Mapping quantitative trait loci in inbred backcross lines of<i>Lycopersicon pimpinellifolium</i>(LA1589)
Bibliographic record
Abstract
Although tomato has been the subject of extensive quantitative trait loci (QTLs) mapping experiments, most of this work has been conducted on transient populations (e.g., F2 or backcross) and few homozygous, permanent mapping populations are available. To help remedy this situation, we have developed a set of inbred backcross lines (IBLs) from the interspecific cross between Lycopersicon esculentum cv. E6203 and L. pimpinellifolium (LA1589). A total of 170 BC2F1 plants were selfed for five generations to create a set of homozygous BC2F6 lines by single-seed descent. These lines were then genotyped for 127 marker loci covering the entire tomato genome. These IBLs were evaluated for 22 quantitative traits. In all, 71 significant QTLs were identified, 15% (11/71) of which mapped to the same chromosomal positions as QTLs identified in earlier studies using the same cross. For 48% (34/71) of the detected QTLs, the wild allele was associated with improved agronomic performance. A number of new QTLs were identified including several of significant agronomic importance for tomato production: fruit shape, firmness, fruit color, scar size, seed and flower number, leaf curliness, plant growth, fertility, and flowering time. To improve the utility of the IBL population, a subset of 100 lines giving the most uniform genome coverage and map resolution was selected using a randomized greedy algorithm as implemented in the software package MapPop (http://www.bio.unc.edu/faculty/vision/lab/ mappop/). The map, phenotypic data, and seeds for the IBL population are publicly available (http://soldb.cit.cornell.edu) and will provide tomato geneticists and breeders with a genetic resource for mapping, gene discovery, and breeding.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 0.001 |
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".