logical processes account in part for the distribution of metabolic proﬁles and disease-related traits across ethnic groups [88]. The idea that alleles that confer adaptations to climate have opposing effects on disease susceptibility is not entirely unexpected; this is probably because positive selection acted on phenotypes that are related, but distinct from the metabolic disorders that reach high prevalence among contemporary populations or that account only for a portion of all patients. For example, a haplotype in the TCF7L2 gene was reported to carry a signature of positive selection and this haplotype includes the derived allele at SNP rs7903146 that protects against T2D [66]. Interestingly, this haplotype is associated with higher body mass index and altered concen- trations of the hunger-satiety hormones ghrelin and leptin, suggesting that it conferred a selective advantage on energy metabolism. Our ﬁnding that SNP rs7903146 has a signiﬁcant signal of spatially varying selection and that the derived allele increases in frequency outside Africa is consistent with this proposal. Furthermore, it illustrates how an adaptation to climate may partially, but not entirely overlap with the disease phenotype. A key feature of our approach was the use of a network algorithm to choose a set of candidate genes likely to have experienced similar selective pressures, hence leading to the expectation that these genes, as a group, would show a signal of spatially varying selection. A network approach seemed particularly appropriate because of the widely accepted idea that the clustering of quantitative phenotypes typical of the metabolic syndrome is due to a small set of shared metabolic defects. By choosing seed genes implicated in all or most of the phenotypes that characterize the metabolic syndrome, we aimed to extract information regarding the core portion of the metabolic syndrome subnetwork; the genes in this core subnetwork are likely to be responsible for the concomitant increase in prevalence of metabolic syndrome phenotypes and to have been targets of natural selection in ancestral populations. The observed excess of signals of spatially varying selection in this group of genes suggests that our strategy for identifying candidate genes with similar evolu- tionary histories was successful. Interestingly, we found a similar proportion of seed and non-seed genes with signals of spatially varying selection, consistent with the idea that network theory provides a useful framework for elucidating the impact of natural selection. From the population genetics standpoint, our results suggest that searching for a correlation between allele frequencies and speciﬁed environmental variables comple- ments other approaches to detecting the signature of selection that also compare allele frequencies across pop- ulations. For example, there is a long history of using FST values for detecting adaptations to local environments [89– 91] and this approach has been recently applied to genome- wide variation data [92,93]. Even though spatially varying selection may lead to high FST values, a strong and signiﬁcant correlation between allele frequencies and environmental variables can be detected even if allele frequencies at opposite extremes of the environmental range are not highly differentiated. This is what we observed, for example, in the RAPTOR gene, which carries the strongest signal with latitude. Six SNPs are signiﬁcant with empirical p , 0.05; however, when compared to the HapMap data, the FST values Adaptations to Climate for these same SNPs are unremarkable, with 12% to 86% of HapMap SNPs showing more extreme values. The power and accuracy of our approach is likely to depend on a number of factors, including the strength of selection relative to gene ﬂow. If selection is not sufﬁciently strong to spread favored alleles between subdivided popula- tions, allele frequencies may not show the expected clines with the appropriate environmental variable. This may be the case for some of the signals observed in this study. For example, the allele frequencies at candidate SNPs for skin pigmentation are not most strongly correlated with the variable, i.e. short-wave radiation, that most closely matches the selective pressure acting on these variants. Indeed, several of these variants reach high frequency only in Eurasian populations living west of the Himalayas, while East Asian populations at similar latitudes have allele frequencies as low as those found in Sub-Saharan Africa [15]. Although further work is necessary to estimate the power of our approach for a range of selective coefﬁcients and migration rates, our empirical investigation based on the skin pigmentation SNPs suggests that strong signals can be detected even if the spatial distribution of allele frequencies does not match exactly the spatial distribution of the intensity of selection. Additional factors that may affect the power and accuracy of our approach include threshold effects on ﬁtness, so that a correlation between an allele frequency and an environ- mental variable is expected only over a portion of the range of environmental variation. Furthermore, as noted for skin pigmentation variants [15], parallel mutations that arose in different populations may obscure the expected correlation with the relevant environmental variable. Although we tested for a correlation between allele frequencies and climate variables, it is unclear whether all the signals of spatially varying selection reported here are the result of adaptations to climate rather than other environ- mental variables. To the extent that other features of the environment are partially correlated with climate, our method is unlikely to distinguish between selective pressures. For example, UV radiation, which was included among the climate variables in our analysis, is correlated with the other climate variables and with latitude (data not shown). Clearly, UV radiation inﬂuences environmental temperature and, therefore, it is expected to exert a selective pressure on genes involved in diverse biological processes, from energy metab- olism to melanin production. In some cases, climate may be an important determinant of other aspects of the environ- ment. An important example is the diversity of parasitic and infectious disease species, which is known to decrease with latitude mainly as a result of climatic factors [94]. Likewise, resource availability, which in turn may inﬂuence subsistence and diet, is likely to be partially correlated with climate (see, for example [95,96]). Therefore, the signals of spatially varying selection, detected in this study are likely to be the result of both the direct and indirect inﬂuence of climate on human genetic variation; they may also be due to selective pressures entirely unrelated to climate, but partially corre- lated with it. Despite these caveats, our results indicate that searching for spatial patterns of allele frequencies that correlate with environmental variables will add to our knowledge concerning the history of selective pressures acting on the human genome. PLoS Genetics | www.plosgenetics.org 0009 February 2008 | Volume 4 | Issue 2 | e32