Exploring the potential and limitations of genotyping-by-sequencing for SNP discovery and genotyping in tetraploid potato
Bibliographic record
Abstract
Genotyping-by-sequencing (GBS) potentially offers a cost-effective alternative for SNP discovery and genotyping. Here, we report the exploration of GBS in tetraploid potato. Both ApeKI and PstI/MspI enzymes were used for library preparation on eight diverse potato genotypes. ApeKI yielded more markers than PstI/MspI but provided a lower read coverage per marker, resulting in more missing data and limiting effective genotyping to the tetraploid mode. We then assessed the accuracy of these SNPs by comparison with SolCAP data (5824 data points in diploid mode and 3243 data points in tetraploid mode) and found the match rates between genotype calls was 90.4% and 81.3%, respectively. Imputation of missing data did not prove very accurate because of incomplete haplotype discovery, suggesting caution in setting the allowance for missing data. To further assess the quality of GBS-derived data, a genome-wide association analysis was performed for flower color on 318 clones (with ApeKI). A strong association signal on chromosome 2 was obtained with the most significant SNP located in the middle of the dihydroflavonol 4-reductase (DFR) gene. We conclude that an appropriate choice of enzyme for GBS library preparation makes it possible to obtain high-quality SNPs in potato and will be helpful for marker-assisted genomics.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".