Materials and Methods Population samples. We genotyped 971 individuals from 52 worldwide populations from the CEPH Human Genome Diversity Project (HGDP) panel [33] and 10 duplicate controls. From the original panel of 1064 samples, 93 were excluded from analysis because they had been inferred to be either from members of ﬁrst- degree relative pairs, duplicates or atypical [97]. In all analyses, we combined all individuals from South African Bantu populations into one group, which we considered to be different from the Bantu sample from Kenya. In addition, we genotyped 72 individuals from two Cameroonian populations. We included 38 individuals from the Ewondo Beti, who belong to the Bantu language group, and 34 individuals from the Chadic-speaking Hausa; all Cameroonian individuals were sampled in Yaounde. This study was approved by the University of Chicago Institutional Review Board. Candidate gene selection using network analysis. Primary evidence scores used for running the molecular triangulation program were assigned based on the types of evidence found in the literature review. For each type of evidence, points were added to the primary evidence score in the following manner: ten points were added if there was signiﬁcant evidence of association from a meta-analysis, one point was added if there was signiﬁcant evidence of association in a single analysis, four points were added if a Mendelian phenotype results from mutations in a given gene, four points were added if the gene product is a drug target, one point was added if the gene was found to affect a given phenotype in an animal model, one point was added if there was physiological evidence a gene was involved in a phenotype and for genes with at least one of the previous types of evidence, we gave one point for genes that fell under a linkage peak. In order to be included as a ‘‘seed’’ gene in the network analysis, a gene needed to have a primary evidence score 3, except in the case of metabolic syndrome, where we only required a score 1. The score required was lower for metabolic syndrome because this phenotype is likely to represent a defect in the core metabolic pathway and because few studies have examined metabolic syndrome directly. We chose genes using the molecular triangulation method in two ways: (1) by running the algorithm on each phenotype separately and then combining the p-values and (2) by running the algorithm on genes with strength of evidence scores reﬂecting evidence for all pheno- types together. For the separate phenotype analysis, we ﬁrst ran the molecular triangulation algorithm on each phenotype separately and summed the p-values associated with the secondary evidence scores across all phenotypes. Then, we chose 50 ‘‘seed’’ and 50 ‘‘non-seed’’ genes with the lowest sums of the p-values. For the combined phenotype analysis, we summed the strength of evidence scores for all phenotypes, ran the molecular triangulation algorithm, and chose the most signiﬁcant 20 ‘‘seed’’ and 20 ‘‘non-seed’’ genes. This list of genes was reduced by choosing the 39 seed and 35 non-seed genes that had the best coverage of tag SNPs in the HapMap (a total of 34 out 140 genes were omitted due to poor tagging in the HapMap). We also selected an additional 8 genes with strong evidence of involvement in MetS phenotypes that were not present in the network. These genes are described in greater detail in the Results section. In total, we included 82 genes in the analysis. Selection of SNPs in candidate genes. Genic SNPs were selected from among HapMap Phase II (Build 35) SNPs located in a region that spans from 100 kb upstream to 100 kb downstream of each of the 82 genes. The frequency of the global minor allele (GMA) for each SNP was estimated based on the allele counts of the combined sample of 180 unrelated HapMap individuals (60 CEU parents, 60 YRI parents, 30 JPT þ 30 CHB chosen at random from the 90 Asian samples). Given these allele frequencies, the SNPs selected for genotyping were further ﬁltered using the following steps. First, SNPs with GMA frequency less than 10% or greater than 90% in all three regional populations were omitted. Second, SNPs from step 1 were sorted into tag bins using ldSelect (v 1.0) [98], applying an r2 threshold of 0.8. The process of sorting into tag bins was done both for the global sample and the non-African (CEU þ ASN) sample. Third, tag SNPs were selected from each bin observed in the global sample; in addition, tag SNPs were selected from bins that were found only in the non-African sample. If a tag bin had multiple tag SNPs, the SNP with the highest Illumina assay score was chosen. SNPs were also preferentially included if they were nonsynonymous or if they had been previously associated with disease phenotypes. For all genes, tag SNPs were chosen from among those bins whose span (the smallest region containing all SNPs in the bin) intersected the region between 5 kb upstream and 5 kb downstream of the gene. Selection of control SNPs. To control for the effect of population structure in assessing the relationship between climate variables