Genome Wide Association Mapping of <i>Sclerotinia sclerotiorum</i> Resistance in Soybean with a Genotyping‐by‐Sequencing Approach
Bibliographic record
Abstract
Sclerotinia stem rot (SSR) is one of the most important pests in cool soybean growing regions of the Northeastern United States and Canada. However, the intensity of infestations varies considerably from year to year according to weather conditions, thus making it difficult for breeders to select under uniform disease pressure. Selection for resistance to SSR would be greatly facilitated by the use of molecular markers. In this work, a collection of 130 lines was inoculated using the cotton pad method and was genetically characterized using a genotyping‐by‐sequencing (GBS) protocol optimized for soybean. Genome‐wide association mapping (AM) and linkage disequilibrium (LD) analyses were performed with 7864 single nucleotide polymorphisms (SNPs). Linkage disequilibrium varied considerably over physical distance, reaching a r 2 value of 0.2 after 8.5 Mb in the pericentromeric region and 0.5 Mb in the telomeric region. The mixed linear model (MLM) performed very well in accounting for population structure and relatedness, as only 5.5% of the observed p ‐values were < 0.05. The strongest association was found on chromosome Gm15 ( p ‐value = 1.38 × 10 –6 ; q ‐value [adjusted p ‐value] = 0.011). Two additional SNP markers in the vicinity had a q ‐value < 0.1. This marker was validated in the progeny of a biparental cross, where F 4:6 lines carrying the susceptibility allele developed lesions 17.6 mm longer than lines carrying the resistance allele. Interestingly, other genes contributing to resistance to pathogens have been reported in this region of Gm15. Three other association peaks having a q ‐value < 0.1 were detected on chromosomes Gm01, Gm19, and Gm20.
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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 | 0.001 |
| 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".