Linkage map construction and quantitative trait loci (QTL) mapping using intermated vs. selfed recombinant inbred maize line (<i>Zea mays</i> L.)
Bibliographic record
Abstract
Khanal, R., Navabi, A. and Lukens, L. 2015. Linkage map construction and quantitative trait loci (QTL) mapping using intermated vs. selfed recombinant inbred maize line (Zea mays L.). Can. J. Plant Sci. 95: 1133–1144. Intermating of individuals in an F2 population increases genetic recombination between markers, which is useful for linkage map construction and quantitative trait loci (QTL) mapping. The objectives of this study were to compare the linkage maps and precision of QTL detection in an intermated recombinant inbred line (IRIL) population and a selfed recombinant inbred line (RIL) population. Both, IRIL and RIL, populations were developed from Zea mays inbred lines CG60 and CG102. The populations were grown in two environments to evaluate traits, and inbred lines from each population were genotyped with SSR and SNP markers for linkage map construction and QTL identification. In addition, we simulated RIL and IRIL populations from two inbred parents to compare the precision of QTL detection between simulated RIL and IRIL populations. In the empirical study, the linkage map was longer in RIL as compared with IRIL, and the average QTL support interval was reduced by 1.37-fold in the IRIL population compared with the RIL population. We detected 16 QTL for flowering time, plant height, leaf number, and stay green in at least one recombinant inbred line population. Two out of 16 QTL were shared between two recombinant inbred line populations. In the simulation study, the QTL support interval was reduced by 1.66-fold in the IRIL population as compared with the RIL population and linked QTL were identified more frequently in IRIL population as compared with RIL population. This study supports the utility of intermated RIL populations for precise QTL mapping.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".