analysis, is designed to test for a correlation between an environ- mental variable and the frequency of an allele across populations, while accounting for differences in sample size and the neutral covariation of allele frequencies among populations. This approach involves two stages. First, we use a set of SNPs (which can be treated as randomly chosen for our purposes and which were typed by Conrad et al., 2006 [102]) to estimate the covariance matrix (X) whose entries give the covariance of allele frequencies between pairs of popula- tions. To estimate this matrix we make a number of assumptions. The allele counts at a SNP in a population are assumed to be a binomial draw from the unknown underlying population allele frequency. We assume the population allele frequency within a population margin- ally has a normal distribution where the mass of the normal distribution below zero is placed as a point mass on zero and the mass above one is placed on one. The population frequency across populations is assumed to have a multivariate normal distribution, with an unknown covariance matrix X and mean l. Given these assumptions we used Markov Chain Monte Carlo (MCMC) to sample the posterior distribution of the covariance matrix (using standard priors). Second, for each genic SNP and each climate variable, we tested for correlation with the climate variable (whose values across the 52 populations is a vector Y), while controlling for population structure using the covariance matrix estimated in the ﬁrst step. To perform this test we included a linear effect bY (where b is the coefﬁcient) in the mean of the multivariate normal, and estimated the posterior of b using MCMC. To summarize the posterior in a single test score we recorded the proportion of the posterior density, which was above zero. If the data has no strong signal of an effect of the environmental variable (Y) the posterior of b will be reasonably symmetrically distributed around zero resulting in scores close to 0.5, a posterior strongly skewed towards positive b will have a score close to one, while a posterior distribution strong skewed towards negative b will give a score close to zero. We also ran a test for each environmental variable on each control SNP and use these scores to estimate, for each environmental variable, an empirical two-tailed p- value for each genic SNP. We included the two Cameroonian populations (Hausa and Beti) when we ran the Spearman method, but we excluded these populations when we ran the Bayesian geographic analysis method since the covariance matrix was created from a dataset that did not include these populations. Testing for excess signiﬁcance for climate variables. Since we performed many tests and the SNPs within a given gene are not independent, we asked whether the genic SNPs as a group show a larger proportion of low p values than expected by chance. To this end, we chose several type I error levels (a ¼ 0.01, 0.05, 0.1 and 0.5) and, for each variable, we calculated the proportion of genic SNPs with empirical p-values less than a. Furthermore, because the SNPs within any given genic region are not completely independent while the control SNPs are, it is possible that the observed excess of genic SNPs with p , a is driven by a large number of correlated SNPs within one or few genes rather than by SNPs in many genes. To distinguish between these two possibilities, we bootstrap re-sampled across genic and control SNPs and recalculated, for each of 1000 bootstrap replicates, the proportion of SNPs with p-values lower than each cutoff. This distribution of proportions was then used to get a measure of the conﬁdence that the excess we observed was robust to the choice of genes and control SNPs. More speciﬁcally, for each replicate, we drew a set of 81 genes with replacement from the original set of genes and a set of 210 controls with replacement from the list of control SNPs. Then, we calculated empirical p-values for each of the genic SNPs using the distribution of control SNPs. Next, we determined the proportion of genic SNPs that had p-values less than or equal to each a cutoff. Finally, we found the proportion of bootstrap replicates where the proportion of SNPs with p-values less than or equal to the cutoff was greater than expected by chance. References 1. Storey JM, Storey KB (2004) Cold hardiness and freeze tolerance.Storey KB, editor. Functional metabolism: regulation and adaptation. Hoboken (New Jersey): John Wiley & Sons, Inc. 2. Beckman G, Birgander R, Sjalander A, Saha N, Holmberg PA, et al. (1994) Is p53 polymorphism maintained by natural selection? Hum Hered 44: 266–270. 