Thermal adaptation rather than demographic history drives genetic structure inferred by copy number variants in a marine fish
Bibliographic record
Abstract
Abstract Increasing evidence shows that structural variants represent an overlooked aspect of genetic variation with consequential evolutionary roles. Among those, copy number variants (CNVs), including duplicated genomic region and transposable elements (TEs) may contribute to local adaptation and/or reproductive isolation among divergent populations. Those mechanisms suppose that CNVs could be important drivers of population genetic structure, whose study is generally restricted to the use of SNPs. Taking advantage of recent developments allowing CNV analysis from RAD-seq data, we investigated how variation in fitness-related traits, local thermal conditions and demographic history are associated with CNVs, and how subsequent copy number variation drives population genetic structure in a marine fish, the capelin ( Mallotus villosus ). We collected 1536 DNA samples from 35 sampling sites in the north Atlantic Ocean and identified 6620 CNVs. We found associations between CNVs and the gonadosomatic index, suggesting that duplicated regions could affect female fitness by modulating oocyte production. We also detected 105 CNV candidates associated with water temperature, among which 20% corresponded to genomic regions located within the sequence of protein-coding genes, suggesting local adaptation to cold water by means of gene amplification. We also identified 175 CNVs associated with the divergence of three parapatric glacial lineages, of which 24% were located within protein-coding genes, which might contribute to genetic incompatibilities and ultimately, reproductive isolation. Lastly, our analyses unveiled a hierarchical, complex CNV population structure determined by temperature and local geography, that was very different from that inferred based on SNPs in a previous study. Our findings underscore the complementarity of those two types of markers in population genomics studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".