Pleiotropic genes for metabolic syndrome and inflammation

Aldi T. Kraja; Daniel I. Chasman; Kari E. North; Alexander P. Reiner; Lisa R. Yanek; Tuomas O. Kilpeläinen; Jennifer A. Smith; Abbas Dehghan; Josée 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 Vaez; Honghuang Lin; Symen Ligthart; Letizia Marullo; Rebecca Rohde; Yaming Shao; Mark A. Ziegler; Hae Kyung Im; Renate B. Schnabel; Torben Jørgensen; Marit E. Jørgensen; 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; Eric Boerwinkle; Alanna C. Morrison; Paul M. Ridker; Diane M. Becker; Jerome I. Rotter; Sharon L.R. Kardia; Ruth J.F. Loos; Martin G. Larson; Yi Hsiang Hsu; Michael A. Province; Russell Tracy; Benjamin F. Voight; Dhananjay Vaidya; Christopher J. O'Donnell; Emelia J. Benjamin; Behrooz Z. Alizadeh; Inga Prokopenko; James B. Meigs; Ingrid B. Borecki

doi:10.1016/j.ymgme.2014.04.007

Pleiotropic genes for metabolic syndrome and inflammation

Aldi T. Kraja, Daniel I. Chasman, Kari E. North, Alexander P. Reiner, Lisa R. Yanek, Tuomas O. Kilpeläinen, Jennifer A. Smith, Abbas Dehghan, Josée 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, Yaming Shao, Mark A. Ziegler, Hae Kyung Im, Renate B. Schnabel, Torben Jørgensen, Marit E. Jørgensen, 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, Eric Boerwinkle, Alanna C. Morrison, Paul M. Ridker, Diane M. Becker, Jerome I. Rotter, Sharon L.R. Kardia, Ruth J.F. Loos, Martin G. Larson, Yi Hsiang Hsu, Michael A. Province, Russell Tracy, Benjamin F. Voight, Dhananjay Vaidya, Christopher J. O'Donnell, Emelia J. Benjamin, Behrooz Z. Alizadeh, Inga Prokopenko, James B. Meigs, Ingrid B. Borecki

Research output: Contribution to journal › Article › peer-review

107 Scopus citations

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.

Original language	English
Pages (from-to)	317-338
Number of pages	22
Journal	Molecular genetics and metabolism
Volume	112
Issue number	4
DOIs	https://doi.org/10.1016/j.ymgme.2014.04.007
State	Published - Aug 2014

Keywords

Inflammatory markers
Meta-analysis
Metabolic syndrome
Pleiotropic associations
Regulome

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10.1016/j.ymgme.2014.04.007

Cite this

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., ... Borecki, I. B. (2014). Pleiotropic genes for metabolic syndrome and inflammation. Molecular genetics and metabolism, 112(4), 317-338. https://doi.org/10.1016/j.ymgme.2014.04.007

@article{a6a672ed31f542cc86916dba7135c737,

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 = "Inflammatory markers, Meta-analysis, Metabolic syndrome, Pleiotropic associations, Regulome",

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 Hsu, {Yi Hsiang} 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.}",

