other traits. Understanding genetic diversity of the crop is important to guide breeding decisions. The objectives of this research were to identify SNPs among 64 cultivated lines from different geographic areas and assess genetic variation, population structure and linkage disequilibrium. More than 55,000 SNPs were identified by GBS and subjected to filtration for minor allele frequency and missing data, resulting in 12,886 markers for all subsequent analysis. Markers exhibited a wide range of Expected Heterozygosity (He), Polymorphic Information Content (PIC) and Observed Heterozygosity (Ho) with mean values of 0.370, 0.310, and 0.450 respectively. Population STRUCTURE analysis indicated that the 64 lines were grouped into two, four, seven, and nine subpopulations. For K = 4, four distinct groups were defined: (1) New Zealand, New Jersey, France, and California; (2) Canada; (3) China, The Netherlands, and Germany; and (4) England, Denmark, Spain, Turkey, and India. The results were further confirmed by PCA, and a phylogenetic tree. LD declined rapidly with an increase in physical distance. A considerable amount of genetic diversity was observed, despite previous suggestions that asparagus cultivars may have originated from one open-pollinated population.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".