Molecular characterization and diversity analysis of selected maize inbred lines using single-nucleotide polymorphism markers
Bibliographic record
Abstract
The level of genetic diversity among the available breeding materials determines the potential success of a breeding program. In this study, 92 maize inbred lines were genotyped with 3047 single-nucleotide polymorphism (SNP) markers using a Kompetitive allele-specific polymerase chain reaction (KASP™) genotyping protocol. The objectives were to determine the level and pattern of genetic diversity and define potential heterotic groups of maize inbred lines developed by the Agricultural Research Council maize program of South Africa. More than 91% of the SNPs used were polymorphic with a mean polymorphic information content of 0.36. Gene diversity ranged from 0.35 to 0.37, with a mean of 0.36. Cluster analysis revealed the presence of three distinct subpopulations. Analysis of molecular variance revealed low but highly significant (P < 0.0001) variations among populations and high within and among individual variations. Variation among individuals contributed 83% of the total variation, whereas variation within individuals and among populations contributed 14% and 3%, respectively. Low mean population differentiation observed in this study suggested that the inbred lines might be developed from parental genotypes with similar genetic backgrounds. The mean percentage of genetic purity among the inbred lines was 4.8, with more than 79% of the inbred lines exhibiting less than 5% heterozygosity, indicating the inbred lines are fixed. Genotypes in different clusters may be earmarked as belonging to distinct heterotic groups, and their crosses may result in better heterosis. Thus, the findings of this study set the basis for earmarking heterotic groups and parental selection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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".