note = "Funding Information: GeneSTAR was supported by the National Heart, Lung, and Blood Institute (NHLBI) through the PROGENI (U01 HL72518) consortium as well as grants HL58625-01A1, HL59684, and HL071025-01A1, and a grant from the NIH/National Institute of Nursing Research (NR0224103). Additional support was provided by a grant from the NIH/National Center for Research Resources (M01-RR000052) to the Johns Hopkins General Clinical Research Center. Funding Information: The LifeLines Cohort Study is supported by the Netherlands Organization of Scientific Research NWO (grant 175.010.2007.006), the Economic Structure Enhancing Fund (FES) of the Dutch government, the Ministry of Economic Affairs, the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the Northern Netherlands Collaboration of Provinces (SNN), the Province of Groningen, University Medical Center Groningen, the University of Groningen, Dutch Kidney Foundation and Dutch Diabetes Research Foundation. The authors wish to acknowledge the services of the LifeLines Cohort Study, the contributing research centers delivering data to LifeLines, and all the study participants. Funding Information: The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C. A listing of WHI investigators can be found at https://cleo.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Short%20List.pdf Funding Information: The Rotterdam Study is supported by the Erasmus Medical Center and Erasmus University Rotterdam; the Netherlands Organization for Scientific Research (NWO); the Netherlands Organization for Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly (RIDE); the Netherlands Heart Foundation; the Ministry of Education, Culture and Science; the Ministry of Health Welfare and Sports; the European Commission; and the Municipality of Rotterdam. Funding Information: Support for genotyping was provided by the Netherlands Organisation of Scientific Research NWO Investments (nr. 175.010.2005.011, 911-03-012), the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Consortium for Healthy Aging (NCHA) project nr. 050-060-810. This work is supported also by NWO grant (veni, 916.12.154) and the EUR Fellowship (A.D.). Funding Information: This research was supported in part by the Intramural Research Program of the NIH , National Institute on Aging, Z01-AG000932-06 , (M.N.). This study utilized the high-performance computational capabilities of the Biowulf Linux cluster ( http://biowulf.nih.gov ) at the National Institutes of Health, Bethesda, MD (M.N.). Funding Information: Coronary Artery Risk in Young Adults was supported by University of Alabama at Birmingham (N01-HC-48047), University of Minnesota (N01-HC-48048), Northwestern University (N01-HC-48049), Kaiser Foundation Research Institute (N01-HC-48050), University of Alabama at Birmingham (N01-HC-95095), Tufts-New England Medical Center (N01-HC-45204), Wake Forest University (N01-HC-45205), Harbor-UCLA Research and Education Institute (N01-HC-05187), University of California, Irvine (N01-HC-45134, N01-HC-95100). Funding Information: This work was supported in part by NIDDK grant 1R01DK8925601 (I.B.B.). Funding Information: The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). This work is also funded in part by R01DK075681 (K.E.N.). Funding Information: The WGHS is supported by HL043851 and HL080467 from the National Heart, Lung, and Blood Institute and CA047988 from the National Cancer Institute, and the Donald W. Reynolds Foundation, with collaborative scientific support and funding for genotyping provided by Amgen. This research was partially supported by U01 HL108630. Funding Information: This research was conducted in part using data and resources from the Framingham Heart Study of the National Heart Lung and Blood Institute of the National Institutes of Health and Boston University School of Medicine. The analyses reflect intellectual input and resource development from the Framingham Heart Study investigators participating in the SNP Health Association Resource (SHARe) project. This work was partially supported by the National Heart, Lung and Blood Institute's Framingham Heart Study (Contract No. N01-HC-25195) and its contract with Affymetrix, Inc for genotyping services (Contract No. N02-HL-6-4278). A portion of this research utilized the Linux Cluster for Genetic Analysis (LinGA‐II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. Also supported by National Institute for Diabetes and Digestive and Kidney Diseases R01 DK078616 and K24 DK080140 (J.B.M.), and 1RO1 HL64753, R01 HL076784, 1R01 AG028321, 1R01HL092577 (E.J.B.). Funding Information: The Inter99 Study was initiated by Torben J{\o}rgensen, Knut Borch-Johnsen, Hans Ibsen and Troels F. Thomsen. The steering committee comprises Torben J{\o}rgensen, Knut Borch-Johnsen and Charlotta Pisinger. The phenotyping was financially supported by grants from the Danish Medical Research Council, The Danish Centre for Health Technology Assessment, Novo Nordisk, Copenhagen County, The Danish Heart Foundation, The Danish Pharmaceutical Association, The Augustinus Foundation, The Ib Henriksen Foundation, and the Becket Foundation. The genetic research was supported by grants from the Lundbeck Foundation ( www.lucamp.org ) and the Novo Nordisk Foundation ( metabol.ku.dk ). This work is carried out as a part of the research program of the UNIK: Food, Fitness & Pharma for Health and Disease (see www.foodfitnesspharma.ku.dk ). The UNIK project is supported by the Danish Ministry of Science, Technology and Innovation. ",

year = "2014",

month = aug,

doi = "10.1016/j.ymgme.2014.04.007",

language = "English",

volume = "112",

pages = "317--338",

journal = "Molecular genetics and metabolism",

issn = "1096-7192",

number = "4",

}

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, YH, Province, MA, Tracy, R, Voight, BF, Vaidya, D, O'Donnell, CJ, Benjamin, EJ, Alizadeh, BZ, Prokopenko, I, Meigs, JB & Borecki, IB 2014, 'Pleiotropic genes for metabolic syndrome and inflammation', Molecular genetics and metabolism, vol. 112, no. 4, pp. 317-338. https://doi.org/10.1016/j.ymgme.2014.04.007

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.

