We find particularly strong signals associated with polar ecoregions, with foraging, and with a diet rich in roots and tubers. Interestingly, several of the strongest signals overlap with those implicated in energy metabolism phenotypes from genome-wide association studies, including SNPs influencing glucose levels and susceptibility to type 2 diabetes. Furthermore, several pathways, including those of starch and sucrose metabolism, are enriched for strong signals of adaptations to a diet rich in roots and tubers, whereas signals associated with polar ecoregions are overrepresented in genes associated with energy metabolism pathways.
Modern humans evolved in Africa approximately 100–200 kya (White et al., 2003), and since then human populations have expanded and diversified to occupy an exceptionally broad range of habitats and to use a variety of subsistence modes. There is wide physiologic and morphologic variation among populations, some of which was undoubtedly shaped by genetic adaptations to local environments. However, identifying the polymorphisms underlying adaptive phenotypes is challenging because current patterns of human genetic variation result not only from selective but also from demographic processes.
Previous studies examined evidence of positive selection by scanning genome-wide SNP data using approaches that are generally agnostic to the underlying selective pressures. These studies detected outliers on the basis of differentiation of allele frequencies between broadly defined populations (Barreiro et al., 2008; Coop et al., 2009), extended regions of haplotype homozygosity (Voight et al., 2006; Wang et al., 2006; Pickrell et al., 2009), frequency spectrum-based statistics (Carlson et al., 2005; Williamson et al., 2007), or some combination of these methods (Sabeti et al., 2007; Jakobsson et al., 2008). These approaches are well suited to detect cases in which selection quickly drove an advantageous allele to high frequency, thereby generating extreme deviations from genome-wide patterns of variation. However, selection acting on polygenic traits may lead to subtle shifts in allele frequency at many loci, with each allele making a small contribution to the phenotype [see Pritchard et al. (2010) for a discussion]. Recent genome-wide association studies (GWAS) support this view in that most traits are associated with many variants with small effects and involve a large number of different loci (Manolio et al., 2009). Given that most phenotypic variation is polygenic, adaptations due to small changes in allele frequencies are likely to be widespread.
Detection of beneficial alleles that evolved under a polygenic selection model may be achieved by an approach that simultaneously considers the spatial distributions of the allele frequencies and the underlying selective pressures. Such an approach was used in the past to identify