Table 1. Proportion of SNPs Significant with Climate Variables Using a Parametric (Bayesian Geographic Analysis) and a Nonparametric (Spearman Rank Correlation) Method Method Bayesian geographic analysis Spearman rank correlationa Variable Absolute latitude Summer PC1 Summer PC2 Winter PC1 Winter PC2 Absolute latitude Summer PC1 Summer PC2 Winter PC1 Winter PC2 a a ¼ 0.01 0.0169 0.0545 0.0206 0.0303 0.0109 0.0339 0.0206 0.0206 0.0303 0.0315 0.05 0.0714 0.1017 0.0557 0.1102 0.0351 0.0787 0.0605 0.0702 0.0835 0.0460 0.1 0.1344 0.1598 0.1077 0.1622 0.0993 0.1283 0.1308 0.1223 0.1646 0.0751 Adaptations to Climate 0.5 0.6090 0.5218 0.4843 0.6005 0.4891 0.5545 0.4903 0.5811 0.5908 0.5387 aValues are bold if there is an excess in at least 95% of the bootstrap replicates when bootstrapping was performed across genes. doi:10.1371/journal.pgen.0040032.t001 of signiﬁcant correlations with all climate variables, as assessed against the theoretical distribution (see Figure S1). Signals of Spatially Varying Selection on Metabolism Genes We found that many genic SNPs had signiﬁcant spatial patterns with climate variables (absolute latitude, summer PC1, summer PC2, winter PC1, winter PC2). To assess the evidence for climate adaptations in metabolism genes, we asked if genic SNPs as a group show an excess of signals for spatially varying selection. Our expectation was that, even if climate had exerted a strong selective pressure on these genes, only a fraction of genic SNPs would show evidence for spatially varying selection. Therefore, we asked if there was an excess of SNPs in the tail of the distribution of empirical p- values for the genic SNPs for several type I error (a) levels. As shown in Table 1, we found an excess of genic SNPs with p- values above the type I error rate for most climate variables. To rule out the possibility that this pattern was driven by linkage disequilibrium in a few genes with strong signals, we bootstrap resampled across genes and control SNPs and determined how often we found an excess (for details, see Materials and Methods). The RAPTOR gene had been included in our survey because of a known correlation between latitude and a microsatellite within this gene (Witonsky and Di Rienzo, unpublished data); therefore, RAPTOR was omitted from this analysis. (When RAPTOR was included in this analysis, we obtained similar results; see Table S9.) This analysis revealed strong signals for summer PC1 and winter PC1, consistent with the idea that these summaries of climate variables reﬂect important selective pressures that shaped the spatial distribution of allele frequencies of metabolism genes in humans. Note that these two PCs are not strongly correlated with each other as they summarize somewhat different aspects of climate (i.e. relative humidity, short-wave radiation ﬂux and precipitation for summer PC1 and temperature variables for winter PC1) (see Table S7). Interestingly, the Bayesian geographic analysis tends to detect a stronger signal compared to the rank correlation analysis. Computer simulations showed that, under some scenarios, the Bayesian analysis is more powerful than the rank correlation analysis (G. Coop, unpublished observations); therefore, it is possible that the difference is simply due to a difference in power. Next, we considered the signals of spatially varying selection at individual SNPs; for this analysis, we focused on SNPs that are signiﬁcant across both methods on the assumption that they represent the most robust signals. We found 72 SNPs in 32 genes that were in the top 5% of the empirical distribution (i.e. the control SNP distribution) with the same climate variable for both methods (Table 2). At the 0.01 signiﬁcance level, there were four SNPs in two genes (FABP2 and RAPTOR) signiﬁcant with latitude, nine SNPs in six genes (CD36, DSCR1, MAPK14, PON1, SOD1, and TCF7L2) with summer PC1, four SNPs in four genes (CETP, DSCR1, EGFR, and NPPA) signiﬁcant with summer PC2, ten SNPs in ﬁve genes signiﬁcant with winter PC1 (EPHX2, LEPR, MAPK1, RAPTOR, and UCP3) and two SNPs in two genes (LPA and MMRN1) signiﬁcant with winter PC2. In total, 26 SNPs in 17 genes had empirical p , 0.01 with both analysis methods. Of these 17 genes, eight were seed genes, six were non-seed genes and three were other candidates. Although we did not detect an enrichment of signals of spatially varying selection at non-synonymous SNPs, several genes carry particularly strong signals at a nonsynonymous SNP. The gene coding for the leptin receptor, LEPR, is among the strongest signals detected by our analysis. LEPR is an exceptionally strong candidate gene as it is involved in the regulation of satiety and energy balance and is part of the thermogenesis pathway [58]. A nonsynonymous SNP (K109R), rs1137100, is strongly correlated with winter PC1 (p , 0.01). Three SNPs in the PON1 gene, which codes for an esterase present in high density lipoproteins (HDLs), have strong patterns with summer PC1, and one of these, rs662, is a nonsynonymous SNP (R192Q) that was shown to affect enzymatic activity in vitro [59] (Figure 1C and 1D). Additional nonsynonymous SNPs with strong climate signals are found in the FABP2 and the EPHX2 genes, which code for the intestinal fatty acid binding protein 2 and the cytoplasmic epoxide hydrolase 2, respectively. The derived allele at SNP (A54T), rs1799883, in FABP2 increases strongly with latitude (p , 0.01) and SNP (R287Q), rs751141, in EPHX2 has a signiﬁcant correlation (p , 0.01) with winter PC1 with both PLoS Genetics | www.plosgenetics.org 0004 February 2008 | Volume 4 | Issue 2 | e32