High throughput SNP discovery and genotyping in grapevine (Vitis vinifera L.) by combining a re-sequencing approach and SNPlex technology
Bibliographic record
Abstract
BACKGROUND: Single-nucleotide polymorphisms (SNPs) are the most abundant type of DNA sequence polymorphisms. Their higher availability and stability when compared to simple sequence repeats (SSRs) provide enhanced possibilities for genetic and breeding applications such as cultivar identification, construction of genetic maps, the assessment of genetic diversity, the detection of genotype/phenotype associations, or marker-assisted breeding. In addition, the efficiency of these activities can be improved thanks to the ease with which SNP genotyping can be automated. Expressed sequence tags (EST) sequencing projects in grapevine are allowing for the in silico detection of multiple putative sequence polymorphisms within and among a reduced number of cultivars. In parallel, the sequence of the grapevine cultivar Pinot Noir is also providing thousands of polymorphisms present in this highly heterozygous genome. Still the general application of those SNPs requires further validation since their use could be restricted to those specific genotypes. RESULTS: In order to develop a large SNP set of wide application in grapevine we followed a systematic re-sequencing approach in a group of 11 grape genotypes corresponding to ancient unrelated cultivars as well as wild plants. Using this approach, we have sequenced 230 gene fragments, what represents the analysis of over 1 Mb of grape DNA sequence. This analysis has allowed the discovery of 1573 SNPs with an average of one SNP every 64 bp (one SNP every 47 bp in non-coding regions and every 69 bp in coding regions). Nucleotide diversity in grape (pi = 0.0051) was found to be similar to values observed in highly polymorphic plant species such as maize. The average number of haplotypes per gene sequence was estimated as six, with three haplotypes representing over 83% of the analyzed sequences. Short-range linkage disequilibrium (LD) studies within the analyzed sequences indicate the existence of a rapid decay of LD within the selected grapevine genotypes. To validate the use of the detected polymorphisms in genetic mapping, cultivar identification and genetic diversity studies we have used the SNPlextrade mark genotyping technology in a sample of grapevine genotypes and segregating progenies. CONCLUSION: These results provide accurate values for nucleotide diversity in coding sequences and a first estimate of short-range LD in grapevine. Using SNPlextrade mark genotyping we have shown the application of a set of discovered SNPs as molecular markers for cultivar identification, linkage mapping and genetic diversity studies. Thus, the combination a highly efficient re-sequencing approach and the SNPlextrade mark high throughput genotyping technology provide a powerful tool for grapevine genetic analysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".