Forty-three loci associated with plasma lipoprotein size, concentration, and cholesterol content in genome-wide analysis.
Forty-three loci associated with plasma lipoprotein size, concentration, and cholesterol content in genome-wide analysis.
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DOI：
10.1371/journal.pgen.1000730
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发表时间：
2009-11
期刊：
影响因子：
4.5
通讯作者：
Ridker PM
中科院分区：
文献类型：
--
作者：
Chasman DI;Paré G;Mora S;Hopewell JC;Peloso G;Clarke R;Cupples LA;Hamsten A;Kathiresan S;Mälarstig A;Ordovas JM;Ripatti S;Parker AN;Miletich JP;Ridker PM
虽然传统的LDL-C、HDL-C和甘油三酯测量反映了血浆脂蛋白组分的聚集特性，但基于NMR的测量更准确地反映了根据类别（LDL、HDL和VLDL）和粒度（小、中和大）的脂蛋白颗粒浓度。这些脂蛋白亚组分的浓度可能与心血管疾病和相关代谢紊乱的风险有关。我们对来自妇女基因组健康研究（WGHS）的17，296名妇女进行了一项全基因组关联研究，其中包括通过NMR测定的17项脂蛋白指标以及LDL-C、HDL-C、甘油三酯、ApoA 1和ApoB。在36个具有全基因组意义的位点中，（P<5×10−8）在主要和次要分析中，10（PCCB/STAG 1（3q22.3），GMPR/MYLIP（6p22.3），BTNL 2（6p21.32），KLF14（7q32.2），8p23.1，JM JD1C（10q21.3），SBF 2（11p15.4），12q23.2，CCDC92/DNAH10/ZNF 664（12q24.31.B）和WIPI 1（17q24.2））在先前的血浆脂质浓度的全基因组关联研究中尚未报道。在4个位点（7q11.23、LPL（8p21.3）、12q24.31.B和LIPG（18q21.1））发现LDL与平均脂蛋白颗粒大小相关，而在1个位点（GCKR（2p23.3））发现HDL与胆固醇含量无关。此外，在许多位点发现了总IDL和总VLDL浓度的遗传决定因素，最强的分别是LIPC（15q22.1）和APOC-APOE复合体（19q13.32）。先前已知的7个位点对常规血脂测量的影响揭示了对脂蛋白谱的额外遗传影响，使位点总数达到43个。因此，全基因组关联鉴定了涉及脂蛋白代谢的新位点，包括影响基于NMR的LDL、HDL和VLDL颗粒浓度或大小测量的位点，脂蛋白谱的所有特征都可能影响疾病风险，但无法通过常规测定获得。血浆脂蛋白组分的全基因组关联研究（GWAS）为了解脂质代谢及其在心血管疾病和相关疾病中的中心作用提供了巨大的希望。脂蛋白状态的常规测定法测定低或高密度脂蛋白颗粒的总胆固醇含量（分别为LDL-C或HDL-C）或总血浆甘油三酯含量（作为极低密度脂蛋白颗粒浓度[VLDL]的估计值）。所有这三项措施都是最近全球WAS的目标。然而，脂蛋白代谢的GWAS的更精确目标是根据类别（LDL、HDL、VLDL）和大小（小、中、大）的单个脂蛋白颗粒的浓度，所有这些都可以通过基于NMR的方法测量。在来自妇女基因组健康研究的17，296名欧洲血统妇女的人群中，我们对来自基于NMR和常规测定的22种脂蛋白测量进行了GWAS。共发现43个与脂蛋白代谢相关的基因位点，其中包括10个新的基因位点。结果提供了一个更清晰的图片脂蛋白代谢的常见遗传影响比以前可用，包括遗传对LDL，HDL和VLDL颗粒大小的分布，以及IDL和VLDL颗粒浓度的影响，这两者都不能通过常规措施进行评估。
While conventional LDL-C, HDL-C, and triglyceride measurements reflect aggregate properties of plasma lipoprotein fractions, NMR-based measurements more accurately reflect lipoprotein particle concentrations according to class (LDL, HDL, and VLDL) and particle size (small, medium, and large). The concentrations of these lipoprotein sub-fractions may be related to risk of cardiovascular disease and related metabolic disorders. We performed a genome-wide association study of 17 lipoprotein measures determined by NMR together with LDL-C, HDL-C, triglycerides, ApoA1, and ApoB in 17,296 women from the Women's Genome Health Study (WGHS). Among 36 loci with genome-wide significance (P<5×10−8) in primary and secondary analysis, ten (PCCB/STAG1 (3q22.3), GMPR/MYLIP (6p22.3), BTNL2 (6p21.32), KLF14 (7q32.2), 8p23.1, JMJD1C (10q21.3), SBF2 (11p15.4), 12q23.2, CCDC92/DNAH10/ZNF664 (12q24.31.B), and WIPI1 (17q24.2)) have not been reported in prior genome-wide association studies for plasma lipid concentration. Associations with mean lipoprotein particle size but not cholesterol content were found for LDL at four loci (7q11.23, LPL (8p21.3), 12q24.31.B, and LIPG (18q21.1)) and for HDL at one locus (GCKR (2p23.3)). In addition, genetic determinants