Construction of a High-Density Genetic Linkage Map and QTL Analysis of Fiber Yield Traits in Kenaf (<i>Hibiscus Cannabinus</i> L.) via SLAF-seq
Bibliographic record
Abstract
High-density genetic maps are vital for quantitative trait loci (QTL) mapping, genome assembly, and marker-assisted selection (MAS) in plants. Meanwhile, kenaf (Hibiscus cannabinus L.) is an important crop that produces raw fiber to many industries. However, both high-density genetic map construction and QTL identification have been limited in this crop due to insufficient molecular markers. Here, we constructed a high-density genetic linkage map in kenaf via specific-locus amplified fragment sequencing (SLAF-seq). An F7:8 mapping population of 138 recombinant inbred lines was developed by crossing between Alian and Fuhong 992. In total, 220,484 high-quality SLAFs were detected, among which 52,832 were polymorphic; 4,167 polymorphic markers were then utilized to construct a genetic map. The assembled genetic map contained 18 linkage groups, spanning 1,952.68 cM with a mean distance of 0.47 cM among the adjacent markers. Phenotypic data from 10 fiber yield-related traits were used for QTL analysis, and a total of 85 QTLs were detected for 10 traits. We were able to construct the densest linkage map yet reported in kenaf and our findings will aid in further QTL mapping, genome comparisons, and MAS breeding in kenaf.
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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 | 0.001 |
| 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".