3 research outputs found
Blood pressure loci identified with a gene-centric array
Raised blood pressure (BP) is a major risk factor for cardiovascular disease. Previous studies have identified 47 distinct genetic variants robustly associated with BP, but collectively these explain only a few percent of the heritability for BP phenotypes. To find additional BP loci, we used a bespoke gene-centric array to genotype an independent discovery sample of 25,118 individuals that combined hypertensive case-control and general population samples. We followed up four SNPs associated with BP at our p < 8.56 Ă 10â7 study-specific significance threshold and six suggestively associated SNPs in a further 59,349 individuals. We identified and replicated a SNP at LSP1/TNNT3, a SNP at MTHFR-NPPB independent (r2 = 0.33) of previous reports, and replicated SNPs at AGT and ATP2B1 reported previously. An analysis of combined discovery and follow-up data identified SNPs significantly associated with BP at p < 8.56 Ă 10â7 at four further loci (NPR3, HFE, NOS3, and SOX6). The high number of discoveries made with modest genotyping effort can be attributed to using a large-scale yet targeted genotyping array and to the development of a weighting scheme that maximized power when meta-analyzing results from samples ascertained with extreme phenotypes, in combination with results from nonascertained or population samples. Chromatin immunoprecipitation and transcript expression data highlight potential gene regulatory mechanisms at the MTHFR and NOS3 loci. These results provide candidates for further study to help dissect mechanisms affecting BP and highlight the utility of studying SNPs and samples that are independent of those studied previously even when the sample size is smaller than that in previous studies
Common variants at 10 genomic loci influence hemoglobin Aâ(C) levels via glycemic and nonglycemic pathways.
Glycated hemoglobin (HbAâ(c)), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbAâ(c). We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbAâ(c) levels
Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals
Circulating levels of adiponectin, a hormone produced predominantly by adipocytes, are highly heritable and are inversely associated with type 2 diabetes mellitus (T2D) and other metabolic traits. We conducted a meta-analysis of genome-wide association studies in 39,883 individuals of European ancestry to identify genes associated with metabolic disease. We identified 8 novel loci associated with adiponectin levels and confirmed 2 previously reported loci (Pâ=â4.5Ă10(-8)-1.2Ă10(-43)). Using a novel method to combine data across ethnicities (Nâ=â4,232 African Americans, Nâ=â1,776 Asians, and Nâ=â29,347 Europeans), we identified two additional novel loci. Expression analyses of 436 human adipocyte samples revealed that mRNA levels of 18 genes at candidate regions were associated with adiponectin concentrations after accounting for multiple testing (p<3Ă10(-4)). We next developed a multi-SNP genotypic risk score to test the association of adiponectin decreasing risk alleles on metabolic traits and diseases using consortia-level meta-analytic data. This risk score was associated with increased risk of T2D (pâ=â4.3Ă10(-3), nâ=â22,044), increased triglycerides (pâ=â2.6Ă10(-14), nâ=â93,440), increased waist-to-hip ratio (pâ=â1.8Ă10(-5), nâ=â77,167), increased glucose two hours post oral glucose tolerance testing (pâ=â4.4Ă10(-3), nâ=â15,234), increased fasting insulin (pâ=â0.015, nâ=â48,238), but with lower in HDL-cholesterol concentrations (pâ=â4.5Ă10(-13), nâ=â96,748) and decreased BMI (pâ=â1.4Ă10(-4), nâ=â121,335). These findings identify novel genetic determinants of adiponectin levels, which, taken together, influence risk of T2D and markers of insulin resistance