An amplicon panel for high-throughput and low-cost genotyping of Pacific oyster
Bibliographic record
Abstract
Maintaining genetic diversity in cultured shellfish can be challenging due to high variance in individual reproductive success, founder effects, and rapid genetic drift, but is important to retain adaptive potential and avoid inbreeding depression. To support broodstock management and selective breeding in cultured Pacific oysters (Crassostrea (Magallana) gigas), we developed an amplicon panel targeting 592 genomic regions and SNP variants with an average of 50 amplicons per chromosome. Target SNPs were selected based on elevated observed heterozygosity or differentiation in Pacific oyster populations in British Columbia, Canada. The use of the panel for parentage applications was evaluated using multiple generations of oysters from a breeding program on Vancouver Island, Canada (n = 181) and families selected for Ostreid herpesvirus-1 resistance from the Molluscan Broodstock Program in Oregon, USA (n = 136). Population characterization was evaluated using wild, naturalized, farmed, or hatchery oysters sampled throughout the Northern Hemisphere (n = 189). Technical replicates showed high genotype concordance (97.5%; n = 68 replicates). Parentage analysis found suspected pedigree and sample handling errors, demonstrating the panel's value for quality control in breeding programs. Suspected null alleles were identified and found to be largely population dependent, suggesting population-specific variation impacting target amplification. Null alleles were identified using existing data without the need for pedigree information, and once they were removed, assignment rates increased to 93.0 and 86.0% of possible assignments in the two breeding program datasets. A pipeline for analyzing the amplicon sequence data from sequencer output, amplitools, is also provided.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".