Genetics of Height and Branching in Faba Bean (Vicia faba)
Bibliographic record
Abstract
A better understanding of the genetics of plant architecture, including height and branching, could improve faba bean breeding for varieties with better fit into specific cropping systems. This study aimed to determine the inheritance and genetic interactions of the sources of the dwarf gene (dwf1) and semi-dwarf gene (dwarf1), and to investigate the genetics of branching in the faba bean. We chose inbred lines IG 12658 (dwarf, carrying dwf1) and Rinrei (semi-dwarf, carrying dwarf1) along with Aurora/2 and IG 114476 as sources of non-dwarf faba bean genotypes and crossed them (Aurora/2 × IG 12658, IG 114476 × IG 12658, Rinrei × IG 12658, IG 114476 × Rinrei, and Rinrei × Aurora/2). IG 114476 was also used as a genetic source of a highly branching phenotype and crossed with IG 12658, Rinrei, and Aurora/2 to study the genetics of branching. Parental lines, F1s, and F2 populations were evaluated under growth chamber and field conditions in 2018. The segregating F2 populations were tested for 3:1 single recessive gene inheritance using Chi-square tests. Both dwarfing/semi-dwarfing genes fit 3:1 recessive, and 15:1 for double recessive. Rinrei was not a true dwarf, and the gene creating the dwarf appearance reduced the initial growth rate, but this corrected over time. Multiple F2 populations were also tested for a 3:1 single dominant gene hypothesis for highly branched phenotypes. These populations showed a bell-shaped phenotypic distribution for branch number, with no discernable classes, and revealed that branching was likely quantitatively controlled. In conclusion, dwarfism and branching in faba bean were controlled qualitatively and quantitatively, respectively.
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".