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Sunuwar L, Frkatović A, Sharapov S, Wang Q, Neu HM, Wu X, Haritunians T, Wan F, Michel S, Wu S, Donowitz M, McGovern D, Lauc G, Sears C, Melia J. Pleiotropic ZIP8 A391T implicates abnormal manganese homeostasis in complex human disease. JCI Insight 2020; 5:140978. [PMID: 32897876 PMCID: PMC7605523 DOI: 10.1172/jci.insight.140978] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 08/26/2020] [Indexed: 12/15/2022] Open
Abstract
ZIP8 is a metal transporter with a role in manganese (Mn) homeostasis. A common genetic variant in ZIP8 (rs13107325; A391T) ranks in the top 10 of pleiotropic SNPs identified in GWAS; A391T has associations with an increased risk of schizophrenia, obesity, Crohn’s disease, and reduced blood Mn. Here, we used CRISPR/Cas9-mediated knockin (KI) to generate a mouse model of ZIP8 A391T (Zip8 393T-KI mice). Recapitulating the SNP association with blood Mn, blood Mn was reduced in Zip8 393T-KI mice. There was restricted abnormal tissue Mn homeostasis, with decreases in liver and kidney Mn and a reciprocal increase in biliary Mn, providing in vivo evidence of hypomorphic Zip8 function. Upon challenge in a chemically induced colitis model, male Zip8 393T-KI mice exhibited enhanced disease susceptibility. ZIP8 391-Thr associated with reduced triantennary plasma N-glycan species in a population-based cohort to define a genotype-specific glycophenotype hypothesized to be linked to Mn-dependent glycosyltransferase activity. This glycophenotype was maintained in a cohort of patients with Crohn’s disease. These data and the pleiotropic disease associations with ZIP8 391-Thr suggest underappreciated roles of Mn homeostasis in complex human disease. Abnormal manganese homeostasis is implicated by a GWAS disease-associated SNP, rs13107325 (ZIP8 A391T), studied in a knockin mouse model and human N-glycome analyses.
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Affiliation(s)
- Laxmi Sunuwar
- Department of Medicine, Division of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - Sodbo Sharapov
- Laboratory of Glycogenomics, Institute of Cytology and Genetics of Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Qinchuan Wang
- Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Heather M Neu
- University of Maryland School of Pharmacy, University of Maryland, Baltimore, Maryland, USA
| | - Xinqun Wu
- Department of Medicine, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Talin Haritunians
- F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Fengyi Wan
- Department of Biochemistry and Molecular Biology and.,Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Sarah Michel
- University of Maryland School of Pharmacy, University of Maryland, Baltimore, Maryland, USA
| | - Shaoguang Wu
- Department of Medicine, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Mark Donowitz
- Department of Medicine, Division of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Dermot McGovern
- F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Gordan Lauc
- Genos Glycoscience Research Laboratory, Zagreb, Croatia
| | - Cynthia Sears
- Department of Medicine, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Joanna Melia
- Department of Medicine, Division of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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Adi D, Abuzhalihan J, Wang YH, Baituola G, Wu Y, Xie X, Fu ZY, Yang YN, Ma X, Li XM, Chen BD, Liu F, Ma YT. IDOL gene variant is associated with hyperlipidemia in Han population in Xinjiang, China. Sci Rep 2020; 10:14280. [PMID: 32868861 PMCID: PMC7459279 DOI: 10.1038/s41598-020-71241-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 08/12/2020] [Indexed: 12/11/2022] Open
Abstract
Hyperlipidemia is one of the main risk factors that contributed to atherosclerosis and coronary artery disease (CAD). In the present study, our objective was to explore whether some genetic variants of human IDOL gene were associated with hyperlipidemia among Han population in Xinjiang, China. We designed a case–control study. A total of 1,172 subjects (588 diagnosed hyperlipidemia cases and 584 healthy controls) of Chinese Han were recruited. We genotyped three SNPs (rs9370867, rs909562, and rs2072783) of IDOL gene in all subjects by using the improved multiplex ligation detection reaction (iMLDR) method. Our study demonstrated that the distribution of the genotypes, the dominant model (AA vs GG + GA), and the overdominant model (AA + GG vs GA) of the rs9370867 SNP had significant differences between the case group and controls (all P < 0.001). For rs909562 and rs2072783, the distribution of the genotypes, the recessive model (AA + GA vs GG) showed significant differences between the case subjects and controls (P = 0.002, P = 0.007 and P = 0.045, P = 0.02, respectively). After multivariate adjustment for several confounders, the rs9370867 SNP is still an independent risk factor for hyperlipidemia [odds ratio (OR) = 1.380, 95% confidence interval (CI) = 1.201–1.586, P < 0.001]. The rs9370867 of human IDOL gene was associated with hyperlipidemia in Han population.
