A Unified Single Nucleotide Polymorphism Map of Sunflower (<i>Helianthus annuus</i> L.) Derived from Current Genomic Resources
Bibliographic record
Abstract
ABSTRACT Dense genetic maps are critical tools for plant breeders and geneticists. While many maps have been developed for sunflower in the last few decades, most have been based on low‐throughput technologies and include marker numbers in the hundreds. However, two maps with reasonably dense coverage of about 5000 and 9000 single nucleotide polymorphism (SNP) loci each have recently been produced using high‐throughput genotyping methods. Unfortunately, no mapping population is common between the two maps, making the development of a joint map a challenge. With genome sequencing and resequencing of mapping populations currently in progress, there will be opportunities in the near future to develop much more informative resources. In the meantime, there is much demand from the sunflower community, particularly plant breeders, to combine these two maps to develop a denser map for immediate needs. In this paper, we used an in silico approach to join the two SNP maps by placing our existing marker sequences on draft genome scaffolds. Genetic map positions of the markers were determined from a resequenced mapping population aligned to the same draft genome scaffolds. In this way, we were able to directly place 10,247 SNP and insertion‐deletion (Indel) loci on a common linkage map, and also provide the ability to infer genetic position of a further 6724 SNP loci from both previously published maps. These results will allow researchers to compare previous genetics research conducted on the separate maps, and facilitate collaborative work on marker‐assisted breeding approaches in sunflower.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".