Genotyping by sequencing for SNP marker development in onion
Bibliographic record
Abstract
Onion (Allium cepa) is not highly tractable for development of molecular markers due to its large (16 gigabases per 1C) nuclear genome. Single nucleotide polymorphisms (SNPs) are useful for genetic characterization and marker-aided selection of onion because of codominance and common occurrence in elite germplasm. We completed genotyping by sequencing (GBS) to identify SNPs in onion using 46 F2 plants, parents of the F2 plants (Ailsa Craig 43 and Brigham Yellow Globe 15-23), two doubled haploid (DH) lines (DH2107 and DH2110), and plants from 94 accessions in the USDA National Plant Germplasm System (NPGS). SNPs were called using the TASSEL 3.0 Universal Network Enabled Analysis (UNEAK) bioinformatics pipeline. Sequences from the F2 and DH plants were used to construct a pseudo-reference genome against which genotypes from all accessions were scored. Quality filters were used to identify a set of 284 high quality SNPs, which were placed onto an existing genetic map for the F2 family. Accessions showed a moderate level of diversity (mean He = 0.341) and evidence of inbreeding (mean F = 0.592). GBS is promising for SNP discovery in onion, although lack of a reference genome required extensive custom scripts for bioinformatics analyses to identify high quality markers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".