Identifying Susceptibility Genes for Metabolic Syndrome

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The metabolic syndrome (MS) describes the clustering of insulin resistance and metabolic CVD risk factors. Given the substantial clinical and public health burden imposed by this condition, further understanding of its genetic etiology is important. A recent genome scans for a composite measure of the MS in the NHLBI Family Heart Study (FHS) found significant linkage on chromosome 2 at 240 cM (LOD=3.34, p=0.00004). This same broad region of chromosome 2 has been implicated by at least 14 other studies for phenotypes related to the MS. The overall goal of this 3-year project is to characterize a 19.5 Mb region on chromosome 2q35-2q37 using linkage disequilibrium (LD) mapping to identify genes influencing susceptibility to the MS. Multiple strategies will be used to identify, characterize, and confirm MS genes in this region. Single nucleotide polymorphisms (SNPs) will be genotyped in 16 candidate genes prioritized by biological function (average density 1 per 10 kb) in 1122 subjects from the 150 FHS pedigrees contributing to the linkage peak. For regions outside these candidate genes, SNPs will be typed an average density 1 per 30 kb. State-of the-art methods will be used to model patterns of LD and identify recombination hotspots. Family-based methods will be used to test associations between individual SNPs and multilocus haplotypes and the MS composite measure. Building on initial results, selected regions and candidate genes will be saturated with additional SNPs in an effort to identify causal variants. To confirm the most compelling findings, associations between SNPs or haplotypes and the composite MS measure will be tested in an independent sample of randomly selected unrelated subjects (n=750) from FHS. In addition, the effects of implicated SNPs on gene expression will be tested in lymphoblast cell lines, preadipocyte tissue, and brain samples. Identification of genes influencing the MS will not only further the understanding of etiologic pathways that explain the clustering of abnormalities, but also may offer novel strategies for screening and prevention.