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Pleiotropic genes for metabolic syndrome and inflammation

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Pleiotropic genes for metabolic syndrome and inflammation. / Kraja, Aldi T; Chasman, Daniel I; North, Kari E; Reiner, Alexander P; Yanek, Lisa R; Kilpeläinen, Tuomas O; Smith, Jennifer A; Dehghan, Abbas; Dupuis, Josée; Johnson, Andrew D; Feitosa, Mary F; Tekola-Ayele, Fasil; Chu, Audrey Y; Nolte, Ilja M; Dastani, Zari; Morris, Andrew; Pendergrass, Sarah A; Sun, Yan V; Ritchie, Marylyn D; Vaez, Ahmad; Lin, Honghuang; Ligthart, Symen; Marullo, Letizia; Rohde, Rebecca; Shao, Yaming; Ziegler, Mark A; Im, Hae Kyung; Schnabel, Renate B; Jørgensen, Torben; Jørgensen, Marit E; Hansen, Torben; Pedersen, Oluf; Stolk, Ronald P; Snieder, Harold; Hofman, Albert; Uitterlinden, Andre G; Franco, Oscar H; Ikram, M Arfan; Richards, J Brent; Rotimi, Charles; Wilson, James G; Lange, Leslie; Ganesh, Santhi K; Nalls, Mike; Rasmussen-Torvik, Laura J; Pankow, James S; Coresh, Josef; Tang, Weihong; Linda Kao, W H; Boerwinkle, Eric; Morrison, Alanna C; Ridker, Paul M; Becker, Diane M; Rotter, Jerome I; Kardia, Sharon L R; Loos, Ruth J F; Larson, Martin G; Hsu, Yi-Hsiang; Province, Michael A; Tracy, Russell; Voight, Benjamin F; Vaidya, Dhananjay; O'Donnell, Christopher J; Benjamin, Emelia J; Alizadeh, Behrooz Z; Prokopenko, Inga; Meigs, James B; Borecki, Ingrid B; Cross Consortia Pleiotropy Group.

in: MOL GENET METAB, Jahrgang 112, Nr. 4, 08.2014, S. 317-338.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Kraja, AT, Chasman, DI, North, KE, Reiner, AP, Yanek, LR, Kilpeläinen, TO, Smith, JA, Dehghan, A, Dupuis, J, Johnson, AD, Feitosa, MF, Tekola-Ayele, F, Chu, AY, Nolte, IM, Dastani, Z, Morris, A, Pendergrass, SA, Sun, YV, Ritchie, MD, Vaez, A, Lin, H, Ligthart, S, Marullo, L, Rohde, R, Shao, Y, Ziegler, MA, Im, HK, Schnabel, RB, Jørgensen, T, Jørgensen, ME, Hansen, T, Pedersen, O, Stolk, RP, Snieder, H, Hofman, A, Uitterlinden, AG, Franco, OH, Ikram, MA, Richards, JB, Rotimi, C, Wilson, JG, Lange, L, Ganesh, SK, Nalls, M, Rasmussen-Torvik, LJ, Pankow, JS, Coresh, J, Tang, W, Linda Kao, WH, Boerwinkle, E, Morrison, AC, Ridker, PM, Becker, DM, Rotter, JI, Kardia, SLR, Loos, RJF, Larson, MG, Hsu, Y-H, Province, MA, Tracy, R, Voight, BF, Vaidya, D, O'Donnell, CJ, Benjamin, EJ, Alizadeh, BZ, Prokopenko, I, Meigs, JB, Borecki, IB & Cross Consortia Pleiotropy Group 2014, 'Pleiotropic genes for metabolic syndrome and inflammation', MOL GENET METAB, Jg. 112, Nr. 4, S. 317-338. https://doi.org/10.1016/j.ymgme.2014.04.007

APA

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., ... Cross Consortia Pleiotropy Group (2014). Pleiotropic genes for metabolic syndrome and inflammation. MOL GENET METAB, 112(4), 317-338. https://doi.org/10.1016/j.ymgme.2014.04.007

Vancouver

Kraja AT, Chasman DI, North KE, Reiner AP, Yanek LR, Kilpeläinen TO et al. Pleiotropic genes for metabolic syndrome and inflammation. MOL GENET METAB. 2014 Aug;112(4):317-338. https://doi.org/10.1016/j.ymgme.2014.04.007

