Genome-Wide Screen for Metabolic Syndrome Susceptibility Loci Reveals Strong Lipid Gene Contribution But No Evidence for Common Genetic Basis for Clustering of Metabolic Syndrome Traits

Author:

Kristiansson Kati,Perola Markus,Tikkanen Emmi,Kettunen Johannes,Surakka Ida,Havulinna Aki S.,Stančáková Alena,Barnes Chris,Widen Elisabeth,Kajantie Eero,Eriksson Johan G.,Viikari Jorma,Kähönen Mika,Lehtimäki Terho,Raitakari Olli T.,Hartikainen Anna-Liisa,Ruokonen Aimo,Pouta Anneli,Jula Antti,Kangas Antti J.,Soininen Pasi,Ala-Korpela Mika,Männistö Satu,Jousilahti Pekka,Bonnycastle Lori L.,Järvelin Marjo-Riitta,Kuusisto Johanna,Collins Francis S.,Laakso Markku,Hurles Matthew E.,Palotie Aarno,Peltonen Leena,Ripatti Samuli,Salomaa Veikko

Abstract

Background— Genome-wide association (GWA) studies have identified several susceptibility loci for metabolic syndrome (MetS) component traits, but have had variable success in identifying susceptibility loci to the syndrome as an entity. We conducted a GWA study on MetS and its component traits in 4 Finnish cohorts consisting of 2637 MetS cases and 7927 controls, both free of diabetes, and followed the top loci in an independent sample with transcriptome and nuclear magnetic resonance-based metabonomics data. Furthermore, we tested for loci associated with multiple MetS component traits using factor analysis, and built a genetic risk score for MetS. Methods and Results— A previously known lipid locus, APOA1/C3/A4/A5 gene cluster region (SNP rs964184 ), was associated with MetS in all 4 study samples ( P =7.23×10 −9 in meta-analysis). The association was further supported by serum metabolite analysis, where rs964184 was associated with various very low density lipoprotein, triglyceride, and high-density lipoprotein metabolites ( P =0.024–1.88×10 −5 ). Twenty-two previously identified susceptibility loci for individual MetS component traits were replicated in our GWA and factor analysis. Most of these were associated with lipid phenotypes, and none with 2 or more uncorrelated MetS components. A genetic risk score, calculated as the number of risk alleles in loci associated with individual MetS traits, was strongly associated with MetS status. Conclusions— Our findings suggest that genes from lipid metabolism pathways have the key role in the genetic background of MetS. We found little evidence for pleiotropy linking dyslipidemia and obesity to the other MetS component traits, such as hypertension and glucose intolerance.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Genetics (clinical),Cardiology and Cardiovascular Medicine,Genetics

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