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Kraja, A.T.* ; Chasman, D.I.* ; North, K.E.* ; Reiner, A.P.* ; Yanek, L.R.* ; Kilpeläinen, T.O.* ; Smith, J.A.* ; Dehghan, A.* ; Dupuis, J.* ; Johnson, A.D.* ; Feitosa, M.F.* ; Tekola-Ayele, F.* ; Chu, A.Y.* ; Nolte, I.M.* ; Dastani, Z.* ; Morris, A.* ; Pendergrass, S.A.* ; Sun, Y.V.* ; Ritchie, M.D.* ; Vaez, A.* ; Lin, H.* ; Ligthart, S.* ; Marullo, L.* ; Rohde, R.* ; Shao, Y.* ; Ziegler, M.A.* ; Im, H.K.* ; Cross Consortia Pleiotropy (XC-Pleiotropy) (*) ; CHARGE Consortium (*) ; GIANT Consortium (Albrecht, E. ; Grallert, H. ; Thorand, B. ; Gieger, C. ; Peters, A. ; Wichmann, H.-E. ; Illig, T. ; Müller-Nurasyid, M. ; Heid, I.M.) ; Global Lipids Genetics Consortium (*) ; MAGIC Consortium (*) ; Global BPgen Consortium (Eyheramendy, S. ; Döring, A. ; Meitinger, T. ; Pfeufer, A.) ; ADIPOGen Consortium (*) ; Women's Genome Health Study (*) ; Howard University Family Study (*) ; Schnabel, R.B.* ; Jorgensen, T.* ; Jorgensen, M.E.* ; Hansen, T.* ; Pedersen, O.* ; Stolk, R.P.* ; Snieder, H.* ; Hofman, A.* ; Uitterlinden, A.G.* ; Franco, O.H.* ; Ikram, M.A.* ; Richards, J.B.* ; Rotimi, C.N.* ; Wilson, J.G.* ; Lange, L.A.* ; Ganesh, S.K.* ; Nalls, M.* ; Rasmussen-Torvik, L.J.* ; Pankow, J.S.* ; Coresh, J.* ; Tang, W.* ; Kao, W.H.L.* ; Boerwinkle, E.* ; Morrison, A.C.* ; Ridker, P.M.* ; Becker, D.M.* ; Rotter, J.I.* ; Kardia, S.L.R.* ; Loos, R.J.F.* ; Larson, M.G.* ; Hsu, Y.H.* ; Province, M.A.* ; Tracy, R.* ; Voight, B.F.* ; Vaidya, D.* ; O'Donnell, C.J.* ; Benjamin, E.J.* ; Alizadeh, B.Z.* ; Prokopenko, I.* ; Meigs, J.B.* ; Borecki, I.B.*

Pleiotropic genes for metabolic syndrome and inflammation.

Mol. Genet. Metab. 112, 317-338 (2014)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Metabolic Syndrome ; Inflammatory Markers ; Pleiotropic Associations ; Meta-analysis ; Regulome; Genome-wide Association; Density-lipoprotein Cholesterol; Coronary-artery-disease; Diabetes Susceptibility Loci; C-reactive Protein; Nf-kappa-b; Insulin-resistance; Blood-pressure; Cardiovascular-disease; Circulating Adiponectin
Sprache englisch
Veröffentlichungsjahr 2014
HGF-Berichtsjahr 2014
ISSN (print) / ISBN 1096-7192
e-ISSN 1096-7192
Zeitschrift Molecular Genetics and Metabolism
Quellenangaben Band: 112, Heft: 4, Seiten: 317-338 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort San Diego
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Genetic Epidemiology (IGE)
CCG Nutrigenomics and Type 2 Diabetes (KKG-KDN)
Institute of Epidemiology (EPI)
Institute of Human Genetics (IHG)
POF Topic(s) 30501 - Systemic Analysis of Genetic and Environmental Factors that Impact Health
30502 - Diabetes: Pathophysiology, Prevention and Therapy
30202 - Environmental Health
30503 - Chronic Diseases of the Lung and Allergies
Forschungsfeld(er) Genetics and Epidemiology
PSP-Element(e) G-504100-001
G-521600-002
G-504091-004
G-504000-002
G-504000-006
G-503900-001
G-500700-001
DOI 10.1016/j.ymgme.2014.04.007
WOS ID WOS:000340354000009
Erfassungsdatum 2014-09-29
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