Assessment of genetic diversity in 35 Pisum sativum accessions using microsatellite markers
Bibliographic record
Abstract
Ahmad, S., Singh, M., Lamb-Palmer, N. D., Lefsrud, M. and Singh, J. 2012. Assessment of genetic diversity in 35 Pisum sativum accessions using microsatellite markers. Can. J. Plant Sci. 92: 1075-1081. Field pea is an important Canadian pulse crop and therefore developing high-performing cultivars is critical for Canadian pea growers. Information about genetic diversity is a key component for the creation of novel and desirable germplasm to develop elite pea breeding lines. The objective of the present study is to assess genetic diversity in 35 diverse Pisum accessions using 15 polymorphic microsatellites located on different pea chromosomes. Microsatellites were found to be polymorphic, amplifying a total of 41 alleles and were able to differentiate all 35 Pisum genotypes. These markers were scored by their polymorphic information content (PIC), ranging from 0.055 (AA206) to 0.660 (AB72) with an average of 0.460, and by their discriminating power (D), which varied from 0.057 (AA206) to 0.679 (AB 72) with an average of 0.475. Genetic similarity values ranged from 0.074 (between Maple pea NZ and Line 45760) to 0.875 (between Galena and Dakota) with an average of 0.336. Unweighted pair group method with arithmetic averages (UPGMA) cluster analysis grouped the 35 pea accessions into two major clusters and eight sub-clusters. The majority of Canadian and European genotypes were grouped separately, suggesting both these groups are from genetically distinct gene pools. The genetically diverse groups identified in this study can be used to derive parental lines for pea breeding.
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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) | 0.000 | 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".