and Adaptations to Climate allele frequencies at candidate genes, we genotyped a set of control SNPs from unconstrained genomic regions. These regions were chosen from among noncoding regions that are neither conserved nor in strong LD with deeply conserved sequence elements. To select unconserved regions, sequence conservation was calculated over sliding windows (100 bp window size, 1 bp step size) in the human- chicken, human-zebraﬁsh, human-mouse and human-rat genome alignments, which were obtained from the UCSC Genome Browser (http://genome.ucsc.edu), and a region was considered conserved if sequence identity exceeded 70%. Then, using estimates of the population recombination rate parameter q ( 4Nr, where N is the ¼ effective population size and r is the cross-over rate between adjacent sites) [99], regions were selected to be at least 200 kb in length and at least 40 q units away from regions conserved between human and chicken or between human and zebraﬁsh, to be unconserved between human and mouse or between human and rat, and not to contain coding regions. Among these regions, we selected a subset so that their lengths matched the distribution of lengths for genic regions. SNPs from these regions were chosen using a procedure similar to the procedure used in the selection of genic SNPs. Brieﬂy, tag SNPs were chosen so that their allele frequency distribution matched that of SNPs in genic regions. One SNP was selected at random from the set of tag SNPs each control region. SNP genotyping. 1130 SNPs were genotyped using the Illumina BeadArray technology at the UCLA Southern California Genotyping Consortium Facility. Thirty-nine SNPs had 100% missing data. In addition, data were ﬁltered so that three SNPs with missing data for more than 5% of the genotypes, three SNPs with high population- speciﬁc missing data, and two monomorphic SNPs were excluded from analyses. Seven HGDP individuals, one Hausa and one Beti individuals with more than 10% missing data were excluded from analyses. Data quality was assessed by genotyping 10 duplicate samples; we found only one genotyped pair with a discrepant call, for an error rate of 9.25 3 10 5. Tag SNPs in the SLC24A5 gene were genotyped using TaqMan Assays on Demand, for which 2ll of 40ng/lL DNA was added to 3.5 lL TaqMan SNP Genotyping MasterMix, 0.125 Assay on Demand solution and 2.5 lL ddH2O. Fluorescence was visualized on an Applied Biosystems 7900 HT Fast Real Time PCR machine. Data quality was assessed by genotyping 7 duplicate samples; we did not ﬁnd any discrepant call in these duplicates. For all analyses, ancestral state was inferred based on the human- chimpanzee genome alignment. When the ancestral state could not be unambiguously inferred, the major allele in the combined African HGDP populations was assumed to be ancestral. Climate variables. Climate data were obtained for each population based on coordinates provided with the HGDP panel. For the Cameroonian samples, coordinates for the birthplaces of each individual’s grandparents were obtained from Mapquest (http:// www.mapquest.com). We selected climate variables separately for the summer and winter seasons from the NCEP/NCAR database [100]; they included: precipitation rate, relative humidity, minimum temperature, maximum temperature, mean temperature and short wave radiation ﬂux. Values for summer and winter seasons were obtained for each variable by averaging variables over these seasons. We ran principal components analysis separately for summer and winter on a matrix that included the six climate variables as well as latitude and longitude using the prcomps() function in R [101]. Latitude and longitude were included because they might capture the long-term climate of human populations better than the climate variables measured over the past 50 years. Principal components analysis results in new variables, which are linear combinations of the original data weighted by the eigenvectors. Eigenvectors for these PCs and the proportion of the variance explained by each PC are shown in Tables S7 and S8. Detecting SNPs with climate patterns. We used two different methods to determine whether SNP allele frequencies at the candidate genes had been shaped by selective pressures related to climate. In the ﬁrst method, a Spearman rank correlation coefﬁcient is calculated for each SNP and each climate variable or PC. The data from the control SNPs are used to provide an empirical distribution of rank correlation coefﬁcients that reﬂects the history of human migrations and population structure. Speciﬁcally, for the rank correlation coefﬁcient value of each genic SNP we found the percentile value in the control SNP distribution; this percentile value is referred to as the empirical p value for a given genic SNP and a given climate variable or PC. The Spearman rank correlation method does not assume that the relationship between SNP allele frequency and climate variable or PC is linear. The second method, which we refer to as Bayesian geographic PLoS Genetics | www.plosgenetics.org 0010 February 2008 | Volume 4 | Issue 2 | e32