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.

Figure 1. Climate variables used for the analysis.

(A) Maps show the distributions of summer and winter climate variables: maximum summer temperature, minimum winter temperature and solar radiation, precipitation rate and relative humidity in the summer and winter. (B) A heatmap shows the absolute values of Spearman rank correlation coefficients between pairs of climate variables.

Figure 2. Mean-centered allele frequency plotted against population for SNPs with the strongest signals (transformed rank statistic <10⁻⁵).

The variables shown are: (A) winter solar radiation in the worldwide analysis, (B) summer precipitation rate in the worldwide analysis, and winter solar radiation in (C) the AWE population subset and (D) the AEA population subset. Since the particular patterns that result in strong correlations in the worldwide analysis are diverse, SNPs for these variables were split into two clusters using the results of an eigen analysis of the matrix of SNPs and populations. SNPs were assigned to clusters based on the eigenvector term for the eigenvector corresponding to the first eigenvalue . Mean-centered allele frequencies were computed by subtracting the mean allele frequency across populations. SNPs with rank statistics less than 10⁻⁵ are included in the plots. Population names and means are colored based on membership in one of five major geographical regions (sub-Saharan Africa, Western Eurasia, East Asia, Oceania, and the Americas) and ordered, within each region, so that the climate variable values increase from left to right across the x-axis. Alleles are polarized based on the signs of the Spearman correlations with the climate variable. Each gray dot represents an individual SNP and fitted lines (obtained using the lm function in R) for each region are shown in color. The ranges of the climate variable values across each geographic region are shown above the horizontal axis.

Figure 3. Global variation in allele frequencies for SNPs with strong signals with climate.

Two NS SNPs from the worldwide analysis: (A) A SNP (rs3782489) in keratin 77 (KRT77), is strongly correlated with summer solar radiation, and (B) a SNP (rs2075756) in the thyroid receptor interacting protein (TRIP6) is strongly correlated with absolute latitude. Two SNPs from the population subset analysis: (C) A SNP (rs4558836) in CORIN has a signal in the AEA population subset with winter minimum temperature, but not in the AWE subset, and (D) a NS SNP (rs5743810) in TLR6 has a signal in the AWE population subset with winter solar radiation, but not in the AEA subset. Two SNPs that are associated with autoimmune disease from GWAS: (E) A SNP (rs2313132) upstream of PCDH18 that is associated with SLE is strongly correlated with summer solar radiation, and (F) a SNP (rs6074022) upstream of CD40 that is associated with multiple sclerosis is strongly correlated with minimum winter temperature. For each plot, gray points represent individual SNPs and colored lines represent fitted lines (obtained using the lm function in R) for each region. The ranges of the climate variable values for each region are shown at the bottom of the corresponding segment of the plot.

Figure 4. Venn diagrams showing the overlap between lower tails of rank statistics from the worldwide analysis and each population subset analysis.

The Venn diagram on the right shows the overlap expected between the results of the worldwide analysis and a set of randomly drawn SNPs.