N1 - Funding Information: GeneSTAR was supported by the National Heart, Lung, and Blood Institute (NHLBI) through the PROGENI (U01 HL72518) consortium as well as grants HL58625-01A1, HL59684, and HL071025-01A1, and a grant from the NIH/National Institute of Nursing Research (NR0224103). Additional support was provided by a grant from the NIH/National Center for Research Resources (M01-RR000052) to the Johns Hopkins General Clinical Research Center. Funding Information: The LifeLines Cohort Study is supported by the Netherlands Organization of Scientific Research NWO (grant 175.010.2007.006), the Economic Structure Enhancing Fund (FES) of the Dutch government, the Ministry of Economic Affairs, the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the Northern Netherlands Collaboration of Provinces (SNN), the Province of Groningen, University Medical Center Groningen, the University of Groningen, Dutch Kidney Foundation and Dutch Diabetes Research Foundation. The authors wish to acknowledge the services of the LifeLines Cohort Study, the contributing research centers delivering data to LifeLines, and all the study participants. Funding Information: The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C. A listing of WHI investigators can be found at https://cleo.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Short%20List.pdf Funding Information: The Rotterdam Study is supported by the Erasmus Medical Center and Erasmus University Rotterdam; the Netherlands Organization for Scientific Research (NWO); the Netherlands Organization for Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly (RIDE); the Netherlands Heart Foundation; the Ministry of Education, Culture and Science; the Ministry of Health Welfare and Sports; the European Commission; and the Municipality of Rotterdam. Funding Information: Support for genotyping was provided by the Netherlands Organisation of Scientific Research NWO Investments (nr. 175.010.2005.011, 911-03-012), the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Consortium for Healthy Aging (NCHA) project nr. 050-060-810. This work is supported also by NWO grant (veni, 916.12.154) and the EUR Fellowship (A.D.). Funding Information: This research was supported in part by the Intramural Research Program of the NIH , National Institute on Aging, Z01-AG000932-06 , (M.N.). This study utilized the high-performance computational capabilities of the Biowulf Linux cluster ( http://biowulf.nih.gov ) at the National Institutes of Health, Bethesda, MD (M.N.). Funding Information: Coronary Artery Risk in Young Adults was supported by University of Alabama at Birmingham (N01-HC-48047), University of Minnesota (N01-HC-48048), Northwestern University (N01-HC-48049), Kaiser Foundation Research Institute (N01-HC-48050), University of Alabama at Birmingham (N01-HC-95095), Tufts-New England Medical Center (N01-HC-45204), Wake Forest University (N01-HC-45205), Harbor-UCLA Research and Education Institute (N01-HC-05187), University of California, Irvine (N01-HC-45134, N01-HC-95100). Funding Information: This work was supported in part by NIDDK grant 1R01DK8925601 (I.B.B.). Funding Information: The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). This work is also funded in part by R01DK075681 (K.E.N.). Funding Information: The WGHS is supported by HL043851 and HL080467 from the National Heart, Lung, and Blood Institute and CA047988 from the National Cancer Institute, and the Donald W. Reynolds Foundation, with collaborative scientific support and funding for genotyping provided by Amgen. This research was partially supported by U01 HL108630. Funding Information: This research was conducted in part using data and resources from the Framingham Heart Study of the National Heart Lung and Blood Institute of the National Institutes of Health and Boston University School of Medicine. The analyses reflect intellectual input and resource development from the Framingham Heart Study investigators participating in the SNP Health Association Resource (SHARe) project. This work was partially supported by the National Heart, Lung and Blood Institute's Framingham Heart Study (Contract No. N01-HC-25195) and its contract with Affymetrix, Inc for genotyping services (Contract No. N02-HL-6-4278). A portion of this research utilized the Linux Cluster for Genetic Analysis (LinGA‐II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. Also supported by National Institute for Diabetes and Digestive and Kidney Diseases R01 DK078616 and K24 DK080140 (J.B.M.), and 1RO1 HL64753, R01 HL076784, 1R01 AG028321, 1R01HL092577 (E.J.B.). Funding Information: The Inter99 Study was initiated by Torben Jørgensen, Knut Borch-Johnsen, Hans Ibsen and Troels F. Thomsen. The steering committee comprises Torben Jørgensen, Knut Borch-Johnsen and Charlotta Pisinger. The phenotyping was financially supported by grants from the Danish Medical Research Council, The Danish Centre for Health Technology Assessment, Novo Nordisk, Copenhagen County, The Danish Heart Foundation, The Danish Pharmaceutical Association, The Augustinus Foundation, The Ib Henriksen Foundation, and the Becket Foundation. The genetic research was supported by grants from the Lundbeck Foundation ( www.lucamp.org ) and the Novo Nordisk Foundation ( metabol.ku.dk ). This work is carried out as a part of the research program of the UNIK: Food, Fitness & Pharma for Health and Disease (see www.foodfitnesspharma.ku.dk ). The UNIK project is supported by the Danish Ministry of Science, Technology and Innovation.

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 - Inflammatory markers

KW - Meta-analysis

KW - Metabolic syndrome

KW - Pleiotropic associations

KW - Regulome

UR - http://www.scopus.com/inward/record.url?scp=84905264016&partnerID=8YFLogxK

U2 - 10.1016/j.ymgme.2014.04.007

DO - 10.1016/j.ymgme.2014.04.007

M3 - Article

C2 - 24981077

AN - SCOPUS:84905264016

SN - 1096-7192

VL - 112

SP - 317

EP - 338

JO - Molecular genetics and metabolism

JF - Molecular genetics and metabolism

IS - 4

ER -

Pleiotropic genes for metabolic syndrome and inflammation

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