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Affiliation(s)
- Dilare Adi
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People's Republic of China.,Xinjiang Key Laboratory of Cardiovascular Disease Research, Urumqi, 830054, People's Republic of China
| | - Jialin Abuzhalihan
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People's Republic of China.,Xinjiang Key Laboratory of Cardiovascular Disease Research, Urumqi, 830054, People's Republic of China
| | - Ying-Hong Wang
- Health Checkup Department of the First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People's Republic of China
| | - Gulinaer Baituola
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People's Republic of China.,Xinjiang Key Laboratory of Cardiovascular Disease Research, Urumqi, 830054, People's Republic of China
| | - Yun Wu
- Department of General Practice, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, People's Republic of China
| | - Xiang Xie
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People's Republic of China.,Xinjiang Key Laboratory of Cardiovascular Disease Research, Urumqi, 830054, People's Republic of China
| | - Zhen-Yan Fu
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People's Republic of China.,Xinjiang Key Laboratory of Cardiovascular Disease Research, Urumqi, 830054, People's Republic of China
| | - Yi-Ning Yang
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People's Republic of China.,Xinjiang Key Laboratory of Cardiovascular Disease Research, Urumqi, 830054, People's Republic of China
| | - Xiang Ma
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People's Republic of China.,Xinjiang Key Laboratory of Cardiovascular Disease Research, Urumqi, 830054, People's Republic of China
| | - Xiao-Mei Li
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People's Republic of China.,Xinjiang Key Laboratory of Cardiovascular Disease Research, Urumqi, 830054, People's Republic of China
| | - Bang-Dang Chen
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People's Republic of China.,Xinjiang Key Laboratory of Cardiovascular Disease Research, Urumqi, 830054, People's Republic of China
| | - Fen Liu
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People's Republic of China
| | - Yi-Tong Ma
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People's Republic of China. .,Xinjiang Key Laboratory of Cardiovascular Disease Research, Urumqi, 830054, People's Republic of China.
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53
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Morjane I, Charoute H, Ouatou S, Elkhattabi L, Benrahma H, Saile R, Rouba H, Barakat A. Association of c.56C > G (rs3135506) Apolipoprotein A5 Gene Polymorphism with Coronary Artery Disease in Moroccan Subjects: A Case-Control Study and an Updated Meta-Analysis. Cardiol Res Pract 2020; 2020:5981971. [PMID: 32832146 PMCID: PMC7424381 DOI: 10.1155/2020/5981971] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 02/29/2020] [Accepted: 03/03/2020] [Indexed: 11/18/2022] Open
Abstract
PURPOSE Coronary artery diseases (CAD) are clinical cardiovascular events associated with dyslipidemia in common. The interaction between environmental and genetic factors can be responsible for CAD. The present paper aimed to examine the association between c.56C > G (rs3135506) APOA5 gene polymorphism and CAD in Moroccan individuals and to perform an association update meta-analysis. MATERIALS AND METHODS The c.56C > G variant was genotyped in 122 patients with CAD and 134 unrelated controls. Genetic association analysis and comparison of biochemical parameters were performed using R statistical language. In addition, a comprehensive meta-analysis including eleven published studies in addition to our case-control study results was conducted using Review Manager 5.3. Publication bias was examined by Egger's test and funnel plot. RESULTS The case-control study data showed that the c.56C > G polymorphism was associated with CAD susceptibility under codominant (P-value = 0.001), recessive (P-value <0.001) and log-additive (P-value = 0.008) inheritance models. In addition, this polymorphism was significantly associated with increased levels of systolic and diastolic blood pressures, triglycerides, glycemia, and total cholesterol. Furthermore, meta-analysis showed a significant association between the c.56C > G gene polymorphism and increased risk of CAD under recessive (OR = 3.39[1.77-6.50], P value <0.001) and homozygote codominant (OR = 3.96[2.44-6.45], P value <0.001) models. CONCLUSION Our case-control study revealed a significant association between c.56C > G polymorphism and CAD in the Moroccan population. In addition, meta-analysis data supported the implication of this polymorphism in CAD susceptibility.
