Genomic ancestry estimation quantifies use of wild species in grape breeding
Bibliographic record
Abstract
BACKGROUND: Grapes are one of the world's most valuable crops and most are made into wine. Grapes belong to the genus Vitis, which includes over 60 inter-fertile species. The most common grape cultivars derive their entire ancestry from the species Vitis vinifera, but wild relatives have also been exploited to create hybrid cultivars, often with increased disease resistance. RESULTS: We evaluate the genetic ancestry of some of the most widely grown commercial hybrids from North America and Europe. Using genotyping-by-sequencing (GBS), we generated 2482 SNPs and 56 indels from 7 wild Vitis, 7 V. vinifera, and 64 hybrid cultivars. We used a principal component analysis (PCA) based ancestry estimation procedure and verified its accuracy with both empirical and simulated data. V. vinifera ancestry ranged from 11 % to 76 % across hybrids studied. Approximately one third (22/64) of the hybrids have ancestry estimates consistent with F1 hybridization: they derive half of their ancestry from wild Vitis and half from V. vinifera. CONCLUSIONS: Our results suggest that hybrid grape breeding is in its infancy. The distribution of V. vinifera ancestry across hybrids also suggests that backcrosses to wild Vitis species have been more frequent than backcrosses to V. vinifera during hybrid grape breeding. This pattern is unusual in crop breeding, as it is most common to repeatedly backcross to elite, or domesticated, germplasm. We anticipate our method can be extended to facilitate marker-assisted selection in order to introgress beneficial wild Vitis traits, while allowing for offspring with the highest V. vinifera content to be selected at the seedling stage.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".