Adaptations to Climate Table 4. Coefficients and p-Values (in Parentheses) for Tag SNPs in SLC24A5 Method SNP Latitude Summer PC1 Summer PC2 Winter PC1 Winter PC2 Summer SWRF Winter SWRF Bayesian geographic analysisa rs1834640 rs2675345 rs2459391 rs2433356 rs16960620 rs8040016 rs16960624 rs1426654b rs8028919 rs2413887 Spearman rank correlationa rs1834640 rs2675345 rs2459391 rs2433356 rs16960620 rs8040016 rs16960624 rs1426654b rs8028919 rs2413887 0.92 (0.22) 1 (0) 0.91 (0.22) 0.91 (0.22) 0.36 (0.85) 0.11 (0.29) 0.28 (0.68) 0 (0) 0.98 (0.06) 1 (0) 0.55 (0.02) 0.74 (0) 0.63 (0) 0.62 (0) 0.15 (0.89) 0.34 (0.34) 0.43 (0.13) 0.54 (0.01) 0.65 (0) 0.65 (0) 1 (0) 1 (0) 0.81 (0.31) 0.94 (0.13) 0.85 (0.21) 0.02 (0.03) 0.41 (0.88) 0 (0) 1 (0) 1 (0) 0.69 (0) 0.66 (0) 0.45 (0.03) 0.44 (0.05) 0.34 (0.11) 0.45 (0.15) 0.19 (0.43) 0.67 (0) 0.68 (0) 0.71 (0) 0.43 (0.92) 0.06 (0.12) 0.06 (0.12) 0.16 (0.4) 0.73 (0.51) 0.84 (0.33) 0.22 (0.59) 0.8 (0.4) 0.15 (0.38) 0.17 (0.4) 0.14 (0.55) 0.05 (0.76) 0.05 (0.76) 0.04 (0.81) 0.28 (0.12) 0.24 (0.24) 0.11 (0.67) 0.04 (1) 0.1 (0.67) 0.14 (0.52) 0.84 (0.38) 1 (0) 0.89 (0.3) 0.9 (0.24) 0.37 (0.78) 0.12 (0.24) 0.49 (0.99) 0 (0) 0.97 (0.1) 1 (0) 0.12 (0.5) 0.26 (0.2) 0.32 (0.12) 0.31 (0.13) 0.2 (0.41) 0.07 (0.87) 0.25 (0.21) 0.38 (0.16) 0.17 (0.39) 0.22 (0.25) 0.33 (0.64) 0.06 (0.15) 0.33 (0.64) 0.33 (0.64) 0.49 (0.94) 0.51 (0.96) 0.96 (0.11) 0.96 (0.1) 0.36 (0.64) 0.03 (0.12) 0.12 (0.51) 0.01 (0.95) 0.02 (0.96) 0.02 (0.96) 0.12 (0.52) 0.1 (0.42) 0.08 (0.69) 0.03 (0.74) 0.05 (0.9) 0.08 (0.74) 1 (0) 1 (0) 0.76 (0.39) 0.93 (0.12) 0.77 (0.38) 0.02 (0.09) 0.3 (0.73) 0 (0) 1 (0) 1 (0) 0.57 (0.01) 0.64 (0) 0.48 (0.02) 0.48 (0.02) 0.25 (0.21) 0.49 (0.06) 0.25 (0.21) 0.61 (0.04) 0.63 (0) 0.65 (0) 0.44 (0.91) 0.01 (0.03) 0.46 (0.95) 0.41 (0.86) 0.53 (0.91) 0.6 (0.82) 0.25 (0.55) 0.91 (0.18) 0.23 (0.53) 0.04 (0.1) 0.52 (0.13) 0.65 (0) 0.53 (0.12) 0.54 (0.11) 0.17 (0.85) 0.18 (0.84) 0.53 (0.12) 0.4 (0.11) 0.58 (0.09) 0.52 (0.12) aBayesian geographic analysis coefficients range from zero to one, whereas Spearman rank correlation coefficients range from negative one to one. For both statistics, empirical p-values are two-tailed. Bolded values represent significance with p 0.01. bPutative causative variant. doi:10.1371/journal.pgen.0040032.t004 Interestingly, a genome scan for selection signals based on the HapMap data showed that there is extended haplotype homozygosity around the LEPR gene, as summarized by the integrated Haplotype Score (iHS) [17]. More speciﬁcally, several SNPs in LEPR that had signiﬁcant patterns with winter PC1 also had signiﬁcant iHS values (i.e. rs1892535, rs1137100, rs1022981, and rs12405556 in Asians and rs4655518 in both Asians and CEPH Europeans). This is consistent with the idea that variants that are deleterious in hot equatorial climates become advantageous (rather than simply neutral) in colder climates. A similarly interesting example is the signal observed at the PON1 gene, whose protein product protects lipids from oxidation [78] and is involved in vasodilation. A derived nonsynonymous allele (192Q), which increases in frequency with latitude and affects enzymatic