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Affiliation(s)
- Imane Morjane
- Laboratory of Genomics and Human Genetics, Institut Pasteur Du Maroc, Casablanca, Morocco
- Laboratory of Biology and Health, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca, Casablanca, Morocco
| | - Hicham Charoute
- Laboratory of Genomics and Human Genetics, Institut Pasteur Du Maroc, Casablanca, Morocco
| | - Sanaa Ouatou
- Laboratory of Genomics and Human Genetics, Institut Pasteur Du Maroc, Casablanca, Morocco
| | - Lamiae Elkhattabi
- Laboratory of Genomics and Human Genetics, Institut Pasteur Du Maroc, Casablanca, Morocco
- Laboratory of Biology and Health, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca, Casablanca, Morocco
| | - Houda Benrahma
- Laboratory of Genomics and Human Genetics, Institut Pasteur Du Maroc, Casablanca, Morocco
- National Reference Laboratory (LNR), Faculty of Medicine, Mohammed VI University of Health Sciences (UM6SS), Casablanca, Morocco
| | - Rachid Saile
- Laboratory of Biology and Health, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca, Casablanca, Morocco
| | - Hassan Rouba
- Laboratory of Genomics and Human Genetics, Institut Pasteur Du Maroc, Casablanca, Morocco
| | - Abdelhamid Barakat
- Laboratory of Genomics and Human Genetics, Institut Pasteur Du Maroc, Casablanca, Morocco
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54
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Mealer RG, Jenkins BG, Chen CY, Daly MJ, Ge T, Lehoux S, Marquardt T, Palmer CD, Park JH, Parsons PJ, Sackstein R, Williams SE, Cummings RD, Scolnick EM, Smoller JW. The schizophrenia risk locus in SLC39A8 alters brain metal transport and plasma glycosylation. Sci Rep 2020; 10:13162. [PMID: 32753748 PMCID: PMC7403432 DOI: 10.1038/s41598-020-70108-9] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 07/20/2020] [Indexed: 12/15/2022] Open
Abstract
A common missense variant in SLC39A8 is convincingly associated with schizophrenia and several additional phenotypes. Homozygous loss-of-function mutations in SLC39A8 result in undetectable serum manganese (Mn) and a Congenital Disorder of Glycosylation (CDG) due to the exquisite sensitivity of glycosyltransferases to Mn concentration. Here, we identified several Mn-related changes in human carriers of the common SLC39A8 missense allele. Analysis of structural brain MRI scans showed a dose-dependent change in the ratio of T2w to T1w signal in several regions. Comprehensive trace element analysis confirmed a specific reduction of only serum Mn, and plasma protein N-glycome profiling revealed reduced complexity and branching. N-glycome profiling from two individuals with SLC39A8-CDG showed similar but more severe alterations in branching that improved with Mn supplementation, suggesting that the common variant exists on a spectrum of hypofunction with potential for reversibility. Characterizing the functional impact of this variant will enhance our understanding of schizophrenia pathogenesis and identify novel therapeutic targets and biomarkers.
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Affiliation(s)
- Robert G Mealer
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- The Stanley Center for Psychiatric Research at Broad Institute of Harvard/MIT, Cambridge, MA, USA.
