Author Summary The human species inhabits a wide geographical range encompass- ing a diversity of climates, and adaptation to these climates likely played an important role in shaping genetic and phenotypic variation among populations. We hypothesized that spatially varying selective pressures related to climate shaped the frequen- cies of genetic variants in the energy metabolic pathway. To test this hypothesis, we examined patterns of genetic variation in 82 candidate genes for common metabolic disorders across the 52 globally dispersed populations of the Human Genome Diversity Project. We applied a combination of statistical approaches to test whether the geographic distribution of these variants could be accounted for by differing climates, consistent with a signal of spatially varying positive selection. For several climate variables, we observed signals in excess of that expected from human population history and chance alone. Significantly, many of these signals were from genes previously shown to affect cold tolerance and disease risk. Our results provide evidence that variation among human populations in susceptibility to common metabolic diseases may be due, in part, to different histories of selective pressures on genes in these disease pathways. Furthermore, our results point to additional genes and variants that are suitable targets for follow-up disease association studies. ecological rules that individuals inhabiting colder regions are bulkier and have shorter relative limb lengths [23,24]. More speciﬁcally, classical work by Roberts showed that mean annual temperature is signiﬁcantly correlated with body mass and relative sitting height [25]. Forty years later, a re- examination of body mass measures found a strong correla- tion with mean annual temperature, but the slope of the correlation was smaller, probably due to nutritional changes in a subset of populations [26]. These results imply that climate had a strong inﬂuence on human morphology. Importantly, this inﬂuence can be detected even though, due to climate changes and population movements, the current climate conditions are only weak proxies for the long-term climate experienced by each population. In addition to clines of body mass measures, it was shown that basal metabolic rate is consistently elevated in indigenous circumpolar populations of North America and Siberia [27]. Although not all of this phenotypic variation has a genetic basis, these ﬁndings suggest that the metabolic state of individuals differs across populations and that this variation has adaptive signiﬁcance. The biological processes that inﬂuence tolerance to climatic extremes are likely to play important roles in the pathogenesis of common metabolic disorders, such as type 2 diabetes (T2D), obesity, dyslipidemia, and hypertension. For example, obesity risk is likely to be related to variation in energy balance, through variation in food intake, body mass index, and basal metabolic rate. Additional examples include the relationships between T2D risk and variation in blood glucose levels, between lipid levels and intake, processing and storage of lipids, and between hypertension risk and sodium retention and arterial vessel tone. Therefore, susceptibility genes for these common metabolic disorders are promising candidate targets of climate adaptations. Interestingly, these phenotypes tend to co-occur in individuals, an observation that gave rise to the deﬁnition of the metabolic syndrome as a clustering of quantitative phenotypes related to T2D, obesity, Adaptations to Climate hypertension and dyslipidemia [28,29]. The clustering in individuals, along with their concomitant increase in preva- lence, suggests that these diseases result from a set of shared metabolic defects and that these defects are adaptations to the life style, diet and climates experienced by human populations over long-term evolutionary time [20,30,31]. With the goal of selecting genes that experienced similar selective pressures, we used a bioinformatics approach that aims to characterize the core gene subnetwork of the metabolic syndrome phenotypes [32]. The genes in this subnetwork are likely to harbor those shared metabolic defect(s) that result in the clustering of metabolic disorders. To test the hypothesis that these genes adapted to the different climates experienced by human populations, we captured genetic variation in these genes by tag SNPs, which were genotyped in a worldwide panel of individuals [33]. Statistical tests were then performed to detect spatial patterns of allele frequencies that reﬂect the action of climate-related selective pressures. We ﬁnd that these genes, as a group, harbor an excess of tag SNPs whose allele frequencies are correlated with climate variables. This suggests that climate was an important selective pressure acting on the biological processes underlying common metabolic disorders. Results To identify spatial patterns of allele frequencies that result from adaptations to climate, we analyzed newly collected genotype data for 1083 SNPs in a worldwide panel of 1034 individuals from 54 populations, 52 of which belong to the Human Genome Diversity Project (HGDP) panel [33]. Of the 1083 SNPs, 873 are tag SNPs in 82 genes belonging to the core network of the metabolic syndrome phenotypes and 210 are control SNPs from unconstrained genomic regions. We characterized the signal of spatially varying selection in terms of the correlation between allele frequencies and four summaries of climate. To assess the signiﬁcance of the correlation between genic SNP allele frequencies and climate, we compared the patterns observed at genic and control SNPs. In this analysis, the control SNPs are meant to provide an empirical distribution for the spatial patterns of variation generated by human population history alone. Finally, to explore the performance of our approach in an empirical setting, we genotyped tag SNPs from a gene, SLC24A5, which is known to inﬂuence variation in skin pigmentation [13]. Selection of Candidate Genes in the Metabolic Syndrome Core Subnetwork In order to select a broad set of genes playing a role in metabolic disease phenotypes, we used a network-based algorithm, referred to as Molecular Triangulation [32], which uses ‘‘seed genes’’, i.e. genes previously implicated in disease risk, to search through the total network of gene-gene interactions to deﬁne the disease sub-network. The seed genes can be thought of as casting a shadow on the network, the size of which is proportional to the strength of the evidence implicating the seed in the disease phenotype. Therefore, a non-seed gene interacting with a strongly supported seed gene has a high probability of belonging to the disease subnetwork; note that the disease subnetwork will PLoS Genetics | www.plosgenetics.org 0002 February 2008 | Volume 4 | Issue 2 | e32