Construction of High-Density Linkage Maps and Identification of Quantitative Trait Loci Associated with Verticillium Wilt Resistance in Autotetraploid Alfalfa (<i>Medicago sativa</i>L.)
Bibliographic record
Abstract
Verticillium wilt (VW) of alfalfa is a devastating disease that causes forage yield reductions of up to 50% in the northern United States and Canada. The most effective method for controlling the disease is through the development and use of resistant varieties. To identify quantitative trait loci (QTL) for VW resistance in alfalfa, we used a full-sib population segregating for VW resistance. High-density linkage maps for both resistant and susceptible parents were constructed using single-dose alleles of single-nucleotide polymorphism markers generated by genotyping-by-sequencing. Five QTL associated with VW resistance were identified and they were in four linkage groups (4D, 6B, 6D, and 8C). Of those, three QTL (qVW-6D-1, qVW-6D-2, and qVW-8C) had higher logarithm of odds. Two putative candidates of nucleotide-binding site leucine-rich repeat disease resistance genes were identified in the QTL intervals of qVW-6D-2 and qVW-8C, respectively. The result agreed with our previous studies, in which similar resistance loci were identified in an association panel using genome-wide association. The results provide insight into the quantitative resistance to VW in alfalfa. The resistance loci and closely linked markers identified in the present study can be used in developing new alfalfa varieties with enhanced resistance to VW.
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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".