- National Center for Functional Glycomics, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
| | - Bruce G Jenkins
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Chia-Yen Chen
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- The Stanley Center for Psychiatric Research at Broad Institute of Harvard/MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mark J Daly
- The Stanley Center for Psychiatric Research at Broad Institute of Harvard/MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- The Stanley Center for Psychiatric Research at Broad Institute of Harvard/MIT, Cambridge, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Sylvain Lehoux
- National Center for Functional Glycomics, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Thorsten Marquardt
- Klinik und Poliklinik für Kinder- und Jugendmedizin-Allgemeine Pädiatrie, Universitätsklinikum Münster, Münster, Germany
| | - Christopher D Palmer
- Laboratory of Inorganic and Nuclear Chemistry, Wadsworth Center, New York State Department of Health, Albany, NY, USA
- Department of Environmental Health Sciences, School of Public Health, University at Albany, Albany, NY, USA
| | - Julien H Park
- Klinik und Poliklinik für Kinder- und Jugendmedizin-Allgemeine Pädiatrie, Universitätsklinikum Münster, Münster, Germany
| | - Patrick J Parsons
- Laboratory of Inorganic and Nuclear Chemistry, Wadsworth Center, New York State Department of Health, Albany, NY, USA
- Department of Environmental Health Sciences, School of Public Health, University at Albany, Albany, NY, USA
| | - Robert Sackstein
- Department of Translational Medicine, Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA
| | - Sarah E Williams
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- National Center for Functional Glycomics, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Richard D Cummings
- National Center for Functional Glycomics, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Edward M Scolnick
- The Stanley Center for Psychiatric Research at Broad Institute of Harvard/MIT, Cambridge, MA, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- The Stanley Center for Psychiatric Research at Broad Institute of Harvard/MIT, Cambridge, MA, USA
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55
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Ahmad TR, Higuchi S, Bertaggia E, Hung A, Shanmugarajah N, Guilz NC, Gamarra JR, Haeusler RA. Bile acid composition regulates the manganese transporter Slc30a10 in intestine. J Biol Chem 2020; 295:12545-12558. [PMID: 32690612 DOI: 10.1074/jbc.ra120.012792] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 07/10/2020] [Indexed: 12/11/2022] Open
Abstract
Bile acids (BAs) comprise heterogenous amphipathic cholesterol-derived molecules that carry out physicochemical and signaling functions. A major site of BA action is the terminal ileum, where enterocytes actively reuptake BAs and express high levels of BA-sensitive nuclear receptors. BA pool size and composition are affected by changes in metabolic health, and vice versa. One of several factors that differentiate BAs is the presence of a hydroxyl group on C12 of the steroid ring. 12α-Hydroxylated BAs (12HBAs) are altered in multiple disease settings, but the consequences of 12HBA abundance are incompletely understood. We employed mouse primary ileum organoids to investigate the transcriptional effects of varying 12HBA abundance in BA pools. We identified Slc30a10 as one of the top genes differentially induced by BA pools with varying 12HBA abundance. SLC30A10 is a manganese efflux transporter critical for whole-body manganese excretion. We found that BA pools, especially those low in 12HBAs, induce cellular manganese efflux and that Slc30a10 induction by BA pools is driven primarily by lithocholic acid signaling via the vitamin D receptor. Administration of lithocholic acid or a vitamin D receptor agonist resulted in increased Slc30a10 expression in mouse ileum epithelia. These data demonstrate a previously unknown role for BAs in intestinal control of manganese homeostasis.
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Affiliation(s)
- Tiara R Ahmad
- Department of Pathology and Cell Biology, Columbia University, New York, New York, USA.,Naomi Berrie Diabetes Center, Columbia University, New York, New York, USA
| | - Sei Higuchi
- Department of Pathology and Cell Biology, Columbia University, New York, New York, USA.,Naomi Berrie Diabetes Center, Columbia University, New York, New York, USA
| | - Enrico Bertaggia
- Department of Pathology and Cell Biology, Columbia University, New York, New York, USA.,Naomi Berrie Diabetes Center, Columbia University, New York, New York, USA
| | - Allison Hung
- Naomi Berrie Diabetes Center, Columbia University, New York, New York, USA
| | - Niroshan Shanmugarajah
- Department of Pathology and Cell Biology, Columbia University, New York, New York, USA.,Naomi Berrie Diabetes Center, Columbia University, New York, New York, USA
