Using soybean pedigrees to identify genomic selection signatures associated with long-term breeding for cultivar improvement
Bibliographic record
Abstract
Genetic hitchhiking methods used to uncover selection signatures related to traits of agronomic importance in crops have primarily been used at the level of domestication by comparing groups of wild germplasm to landraces or elite breeding lines. In this study, two groups of cultivars defined by an elite Canadian soybean cultivar, ‘OAC Bayfield’, were compared to identify selection signatures related to long-term breeding within a specific region. Cultivars were assigned to either a pre- or post-OAC Bayfield group. Of the 162 simple sequence repeat markers used to genotype members of the pedigree, 14 were fixed and 19 exhibited a selective signature. An in silico analysis compared the results in this study to quantitative trait loci (QTL) reported in SoyBase and showed that 18 out of the 19 markers with a selective signature were associated with at least one QTL. From the 80 QTL associated with the 18 markers, half were related to plant architecture, yield, or maturity. In addition, the number and type of QTL associated with the fixed versus selected loci differed, particularly for yield. Genomic regions exhibiting a selection signature may contain important loci that either need to be conserved for agronomic performance or be targeted for introgressive breeding and germplasm enrichment.
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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 | 0.001 |
| 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".