Leveraging whole genomes, mitochondrial DNA, and haploblocks to decipher complex demographic histories: an example from a broadly admixed arctic fish
Bibliographic record
Abstract
Abstract The study of phylogeography has transitioned from mitochondrial haplotypes to genome-wide analyses, blurring the line between this field and population genomics. Whole-genome sequencing offers the opportunity to join use both and provides the density of markers necessary to investigate genetic linkage and recombination along the genome. This facilitates the unraveling of complex demographic histories of admixture between divergent lineages, as is often the case in species evolving in recently deglaciated habitats. In this study, we sequenced 1120 Arctic Char genomes from 33 populations across Canada and Western Greenland to characterize patterns of genetic variation and diversity, and how they are shaped by hybridization between the Arctic and Atlantic glacial lineages. Several lines of evidence supported mito-nuclear discordance in lineage distribution, with all Canadian populations under the 66 th parallel being characterized by introgression from the Atlantic lineage, leading to higher nuclear genetic diversity. By scanning the genome using local PCAs, we identified putative low-recombining haploblocks as local ancestry tracts from either lineage and described the impacts of recombination on the introgression landscape in admixed populations. Finally, we inferred conflicting origins of recolonization using whole genomes vs. ancestry tracts for the Arctic lineage, suggesting that haplotypes sheltered from introgression by low recombination could enlighten complex post-glacial histories. Overall, we argue that Whole-Genome Sequencing, even at low depths of coverage, provides a versatile approach to the study of phylogeographic dynamics.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".