| Literature DB >> 24981077 |
Aldi T Kraja1, Daniel I Chasman2, Kari E North3, Alexander P Reiner4, Lisa R Yanek5, Tuomas O Kilpeläinen6, Jennifer A Smith7, Abbas Dehghan8, Josée Dupuis9, Andrew D Johnson10, Mary F Feitosa11, Fasil Tekola-Ayele12, Audrey Y Chu13, Ilja M Nolte14, Zari Dastani15, Andrew Morris16, Sarah A Pendergrass17, Yan V Sun18, Marylyn D Ritchie19, Ahmad Vaez20, Honghuang Lin21, Symen Ligthart22, Letizia Marullo23, Rebecca Rohde24, Yaming Shao25, Mark A Ziegler26, Hae Kyung Im27, Renate B Schnabel28, Torben Jørgensen29, Marit E Jørgensen30, Torben Hansen31, Oluf Pedersen32, Ronald P Stolk33, Harold Snieder34, Albert Hofman35, Andre G Uitterlinden36, Oscar H Franco37, M Arfan Ikram38, J Brent Richards39, Charles Rotimi40, James G Wilson41, Leslie Lange42, Santhi K Ganesh43, Mike Nalls44, Laura J Rasmussen-Torvik45, James S Pankow46, Josef Coresh47, Weihong Tang48, W H Linda Kao49, Eric Boerwinkle50, Alanna C Morrison51, Paul M Ridker52, Diane M Becker53, Jerome I Rotter54, Sharon L R Kardia55, Ruth J F Loos56, Martin G Larson57, Yi-Hsiang Hsu58, Michael A Province59, Russell Tracy60, Benjamin F Voight61, Dhananjay Vaidya62, Christopher J O'Donnell63, Emelia J Benjamin64, Behrooz Z Alizadeh65, Inga Prokopenko66, James B Meigs67, Ingrid B Borecki68.
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.Entities:
Keywords: Inflammatory markers; Meta-analysis; Metabolic syndrome; Pleiotropic associations; Regulome
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Year: 2014 PMID： 24981077 PMCID： PMC4122618 DOI： 10.1016/j.ymgme.2014.04.007
Source DB: PubMed Journal: Mol Genet Metab ISSN： 1096-7192 Impact factor: 4.797