Adaptations to new environments in humans: the role of subtle allele frequency shifts

Abstract

Humans show tremendous phenotypic diversity across geographically distributed populations, and much of this diversity undoubtedly results from genetic adaptations to different environmental pressures. The availability of genome-wide genetic variation data from densely sampled populations offers unprecedented opportunities for identifying the loci responsible for these adaptations and for elucidating the genetic architecture of human adaptive traits. Several approaches have been used to detect signals of selection in human populations, and these approaches differ in the assumptions they make about the underlying mode of selection. We contrast the results of approaches based on haplotype structure and differentiation of allele frequencies to those from a method for identifying single nucleotide polymorphisms strongly correlated with environmental variables. Although the first group of approaches tends to detect new beneficial alleles that were driven to high frequencies by selection, the environmental correlation approach has power to identify alleles that experienced small shifts in frequency owing to selection. We suggest that the first group of approaches tends to identify only variants with relatively strong phenotypic effects, whereas the environmental correlation methods can detect variants that make smaller contributions to an adaptive trait.

Figure 1.

Variation in allele frequencies across populations for (a) rs7954576, a SNP 8 kb upstream of IL22, and (b) for rs7071213, a SNP in the PNLIPRP3 coding region, which show strong signals of selection with foraging as a mode of subsistence. Populations, ordered by geographical region, are shown on the x-axis and allele frequency is shown on the y-axis. Populations that use foraging as the mode of subsistence are shown in red and all others are shown in blue. Points represent allele frequencies for individual SNPs, and the horizontal bars represent the mean allele frequencies across regions.

Figure 2.

(Opposite.) Plots comparing the distributions of inferred allele frequency shifts caused by selection for SNPs with the strongest signatures (i.e. the top 0.01% signals). (a) Distributions of inferred frequency shifts for 36 populations in the AEA subset (see main text for details on calculations of frequency shifts). F_ST and climate correlations are calculated for the same set of 36 populations, and iHS is calculated for the HapMap Asian population. (b) Distributions of inferred frequency shifts for 33 populations in the AWE subset (see main text for details on calculations of frequency shifts). F_ST and climate correlations are calculated for the same set of 33 populations, and iHS is calculated for the HapMap CEPH European population. (c) Distributions of frequency shifts for worldwide populations with dichotomous variables. For these variables, the shift is defined as the average regional difference between allele frequencies for the two classes of populations. In all plots, SNPs are pruned so that only the SNP with the strongest signal is included for each 1 Mb genomic region.

Figure 3.

Signals of selection for SNPs associated with pigmentation and metabolism phenotypes (p < 1 × 10⁻⁸). SNPs were pruned so that only the SNP with the lowest GWAS p-value was included for any 1 Mb region. The colours of cells in the table denote the strength of evidence of selection for each SNP, based on the rank of the SNP in the genome-wide distribution of the selection test statistic, i.e. the ‘empirical p-value’. The empirical p-values on the right-hand side of the table are for environmental correlations. Each environmental variable empirical p-value is the rank of the minimum p-value calculated across a set of environmental variables related to subsistence, ecoregion or climate.