Allelic Shifts and Quantitative Trait Loci in a Recurrent Selection Population of Oat
Bibliographic record
Abstract
Recurrent selection to enhance grain yield of oat (Avena sativa L.) has been ongoing at the University of Minnesota since 1968. Grain yield was increased by 21.7% after seven cycles of recurrent selection. The objectives of this study were to monitor the long‐term genetic changes in this recurrent selection population using restriction fragment length polymorphisms (RFLPs). Ninety‐seven RFLP loci detected by 73 cDNA clones were used to evaluate changes in allelic frequencies during the recurrent selection process. Significant allelic shifts were detected in eight genomic regions. Four linkage groups were studied in greater detail to localize putative quantitative trait loci (QTL). In total, seven primary or major QTL regions were identified using allelic shift, correlation, and single‐factor analysis of variance (ANOVA) data. Six of these regions were associated with grain yield and one was associated with plant height. Thirty‐three other minor QTL were detected using correlation and/or ANOVA data. Multiple regression models for grain yield, heading date, and plant height indicated that associated markers accounted for 30, 38, and 27% of the phenotypic variance, respectively. Our results indicate that we have identified genomic regions containing favorable alleles selected during the recurrent selection process. Thirteen of the 40 QTL identified for the individual traits in the recurrent selection population were previously identified in the Kanota × Ogle recombinant inbred mapping population. Therefore, these QTL may be generally important in oat.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".