Fasting glucose is associated with future risk of type 2 diabetes and ischemic heart disease and is tightly regulated despite considerable variation in quantity, type, and timing of food intake. In pregnancy, maternal fasting glucose concentration is an important determinant of offspring birth weight. The key determinant of fasting glucose is the enzyme glucokinase (GCK). Rare mutations of GCK cause fasting hyperglycemia and alter birth weight. The extent to which common variation of GCK explains normal variation of fasting glucose and birth weight is not known. We aimed to comprehensively define the role of variation of GCK in determination of fasting glucose and birth weight, using a tagging SNP (tSNP) approach and studying 19,806 subjects from six population-based studies. Using 22 tSNPs, we showed that the variant rs1799884 is associated with fasting glucose at all ages in the normal population and exceeded genomewide levels of significance (P=10-9). rs3757840 was also highly significantly associated with fasting glucose (P=8x10-7), but haplotype analysis revealed that this is explained by linkage disequilibrium (r2=0.2) with rs1799884. A maternal A allele at rs1799884 was associated with a 32-g (95% confidence interval 11-53 g) increase in offspring birth weight (P=.002). Genetic variation influencing birth weight may have conferred a selective advantage in human populations. We performed extensive population-genetics analyses to look for evidence of recent positive natural selection on patterns of GCK variation. However, we found no strong signature of positive selection. In conclusion, a comprehensive analysis of common variation of the glucokinase gene shows that this is the first gene to be reproducibly associated with fasting glucose and fetal growth.

Production of heat via nonshivering thermogenesis (NST) is critical for temperature homeostasis in mammals. Uncoupling protein UCP1 plays a central role in NST by uncoupling the proton gradients produced in the inner membranes of mitochondria to produce heat; however, the extent to which UCP1 homologues, UCP2 and UCP3, are involved in NST is the subject of an ongoing debate. We used an evolutionary approach to test the hypotheses that variants that are associated with increased expression of these genes (UCP1 −3826A, UCP2 −866A, and UCP3 −55T) show evidence of adaptation with winter climate. To that end, we calculated correlations between allele frequencies and winter climate variables for these single-nucleotide polymorphisms (SNPs), which we genotyped in a panel of 52 worldwide populations. We found significant correlations with winter climate for UCP1 −3826G/A and UCP3 −55C/T. Further, by analyzing previously published genotype data for these SNPs, we found that the peak of the correlation for the UCP1 region occurred at the disease-associated −3826A/G variant and that the UCP3 region has a striking signal overall, with several individual SNPs showing interesting patterns, including the −55C/T variant. Resequencing of the regions in a set of three diverse population samples helped to clarify the signals that we found with the genotype data. At UCP1, the resequencing data revealed modest evidence that the haplotype carrying the −3826A variant was driven to high frequency by selection. In the UCP3 region, combining results from the climate analysis and resequencing survey suggest a more complex model in which variants on multiple haplotypes may independently be correlated with temperature. This is further supported by an excess of intermediate frequency variants in the UCP3 region in the Han Chinese population. Taken together, our results suggest that adaptation to climate influenced the global distribution of allele frequencies in UCP1 and UCP3 and provide an independent source of evidence for a role in cold resistance for UCP3.

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As more SNP marker data becomes available, researchers have used haplotypes of markers, rather than individual polymorphisms, for association analysis of candidate genes. In order to perform haplotype analysis in a population-based case-control study, haplotypes must be determined by estimation in the absence of family information or laboratory methods for establishing phase. Here, we test the accuracy of the Expectation-Maximization (EM) algorithm for estimating haplotype state and frequency in the CCR2-CCR5 gene region by comparison with haplotype state and frequency determined by pedigree analysis. To do this, we have characterized haplotypes comprising alleles at seven biallelic loci in the CCR2-CCR5 chemokine receptor gene region, a span of 20 kb on chromosome 3p21. Three-generation CEPH families (n=40), totaling 489 individuals, were genotyped by the 5'nuclease assay (TaqMan). Haplotype states and frequencies were compared in 103 grandparents who were assumed to have mated at random. Both pedigree analysis and the EM algorithm yielded the same small number of haplotypes for which linkage disequilibrium was nearly maximal. The haplotype frequencies generated by the two methods were nearly identical. These results suggest that the EM algorithm estimation of haplotype states, frequency, and linkage disequilibrium analysis will be an effective strategy in the CCR2-CCR5 gene region. For genetic epidemiology studies, CCR2-CCR5 allele and haplotype frequencies were determined in African-American (n=30), Hispanic (n=24) and European-American (n=34) populations.