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Glucocorticoids regulate gene expression via binding of the ligand-activated glucocorticoid receptor (GR) to glucocorticoid-responsive elements (GRE). To identify GR-binding sites, we developed a modified yeast one-hybrid system which enables rapid and efficient identification of genomic targets for DNA-binding proteins. The human GR expression vector was transformed into yeast cells containing a library of human genomic fragments cloned upstream of the reporter gene URA3. The genomic fragments with GR-binding sites were identified by growth of yeast clones in media lacking uracil but containing dexamethasone. DNA fragments were recovered by colony-direct PCR and GRE sequences were predicted by in silico analysis. Using electrophoretic mobility shift assay and fluorescence correlation spectroscopy, we demonstrated that 314 predicted GREs could directly interact with recombinant human GR proteins. In addition, when the genomic fragments were inserted in front of the heterologous SV40 promoter, at least 150 fragments could function as GREs in HEK293 cells. Furthermore, we identified four functional regulatory polymorphisms which may influence individual variation in sensitivity to glucocorticoids. These results provide insights into the molecular mechanisms underlying the physiological and pathological actions of glucocorticoid.

Single nucleotide polymorphisms (SNPs) have been proposed to be grouped into haplotype blocks harboring a limited number of haplotypes. Within each block, the portion of haplotypes is expected to be tagged by a selected subset of SNPs; however, none of the proposed selection algorithms have been definitive. To address this issue, we developed a tag SNP selection algorithm based on grouping of SNPs by the linkage disequilibrium (LD) coefficient r(2) and examined five genes in three ethnic populations--the Japanese, African Americans, and Caucasians. Additionally, we investigated ethnic diversity by characterizing 979 SNPs distributed throughout the genome. Our algorithm could spare 60% of SNPs required for genotyping and limit the imprecision in allele-frequency estimation of nontag SNPs to 2% on average. We discovered the presence of a mosaic pattern of LD plots within a conventionally inferred haplotype block. This emerged because multiple groups of SNPs with strong intragroup LD were mingled in their physical positions. The pattern of LD plots showed some similarity, but the details of tag SNPs were not entirely concordant among three populations. Consequently, our algorithm utilizing LD grouping allows selection of a more faithful set of tag SNPs than do previous algorithms utilizing haplotype blocks.