Adaptations to Climate-Mediated Selective Pressures in Humans

Abstract

Humans inhabit a remarkably diverse range of environments, and adaptation through natural selection has likely played a central role in the capacity to survive and thrive in extreme climates. Unlike numerous studies that used only population genetic data to search for evidence of selection, here we scan the human genome for selection signals by identifying the SNPs with the strongest correlations between allele frequencies and climate across 61 worldwide populations. We find a striking enrichment of genic and nonsynonymous SNPs relative to non-genic SNPs among those that are strongly correlated with these climate variables. Among the most extreme signals, several overlap with those from GWAS, including SNPs associated with pigmentation and autoimmune diseases. Further, we find an enrichment of strong signals in gene sets related to UV radiation, infection and immunity, and cancer. Our results imply that adaptations to climate shaped the spatial distribution of variation in humans.

Author Summary

Classical studies that examined the global distributions of human physiological traits such as pigmentation, basal metabolic rate, and body shape and size suggested that natural selection related to climate has been important during recent human evolutionary history. We scanned the human genome using data for about 650,000 variants in 61 worldwide populations to look for correlations between allele frequencies and 9 climate variables and found evidence for adaptations to climate at the genome-wide level. In addition, we detected compelling signals for individual SNPs involved in pigmentation and immune response, as well as for pathways related to UV radiation, infection and immunity, and cancer. A particularly appealing aspect of this approach is that we identify a set of candidate advantageous SNPs associated with specific biological hypotheses, which will be useful for follow-up testing. We developed an online resource to browse the results of our data analyses, allowing researchers to quickly assess evidence for selection in a particular genomic region and to compare it across several studies.