3. Cavalli-Sforza LL, Menozzi P, Piazza A (1994) History and geography of human genes. Princeton (New Jersey): Princeton University Press. 4. Costa R, Peixoto AA, Barbujani G, Kyriacou CP (1992) A latitudinal cline in a Drosophila clock gene. Proc Biol Sci 250: 43–49. 5. Oakeshott JG, Wilson SR, Knibb WR (1988) Selection affecting enzyme Adaptations to Climate Supporting Information Figure S1. Correlation between Climate Variables and Control SNPs (A) shows the frequency distribution of the Spearman rank correlation test statistic (q) for the control SNPs for each of the climate variables. The critical values for a ¼ 0.01 from the theoretical distribution are shown by vertical lines. Panel B shows the distribution of Spearman rank correlation p-values from the theoretical distribution. The horizontal line shows the expectation if there was no effect due to population structure (i.e., if the p-values were uniformly distributed). Found at doi:10.1371/journal.pgen.0040032.sg001 (2.0 MB TIF). Table S1. Literature Review Results for Hypertension Found at doi:10.1371/journal.pgen.0040032.st001 (636 KB DOC). Table S2. Literature Review Results for Type 2 Diabetes Found at doi:10.1371/journal.pgen.0040032.st002 (513 KB DOC). Table S3. Literature Review Results for Obesity Found at doi:10.1371/journal.pgen.0040032.st003 (1.0 MB DOC). Table S4. Literature Review Results for Dyslipidemia Found at doi:10.1371/journal.pgen.0040032.st004 (374 KB DOC). Table S5. Literature Review Results for Metabolic Syndrome Found at doi:10.1371/journal.pgen.0040032.st005 (113 KB DOC). Table S6. Selection of Candidate Genes for Genotyping Found at doi:10.1371/journal.pgen.0040032.st006 (93 KB DOC). Table S7. Eigenvectors from Principal Components Analysis Found at doi:10.1371/journal.pgen.0040032.st007 (35 KB DOC). Table S8. Proportion of Variance Explained by Each Principal Component Found at doi:10.1371/journal.pgen.0040032.st008 (30 KB DOC). Table S9. Proportion of SNPs Signiﬁcant with RAPTOR Included in the Analysis Found at doi:10.1371/journal.pgen.0040032.st009 (37 KB DOC). Acknowledgments We are grateful to M. Przeworski for comments on the manuscript and to A. Rzhetsky, N. Cox, and F. Luca for helpful discussions. We also gratefully acknowledge F.G. Sperone for creating GIS-generated maps of climate variables and allele frequencies as well as the following experts on metabolic syndrome phenotypes who consulted on the list of seed genes used in the network analysis: D. Allison, R. Cooper, T. Frayling, H. Hobbs, M. Olivier, A. Scanu, and R. Watanabe. H. Norton kindly provided the genotype data for the six skin pigmentation SNPs. Author contributions. AMH, JKP, GC, and AD conceived and designed the study. ASG contributed to data collection. AMH, DBW, and AD analyzed the data. GE contributed analysis tools. AMH and AD wrote the paper. Funding. This study was supported by National Institutes of Health (NIH) grants DK056670 and GM61393 to AD. AMH was partially supported by an NIH Genetics Training Grant and a predoctoral fellowship from the American Heart Association. Competing interests. The authors have declared that no competing interests exist. polymorphisms in enclosed Drosophila populations maintained in a natural environment. Proc Natl Acad Sci U S A 85: 293–297. 6. Schmidt PS, Duvernell DD, Eanes WF (2000) Adaptive evolution of a candidate gene for aging in Drosophila. Proc Natl Acad Sci U S A 97: 10861–10865. 7. Sezgin E, Duvernell DD, Matzkin LM, Duan Y, Zhu CT, et al. (2004) Single- locus latitudinal clines and their relationship to temperate adaptation in metabolic genes and derived alleles in Drosophila melanogaster. Genetics 168: 923–931. 8. Verrelli BC, Eanes WF (2001) Clinal variation for amino acid poly- morphisms at the Pgm locus in Drosophila melanogaster. Genetics 157: 1649–1663. PLoS Genetics | www.plosgenetics.org 0011 February 2008 | Volume 4 | Issue 2 | e32