Genetic diversity of Asian and European common wheat lines assessed by fluorescence in situ hybridization
Bibliographic record
Abstract
Understanding the genetic diversity of wheat is important for wheat breeding and improvement. However, there have been limited attempts to evaluate wheat diversity using fluorescence in situ hybridization (FISH). In this study, the chromosomal structures of 149 wheat accessions from 13 countries located between the latitudes of 30°N and 45°N, the principal growing region for wheat, were characterized using FISH with pTa535 and pSc119.2 probes. The ranges of the numbers of FISH types in the A-, B-, and D-genome chromosomes were 2-8, 3-7, and 2-4, respectively, and the average numbers in the A and B genomes were greater than in the D genome. Chromosomal translocations were detected by these probes, and previously undescribed translocations were also observed. Using the FISH, the genetic relationships among the 149 common wheat lines were divided into three groups (G1, G2, and G3). G1 mainly consisted of southern European lines, G2 consisted of most lines from Japan and some lines from western Asia, China, and Korea, and G3 consisted of the other lines from southern Europe and most of the lines from western Asia, China, and Korea. FISH karyotypes of wheat chromosomes distinguished chromosomal structural variations, revealing the genetic diversity among wheat varieties. Furthermore, these results provide valuable information for the further genetic improvement of wheat in China.
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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 | 0.000 |
| 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".