activity in vitro [59], was found to inﬂuence LDL size [79] and to be protective against coronary artery disease [80], stroke [81] and myocardial infarction risk [82]. Additional signals of spatially varying selection for nonsynonymous SNPs are found at the FABP2 gene and the EPHX2 gene. The FABP2 protein participates in the uptake, intracellular metabolism and transport of long- chain fatty acids. The derived allele at SNP rs1799883, which increases strongly with latitude, was found to increase afﬁnity for long-chain fatty acids [83], consistent with a role in protection against cold temperatures by increasing BMI and increasing the fuel for heat production. In addition, it was associated with an increase in insulin resistance [83], increased plasma concentrations of cholesterol and trigly- cerides [84], and total abdominal and subcutaneous abdomi- nal fat [85]. SNP (R287Q) rs751141 in EPHX2 has a signiﬁcant correlation with winter PC1 and was previously shown to modify risk of familial hypercholesterolemia. Among carriers of causative mutations for familial hypercholesterolemia, carriers of the EPHX2 287Q variant had higher plasma cholesterol and triglyceride levels [86]. In addition, the 287Q variant was associated with a two-fold increased risk of coronary artery calciﬁcation in African Americans, but no relationship was found in Caucasians [87]. Interestingly, for all four nonsynonymous SNPs discussed above, the derived allele increases in frequency outside Africa. However, two of these derived alleles (LEPR 109R and PON1 192Q) decrease risk to disease phenotypes (obesity and CAD), while the other two (FABP2 54T and EPHX2 287Q) increase risk for disease traits (obesity and coronary artery calciﬁcation). Moreover, the frequencies of two additional derived alleles that protect against T2D (i.e. rs7903146 in TCF7L2 and P12A (rs1805192) in PPARG) and show signiﬁ- cant climate signals also increase outside Africa. Therefore, these derived alleles have opposing effects on disease susceptibility, even though they are all likely to be involved in adaptations to cold climates. This observation contrasts with the results of a survey of variants inﬂuencing risk to hypertension; in all cases, the frequencies of the protective alleles were found to increase outside Africa, but some of these alleles were ancestral rather than derived [22]. A possible explanation is that the impact of climate adaptations on the susceptibility to common diseases varies depending on the biological process underlying the speciﬁc disease trait. For example, it is reasonable to expect that adaptations to heat and cold stress involve a different set of genes and biological processes, e.g. sodium homeostasis and energy metabolism, respectively. Therefore, the evidence for climate adaptations in candidate genes for metabolic disorders does not lead to the expectation that these conditions will systematically occur at higher frequency in one ethnic group compared to another. On the other hand, one might speculate that climate adaptations involving different bio- PLoS Genetics | www.plosgenetics.org 0008 February 2008 | Volume 4 | Issue 2 | e32