Genetic architecture of ideotype-related traits in middle American beans through single trait, multi-trait and epistatic genome-wide analyses
Bibliographic record
Abstract
Common bean is one of the major legume crops for direct human consumption. The genetic improvement of common bean is a primary approach to increase crop adaptability to climate change conditions while maintaining productivity. A diverse panel of middle American beans was evaluated over three growing seasons (2021, 2022 and 2023) for agronomic traits considered in the crop ideotype such as flowering, maturity, pigment content, lodging, and yield. A study of the genetic regions controlling trait variation was carried out using single-trait and simultaneous (joint) multi-trait GWAS approaches. Additionally, genome-wide epistatic interactions were also analyzed. Several previously reported and novel regions were identified as significant for individual traits in the single and multi-year analyses with varying percentages of individual (7-52%) and collective (10 - 59%) phenotypic variance explained. In the single-trait and multi-year analyses, markers detected for lodging showed the highest average of percentage of variance explained (52%) followed by other traits with percentages between 11 and 19%. For yield, new loci were found with estimated effects between -96.19 to 90.96 kg/Ha in the multi-year data. In the multi-trait analyses, marginal loci on Pv02 and another on Pv04 were identified to have interaction effects on flowering and yield. A significant locus on Pv04 showed a common effect between lodging and maturity, characterized by several SNPs. Significant epistatic interactions were found along different chromosomes for all the evaluated traits, with some loci having interactions with multiple regions. In flowering, an interaction between loci on Pv01 and Pv04 explained up to 10.5% of the phenotypic variation, followed by interactions between Pv06 and Pv10 for chlorophyll b and between Pv03 and Pv08 for yield explaining around 6% of the trait variation. Multiple transcription factors were identified as candidate genes, particularly in the pairs of combinations of epistatic effects. Based on the homology analyses of the candidate genes, several showed potential roles in the genetic control of the agronomic traits, particularly for flowering, maturity, and yield. Our results demonstrate the applicability of various approaches for common bean and show a comprehensive and expanded panorama of the genetic basis of agronomic traits. The results provide new resources for crop improvement that can be leveraged in multiple approaches such as selection, modeling and predicting crop performance and genetic gain.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".