Development of <scp>SNP</scp> Panels from Low‐Coverage Whole Genome Sequencing (<scp>lcWGS</scp>) to Support Indigenous Fisheries for Three Salmonid Species in Northern Canada
Bibliographic record
Abstract
Single nucleotide polymorphism (SNP) panels are powerful tools for assessing the genetic population structure and dispersal of fishes and can enhance management practices for commercial, recreational and subsistence mixed-stock fisheries. Arctic Char (Salvelinus alpinus), Brook Trout (Salvelinus fontinalis) and Lake Whitefish (Coregonus clupeaformis) are among the most harvested and consumed fish species in Northern Indigenous communities in Canada, contributing significantly to food security, culture, tradition and economy. However, genetic resources supporting Indigenous fisheries have not been widely accessible to northern communities (e.g. Inuit, Cree, Dene). Here, we developed Genotyping-in-Thousands by sequencing (GT-seq) panels for population assignment and mixed-stock analyses of three salmonids, to support fisheries stewardship or co-management in Northern Canada. Using low-coverage Whole Genome Sequencing data from 418 individuals across source populations in Cambridge Bay (Nunavut), Great Slave Lake (Northwest Territories), James Bay (Québec) and Mistassini Lake (Québec), we developed a bioinformatic SNP filtering workflow to select informative SNP markers from genotype likelihoods. These markers were then used to design GT-seq panels, thus enabling high-throughput genotyping for these species. The three GT-seq panels yielded an average of 413 autosomal loci and were validated using 525 individuals with an average assignment accuracy of 83%. Thus, these GT-seq panels are powerful tools for assessing population structure and quantifying the relative contributions of populations/stocks in mixed-stock fisheries across multiple regions. Interweaving genomic data derived from these tools with Traditional Ecological Knowledge will ensure the sustainable harvest of three culturally important salmonids in Indigenous communities, contributing to food security programmes and the economy in Northern Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".