Comparative analysis of genetic diversity in Canadian barley assessed by SSR, DarT, and pedigree data
Bibliographic record
Abstract
The aim of this study was to measure genetic diversity and population structure among 92 Canadian barley cultivars using two types of molecular markers (SSRs and DArTs) and pedigree data. A total of 368 alleles were identified at 50 SSR loci. The number of alleles per locus ranged between 2 and 13 ([Formula: see text] = 7.36) and PIC values ranged from 0.34 to 0.86 ([Formula: see text] = 0.69). For the biallelic DArT markers, the genetic distance matrix was based on 971 markers whose PIC values ranged between 0.06 and 0.50 ([Formula: see text] = 0.39). A third distance matrix was computed based on the kinship coefficient. Clustering of genotypes was performed based on the genetic distance matrix and the three dendrograms obtained showed the genetic relationships among barley cultivars. The topological similarity of the three dendrograms was estimated using a congruence index and showed the three dendrograms to be in very good agreement. Statistical analysis also showed a highly significant correlation between the SSR and DArT matrices (r = 0.80, p < 0.002) compared with lower yet significant correlations of the pedigree data with both marker types (r = 0.46, p < 0.002; r = 0.52, p < 0.002). Finally, we assessed linkage disequilibrium in this germplasm and found it to be quite extensive, as the mean distance between marker pairs with significant (P < 0.001) r(2) values >0.5 was 3.8 cM. Information obtained from comparing results of different genetic diversity estimation methods should be useful for the improvement and conservation of barley genetic resources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".