Construction of a high-density linkage map and QTL analysis for pistil abortion in apricot (<i>Prunus armeniaca</i> L.)
Bibliographic record
Abstract
A high-density genetic map of apricot (Prunus armeniaca L.) was constructed using an F1 population constructed by crossing two main Chinese cultivars ‘Chuanzhihong’ and ‘Luotuohuang’, coupled with a recently developed reduced representation library (RRL) sequencing. The average sequencing depth was 38.97 in ‘Chuanzhihong’ (female parent), 33.05 in ‘Luotuohuang’ (male parent), and 8.91 in each progeny. Based on the sequencing data, 12 451 polymorphic markers were developed and used in the construction of the genetic linkage map. The final map of apricot comprised eight linkage groups, including 1991 markers, and covered 886.25 cM of the total map length. The average distance between adjacent markers was narrowed to 0.46 cM. Gaps larger than 5 cM only accounted for <0.33%. To our knowledge, this map is the densest genetic linkage map that is currently available for apricot research. It is a valuable linkage map for quantitative trait loci (QTLs) identification of important agronomic traits. Moreover, the high marker density and well-ordered markers that this linkage map provides will be useful for molecular breeding of apricot as well. In this study, we applied this map in the QTL analysis of an important agronomic trait, pistil abortion. Several QTLs were detected and mapped respectively to the middle regions of LG5 (51.005∼59.4 cM) and LG6 (72.884∼76.562 cM), with nine SLAF markers closely linked to pistil abortion. The high-density genetic map and QTLs detected in this study will facilitate marker-assisted breeding and apricot genomic study.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".