Simple sequence repeat (SSR) markers reveal low levels of polymorphism between cotton (Gossypium hirsutum L.) cultivars
Bibliographic record
Abstract
Since their discovery in the 1980s microsatellite or simple sequence repeat (SSR) markers have been widely used in many species to generate relatively dense genetic maps or framework maps on which to anchor more abundant, but anonymous, markers such as amplified fragment length polymorphisms (AFLPs). They are typically highly polymorphic, robust, and often portable, particularly among different mapping populations or crosses and often to related species. They have been useful in species where low levels of genetic diversity limit the use of other markers. Cultivated cotton (Gossypium hirsutum L.) has a history of genetic bottlenecks that have considerably reduced its diversity, with the consequence that most molecular marker genetic linkage studies are done with inter-specific crosses. In this study we evaluated the potential for SSR markers to be used in marker-assisted selection (MAS) breeding in cotton by quantifying the level of polymorphism detected with a set of commercially available SSR markers between and within a collection of cotton cultivars being used in our breeding programs. Although the majority of these markers are polymorphic between the 2 tetraploid species of cotton, G. barbadense and G. hirsutum, they are not highly polymorphic (~5%) either among or within G. hirsutum cultivars. However, 6 of the 8 cultivars studied were found to be segregating for alleles of these SSR markers. This suggests that where polymorphisms exist, heterozygosity within cultivars is maintained by the breeding strategies adopted by many modern cotton breeders. Although SSRs clearly have utility in genetic studies using inter-specific crosses or in the introgression of wild germplasm, they will be more difficult to use for standard cotton breeding until greater numbers are available. The utility of some markers may be reduced in some breeding populations where heterozygosity remains in the parental material.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".