Search

Your search keyword '"Global Lipids Genetics Consortium"' showing total 2 results
Start Over Author "Global Lipids Genetics Consortium" Publisher escholarship, university of california
2 results on '"Global Lipids Genetics Consortium"'

1. Platelet-Related Variants Identified by Exomechip Meta-analysis in 157,293 Individuals

Author

Eicher, John D, Chami, Nathalie, Kacprowski, Tim, Nomura, Akihiro, Chen, Ming-Huei, Yanek, Lisa R, Tajuddin, Salman M, Schick, Ursula M, Slater, Andrew J, Pankratz, Nathan, Polfus, Linda, Schurmann, Claudia, Giri, Ayush, Brody, Jennifer A, Lange, Leslie A, Manichaikul, Ani, Hill, W David, Pazoki, Raha, Elliot, Paul, Evangelou, Evangelos, Tzoulaki, Ioanna, Gao, He, Vergnaud, Anne-Claire, Mathias, Rasika A, Becker, Diane M, Becker, Lewis C, Burt, Amber, Crosslin, David R, Lyytikäinen, Leo-Pekka, Nikus, Kjell, Hernesniemi, Jussi, Kähönen, Mika, Raitoharju, Emma, Mononen, Nina, Raitakari, Olli T, Lehtimäki, Terho, Cushman, Mary, Zakai, Neil A, Nickerson, Deborah A, Raffield, Laura M, Quarells, Rakale, Willer, Cristen J, Peloso, Gina M, Abecasis, Goncalo R, Liu, Dajiang J, Global Lipids Genetics Consortium, Deloukas, Panos, Samani, Nilesh J, Schunkert, Heribert, Erdmann, Jeanette, CARDIoGRAM Exome Consortium, Myocardial Infarction Genetics Consortium, Fornage, Myriam, Richard, Melissa, Tardif, Jean-Claude, Rioux, John D, Dube, Marie-Pierre, de Denus, Simon, Lu, Yingchang, Bottinger, Erwin P, Loos, Ruth JF, Smith, Albert Vernon, Harris, Tamara B, Launer, Lenore J, Gudnason, Vilmundur, Velez Edwards, Digna R, Torstenson, Eric S, Liu, Yongmei, Tracy, Russell P, Rotter, Jerome I, Rich, Stephen S, Highland, Heather M, Boerwinkle, Eric, Li, Jin, Lange, Ethan, Wilson, James G, Mihailov, Evelin, Mägi, Reedik, Hirschhorn, Joel, Metspalu, Andres, Esko, Tõnu, Vacchi-Suzzi, Caterina, Nalls, Mike A, Zonderman, Alan B, Evans, Michele K, Engström, Gunnar, Orho-Melander, Marju, Melander, Olle, O'Donoghue, Michelle L, Waterworth, Dawn M, Wallentin, Lars, White, Harvey D, Floyd, James S, Bartz, Traci M, Rice, Kenneth M, Psaty, Bruce M, Starr, JM, Liewald, David CM, Hayward, Caroline, and Deary, Ian J

Subjects
Blood Platelets, Male, Genetics & Heredity, Platelet Count, 1.1 Normal biological development and functioning, CARDIoGRAM Exome Consortium, Genetic Variation, Global Lipids Genetics Consortium, Hematology, Myocardial Infarction Genetics Consortium, Biological Sciences, Cardiovascular, Medical and Health Sciences, Blood, Underpinning research, Genetics, Humans, Female, Exome, Mean Platelet Volume, Genome-Wide Association Study
Abstract

Platelet production, maintenance, and clearance are tightly controlled processes indicative of platelets' important roles in hemostasis and thrombosis. Platelets are common targets for primary and secondary prevention of several conditions. They are monitored clinically by complete blood counts, specifically with measurements of platelet count (PLT) and mean platelet volume (MPV). Identifying genetic effects on PLT and MPV can provide mechanistic insights into platelet biology and their role in disease. Therefore, we formed the Blood Cell Consortium (BCX) to perform a large-scale meta-analysis of Exomechip association results for PLT and MPV in 157,293 and 57,617 individuals, respectively. Using the low-frequency/rare coding variant-enriched Exomechip genotyping array, we sought to identify genetic variants associated with PLT and MPV. In addition to confirming 47 known PLT and 20 known MPV associations, we identified 32 PLT and 18 MPV associations not previously observed in the literature across the allele frequency spectrum, including rare large effect (FCER1A), low-frequency (IQGAP2, MAP1A, LY75), and common (ZMIZ2, SMG6, PEAR1, ARFGAP3/PACSIN2) variants. Several variants associated with PLT/MPV (PEAR1, MRVI1, PTGES3) were also associated with platelet reactivity. In concurrent BCX analyses, there was overlap of platelet-associated variants with red (MAP1A, TMPRSS6, ZMIZ2) and white (PEAR1, ZMIZ2, LY75) blood cell traits, suggesting common regulatory pathways with shared genetic architecture among these hematopoietic lineages. Our large-scale Exomechip analyses identified previously undocumented associations with platelet traits and further indicate that several complex quantitative hematological, lipid, and cardiovascular traits share genetic factors.

Published
2016

2. Pleiotropic genes for metabolic syndrome and inflammation.

Author

Kraja, Aldi T, 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, Cross Consortia Pleiotropy Group, Cohorts for Heart and, Aging Research in Genetic Epidemiology, Genetic Investigation of Anthropometric Traits Consortium, Global Lipids Genetics Consortium, Meta-Analyses of Glucose, Insulin-related traits Consortium, Global BPgen Consortium, ADIPOGen Consortium, Women's Genome Health Study, Howard University Family Study, 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, WH, Boerwinkle, Eric, Morrison, Alanna C, Ridker, Paul M, Becker, Diane M, Rotter, Jerome I, Kardia, Sharon LR, Loos, Ruth JF, 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, Kraja, Aldi T, 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, Cross Consortia Pleiotropy Group, Cohorts for Heart and, Aging Research in Genetic Epidemiology, Genetic Investigation of Anthropometric Traits Consortium, Global Lipids Genetics Consortium, Meta-Analyses of Glucose, Insulin-related traits Consortium, Global BPgen Consortium, ADIPOGen Consortium, Women's Genome Health Study, Howard University Family Study, 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, WH, Boerwinkle, Eric, Morrison, Alanna C, Ridker, Paul M, Becker, Diane M, Rotter, Jerome I, Kardia, Sharon LR, Loos, Ruth JF, 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, and Borecki, Ingrid B

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

Published
2014

Catalog

Books, media, physical & digital resources
See catalog results