Genomic islands of heterozygosity maintained across caribou populations despite inbreeding
Bibliographic record
Abstract
Abstract Small, isolated populations are prone to inbreeding, increasing the proportion of homozygous sites across the genome that can be quantified as runs of homozygosity (ROH). Caribou (Rangifer tarandus) are declining across their range in Canada; thus, understanding the effects of inbreeding on genetic potential is pertinent for conserving small, isolated populations. We quantified ROH in high-coverage whole genomes of boreal caribou from small, isolated populations in southern Ontario, Canada, in comparison to caribou from the continuous range of Ontario, other caribou ecotypes in Canada, and western Greenland. Sampled populations presented divergent evolutionary histories, differing population sizes, and extents of isolation. We conducted BLAST searches across regions of elevated heterozygosity to identify genes that have maintained variation despite inbreeding. We found caribou from recently isolated populations in Ontario had a large proportion of their genome in long ROH. We observed even larger proportions but shorter ROH in western Greenland, indicating that inbreeding has occurred over a longer period in comparison to other populations. We observed the least inbreeding in barren-ground and eastern migratory caribou, which occur in larger population sizes than boreal caribou. Despite vastly different inbreeding extents, we found regions of high heterozygosity maintained across all populations. Within these islands of heterozygosity, we identified genes associated with immunity, signaling regulation, nucleotide binding, toxin elimination, and feeding behaviour regulation. In this study, we confirm inbreeding in isolated populations of a species at risk, but also uncover high variation in some genes maintained across divergent populations despite inbreeding, suggesting strong balancing selection.
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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 | 0.000 |
| 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 | 0.000 |
| 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".