Extreme distribution of deleterious variation in a historically small and isolated population - insights from the Greenlandic Inuit
Bibliographic record
Abstract
Abstract The genetic consequences of a severe bottleneck on genetic load in humans are widely disputed. Based on exome sequencing of 18 Greenlandic Inuit we show that the Inuit have undergone a severe ~20,000 yearlong bottleneck. This has led to a markedly more extreme distribution of deleterious alleles than seen for any other human population. Compared to populations with much larger population sizes, we see an overall reduction in the number of variable sites, increased numbers of fixed sites, a lower heterozygosity, and increased mean allele frequency as well as more homozygous deleterious genotypes. This means, that the Inuit population is the perfect population to examine the effect of a bottleneck on genetic load. Compared to the European, Asian and African populations, we do not observe a difference in the overall number of derived alleles. In contrast, using proxies for genetic load we find that selection has acted less efficiently in the Inuit, under a recessive model. This fits with our simulations that predict a similar number of derived alleles but a true higher genetic load for the Inuit regardless of the genetic model. Finally, we find that the Inuit population has a great potential for mapping of disease-causing variants that are rare in large populations. In fact, we show that these alleles are more likely to be common, and thus easy to map, in the Inuit than in the Finnish and Latino populations; populations considered highly valuable for mapping studies due to recent bottleneck events.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".