AFLP-based assessment of genetic diversity among nine alfalfa germplasms using bulk DNA templates
Bibliographic record
Abstract
Improving commercial utilization of perennial Medicago collections requires developing approaches that can rapidly and accurately characterize genetic diversity among large numbers of populations. This study evaluated the potential of using amplified fragment length polymorphism (AFLP) DNA markers, in combination with DNA bulking over multiple genotypes, as a strategy for high-throughput characterization of genetic distances (D) among alfalfa (Medicago sativa L.) accessions. Bulked DNA templates from 30 genotypes within each of nine well-recognized germplasms (African, Chilean, Flemish, Indian, Ladak, Medicago sativa subsp. falcata, Medicago sativa subsp. varia, Peruvian, and Turkistan) were evaluated using 34 primer combinations. A total of 3754 fragments were identified, of which 1541 were polymorphic. The number of polymorphic fragments detected per primer combination ranged from 20 to 85. Pairwise D estimates among the nine germplasms ranged from 0.52 to 1.46 with M. sativa subsp. falcata being the most genetically dissimilar. Unweighted pair-group method arithmetic average (UPGMA) analysis of the marker data produced two main clusters, (i) M. sativa subsp. sativa and M. sativa subsp. varia, and (ii) M. sativa subsp. falcata. Cluster-analysis results and D estimates among the Chilean, Peruvian, Flemish, and M. sativa subsp. varia germplasms supported the hypothesis that Peruvian was more similar to original Spanish introductions into Central and South America than Chilean. Hierarchical arrangement of the nine germplasms was supported by their respective geographic, subspecific, and intersubspecific hybrid origins. Subsets of as few as seven highly informative primer pairs were identified that produced comparable D estimates and similar heirarchical arrangements compared with the complete dataset. The results indicate that use of primer-pair subsets for AFLP analysis of bulk DNA templates could serve as a high-throughput system for accurately characterizing genetic diversity among large numbers of alfalfa populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".