Development of a targeted genotyping platform for reproducible results within tetraploid and hexaploid blueberry
Bibliographic record
Abstract
Blueberry ( Vaccinium spp.) is one of the most economically important berry crops worldwide. Validation of genetic mapping studies is often hindered by asynchronous marker technology. The development of a standardized genotyping platform that targets a specific set of polymorphic loci can be a practical solution to unify the scientific and breeding community toward blueberry improvement. The objective of this study was to develop and evaluate a targeted genotyping platform for cultivated blueberries that is affordable, reproducible, and sufficiently high density to warrant large-scale adoption for genomic studies. The Flex-Seq platform was developed in a two-step procedure that resulted in 22,000 loci that yielded 194,365 single nucleotide polymorphisms when assessed in a diversity set of 192 samples including cultivated and other related wild Vaccinium species. Locus recovery averaged 89.4% in the cultivated polyploid blueberry (northern highbush [NHB], southern highbush [SHB], and rabbiteye [RE]) and on average 88.8% were polymorphic. While recovery of these loci was lower in the other Vaccinium species assayed, recovery remained high and ranged between 60.8% and 70.4% depending on the taxonomic distance to the cultivated blueberry targeted in this platform. NHB had the highest mean number of variants per locus at 9.7, followed by RE with 9.1, SHB with 8.5, and a range between 7.7 and 8.5 in other species. As expected, the total number of unique-in-state haplotypes exceeded the total number of variants in the domesticated blueberries. Phylogenetic analysis using a subset of the SNPs and haplotypes mostly conformed to known relationships. The platform also offers flexibility about the number of loci, depth of sequencing for accurate dosage calling, loci and haplotype reconstruction from increased fragment length. This genotyping platform will accelerate the development and improvement of blueberry cultivars through genomic-assisted breeding tools.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".