The Effect of an Extreme and Prolonged Population Bottleneck on Patterns of Deleterious Variation: Insights from the Greenlandic Inuit
Bibliographic record
Abstract
The genetic consequences of population bottlenecks on patterns of deleterious genetic variation in human populations are of tremendous interest. Based on exome sequencing of 18 Greenlandic Inuit we show that the Inuit have undergone a severe ∼20,000-year-long bottleneck. This has led to a markedly more extreme distribution of allele frequencies than seen for any other human population tested to date, making the Inuit the perfect population for investigating the effect of a bottleneck on patterns of deleterious variation. When comparing proxies for genetic load that assume an additive effect of deleterious alleles, the Inuit show, at most, a slight increase in load compared to European, East Asian, and African populations. Specifically, we observe <4% increase in the number of derived deleterious alleles in the Inuit. In contrast, proxies for genetic load under a recessive model suggest that the Inuit have a significantly higher load (20% increase or more) compared to other less bottlenecked human populations. Forward simulations under realistic models of demography support our empirical findings, showing up to a 6% increase in the genetic load for the Inuit population across all models of dominance. Further, the Inuit population carries fewer deleterious variants than other human populations, but those that are present tend to be at higher frequency than in other populations. Overall, our results show how recent demographic history has affected patterns of deleterious variants in human populations.
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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 | 0.001 | 0.000 |
| 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".