New STS molecular markers for assessment of genetic diversity and DNA fingerprinting in hop (Humulus lupulus L.)
Bibliographic record
Abstract
Molecular markers have been increasingly used in genetic studies of crop species for their applicability in breeding programs. In this work, we report on the development of new sequence-tagged site (STS) markers based on sequence information from several identified hop (Humulus lupulus L.) genes. We demonstrate the usefulness of these STS markers and compare them to SSRs for identifying hop genotypes and estimating genetic diversity in a collection of 68 hop cultivars from around the world. We found 3 individual gene variants (A, B, C) of the chs_H1 gene in this collection. The most frequent gene variant, B (AJ304877), was not detected in Mt. Hood, Glacier, and Horizon (US) cultivars. Gene variant A came from an American germplasm through wild hops. We found length polymorphism in intron 1 of the chs2 gene, and 4 different amplified markers were detected in PCRs. The chs3 gene was found in only one third of the cultivars. None of the variants of the studied CHS genes were found in Humulus japonicus. We detected 5 major gene variants of DNA-binding protein in the collection of H. lupulus cultivars and 2 others in H. japonicus. We also found 3 individual gene variants of an endochitinase gene. The distribution of gene variants did not correlate with any resistance. We proved that developed STS markers can be successfully used for the analysis of genetic diversity and can substitute and supplement SSR markers in hop.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".