bioRxivGeneticsPreprint
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
John Lindo, Emilia Huerta-Sánchez, Shigeki Nakagome, Morten Rasmussen, Barbara Petzelt, Joycellyn Mitchell, Jerome S Cybulski, Eske Willerslev, Michael DeGiorgio, Ripan S Malhi
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.
- organism: human
- assay: whole-exome sequencing
- research focus: variant calling
- research focus: gene regulatory network inference
- approach: statistical model
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