Distribution of DArT, AFLP, and SSR markers in a genetic linkage map of a doubled-haploid hexaploid wheat population
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".