The data also suggest that, as expected, local adaptation acts via selection on existing variation rather than requiring new mutations.
Nature: ... Although the average level of population differentiation is low (at sites genotyped in all populations the mean value of Wright’s Fst is 0.071 between CEU and YRI, 0.083 between YRI and CHB+JPT, and 0.052 between CHB+JPT and CEU), we find several hundred thousand SNPs with large allele frequency differences in each population comparison (Fig. 5c). As seen in previous studies4, 37, the most highly differentiated sites were enriched for non-synonymous variants, indicative of the action of local adaptation. The completeness of common variant discovery in the low-coverage resource enables new perspectives in the search for local adaptation. First, it provides a more comprehensive catalogue of fixed differences between populations, of which there are very few: two between CEU and CHB+JPT (including the A111T missense variant in SLC24A5 (ref. 38) contributing to light skin colour), four between CEU and YRI (including the −46 GATA box null mutation upstream of DARC39, the Duffy O allele leading to Plasmodium vivax malaria resistance) and 72 between CHB+JPT and YRI (including 24 around the exocyst complex component gene EXOC6B); see Supplementary Table 7 for a complete list. Second, it provides new candidates for selected variants, genes and pathways. For example, we identified 139 non-synonymous variants showing large allele frequency differences (at least 0.8) between populations (Supplementary Table 8), including at least two genes involved in meiotic recombination—FANCA (ninth most extreme non-synonymous SNP in CEU versus CHB+JPT) and TEX15 (thirteenth most extreme non-synonymous SNP in CEU versus YRI, and twenty-sixth most extreme non-synonymous SNP in CHB+JPT versus YRI). Because we are finding almost all common variants in each population, these lists should contain the vast majority of the near fixed differences among these populations. Finally, it improves the fine mapping of selective sweeps (Supplementary Fig. 14) and analysis of the dynamics of location adaptation. For example, we find that the signal of population differentiation around high Fst genic SNPs drops by half within, on average, less than 0.05 cM (typically 30–50 kb; Fig. 5d). Furthermore, 51% of such variants are polymorphic in both populations. These observations indicate that much local adaptation has occurred by selection acting on existing variation rather than new mutation.
Strangely, recombination rates vary between groups. Again, why?
We estimated a fine-scale genetic map from the phased low-coverage genotypes. Recombination hotspots were narrower than previously estimated4 (mean hotspot width of 2.3 kb compared to 5.5 kb in HapMap II; Fig. 6a), although, unexpectedly, the estimated average peak recombination rate in hotspots is lower in YRI (13 cM Mb−1) than in CEU and CHB+JPT (20 cM Mb−1). In addition, crossover activity is less concentrated in the genome in YRI, with 70% of recombination occurring in 10% of the sequence rather than 80% of the recombination for CEU and CHB+JPT (Fig. 6b). A possible biological basis for these differences is that PRDM9, which binds a DNA motif strongly enriched in hotspots and influences the activity of LD-defined hotspots40, 41, 42, 43, shows length variation in its DNA-binding zinc fingers within populations, and substantial differentiation between African and non-African populations, with a greater allelic diversity in Africa43. This could mean greater diversity of hotspot locations within Africa and therefore a less concentrated picture in this data set of recombination and lower usage of LD-defined hotspots (which require evidence in at least two populations and therefore will not reflect hotspots present only in Africa).