New genetic resources in wheat breeding for an increased grain protein content
Bibliographic record
Abstract
The present review offers an overview of genetic research on grain protein content (GPC) in various Triticum L. and Aegilops L. species. Regularities in geographic variability of GPC and the results of a longterm screening of accessions from the VIR collection for this trait are considered. On the basis of these assessments, a core-collection of genetic sources with high GPC has been formed. It includes the diploid Aegilops species as donors of B, G and D genomes for allopolyploid wheats, as well as accessions of di-, tetra- and hexaploid wheat species. The use of highprotein sources in wheat breeding in the United States and Canada in the 1970’s–1980’s resulted in the bread wheat GPC increase by 0.5–3.0 %; however, further purposeful attempts at increasing GPC by traditional breeding methods failed. A breakthrough in increasing the total GPC has been achieved as a result of molecular genetics methods and molecular markers development. For the first time, a functional locus, or the Gpc-B1 gene (chromosome 6BS) affecting the accumulation of protein, Zn and Fe in grain, was identified in T. dicoccoides, cloned and studied in detail. The application of molecular markers has revealed the active allele of this gene in some landraces and old cul-tivars of T. dicoccum, T. durum, T. spelta and T. aestivum. Moreover, Gpc-A1, Gpc-D1, and Gpc-2 wheat genes have been found in chromosomes 6A, 6D and homeologous group 2, respectively. All these genes have been identified as NAC transcription factors, which play an important role in the accelerated senescence of plants and remobilization of nutrients from leaves to grain. The genes related to Gpc-B1 from T. dicoccoides were found in the G genome of T. timopheevii and B (=S) genome of different species of Aegilops sect. sitopsis. Functional Gpc-B1 alleles have been introduced into commercial tetra- and hexaploid wheat cultivars, and it resulted in the creation of new highprotein and high-yield cultivars and series of nearly isogenic lines in different countries. They are promising sources for research and wheat breeding purposes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".