TY - JOUR

T1 - Pleiotropic genes for metabolic syndrome and inflammation

AU - Kraja, Aldi T

AU - Chasman, Daniel I

AU - North, Kari E

AU - Reiner, Alexander P

AU - Yanek, Lisa R

AU - Oskari Kilpeläinen, Tuomas

AU - Smith, Jennifer A

AU - Dehghan, Abbas

AU - Dupuis, Josée

AU - Johnson, Andrew D

AU - Feitosa, Mary F

AU - Tekola-Ayele, Fasil

AU - Chu, Audrey Y

AU - Nolte, Ilja M

AU - Dastani, Zari

AU - Morris, Andrew

AU - Pendergrass, Sarah A

AU - Sun, Yan V

AU - Ritchie, Marylyn D

AU - Vaez, Ahmad

AU - Lin, Honghuang

AU - Ligthart, Symen

AU - Marullo, Letizia

AU - Rohde, Rebecca

AU - Shao, Yaming

AU - Ziegler, Mark A

AU - Im, Hae Kyung

AU - Schnabel, Renate B

AU - Jørgensen, Torben

AU - Jørgensen, Marit E

AU - Hansen, Torben

AU - Pedersen, Oluf

AU - Stolk, Ronald P

AU - Snieder, Harold

AU - Hofman, Albert

AU - Uitterlinden, Andre G

AU - Franco, Oscar H

AU - Ikram, M Arfan

AU - Richards, J Brent

AU - Rotimi, Charles

AU - Wilson, James G

AU - Lange, Leslie

AU - Ganesh, Santhi K

AU - Nalls, Mike

AU - Rasmussen-Torvik, Laura J

AU - Pankow, James S

AU - Coresh, Josef

AU - Tang, Weihong

AU - Linda Kao, W H

AU - Boerwinkle, Eric

AU - Cross Consortia Pleiotropy (XC-Pleiotropy) Group

N1 - Copyright © 2014 Elsevier Inc. All rights reserved.

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

U2 - 10.1016/j.ymgme.2014.04.007

DO - 10.1016/j.ymgme.2014.04.007

M3 - Journal article

C2 - 24981077

VL - 112

SP - 317

EP - 338

JO - Molecular Genetics and Metabolism

JF - Molecular Genetics and Metabolism

SN - 1096-7192

IS - 4

ER -