Methodology: ssb-MASS: a single seed-based sampling strategy for marker-assisted selection in rice
Bibliographic record
Abstract
BACKGROUND: Integrated breeding approaches such as combining marker-assisted selection and rapid line fixation through single-seed-descent, can effectively increase the frequency of desirable alleles in a breeding program and increase the rate of genetic gain for quantitative traits by shortening the breeding cycle. However, with most genotyping being outsourced to 3rd party service providers' nowadays, sampling has become the bottleneck for many breeding programs. While seed-chipping as prevailed as an automatable seed sampling protocol in many species, the symmetry of rice seeds makes this solution as laborious and costly as sampling leaf tissue. The aim of this study is to develop, validate and deploy a single seed sampling strategy for marker-assisted selection of fixed lines in rice that is more efficient, cost-effective and convenient compared to leaf-based sampling protocols without compromising the accuracy of the marker-assisted selection results. RESULTS: ) with different physical and/or chemical properties were not significantly different. Nor were there significant differences between single seeds collected 15 days after panicle initiation compared to those harvested at maturity. A large-scale comparison between single seed and leaf-based methodologies showed not only high levels of genotypic concordance between both protocols (~ 99%) but also higher SNP call rates in single seed (99.24% vs. 97.5% in leaf). A cost-benefit analysis showed that this single seed sampling strategy decreased the cost of sampling fourfold. An advantage of this approach is that desirable genotypes can be selected before investing in planting activities reducing the cost associated with field operations. CONCLUSION: This study reports the development of a cost-effective and simple single seed genotyping strategy that facilitates the adoption and deployment of marker-assisted selection strategies in rice. This will allow breeders to increase the frequency of favorable alleles and combine rapid generation advancement techniques much more cost-effectively accelerating the process and efficiency of parental selection and varietal development.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".