include both seed and non-seed genes, all prioritized based on the network analysis. To create the total gene network, we compiled a list of human gene-gene interactions from databases containing data from automated and human-curated literature extrac- tion methods (BIND, preBIND, MIPS, MINT, and GeneWays) [34–38] as well as from two large-scale yeast two-hybrid studies that used human proteins [39,40]. This total inter- action network contains 12,743 genes and 224,269 non- redundant gene-gene interactions. To choose the seed genes, we conducted a large-scale literature review to identify genes that had been implicated in the etiology of T2D, obesity, hypertension, and dyslipidemia, separately, or of the metabolic syndrome as a phenotype. Each gene was scored depending on whether it was implicated in a given phenotype based on data from association studies (single and meta-analyses), animal models of the disease, pharmacological and physiological studies, or studies of the Mendelian form of the disease (seed genes for each phenotype are listed in Tables S1–S5). This information was combined to create a primary evidence score for each seed gene that summarizes the strength of the evidence implicating it in a given disease phenotype. The Molecular Triangulation algorithm uses the primary evidence scores and the total network to calculate a secondary evidence score (a function of the primary evidence scores and distances from all seed genes) for each gene and disease; the signiﬁcance of the secondary evidence score is assessed by permutations [32]. The permutation-based p-values were combined across phenotypes for each gene and were used to rank all the genes in the total network (i.e. both seeds and non-seeds), separately. The highest ranking 39 seed and 35 non-seed genes (excluding genes that were poorly tagged in the HapMap data) were selected for analysis. Because current knowledge of the true human gene-gene interaction network is incomplete, not all seed genes are found in the total network (e.g. TCF7L2) and not all strong candidate genes are identiﬁed as high ranking genes by the network analysis (e.g. PPARG). Therefore, we also selected an additional 8 genes with strong evidence of involvement in metabolic syndrome phenotypes. These genes are: PPARG, PPARGC1A, LEPR, TCF7L2, CLOCK, ALMS1, RAPTOR and FRAP1. PPARG, PPARGC1A, LEPR and TCF7L2 were chosen because variation in these genes has repeatedly been found to be associated with T2D risk and/or metabolic syndrome risk [41–47]. CLOCK and ALMS1 were included because they are strong functional candidates for metabolic syndrome [48–51]. RAPTOR was chosen because we previously observed an especially strong correlation between a microsatellite within this gene and latitude (Witonsky and Di Rienzo, unpublished data); this microsatellite had been genotyped in the HGDP at the Marshﬁeld Clinic Center for Human Genetics [52]. In addition, the products of RAPTOR and FRAP1 form a protein complex that has been implicated in energy metabolism and T2D pathways [53]. In total, we included 82 genes in the analysis (listed in Table S6). Testing for Spatially Varying Selection We used the HapMap Phase II data [54] to select 873 tagging SNPs that capture most of the variation in the 82 candidate genes (see Methods). In addition, we selected 210 control SNPs. The control SNPs were chosen from non- Adaptations to Climate coding regions that do not contain and are not tightly linked to evolutionarily constrained sequence elements. In order not to introduce a bias, the same ascertainment scheme was used to select genic and control SNPs (except that only one tag SNP was chosen at random from each control region) (for details, see Materials and Methods). A total of 1,083 SNPs typed in 964 individuals from the HGDP panel as well as in 37 Ewondo Beti and 33 Hausa from Cameroon yielded genotype data that passed our data quality ﬁlter (see Methods). To check that the control SNPs were appropriately matched to the genic SNPs, we calculated the minor allele frequency and FST for each SNP in our data and compared the frequency distributions of these two statistics between the two sets of SNPs. A global FST value, based on the average squared deviation between regional allele frequencies and the global average ([55] p. 167), was calculated among the seven major geographical regions deﬁned by Rosenberg et al. 2002 [56]. Using a two-sample Kolmogorov-Smirnov test, we found no signiﬁcant difference between the distributions of genic and control SNPs for minor allele frequency (D ¼ 0.058, p ¼ 0.61) or FST (D ¼ 0.070, p ¼ 0.38). This suggests that our procedure for matching genic and control SNPs is reliable and that the control SNPs can provide an appropriate empirical distribution for spatial patterns of variation expected for putatively neutral regions. To test the hypothesis that climate-related selection shaped variation in the candidate genes, we obtained climate data for each population in our panel. We selected six climate variables likely to reﬂect the physiological effects of cold and hot climates (i.e. precipitation rate, relative humidity, minimum temperature, maximum temperature, mean tem- perature and short wave radiation ﬂux). Separate values for summer and winter seasons were obtained for each of these variables. Since many of these variables are strongly correlated with one another, we reduced the dimensionality of the climate data using principal components analysis. In all analyses, we used the ﬁrst two principal components (PCs) for each season, which together explained 77.6% and 78.6% of the variance in the complete data set for summer and winter variables, respectively (Tables S7 and S8). It should be noted that because of climate changes and population movements over evolutionary time, the current values of these climate variables may be only partially correlated with the long-term climatic conditions experienced by each population. To assess the relationship between SNP allele frequency and climate, we used two methods that make different assumptions and, therefore, may be complementary. The ﬁrst one, Spearman rank correlation, is a non-parametric method that does not assume a linear relationship between the variables. The second one, which will be denoted Bayesian geographic analysis, is a newly developed method that tests whether a linear relationship between allele frequency and a climate variable provides a signiﬁcantly better ﬁt than the model alone, where the population covariance matrix provides the basis for the model (Coop et al., in preparation). With both methods, statistical signiﬁcance was assessed against the distribution of the test statistic calculated from the set of control SNPs. Although the use of control SNPs may be conservative [57], here it is necessary to reduce the number of spurious signals that would arise from the genome-wide correlation between allele frequencies and climate variables. Indeed, the control SNPs show an excess PLoS Genetics | www.plosgenetics.org 0003 February 2008 | Volume 4 | Issue 2 | e32