Association mapping of six agronomic traits on chromosome 4A of wheat (<em>Triticum aestivum</em> L.)
Bibliographic record
Abstract
Association mapping is a powerful approach to identify associations between traits of interest and genetic markers. In this study, 103 wheat germplasm accessions from China were genotyped using 76 SSR markers and 40 EST-SSR markers. The phenotyping of plant height, spike length, spikelets per spike, spikelets density, grains per spike and thousand-kernel weight were carried out in three locations for three years. Six subpopulations were identified among these accessions by population structure analysis based on 49 SSR and 40 EST-SSR markers. Linkage disequilibrium (LD) on chromosome 4A extended up to ~3 cM with r2=0.054. Based on the mixed linear model considering population structure and relative kinship, a total of 10 SSR markers (p<0.01) on chromosome 4A were significantly associated with six agronomic traits, and six of them were associated with multiple traits. Some of the associated markers were in agreement with previous quantitative trait loci (QTL) analysis. This study demonstrated that association mapping can be successfully applied in wheat breeding context for detection of marker-traits associations. Furthermore, association mapping can enhance previous QTL information and provide additional QTL information for marker-assisted selection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".