| | - Nicole C Guilz
- Naomi Berrie Diabetes Center, Columbia University, New York, New York, USA
| | - Jennifer R Gamarra
- Naomi Berrie Diabetes Center, Columbia University, New York, New York, USA
| | - Rebecca A Haeusler
- Department of Pathology and Cell Biology, Columbia University, New York, New York, USA .,Naomi Berrie Diabetes Center, Columbia University, New York, New York, USA
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56
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Deng GX, Yin RX, Guan YZ, Liu CX, Zheng PF, Wei BL, Wu JZ, Miao L. Association of the NCAN-TM6SF2-CILP2-PBX4-SUGP1-MAU2 SNPs and gene-gene and gene-environment interactions with serum lipid levels. Aging (Albany NY) 2020; 12:11893-11913. [PMID: 32568739 PMCID: PMC7343441 DOI: 10.18632/aging.103361] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Accepted: 05/01/2020] [Indexed: 01/07/2023]
Abstract
This study investigated the association of the NCAN-TM6SF2-CILP2-PBX4-SUGP1-MAU2 SNPs and gene-gene and gene-environment interactions with serum lipid levels in the population of Southwest China. Genotyping of 12 SNPs (i.e., rs2238675, rs2228603, rs58542926, rs735273, rs16996148, rs968525, rs17216525, rs12610185, rs10401969, rs8102280, rs73001065 and rs150268548) was performed in 1248 hyperlipidemia patients and 1248 normal subjects. The allelic and genotypic frequencies of the detected SNPs differed substantially between the normal and hyperlipidemia groups (P < 0.05-0.001), and the association of the 12 SNPs and hyperlipidemia was also observed (P < 0.004-0.0001). Four haplotypes (i.e., NCAN C-C, CILP2 G-T, PBX4-SUGP1 G-C, and MAU2 C-A-G-T) and 5 gene-gene interaction haplotypes (i.e., rs2238675C-rs2228603C, rs16996148G-rs17216525T, rs12610185G-rs10401969C, rs73001065G-rs8102280A-rs150268548G-rs968525C and rs73001065C-rs8102280A-rs150268548G-rs96852)showed a protective effect, whereas four other haplotypes (i.e., TM6SF2 T-A, TM6SF2 C-A, MAU2 G-G-G-C and MAU2 C-G-A-T), as well as 4 gene-gene interaction haplotypes (i.e., rs58542926C-rs735273A, rs58542926T-rs735273A, rs73001065G-rs8102280G-rs150268548G-rs968525C, and rs73001065C-rs8102280G-rs150268548A-rs968525T), exhibited an inverse effect on hyperlipidemia (P < 0.05-0.0001). There were notable three-locus models comprising SNP-SNP, SNP-environment, and haplotype-haplotype interactions (P < 0.05-0.0001). The individuals with some genotypes and haplotypes reduced the prevalence of hyperlipidemia, whereas the individuals with some other genotypes and haplotypes augmented the prevalence of hyperlipidemia. The NCAN-TM6SF2-CILP2-PBX4-SUGP1-MAU2 SNPs and gene-gene and gene-environment interactions on hyperlipidemia were observed in the population of Southwest China.
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Affiliation(s)
- Guo-Xiong Deng
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning 530021, Guangxi, People's Republic of China
| | - Rui-Xing Yin
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning 530021, Guangxi, People's Republic of China.,Guangxi Key Laboratory Base of Precision Medicine in Cardio-cerebrovascular Disease Control and Prevention, Nanning 530021, Guangxi, People's Republic of China.,Guangxi Clinical Research Center for Cardio-cerebrovascular Diseases, Nanning 530021, Guangxi, People's Republic of China
| | - Yao-Zong Guan
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning 530021, Guangxi, People's Republic of China
| | - Chun-Xiao Liu
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning 530021, Guangxi, People's Republic of China
| | - Peng-Fei Zheng
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning 530021, Guangxi, People's Republic of China
| | - Bi-Liu Wei
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning 530021, Guangxi, People's Republic of China
| | - Jin-Zhen Wu
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning 530021, Guangxi, People's Republic of China
| | - Liu Miao
- Department of Cardiology, Liuzhou People's Hospital, Liuzhou 545006, Guangxi, People's Republic of China
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57
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Walker ME, Song RJ, Xu X, Gerszten RE, Ngo D, Clish CB, Corlin L, Ma J, Xanthakis V, Jacques PF, Vasan RS. Proteomic and Metabolomic Correlates of Healthy Dietary Patterns: The Framingham Heart Study. Nutrients 2020; 12:E1476. [PMID: 32438708 PMCID: PMC7284467 DOI: 10.3390/nu12051476] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 05/12/2020] [Accepted: 05/16/2020] [Indexed: 02/07/2023] Open
Abstract
