Identification of QTL for seed coat colour and oil content in <i>Brassica napus</i> by association mapping using SSR markers
Bibliographic record
Abstract
Qu, C., Hasan, M., Lu, K., Liu, L., Zhang, K., Fu, F., Wang, M., Liu, S., Bu, H., Wang, R., Xu, X., Chen, L. and Li, J. 2015. Identification of QTL for seed coat colour and oil content in Brassica napus by association mapping using SSR markers. Can. J. Plant Sci. 95: 387–395. Association mapping identifies quantitative trait loci (QTL) based on the strength of linkage disequilibrium (LD) between markers and functional polymorphisms across a set of diverse germplasms. In this study, we used association mapping to detect QTL and genome-wide simple sequence repeat (SSR) markers linked to seed coat colour and oil content in a population of 217 oilseed rape (Brassica napus L.) accessions. We corrected for the population structure of B. napus using 389 genome-wide SSR markers. In total, 25 and 11 SSR markers linked to seed coat colour and oil content were detected, respectively, and these two sets of markers were in different linkage groups. Nine of these markers for seed coat colour spanned the major QTL region for seed coat colour, and been mapped to chromosome A9. Six of these markers showed high levels of association with both seed coat colour and oil content, and markers H081N08.8 and KS20291 were mapped to the major QTL region for seed coat colour on chromosome A9. Another marker, CB10364, was in high LD with all determined seed coat colour and oil content traits, and was mapped to the co-localized QTL region for them on chromosome A8. These data indicate that seed coat colour was found to be an important contributor to seed oil content. Further, we show that association mapping using a heterogeneous set of genotypes is a suitable approach for complementing and enhancing previously obtained 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 | 0.000 |
| 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".