Genetic Analysis of Tetraploid F1 Populations Using SNP Markers
Bibliographic record
Abstract
Many ornamentals are polyploid. Genetic analysis using molecular markers is well established for diploid crops but until recently the tools for mapping and QTL analysis in tetraploids were not readily available. However, this is rapidly changing: next-generation sequencing enables the identification of large numbers of SNPs; array hybridisation generates large SNP marker data sets; and software for dosage scoring (fitTetra, Voorrips et al.,BMC Bioinform 12:172, 2011) allows efficient assignment of the tetraploid SNP genotypes of individuals. We are generating a SNP data set for two F1 populations of rose and performing dosage scoring of the SNPs. Using the tetraploid dosage scores we will develop linkage maps, and subsequently work on QTL mapping of traits including frost tolerance for garden roses and flower colour, production traits and powdery mildew resistance for cut roses. In preparation for this work we developed software (PedigreeSim, Voorrips and Maliepaard,BMC Bioinform 13:248, 2012) for simulating marker data in a tetraploid crop, which models both bivalent and quadrivalent formation during meiosis and therefore also simulates double reduction (the situation where a gamete receives two copies of the same chromosome segment, which is only possible in polyploids). We present here the tetraploid meiosis and simulation process and the approach we developed for linkage 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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".