Abstract
To identify novel loci for susceptibility to MetS, we conducted genome-wide association and exome wide association studies consisting of a discovery stage cohort (KARE, 1946 cases and 6427 controls), and a replication stage cohort (HEXA, 430 cases and 3,264 controls). For finding genetic variants for MetS, with its components, we performed multivariate analysis for common and rare associations, using a standard logistic regression analysis for MetS. From the discovery and replication GWA studies, we confirmed 21 genome-wide signals significantly associated with MetS. Of these 21, four were previously unreported to associate with any MetS components: rs765547 near LPL; rs3782889 in MYL2; and rs11065756 and rs10849915 in CCDC63. Using exome chip variants, gene-based analysis of rare variants revealed three genes, CETP, SH2B1, and ZFP2, in the discovery stage, among which only CETP was confirmed in the replication stage. Finally, CETP D442G (rs2303790) associated, as a less common variant, with decreased risk of MetS. In conclusion, we discovered a total of five new MetS-associated loci, and their overlap with other disease-related components, suggest roles in the various etiologies of MetS, and its possible preventive strategies.
Highlights
In one investigation of lipid metabolic traits, components of Metabolic syndrome (MetS), a genome-wide linkage analysis correlated the three lipid traits of HDLc, TGs, and low-density lipoprotein particle size, supporting the hypothesis of genetic pleiotropy as a source of correlation among metabolic components[7]
The quantile-quantile (QQ) plots of genomewide P values, for multivariate analysis (MulA), deviated from the null distribution, due to strong associations observed for MetS in GWAS studies (Supplementary Figure S1)
The prior quality control procedure showed no strong evidence of population stratification, in the Korean Association REsource (KARE) data[14], and all samples with cryptic relatedness were removed before analysis[14]
Summary
To identify novel loci for susceptibility to MetS, we conducted genome-wide association and exome wide association studies consisting of a discovery stage cohort (KARE, 1946 cases and 6427 controls), and a replication stage cohort (HEXA, 430 cases and 3,264 controls). Metabolic syndrome (MetS) is defined as a combination of several clinical features, including central obesity, high blood pressure, elevated circulating levels of fasting glucose, high triglyceride (TG) levels, and low concentrations of HDL-cholesterol (HDLc) Since these features associate with increased risk of cardiovascular disease and type-2 diabetes, their increased worldwide prevalence and incidence are alarming. Genetic analyses are complicated by MetS being defined as an inherently heterogeneous pathology This complexity has prompted the use of novel gene discovery methods such as factor analysis[8], network-based enrichment analysis, or phenome-wide association study[9]. Our meta-analysis combined the discovery and replication studies for identifying new risk loci for MetS, using: (1) LR modeling for MetS as a binary outcome; and (2) multivariate analysis (MulA) of the components of MetS to reduce false positive results, and find unknown genetic variants for MetS
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