Distribution of DArT, AFLP, and SSR markers in a genetic linkage map of a doubled-haploid hexaploid wheat population
Why this work is in the frame
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Bibliographic record
Abstract
A genetic linkage mapping study was conducted in 93 doubled-haploid lines derived from a cross between Triticum aestivum L. em. Thell 'Arina' and a Norwegian spring wheat breeding line, NK93604, using diversity arrays technology (DArT), amplified fragment length polymorphism (AFLP), and simple sequence repeat (SSR) markers. The objective of this study was to understand the distribution, redundancy, and segregation distortion of DArT markers in comparison with AFLP and SSR markers. The map contains a total of 624 markers with 189 DArTs, 165 AFLPs and 270 SSRs, and spans 2595.5 cM. All 3 marker types showed significant (p < 0.01) segregation distortion, but it was higher for AFLPs (24.2%) and SSRs (22.6%) than for DArTs (13.8%). The overall segregation distortion was 20.4%. DArTs showed the highest frequency of clustering (27.0%) at < 0.5 cM intervals between consecutive markers, which is 3 and 15 times higher than SSRs (8.9%) and AFLPs (1.8%), respectively. This high proportion of clustering of DArT markers may be indicative of gene-rich regions and (or) the result of inclusion of redundant clones in the genomic representations, which was supported by the presence of very high correlation coefficients (r > 0.98) and multicollinearity among the clustered markers. The present study is the first to compare the utility of DArT with AFLP and SSR markers, and the present map has been successfully used to identify novel QTLs for resistance to Fusarium head blight and powdery mildew and for anther extrusion, leaf segment incubation, and latency.
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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 | 0.000 | 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 it