Genetic structure and diversity of upland rice germplasm using diversity array technology (DArT)-based single nucleotide polymorphism (SNP) markers
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Bibliographic record
Abstract
Abstract Investigating genetic structure and diversity is crucial for rice improvement strategies, including mapping quantitative trait loci with the potential for improved productivity and adaptation to the upland ecology. The present study elucidated the population structure and genetic diversity of 176 rice germplasm adapted to the upland ecology using 7063 genome-wide single nucleotide polymorphism (SNP) markers from the Diversity Array Technology (DArT)-based sequencing platform (DArTseq). The SNPs were reasonably distributed across the rice genome, ranging from 432 SNPs on chromosome 9 to 989 SNPs on chromosome 1. The minimum minor allele frequency was 0.05, while the average polymorphism information content and heterozygosity were 0.25 and 0.03, respectively. The model-based (Bayesian) population structure analysis identified two major groups for the studied rice germplasm. Analysis of molecular variance revealed that all (100%) of the genetic variance was attributable to differences within the population, and none was attributable to the population structure. The estimates of genetic differentiation (PhiPT = 0.001; P = 0.197) further showed a negligible difference among the population structures. The results indicated a high genetic exchange or gene flow (number of migrants, Nm = 622.65) and a substantial level of diversity (number of private alleles, Pa = 1.52 number of different alleles, Na = 2.67; Shannon's information index, I = 0.084; and percentage of polymorphic loci, %PPL = 55.9%) within the population, representing a valuable resource for rice improvement. The findings in this study provide a critical analysis of the genetic diversity of upland rice germplasm that would be useful for rice yield improvement. We suggested further breeding and genetic analyses.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it