Predicting progeny performance in common bean (<i>Phaseolus</i><i>vulgaris</i>L.) using molecular marker-based cluster analysis
Bibliographic record
Abstract
Recovery of superior individuals from a cross based solely on the phenotypic characteristics ofsingle-plant selections is inefficient because some traits, like yield, have low heritabilities, or because it is difficult to create the correct conditions for selection, as with disease resistance. In contrast, molecular markers are highly heritable and unaffected by environmental conditions. The objective of this study was to investigate the potential of molecular markers to identify superior lines in a breeding population by examining relationships between genetic distances (GDs) and phenotypic data for eight agronomic and architectural traits (branch angle, height, hypocotyl diameter, lodging, maturity, upper pods, pods per plant, and yield) obtained from three locations over a two-year period. From an elite common bean (Phaseolus vulgaris L.) cross, 110 recombinant inbred lines (RILs) and the two parents were screened with 116 random amplified polymorphic DNA (RAPD) markers. Pairwise GD values were calculated between each line and a selected "target" (the parent 'OAC Speedvale') using the Jaccard method and correlated to the trait data. The correlations were low and non-significant for all traits, except for branch angle (r = 0.30), maturity (r = -0.25), and pods per plant (r = 0.35). The lines were also grouped according to their cluster-based GD from the target parent using UPGMA cluster analysis. Trait data of lines within groups were combined and correlated to cluster-based GD. Correlation values were large and significant for all traits. Additionally, one-half of the top 10 yielding lines and nearly one-third of the best phenotypically ranked lines were present within the 13% of lines clustered nearest the target. A selection method using marker-based cluster analysis (MBCA) is suggested to assist phenotypic selection by directing a breeder's attention to a subsample of the population containing a high proportion of superior lines.
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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 | 0.000 | 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".