Mapping yield and yield-related traits using diverse common bean germplasm
Bibliographic record
Abstract
Common bean (bean) is one of the most important legume crops, and mapping genes for yield and yield-related traits is essential for its improvement. However, yield is a complex trait that is typically controlled by many loci in crop genomes. The objective of this research was to identify regions in the bean genome associated with yield and a number of yield-related traits using a collection of 121 diverse bean genotypes with different yields. The beans were evaluated in replicated trials at two locations, over two years. Significant variation among genotypes was identified for all traits analyzed in the four environments. The collection was genotyped with the BARCBean6K_3 chip (5,398 SNPs), two yield/antiyield gene-based markers, and seven markers previously associated with resistance to common bacterial blight (CBB), including a Niemann-Pick polymorphism (NPP) gene-based marker. Over 90% of the single-nucleotide polymorphisms (SNPs) were polymorphic and separated the panel into two main groups of small-seeded and large-seeded beans, reflecting their Mesoamerican and Andean origins. Thirty-nine significant marker-trait associations (MTAs) were identified between 31 SNPs and 15 analyzed traits on all 11 bean chromosomes. Some of these MTAs confirmed genome regions previously associated with the yield and yield-related traits in bean, but a number of associations were not reported previously, especially those with derived traits. Over 600 candidate genes with different functional annotations were identified for the analyzed traits in the 200-Kb region centered on significant SNPs. Fourteen SNPs were identified within the gene model sequences, and five additional SNPs significantly associated with five different traits were located at less than 0.6 Kb from the candidate genes. The work confirmed associations between two yield/antiyield gene-based markers (AYD1m and AYD2m) on chromosome Pv09 with yield and identified their association with a number of yield-related traits, including seed weight. The results also confirmed the usefulness of the NPP marker in screening for CBB resistance. Since disease resistance and yield measurements are environmentally dependent and labor-intensive, the three gene-based markers (CBB- and two yield-related) and quantitative trait loci (QTL) that were validated in this work may be useful tools for simplifying and accelerating the selection of high-yielding and CBB-resistant bean cultivars.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".