Demographic and immune-based selection shifts before and after European contact inferred from 50 ancient and modern exomes from the Northwest Coast of North America
Bibliographic record
Abstract
The susceptibility of Native Americans to infectious disease has been postulated as a major factor for their population decline after European contact. To investigate if a preexisting genetic component contributed to this phenomenon, we analyzed 50 exomes of both ancient and modern individuals from the Northwest Coast of North America, dating from before and after European contact. We confirmed the genetic continuity between the ancient and modern individuals and modeled the population collapse after European contact, inferring a 57% reduction in effective population size. We also identified signatures of positive selection on immune-related genes in the ancient but not the modern group. The strongest selection signal in the ancients came from the human leukocyte antigen (HLA) gene HLA-DQA1 , with alleles that are close to fixation. The important immune function of HLA-DQA1 supports an ancient adaptation to the environments of the Americas. The modern individuals show a marked decrease in the frequency of the associated alleles (the most pronounced variant showing a 64% difference). This decrease is likely due to the environmental change associated with European colonization, which resulted in a shift of selection pressures, whereby negative selection may have acted on the same gene after contact. Furthermore, the selection pressure shift could correlate to the European-borne epidemics of the 1800s, suffered in the Northwest Coast region. This is among the first studies to examine a single population through time and exemplifies the power of such studies in uncovering nuanced demographic and adaptive histories.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".