Highly localized divergence within supergenes in Atlantic cod (Gadus morhua) within the Gulf of Maine
Bibliographic record
Abstract
BACKGROUND: Atlantic cod (Gadus morhua), is known to vary genetically across the North Atlantic, Greenland, and Newfoundland. This genetic variation occurs both spatially and temporally through decades of heavy fishing, and is concentrated in three linkage disequilibrium blocks, previously defined by pedigreed linkage mapping analysis. Variation within these genomic regions is correlated with both seawater temperature and behavioral ecotype. The full extent and nature of these linkage groups is important information for interpreting cod genetic structure as a tool for future fisheries management. RESULTS: We conducted whole genome sequencing for 31 individual cod from three sub-populations in the Gulf of Maine. Across the genome, we found 3,390,654 intermediate to high frequency Single Nucleotide Polymorphisms (SNPs). We show that pairwise linkage analysis among these SNPs is a powerful tool to detect linkage disequilibrium clusters by recovering the three previously detected linkage groups and identifying the 1031 genes contained therein. Across these genes, we found significant population differentiation among spawning groups in the Gulf of Maine and between Georges Bank and Gulf of Maine. Coordinated divergence among these genes and their differentiation at both short and long spatial scales suggests that they are acting as linked supergenes in local adaptation of cod populations. CONCLUSIONS: Differentiation between SNPs in linkage disequilibrium blocks is the major signal of genetic differentiation between all groups tested within the Gulf of Maine. Our data provide a map of genes contained in these blocks, allowing an enhanced search for neutral genetic structure for demographic inference and fisheries modeling. Patterns of selection and the history of populations may be possible to identify in cod using this description of linkage disequilibrium blocks and future data sets to robustly separate neutral and selected genetic markers.
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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 | 0.000 |
| 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".