of total IDL and total VLDL concentration were found at many loci, most strongly at LIPC (15q22.1) and APOC-APOE complex (19q13.32), respectively. Associations at seven more loci previously known for effects on conventional plasma lipid measures reveal additional genetic influences on lipoprotein profiles and bring the total number of loci to 43. Thus, genome-wide associations identified novel loci involved with lipoprotein metabolism—including loci that affect the NMR-based measures of concentration or size of LDL, HDL, and VLDL particles—all characteristics of lipoprotein profiles that may impact disease risk but are not available by conventional assay. Genome-wide association studies (GWAS) of plasma lipoprotein fractions hold great promise for understanding lipid metabolism and its central role in cardiovascular disease and related disorders. Conventional assays for lipoprotein status determine total cholesterol content of low- or high-density lipoprotein particles (LDL-C or HDL-C, respectively) or total plasma triglyceride content (as an estimate of very-low density lipoprotein particle concentration [VLDL]). All three measures have been targets for recent GWAS. However, a more precise target for GWAS of lipoprotein metabolism would be the concentration of the individual lipoprotein particles according to class (LDL, HDL, VLDL) and size (small, medium, and large), all of which can be measured by NMR-based methods. In a population of 17,296 women of European ancestry from the Women's Genome Health Study, we have performed a GWAS for 22 lipoprotein measures derived from NMR-based and conventional assays. We find 43 genetic loci involved in lipoprotein metabolism, including 10 novel loci. The results offer a clearer picture of common genetic influences on lipoprotein metabolism than available previously, including genetic effects on the distribution of LDL, HDL, and VLDL particle size, as well as on IDL and VLDL particle concentration, neither of which can be assessed by conventional measures.
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影响因子：
64.8
作者：
Frazer, Kelly A.;Ballinger, Dennis G.;Cox, David R.;Hinds, David A.;Stuve, Laura L.;Gibbs, Richard A.;Belmont, John W.;Boudreau, Andrew;Hardenbol, Paul;Leal, Suzanne M.;Pasternak, Shiran;Wheeler, David A.;Willis, Thomas D.;Yu, Fuli;Yang, Huanming;Zeng, Changqing;Gao, Yang;Hu, Haoran;Hu, Weitao;Li, Chaohua;Lin, Wei;Liu, Siqi;Pan, Hao;Tang, Xiaoli;Wang, Jian;Wang, Wei;Yu, Jun;Zhang, Bo;Zhang, Qingrun;Zhao, Hongbin;Zhao, Hui;Zhou, Jun;Gabriel, Stacey B.;Barry, Rachel;Blumenstiel, Brendan;Camargo, Amy;Defelice, Matthew;Faggart, Maura;Goyette, Mary;Gupta, Supriya;Moore, Jamie;Nguyen, Huy;Onofrio, Robert C.;Parkin, Melissa;Roy, Jessica;Stahl, Erich;Winchester, Ellen;Ziaugra, Liuda;Altshuler, David;Shen, Yan;Yao, Zhijian;Huang, Wei;Chu, Xun;He, Yungang;Jin, Li;Liu, Yangfan;Shen, Yayun;Sun, Weiwei;Wang, Haifeng;Wang, Yi;Wang, Ying;Xiong, Xiaoyan;Xu, Liang;Waye, Mary M. Y.;Tsui, Stephen K. W.;Wong, J. Tze-Fei;Galver, Luana