Data on proteomic and metabolomic signatures of healthy dietary patterns are limited. We evaluated the cross-sectional association of serum proteomic and metabolomic markers with three dietary patterns: the Alternative Healthy Eating Index (AHEI), the Dietary Approaches to Stop Hypertension (DASH) diet; and a Mediterranean-style (MDS) diet. We examined participants from the Framingham Offspring Study (mean age; 55 years; 52% women) who had complete proteomic (n = 1713) and metabolomic (n = 2284) data; using food frequency questionnaires to derive dietary pattern indices. Proteins and metabolites were quantified using the SomaScan platform and liquid chromatography/tandem mass spectrometry; respectively. We used multivariable-adjusted linear regression models to relate each dietary pattern index (independent variables) to each proteomic and metabolomic marker (dependent variables). Of the 1373 proteins; 103 were associated with at least one dietary pattern (48 with AHEI; 83 with DASH; and 8 with MDS; all false discovery rate [FDR] ≤ 0.05). We identified unique associations between dietary patterns and proteins (17 with AHEI; 52 with DASH; and 3 with MDS; all FDR ≤ 0.05). Significant proteins enriched biological pathways involved in cellular metabolism/proliferation and immune response/inflammation. Of the 216 metabolites; 65 were associated with at least one dietary pattern (38 with AHEI; 43 with DASH; and 50 with MDS; all FDR ≤ 0.05). All three dietary patterns were associated with a common signature of 24 metabolites (63% lipids). Proteins and metabolites associated with dietary patterns may help characterize intermediate phenotypes that provide insights into the molecular mechanisms mediating diet-related disease. Our findings warrant replication in independent populations.
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Affiliation(s)
- Maura E. Walker
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA; (L.C.); (V.X.); (R.S.V.)
| | - Rebecca J. Song
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA;
| | - Xiang Xu
- Department of Mathematics and Statistics, Boston University College of Arts and Sciences, Boston, MA 02215, USA;
| | - Robert E. Gerszten
- Division of Cardiovascular Medicine Beth Israel Deaconess Medical Center, Boston, MA 02215, USA; (R.E.G.); (D.N.)
| | - Debby Ngo
- Division of Cardiovascular Medicine Beth Israel Deaconess Medical Center, Boston, MA 02215, USA; (R.E.G.); (D.N.)
| | - Clary B. Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA;
| | - Laura Corlin
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA; (L.C.); (V.X.); (R.S.V.)
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA
| | - Jiantao Ma
- Framingham Heart Study, Framingham, MA 01702, USA;
- Nutrition Epidemiology and Data Science, Tufts University Friedman School of Nutrition Science and Policy, Boston, MA 02111, USA;
| | - Vanessa Xanthakis
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA; (L.C.); (V.X.); (R.S.V.)
- Framingham Heart Study, Framingham, MA 01702, USA;
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Paul F. Jacques
- Nutrition Epidemiology and Data Science, Tufts University Friedman School of Nutrition Science and Policy, Boston, MA 02111, USA;
- Nutrition Epidemiology, Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA 02111, USA
| | - Ramachandran S. Vasan
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA; (L.C.); (V.X.); (R.S.V.)
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA;
- Framingham Heart Study, Framingham, MA 01702, USA;
- Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
- Center for Computing and Data Sciences, Boston University, Boston, MA 02215, USA
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Basu S, Nandy A, Biswas D. Keeping RNA polymerase II on the run: Functions of MLL fusion partners in transcriptional regulation. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2020; 1863:194563. [PMID: 32348849 DOI: 10.1016/j.bbagrm.2020.194563] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 01/13/2020] [Accepted: 04/13/2020] [Indexed: 12/21/2022]
Abstract
Since the identification of key MLL fusion partners as transcription elongation factors regulating expression of HOX cluster genes during hematopoiesis, extensive work from the last decade has resulted in significant progress in our overall mechanistic understanding of role of MLL fusion partner proteins in transcriptional regulation of diverse set of genes beyond just the HOX cluster. In this review, we are going to detail overall understanding of role of MLL fusion partner proteins in transcriptional regulation and thus provide mechanistic insights into possible MLL fusion protein-mediated transcriptional misregulation leading to aberrant hematopoiesis and leukemogenesis.
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Affiliation(s)
- Subham Basu
- Laboratory of Transcription Biology, Molecular Genetics Division, CSIR-Indian Institute of Chemical Biology, 4, Raja S. C. Mullick Road, Kolkata 32, India
| | - Arijit Nandy
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Debabrata Biswas
- Laboratory of Transcription Biology, Molecular Genetics Division, CSIR-Indian Institute of Chemical Biology, 4, Raja S. C. Mullick Road, Kolkata 32, India.
