Use of genotyping by sequencing data to develop a high‐throughput and multifunctional <scp>SNP</scp> panel for conservation applications in Pacific lamprey
Bibliographic record
Abstract
Next-generation sequencing data can be mined for highly informative single nucleotide polymorphisms (SNPs) to develop high-throughput genomic assays for nonmodel organisms. However, choosing a set of SNPs to address a variety of objectives can be difficult because SNPs are often not equally informative. We developed an optimal combination of 96 high-throughput SNP assays from a total of 4439 SNPs identified in a previous study of Pacific lamprey (Entosphenus tridentatus) and used them to address four disparate objectives: parentage analysis, species identification and characterization of neutral and adaptive variation. Nine of these SNPs are FST outliers, and five of these outliers are localized within genes and significantly associated with geography, run-timing and dwarf life history. Two of the 96 SNPs were diagnostic for two other lamprey species that were morphologically indistinguishable at early larval stages and were sympatric in the Pacific Northwest. The majority (85) of SNPs in the panel were highly informative for parentage analysis, that is, putatively neutral with high minor allele frequency across the species' range. Results from three case studies are presented to demonstrate the broad utility of this panel of SNP markers in this species. As Pacific lamprey populations are undergoing rapid decline, these SNPs provide an important resource to address critical uncertainties associated with the conservation and recovery of this imperiled species.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".