Pleiotropic genes for metabolic syndrome and inflammation

• Type of Material: Article
• Authors: Aldi T. Kraja, Washington University; Daniel I. Chasman, Brigham & Women's Hospital; Kari E. North, University of North Carolina; Alexander P. Reiner, University of Washington; Lisa R. Yanek, Johns Hopkins University; Tuomas O. Kilpelainen, University of Copenhagen; Jennifer A. Smith, University of Michigan; Abbas Dehghan, Erasmus Medical Center; Josee Dupuis, National Heart, Lung, and Blood Institute; Andrew D. Johnson, National Heart, Lung, and Blood Institute; Mary F. Feitosa, Washington University; Fasil Tekola-Ayele, Boston University; Audrey Y. Chu, Brigham & Women's Hospital; Ilja M. Nolte, University of Groningen; Zari Dastani, McGill University; Andrew Morris, University of Oxford; Sarah A. Pendergrass, Pennsylvania State University; Yan Sun, Emory University; Marylyn D. Ritchie, Pennsylvania State University; Ahmad Vaez, University of Groningen
• Language: English
• Date: 2014-08-01
• Publisher: Elsevier: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2014 Elsevier Inc.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.ymgme.2014.04.007
• Title of Journal or Parent Work: Molecular Genetics and Metabolism
• ISSN: 1096-7192
• Volume: 112
• Issue: 4
• Start Page: 317
• End Page: 338
• Grant/Funding Information: Complete funding list available in full text.
• Keywords: Regulome; INSULIN-RESISTANCE; Genetics & Heredity; ARTERIAL STIFFNESS; Endocrinology & Metabolism; Research & Experimental Medicine; HEPATIC STEATOSIS; Pleiotropic associations; GLUCOSE-HOMEOSTASIS; Medicine, Research & Experimental; Metabolic syndrome; CIRCULATING ADIPONECTIN; Science & Technology; CORONARY-HEART-DISEASE; Meta-analysis; DENSITY-LIPOPROTEIN CHOLESTEROL; SUSCEPTIBILITY LOCI; Inflammatory markers; Life Sciences & Biomedicine; CARDIOVASCULAR-DISEASE; GENOME-WIDE ASSOCIATION
• Research Categories: Biology, Genetics; Health Sciences, Epidemiology

Abstract

Metabolic syndrome (MetS) has become a health and financial burden worldwide. The MetS definition captures clustering of risk factors that predict higher risk for diabetes mellitus and cardiovascular disease. Our study hypothesis is that additional to genes influencing individual MetS risk factors, genetic variants exist that influence MetS and inflammatory markers forming a predisposing MetS genetic network. To test this hypothesis a staged approach was undertaken. (a) We analyzed 17 metabolic and inflammatory traits in more than 85,500 participants from 14 large epidemiological studies within the Cross Consortia Pleiotropy Group. Individuals classified with MetS (NCEP definition), versus those without, showed on average significantly different levels for most inflammatory markers studied. (b) Paired average correlations between 8 metabolic traits and 9 inflammatory markers from the same studies as above, estimated with two methods, and factor analyses on large simulated data, helped in identifying 8 combinations of traits for follow-up in meta-analyses, out of 130,305 possible combinations between metabolic traits and inflammatory markers studied. (c) We performed correlated meta-analyses for 8 metabolic traits and 6 inflammatory markers by using existing GWAS published genetic summary results, with about 2.5 million SNPs from twelve predominantly largest GWAS consortia. These analyses yielded 130 unique SNPs/genes with pleiotropic associations (a SNP/gene associating at least one metabolic trait and one inflammatory marker). Of them twenty-five variants (seven loci newly reported) are proposed as MetS candidates. They map to genes MACF1, KIAA0754, GCKR, GRB14, COBLL1, LOC646736-IRS1, SLC39A8, NELFE, SKIV2L, STK19, TFAP2B, BAZ1B, BCL7B, TBL2, MLXIPL, LPL, TRIB1, ATXN2, HECTD4, PTPN11, ZNF664, PDXDC1, FTO, MC4R and TOMM40. Based on large data evidence, we conclude that inflammation is a feature of MetS and several gene variants show pleiotropic genetic associations across phenotypes and might explain a part of MetS correlated genetic architecture. These findings warrant further functional investigation.