Distribution of genes related to plant height, kernel weight and disease resistance among wheat cultivars from major countries
Bibliographic record
Abstract
Using the molecular markers for dwarfing genes(Rht-B1b and Rht-D1b)and kernel weight related genes(TaCwi-A1a and Hap-6A-A) and Lr34/Yr18/Pm38,the distributions of these genes were detected among 745 cultivars from 21 major wheat-growing countries.The results indicated that:(1) The frequencies of Rht-B1b and Rht-D1b in 745 cultivar were 42.1% and 28.7%,respectively,varying much in different wheat-growing countries.Generally,cultivars from the same country usually carried only one of Rht-B1b and Rht-D1b,frequencies only from Italy and Australia were higher for both Rht-B1b and Rht-D1b,which in high latitude areas such as Canada and Russia were lower due to less requirement for plant height.(2) TaCwi-A1a allele was widely distributed in 21 countries with a total frequency of 78.4%,and cultivars from different countries possessed all higher frequency of TaCwi-A1a except for Japan(50.0%),Germany(45.3%) and Chile(48.8%).The 29.3% of cultivars carried Hap-6A-A allele at TaGW2-6A locus,mainly distributing in spring and weak winter wheat,whereas Hap-6A-G was mainly present in winter and strong winter cultivars.(3) The 22.1% of cultivars had Lr34/Yr18/Pm38 allele,with higher frequency in USA(18.5%),Ukraine(28.6%),Russia(26.1%),Iran(20.0%),Turkey(34.8%),Hungary(50.0%),Bulgaria(38.9%),Romania(87.0%),Japan(80.0%),Canada(34.6%) and Australia(44.6%).(4)The molecular markers CWI 21 and CWI 22 for TaCwi-A1 can well differentiate TaCwi-A1a and TaCwi-A1b alleles,while the CAPS marker of TaGW2-6A can also be used for kernel weight selection due to its ability of discriminating Hap-6A-A and Hap-6A-G with great accuracy and repeatability.The information is very crucial for use of exotic germplasm in Chinese wheat breeding program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".