Use of microsatellite markers for the assessment of bambara groundnut breeding system and varietal purity before genome sequencing
Bibliographic record
Abstract
Maximizing the research output from a limited investment is often the major challenge for minor and underutilized crops. However, such crops may be tolerant to biotic and abiotic stresses and are adapted to local, marginal, and low-input environments. Their development through breeding will provide an important resource for future agricultural system resilience and diversification in the context of changing climates and the need to achieve food security. The African Orphan Crops Consortium recognizes the values of genomic resources in facilitating the improvement of such crops. Prior to beginning genome sequencing there is a need for an assessment of line varietal purity and to estimate any residual heterozygosity. Here we present an example from bambara groundnut (Vigna subterranea (L.) Verdc.), an underutilized drought tolerant African legume. Two released varieties from Zimbabwe, identified as potential genotypes for whole genome sequencing (WGS), were genotyped with 20 species-specific SSR markers. The results indicate that the cultivars are actually a mix of related inbred genotypes, and the analysis allowed a strategy of single plant selection to be used to generate non-heterogeneous DNA for WGS. The markers also confirmed very low levels of heterozygosity within individual plants. The application of a pre-screen using co-dominant microsatellite markers is expected to substantially improve the genome assembly, compared to a cultivar bulking approach that could have been adopted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".