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Zhong Y, De T, Alarcon C, Park CS, Lec B, Perera MA. Discovery of novel hepatocyte eQTLs in African Americans. PLoS Genet 2020; 16:e1008662. [PMID: 32310939 PMCID: PMC7192504 DOI: 10.1371/journal.pgen.1008662] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 04/30/2020] [Accepted: 02/11/2020] [Indexed: 12/21/2022] Open
Abstract
African Americans (AAs) are disproportionately affected by metabolic diseases and adverse drug events, with limited publicly available genomic and transcriptomic data to advance the knowledge of the molecular underpinnings or genetic associations to these diseases or drug response phenotypes. To fill this gap, we obtained 60 primary hepatocyte cultures from AA liver donors for genome-wide mapping of expression quantitative trait loci (eQTL) using LAMatrix. We identified 277 eGenes and 19,770 eQTLs, of which 67 eGenes and 7,415 eQTLs are not observed in the Genotype-Tissue Expression Project (GTEx) liver eQTL analysis. Of the eGenes found in GTEx only 25 share the same lead eQTL. These AA-specific eQTLs are less correlated to GTEx eQTLs. in effect sizes and have larger Fst values compared to eQTLs found in both cohorts (overlapping eQTLs). We assessed the overlap between GWAS variants and their tagging variants with AA hepatocyte eQTLs and demonstrated that AA hepatocyte eQTLs can decrease the number of potential causal variants at GWAS loci. Additionally, we identified 75,002 exon QTLs of which 48.8% are not eQTLs in AA hepatocytes. Our analysis provides the first comprehensive characterization of AA hepatocyte eQTLs and highlights the unique discoveries that are made possible due to the increased genetic diversity within the African ancestry genome.
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Affiliation(s)
- Yizhen Zhong
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Tanima De
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Cristina Alarcon
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - C. Sehwan Park
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Bianca Lec
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Minoli A. Perera
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- * E-mail:
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Du S, Wang H, Cai J, Ren R, Zhang W, Wei W, Shen X. Apolipoprotein E2 modulates cell cycle function to promote proliferation in pancreatic cancer cells via regulation of the c-Myc–p21Waf1signalling pathway. Biochem Cell Biol 2020; 98:191-202. [PMID: 32167787 DOI: 10.1139/bcb-2018-0230] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Apolipoprotein E2 (ApoE2) is reportedly critical for cell proliferation and survival, and has been identified as a potential tumour-associated marker in many kinds of cancer. However, studies of the function and mechanisms of ApoE2 in pancreatic cancer proliferation and development are rare. In this study, we performed an analysis to determine the modulatory effects of ApoE2–LRP8 (lipoprotein receptor-related protein 8) pathway on cell cycle and cell proliferation, and explored its mechanisms in pancreatic cancer. High expression levels of ApoE2–LRP8/c-Myc were detected in tumour tissues and cell lines by immunohistochemistry and Western blotting. It was also shown that ApoE2–LRP8 induced phosphorylation of ERK1/2 to activate c-Myc and contribute to cell-cycle-related protein expression. ApoE2 conditions induced c-Myc binding to target gene sequences in the p21Waf1promoter, resulting in decreased transcription. ERK/c-Myc contributes to the promotion of the expression levels of cyclin D1, cdc2, and cyclin B1, and reduces p21Waf1activity, thereby promoting cell cycle distribution. We demonstrated the function of ApoE2–LRP8 in the activation of the ERK–c-Myc–p21Waf1signalling cascade and the modulation of G1/S and G2/M transition, indicating ApoE2–LRP8’s important role in the cancer cell proliferation. ApoE2 could serve as a diagnostic marker and chemotherapeutic target in pancreatic cancer.
