Association mapping of QTLs for sclerotinia stem rot resistance in a collection of soybean plant introductions using a genotyping by sequencing (GBS) approach
Bibliographic record
Abstract
BACKGROUND: Sclerotinia stem rot (SSR) is the most important soybean disease in Eastern Canada. The development of resistant cultivars represents the most cost-effective means of limiting the impact of this disease. In view of ensuring durable resistance, it is imperative to identify germplasm harbouring different resistance loci and to provide breeders with closely linked molecular markers to facilitate breeding. With this end in view, we assessed resistance using a highly reproducible artificial inoculation method on a diverse collection of 101 soybean lines, mostly composed of plant introductions (PIs) and some of which had previously been reported to be resistant to sclerotinia stem rot. RESULTS: Overall, 50% of the lines exhibited a level of resistance equal to or better than the resistant checks among elite material. Of the 50 lines previously reported to be resistant, only 20 were in this category and a few were highly susceptible under these inoculation conditions. The collection of lines was genetically characterized using a genotyping by sequencing (GBS) protocol that we have optimized for soybean. A total of 8,397 single nucleotide polymorphisms (SNPs) were obtained and used to perform an association analysis for SSR by using a mixed linear model as implemented in the TASSEL software. Three genomic regions were found to exhibit a significant association at a stringent threshold (q = 0.10) and all of the most highly resistant PIs shared the same alleles at these three QTLs. The strongest association was found on chromosome Gm03 (P-value = 2.03 × 10(-6)). The other significantly associated markers were found on chromosomes Gm08 and Gm20 with P-values <10(-5). CONCLUSION: This work will facilitate breeding efforts for increased resistance to Sclerotinia stem rot through the use of these PIs.
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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 | 0.001 | 0.000 |
| 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) | 0.001 | 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".