Leveraging Whole Genomes, Mitochondrial <scp>DNA</scp> and Haploblocks to Decipher Complex Demographic Histories: An Example From a Broadly Admixed Arctic Fish
Bibliographic record
Abstract
The study of phylogeography has transitioned from mitochondrial haplotypes to genome-wide analyses, borrowing from population genomics methods along the way. Whole-genome sequencing allows the study of both mitochondrial and nuclear DNA and provides the density of markers to investigate recombination along the genome. This level of resolution could unravel complex histories of admixture between lineages, which are commonly observed in species evolving in recently deglaciated habitats. In this study, we sequenced 1120 Arctic Char genomes from 33 populations across Canada and Greenland to characterise patterns of genetic variation and diversity, and how they are shaped by hybridisation between the Arctic and Atlantic glacial lineages. Mitochondrial genomes across the study area were predominantly of Arctic origin, except in Greenland, where we observed some Atlantic descent. Through admixture analyses and demographic inferences on nuclear markers, we identified that all Canadian populations under the 66th parallel showed introgression from the Atlantic lineage, leading to higher genetic diversity. By scanning the genome using local principal component analyses, we identified putative large low-recombining haploblocks as local ancestry tracts from either lineage. Since haplotypes might retain different signatures of postglacial histories by sheltering sequences from recombination, we attempted to infer origins of recolonisation using whole genomes vs. ancestry tracts for the Arctic lineage. Despite limitations, we unveiled clues suggesting a complex postglacial history in Arctic Char. Overall, our study demonstrates that, even at low depth, making the most of whole-genome sequencing by analysing several genomic compartments provides a versatile and powerful way to address 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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".