Adaptations to Climate Figure 1. Allele Frequencies for rs12946049 in the RAPTOR Gene Are Mapped onto Winter Maximum Temperature (A) and Plotted against Winter PC1 (B) Allele frequencies for rs662 in the PON1 gene are mapped onto summer SWRF (C) and plotted against summer PC1 (D). doi:10.1371/journal.pgen.0040032.g001 metabolic syndrome. These SNPs include the nonsynonymous SNPs in SLC24A5 and SLC45A2, which were associated with normal variation in skin pigmentation [13,68], as well as a nonsynonymous SNP in TYR (rs1042602), which was recently associated with normal variation in hair color and with freckles [69]. The remaining variants are a nonsynonymous SNP in OCA2 (rs1800404), a SNP in the promoter of ASIP, and an intronic SNP (rs1800410) in OCA2. These SNPs were reported to carry a signature of positive natural selection based on either haplotype structure or inter-population differentiation [13–18]. As shown in Table 3, several of these candidate SNPs showed interesting patterns with climate variables. We expected that short wave radiation ﬂux should have the strongest signal with SNPs in skin pigmentation genes since intensity of solar radiation is likely to be the best proxy for the selective pressure acting on this phenotype [12]. Somewhat surprisingly, however, we did not see the strongest signals with short wave radiation ﬂux. Instead, we found very strong patterns with latitude and with winter PC1. Since climate and, as a result, UV exposure at the earth surface changed over evolutionary time while latitude did not, it is possible that latitude provides a better proxy for the long- term selective pressures that shaped variation in these genes. It is also possible that the power to detect a correlation varies for different climate variables. For example, if the spatial distribution of some climate variables is similar to genome- wide patterns of population structure, neutral allele frequen- cies may be more strongly correlated with these climate variables, possibly leading to a greater reduction in power for some variables compared to others. Indeed, we found that the correlation between control SNP allele frequencies and climate PCs differs across variables (see Figure S1). We also found some difference between the results using the rank correlation and the Bayesian analyses. In agreement with our ﬁndings for metabolic genes, the latter tends to yield more signiﬁcant signals than the nonparametric method. Finally, we wanted to investigate how the signal of spatially varying selection is distributed across tag SNPs within the same gene. For this analysis, we selected the gene SLC24A5 because of its known function in human skin pigmentation: its knockdown has a skin pigmentation phenotype in zebra- ﬁsh and this phenotype can be rescued by the human SLC24A5 [13]. In addition, the nonsynonymous polymor- phism at amino acid 111 (rs1426654) was found to be associated with variation in pigmentation among African- American and African-Caribbean individuals [13]. We chose tagging SNPs for SLC24A5 in the same way as for the metabolic syndrome candidate genes and tested them for a correlation with climate variables. The empirical p-values for both methods are shown in Table 4. With both methods, we found strong signals for the ‘‘causative’’ nonsynonymous SNP as well as for a few additional SNPs. In the case of the rank correlation analysis, some of the signiﬁcant SNPs had stronger correlations with latitude, summer PC1 and summer PLoS Genetics | www.plosgenetics.org 0006 February 2008 | Volume 4 | Issue 2 | e32