Genetic Diversity Analysis of Soybean [Glycine max (L.) Merrill.] Germplasms in Bangladesh Using SSR Markers
Bibliographic record
Abstract
The purpose of the present study was to investigate the genetic diversity and molecular characterization of 50 soybean genotypes in Bangladesh with 20 SSR markers. Genomic DNA was isolated by modified standard cetyl tri-methyl ammonium bromide (CTAB) extraction protocol and alleles were amplified by polymerase chain reaction (PCR). Allele sizes were estimated in comparison with 50 bp DNA ladder. The software NTSYSpc version 2.2 and POPGENE version 1.31 were utilized for molecular data analysis and preparation of dendrogram. Polymorphic Information Content (PIC) values varied from 0.53 (Satt664) to 0.98 (Satt009, Satt330 and Satt522) with the mean value 0.897 and expected heterozygosity varied from 0.4059 (Satt685) to 0.1246 (Satt664) with the mean value 0.244. The dendrogram analysis depicted that the 50 genotypes were grouped in four (4) major clusters. The most diverse genotypes were SBG-1, PM-78-6-3-13, BS-3 and AGS-31, which suggest that the simple sequence repeat (SSR) markers are very efficient for genetic diversity analysis. The similarity matrix revealed the diversity among genotypes. The diverse genetic materials obtained from the present study on genetic diversity of soybean genotypes in Bangladesh may be utilized in the future breeding programme.
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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 | 0.001 |
| 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".