Pleiotropic genes for metabolic syndrome and inflammation

Aldi T. Kraja*, Daniel I. Chasman, Kari E. North, Alexander P. Reiner, Lisa R. Yanek, Tuomas O. Kilpelainen, Jennifer A. Smith, Abbas Dehghan, Josee Dupuis, Andrew D. Johnson, Mary F. Feitosa, Fasil Tekola-Ayele, Audrey Y. Chu, Ilja M. Nolte, Zari Dastani, Andrew Morris, Sarah A. Pendergrass, Yan V. Sun, Marylyn D. Ritchie, Ahmad VaezHonghuang Lin, Symen Ligthart, Letizia Marullo, Rebecca Rohde, Yarning Shao, Mark A. Ziegler, Hae Kyung Im, Renate B. Schnabel, Torben Jorgensen, Marit E. Jorgensen, Torben Hansen, Oluf Pedersen, Ronald P. Stolk, Harold Snieder, Albert Hofman, Andre G. Uitterlinden, Oscar H. Franco, M. Arfan Ikram, J. Brent Richards, Charles Rotimi, James G. Wilson, Leslie Lange, Santhi K. Ganesh, Mike Nalls, Laura J. Rasmussen-Torvik, James S. Pankow, Josef Coresh, Weihong Tang, W. H. Linda Kao, Behrooz Z. Alizadeh, Cross Consortia Pleiotropy, CHARGE, Genetic Invest Anthropomet, GLGC, MAGIC, GBPG, ADIPOGen Consortium, WGHS, HUFS

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

71 Citations (Scopus)


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. (C) 2014 Elsevier Inc. All rights reserved.

Original languageEnglish
Pages (from-to)317-338
Number of pages22
JournalMolecular Genetics and Metabolism
Issue number4
Publication statusPublished - Aug-2014


  • Metabolic syndrome
  • Inflammatory markers
  • Pleiotropic associations
  • Meta-analysis
  • Regulome

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