Bibtex

@article{f02b4924ff05446ea7508d292b07831f,
title = "Pleiotropic genes for metabolic syndrome and inflammation",
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. ",
keywords = "Biomarkers/metabolism, Computational Biology, Gene Regulatory Networks, Genetic Pleiotropy, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Inflammation/epidemiology, Meta-Analysis as Topic, Metabolic Syndrome/epidemiology, Phenotype, Quantitative Trait, Heritable",
author = "Kraja, {Aldi T} and Chasman, {Daniel I} and North, {Kari E} and Reiner, {Alexander P} and Yanek, {Lisa R} and Kilpel{\"a}inen, {Tuomas O} and Smith, {Jennifer A} and Abbas Dehghan and Jos{\'e}e Dupuis and Johnson, {Andrew D} and Feitosa, {Mary F} and Fasil Tekola-Ayele and Chu, {Audrey Y} and Nolte, {Ilja M} and Zari Dastani and Andrew Morris and Pendergrass, {Sarah A} and Sun, {Yan V} and Ritchie, {Marylyn D} and Ahmad Vaez and Honghuang Lin and Symen Ligthart and Letizia Marullo and Rebecca Rohde and Yaming Shao and Ziegler, {Mark A} and Im, {Hae Kyung} and Schnabel, {Renate B} and Torben J{\o}rgensen and J{\o}rgensen, {Marit E} and Torben Hansen and Oluf Pedersen and Stolk, {Ronald P} and Harold Snieder and Albert Hofman and Uitterlinden, {Andre G} and Franco, {Oscar H} and Ikram, {M Arfan} and Richards, {J Brent} and Charles Rotimi and Wilson, {James G} and Leslie Lange and Ganesh, {Santhi K} and Mike Nalls and Rasmussen-Torvik, {Laura J} and Pankow, {James S} and Josef Coresh and Weihong Tang and {Linda Kao}, {W H} and Eric Boerwinkle and Morrison, {Alanna C} and Ridker, {Paul M} and Becker, {Diane M} and Rotter, {Jerome I} and Kardia, {Sharon L R} and Loos, {Ruth J F} and Larson, {Martin G} and Yi-Hsiang Hsu and Province, {Michael A} and Russell Tracy and Voight, {Benjamin F} and Dhananjay Vaidya and O'Donnell, {Christopher J} and Benjamin, {Emelia J} and Alizadeh, {Behrooz Z} and Inga Prokopenko and Meigs, {James B} and Borecki, {Ingrid B} and {Cross Consortia Pleiotropy Group}",
note = "Copyright {\textcopyright} 2014 Elsevier Inc. All rights reserved.",
year = "2014",
month = aug,
doi = "10.1016/j.ymgme.2014.04.007",
language = "English",
volume = "112",
pages = "317--338",
journal = "MOL GENET METAB",
issn = "1096-7192",
publisher = "Academic Press Inc.",
number = "4",

}

RIS

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 - Kilpeläinen, Tuomas O

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 - Morrison, Alanna C

AU - Ridker, Paul M

AU - Becker, Diane M

AU - Rotter, Jerome I

AU - Kardia, Sharon L R

AU - Loos, Ruth J F

AU - Larson, Martin G

AU - Hsu, Yi-Hsiang

AU - Province, Michael A

AU - Tracy, Russell

AU - Voight, Benjamin F

AU - Vaidya, Dhananjay

AU - O'Donnell, Christopher J

AU - Benjamin, Emelia J

AU - Alizadeh, Behrooz Z

AU - Prokopenko, Inga

AU - Meigs, James B

AU - Borecki, Ingrid B

AU - Cross Consortia Pleiotropy Group

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

PY - 2014/8

Y1 - 2014/8

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.

KW - Biomarkers/metabolism

KW - Computational Biology

KW - Gene Regulatory Networks

KW - Genetic Pleiotropy

KW - Genetic Predisposition to Disease

KW - Genome-Wide Association Study

KW - Humans

KW - Inflammation/epidemiology

KW - Meta-Analysis as Topic

KW - Metabolic Syndrome/epidemiology

KW - Phenotype

KW - Quantitative Trait, Heritable

U2 - 10.1016/j.ymgme.2014.04.007

DO - 10.1016/j.ymgme.2014.04.007

M3 - SCORING: Journal article

C2 - 24981077

VL - 112

SP - 317

EP - 338

JO - MOL GENET METAB

JF - MOL GENET METAB

SN - 1096-7192

IS - 4

ER -