M.;Fan, Jian-Bing;Gunderson, Kevin;Murray, Sarah S.;Oliphant, Arnold R.;Chee, Mark S.;Montpetit, Alexandre;Chagnon, Fanny;Ferretti, Vincent;Leboeuf, Martin;Olivier, Jean-Franccois;Phillips, Michael S.;Roumy, Stephanie;Sallee, Clementine;Verner, Andrei;Hudson, Thomas J.;Kwok, Pui-Yan;Cai, Dongmei;Koboldt, Daniel C.;Miller, Raymond D.;Pawlikowska, Ludmila;Taillon-Miller, Patricia;Xiao, Ming;Tsui, Lap-Chee;Mak, William;Song, You Qiang;Tam, Paul K. H.;Nakamura, Yusuke;Kawaguchi, Takahisa;Kitamoto, Takuya;Morizono, Takashi;Nagashima, Atsushi;Ohnishi, Yozo;Sekine, Akihiro;Tanaka, Toshihiro;Tsunoda, Tatsuhiko;Deloukas, Panos;Bird, Christine P.;Delgado, Marcos;Dermitzakis, Emmanouil T.;Gwilliam, Rhian;Hunt, Sarah;Morrison, Jonathan;Powell, Don;Stranger, Barbara E.;Whittaker, Pamela;Bentley, David R.;Daly, Mark J.;de Bakker, Paul I. W.;Barrett, Jeff;Chretien, Yves R.;Maller, Julian;McCarroll, Steve;Patterson, Nick;Pe'er, Itsik;Price, Alkes;Purcell, Shaun;Richter, Daniel J.;Sabeti, Pardis;Saxena, Richa;Schaffner, Stephen F.;Sham, Pak C.;Varilly, Patrick;Altshuler, David;Stein, Lincoln D.;Krishnan, Lalitha;Smith, Albert Vernon;Tello-Ruiz, Marcela K.;Thorisson, Gudmundur A.;Chakravarti, Aravinda;Chen, Peter E.;Cutler, David J.;Kashuk, Carl S.;Lin, Shin;Abecasis, Goncalo R.;Guan, Weihua;Li, Yun;Munro, Heather M.;Qin, Zhaohui Steve;Thomas, Daryl J.;McVean, Gilean;Auton, Adam;Bottolo, Leonardo;Cardin, Niall;Eyheramendy, Susana;Freeman, Colin;Marchini, Jonathan;Myers, Simon;Spencer, Chris;Stephens, Matthew;Donnelly, Peter;Cardon, Lon R.;Clarke, Geraldine;Evans, David M.;Morris, Andrew P.;Weir, Bruce S.;Tsunoda, Tatsuhiko;Johnson, Todd A.;Mullikin, James C.;Sherry, Stephen T.;Feolo, Michael;Skol, Andrew
通讯作者：
Skol, Andrew
与人类血液低密度脂蛋白胆固醇、高密度脂蛋白胆固醇或甘油三酯相关的六个新位点。
影响因子：
30.8
作者：
Kathiresan, Sekar;Melander, Olle;Guiducci, Candace;Surti, Aarti;Burtt, Noel P.;Rieder, Mark J.;Cooper, Gregory M.;Roos, Charlotta;Voight, Benjamin F.;Havulinna, Aki S.;Wahlstrand, Bjorn;Hedner, Thomas;Corella, Dolores;Tai, E. Shyong;Ordovas, Jose M.;Berglund, Goran;Vartiainen, Erkki;Jousilahti, Pekka;Hedblad, Bo;Taskinen, Marja-Riitta;Newton-Cheh, Christopher;Salomaa, Veikko;Peltonen, Leena;Groop, Leif;Altshuler, David M.;Orho-Melander, Marju
通讯作者：
Orho-Melander, Marju
影响因子：
56.9
作者：
McVean, GAT;Myers, SR;Donnelly, P
通讯作者：
Donnelly, P
影响因子：
30.8
作者：
Kathiresan S;Willer CJ;Peloso GM;Demissie S;Musunuru K;Schadt EE;Kaplan L;Bennett D;Li Y;Tanaka T;Voight BF;Bonnycastle LL;Jackson AU;Crawford G;Surti A;Guiducci C;Burtt NP;Parish S;Clarke R;Zelenika D;Kubalanza KA;Morken MA;Scott LJ;Stringham HM;Galan P;Swift AJ;Kuusisto J;Bergman RN;Sundvall J;Laakso M;Ferrucci L;Scheet P;Sanna S;Uda M;Yang Q;Lunetta KL;Dupuis J;de Bakker PI;O'Donnell CJ;Chambers JC;Kooner JS;Hercberg S;Meneton P;Lakatta EG;Scuteri A;Schlessinger D;Tuomilehto J;Collins FS;Groop L;Altshuler D;Collins R;Lathrop GM;Melander O;Salomaa V;Peltonen L;Orho-Melander M;Ordovas JM;Boehnke M;Abecasis GR;Mohlke KL;Cupples LA
通讯作者：
Cupples LA
影响因子：
37.8
作者：
Kathiresan, S;Otvos, JD;Robins, SJ
通讯作者：
Robins, SJ