Polymorphism of PCR-based markers targeting exons, introns, promoter regions, and SSRs in maize and introns and repeat sequences in oat
Bibliographic record
Abstract
Sequence databases could be efficiently exploited for development of DNA markers if it were known which gene regions reveal the most polymorphism when amplified by PCR. We developed PCR primer pairs that target specific regions of previously sequenced genes from Avena and Zea species. Primers were targeted to amplify 40 introns, 24 exons, and 23 promoter regions within 54 maize genes. We surveyed 48 maize inbred lines (previously assayed for simple-sequence repeat (SSR) polymorphism) for amplification-product polymorphism. We also developed primers to target 14 SSRs and 12 introns within 18 Avena genes, and surveyed 22 hexaploid oat cultivars and 2 diploid Avena species for amplification-product polymorphism. In maize, 67% of promoter markers, 58% of intron markers, and 13% of exon markers exhibited amplification-product polymorphisms. Among polymorphic primer pairs in maize, genotype diversity was highest for SSR markers (0.60) followed by intron markers (0.46), exon markers (0.42), and promoter markers (0.28). Among all Avena genotypes, 64% of SSR markers and 58% of intron markers revealed polymorphisms, but among the cultivars only, 21% of SSR markers and 50% of intron markers were polymorphic. Polymorphic-sequence-tagged sites for plant-breeding applications can be created easily by targeting noncoding gene regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".