A highly flexible and repeatable genotyping method for aquaculture studies based on target amplicon sequencing using next-generation sequencing technology
Bibliographic record
Abstract
Studies using genome-wide single nucleotide polymorphisms (SNPs) have become commonplace in genetics and genomics, due to advances in high-throughput sequencing technologies. Since the numbers of required SNPs and samples vary depending on each research goal, genotyping technologies with high flexibility in the number of SNPs/samples and high repeatability have been intensively investigated. For example, the ultrahigh-multiplexed amplicon sequencing, Ion AmpliSeq, has been used as a high-throughput genotyping method mainly for diagnostic purposes. Here, we designed a custom panel targeting 3,187 genome-wide SNPs of fugu, Takifugu rubripes, and applied it for genotyping farmed fugu to test its feasibility in aquaculture studies. We sequenced two libraries consisting of different pools of individuals (n = 326 each) on the Illumina MiSeq sequencer. Consequently, over 99% target regions (3,178 SNPs) were amplified and 2,655 SNPs were available after filtering steps. Strong correlation was observed in the mean depth of coverage of each SNP between duplicate runs (r = 0.993). Genetic analysis using these genotype data successfully detected the known population structure and the sex determining locus of fugu. These results show the method is superior in repeatability and flexibility, and suits genetic studies including molecular breeding, such as marker assisted and genomic selection.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".