Background-Recent advances in high-throughput genomics technology have expanded our ability to catalogue allelic variants in large sets of candidate genes related to premature coronary artery disease. Methods and Results-A total of 398 families were identified in 15 participating medical centers; they fulfilled the criteria of myocardial infarction, revascularization, or a significant coronary artery lesion diagnosed before 45 years in men or 50 years in women. A total of 62 vascular biology genes and 72 single-nucleotide polymorphisms were assessed. Previously undescribed variants in 3 related members of the thrombospondin protein family were prominent among a small set of single-nucleotide polymorphisms that showed a statistical association with premature coronary artery disease. A missense variant of thrombospondin 4 (A387P) showed the strongest association, with an adjusted odds ratio for myocardial infarction of 1.89 (Pϭ0.002 adjusted for covariates) for individuals carrying the P allele. A variant in the 3Ј untranslated region of thrombospondin-2 (change of thymidine to guanine) seemed to have a protective effect against myocardial in individuals homozygous for the variant (adjusted odds ratio of 0.31; Pϭ0.0018). A missense variant in thrombospondin-1 (N700S) was associated with an adjusted odds ratio for coronary artery disease of 11.90 (Pϭ0.041) in homozygous individuals, who also had the lowest level of thrombospondin-1 by plasma assay (Pϭ0.0019). Conclusions-This large-scale genetic study has identified the potential of multiple novel variants in the thrombospondin gene family to be associated with familial premature myocardial infarction. Notwithstanding multiple caveats, thrombospondins specifically and high-throughput genomic technology in general deserve further study in familial ischemic heart disease. (Circulation. 2001;104:2641-2644.)
Genome-wide association studies (GWASs) have identified many SNPs underlying variations in plasma-lipid levels. We explore whether additional loci associated with plasma-lipid phenotypes, such as high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglycerides (TGs), can be identified by a dense gene-centric approach. Our meta-analysis of 32 studies in 66,240 individuals of European ancestry was based on the custom ∼50,000 SNP genotyping array (the ITMAT-Broad-CARe array) covering ∼2,000 candidate genes. SNP-lipid associations were replicated either in a cohort comprising an additional 24,736 samples or within the Global Lipid Genetic Consortium. We identified four, six, ten, and four unreported SNPs in established lipid genes for HDL-C, LDL-C, TC, and TGs, respectively. We also identified several lipid-related SNPs in previously unreported genes: DGAT2, HCAR2, GPIHBP1, PPARG, and FTO for HDL-C; SOCS3, APOH, SPTY2D1, BRCA2, and VLDLR for LDL-C; SOCS3, UGT1A1, BRCA2, UBE3B, FCGR2A, CHUK, and INSIG2 for TC; and SERPINF2, C4B, GCK, GATA4, INSR, and LPAL2 for TGs. The proportion of explained phenotypic variance in the subset of studies providing individual-level data was 9.9% for HDL-C, 9.5% for LDL-C, 10.3% for TC, and 8.0% for TGs. This large meta-analysis of lipid phenotypes with the use of a dense gene-centric approach identified multiple SNPs not previously described in established lipid genes and several previously unknown loci. The explained phenotypic variance from this approach was comparable to that from a meta-analysis of GWAS data, suggesting that a focused genotyping approach can further increase the understanding of heritability of plasma lipids.
To identify genetic factors contributing to type 2 diabetes (T2D), we performed large-scale meta-analyses by using a custom ∼50,000 SNP genotyping array (the ITMAT-Broad-CARe array) with ∼2000 candidate genes in 39 multiethnic population-based studies, case-control studies, and clinical trials totaling 17,418 cases and 70,298 controls. First, meta-analysis of 25 studies comprising 14,073 cases and 57,489 controls of European descent confirmed eight established T2D loci at genome-wide significance. In silico follow-up analysis of putative association signals found in independent genome-wide association studies (including 8,130 cases and 38,987 controls) performed by the DIAGRAM consortium identified a T2D locus at genome-wide significance (GATAD2A/CILP2/PBX4; p = 5.7 × 10(-9)) and two loci exceeding study-wide significance (SREBF1, and TH/INS; p < 2.4 × 10(-6)). Second, meta-analyses of 1,986 cases and 7,695 controls from eight African-American studies identified study-wide-significant (p = 2.4 × 10(-7)) variants in HMGA2 and replicated variants in TCF7L2 (p = 5.1 × 10(-15)). Third, conditional analysis revealed multiple known and novel independent signals within five T2D-associated genes in samples of European ancestry and within HMGA2 in African-American samples. Fourth, a multiethnic meta-analysis of all 39 studies identified T2D-associated variants in BCL2 (p = 2.1 × 10(-8)). Finally, a composite genetic score of SNPs from new and established T2D signals was significantly associated with increased risk of diabetes in African-American, Hispanic, and Asian populations. In summary, large-scale meta-analysis involving a dense gene-centric approach has uncovered additional loci and variants that contribute to T2D risk and suggests substantial overlap of T2D association signals across multiple ethnic groups.
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