Signatures of environmental genetic adaptation pinpoint pathogens as the main selective pressure through human evolution

Abstract

Previous genome-wide scans of positive natural selection in humans have identified a number of non-neutrally evolving genes that play important roles in skin pigmentation, metabolism, or immune function. Recent studies have also shown that a genome-wide pattern of local adaptation can be detected by identifying correlations between patterns of allele frequencies and environmental variables. Despite these observations, the degree to which natural selection is primarily driven by adaptation to local environments, and the role of pathogens or other ecological factors as selective agents, is still under debate. To address this issue, we correlated the spatial allele frequency distribution of a large sample of SNPs from 55 distinct human populations to a set of environmental factors that describe local geographical features such as climate, diet regimes, and pathogen loads. In concordance with previous studies, we detected a significant enrichment of genic SNPs, and particularly non-synonymous SNPs associated with local adaptation. Furthermore, we show that the diversity of the local pathogenic environment is the predominant driver of local adaptation, and that climate, at least as measured here, only plays a relatively minor role. While background demography by far makes the strongest contribution in explaining the genetic variance among populations, we detected about 100 genes which show an unexpectedly strong correlation between allele frequencies and pathogenic environment, after correcting for demography. Conversely, for diet regimes and climatic conditions, no genes show a similar correlation between the environmental factor and allele frequencies. This result is validated using low-coverage sequencing data for multiple populations. Among the loci targeted by pathogen-driven selection, we found an enrichment of genes associated to autoimmune diseases, such as celiac disease, type 1 diabetes, and multiples sclerosis, which lends credence to the hypothesis that some susceptibility alleles for autoimmune diseases may be maintained in human population due to past selective processes.

Figure 1. Enrichment of SNPs for different values of prediction accuracy.

Enrichment of genic (red line, panel A) or non-synonymous (red line, panel B) vs. intergenic SNPs (blue line, both panel) for different values of prediction accuracy. Peach region denotes 90^th confidence interval computed with 1,000 bootstrap resamplings on overlapping blocks of 40 contiguous SNPs.

Figure 2. Enrichment of SNPs for different values of prediction accuracy computed on distinct models.

Enrichment of genic (red line) versus intergenic SNPs (blue line) for different values of prediction accuracy computed on models comprising only pathogen diversity (panel A), subsistence strategies (panel B) and climate conditions (panel C) separately. Peach region denotes 90^th confidence interval computed with 1,000 bootstrap resamplings on blocks of contiguous SNPs.

Figure 3. Frequencies of genes.

Frequencies of genes displaying different values of improvement of explained variance with three distinct models, comprising only pathogen diversity, subsistence strategies and climate conditions, separately (A); and when testing each variable separately and taking the maximum value within each environmental category (B).

Figure 4. Quantile-quantile (QQ) plot of distribution of improvement of explained variance, I(R ²), computed with a model including all pathogen species and models not including one pathogen group.

The distribution under a model not including helminthes is denoted by red, protozoa by blue, bacteria by green, and viruses by pink circles.

Figure 5. Frequencies of genes for different values of improvement of explained variance.

Classes were defined as quartiles of distribution of improvement of explained variance for genes associated to a trait or a disease in GWASs. Bars are colored according to the association to a quantitative trait, a disease or an autoimmune disease.