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Affiliation(s)
- Shaoxia Du
- School of Medicine, Nankai University, 94 Weijin Road, Tianjin 300071, China
| | - Hui Wang
- School of Medicine, Nankai University, 94 Weijin Road, Tianjin 300071, China
| | - Jun Cai
- School of Medicine, Nankai University, 94 Weijin Road, Tianjin 300071, China
| | - Runling Ren
- School of Medicine, Nankai University, 94 Weijin Road, Tianjin 300071, China
| | - Wenwen Zhang
- School of Medicine, Nankai University, 94 Weijin Road, Tianjin 300071, China
| | - Wei Wei
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Huanhu West Road, Tianjin 300060, China
| | - Xiaohong Shen
- School of Medicine, Nankai University, 94 Weijin Road, Tianjin 300071, China
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Czumaj A, Śledziński T. Biological Role of Unsaturated Fatty Acid Desaturases in Health and Disease. Nutrients 2020; 12:E356. [PMID: 32013225 PMCID: PMC7071289 DOI: 10.3390/nu12020356] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 01/20/2020] [Accepted: 01/28/2020] [Indexed: 12/21/2022] Open
Abstract
Polyunsaturated fatty acids (PUFAs) are considered one of the most important components of cells that influence normal development and function of many organisms, both eukaryotes and prokaryotes. Unsaturated fatty acid desaturases play a crucial role in the synthesis of PUFAs, inserting additional unsaturated bonds into the acyl chain. The level of expression and activity of different types of desaturases determines profiles of PUFAs. It is well recognized that qualitative and quantitative changes in the PUFA profile, resulting from alterations in the expression and activity of fatty acid desaturases, are associated with many pathological conditions. Understanding of underlying mechanisms of fatty acid desaturase activity and their functional modification will facilitate the development of novel therapeutic strategies in diseases associated with qualitative and quantitative disorders of PUFA.
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Affiliation(s)
- Aleksandra Czumaj
- Department of Pharmaceutical Biochemistry, Faculty of Pharmacy, Medical University of Gdansk, Dębinki, 80-211 Gdansk, Poland;
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Analysis of the genetic variants associated with circulating levels of sgp130. Results from the IMPROVE study. Genes Immun 2020; 21:100-108. [PMID: 31932740 PMCID: PMC7182533 DOI: 10.1038/s41435-019-0090-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 11/12/2019] [Accepted: 12/23/2019] [Indexed: 01/06/2023]
Abstract
The genes regulating circulating levels of soluble gp130 (sgp130), the antagonist of the inflammatory response in atherosclerosis driven by interleukin 6, are largely unknown. Aims of the present study were to identify genetic loci associated with circulating sgp130 and to explore the potential association between variants associated with sgp130 and markers of subclinical atherosclerosis. The study is based on IMPROVE (n = 3703), a cardiovascular multicentre study designed to investigate the determinants of carotid intima media thickness, a measure of subclinical atherosclerosis. Genomic DNA was genotyped by the CardioMetaboChip and ImmunoChip. About 360,842 SNPs were tested for association with log-transformed sgp130, using linear regression adjusted for age, gender, and population stratification using PLINK v1.07. A p value of 1 × 10−5 was chosen as threshold for significance value. In an exploratory analysis, SNPs associated with sgp130 were tested for association with c-IMT measures. We identified two SNPs significantly associated with sgp130 levels and 24 showing suggestive association with sgp130 levels. One SNP (rs17688225) on chromosome 14 was positively associated with sgp130 serum levels (β = 0.03 SE = 0.007, p = 4.77 × 10−5) and inversely associated with c-IMT (c-IMTmean–maxβ = −0.001 SE = 0.005, p = 0.0342). Our data indicate that multiple loci regulate sgp130 levels and suggest a possible common pathway between sgp130 and c-IMT measures.
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Genome-wide association study of metabolic syndrome in Korean populations. PLoS One 2020; 15:e0227357. [PMID: 31910446 PMCID: PMC6946588 DOI: 10.1371/journal.pone.0227357] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 12/17/2019] [Indexed: 12/24/2022] Open
Abstract
Metabolic syndrome (MetS) which is caused by obesity and insulin resistance, is well known for its predictive capability for the risk of type 2 diabetes mellitus and cardiovascular disease. The development of MetS is associated with multiple genetic factors, environmental factors and lifestyle. We performed a genome-wide association study to identify single-nucleotide polymorphism (SNP) related to MetS in large Korean population based samples of 1,362 subjects with MetS and 6,061 controls using the Axiom® Korean Biobank Array 1.0. We replicated the data in another sample including 502 subjects with MetS and 1,751 controls. After adjusting for age and sex, rs662799 located in the APOA5 gene were significantly associated with MetS. 15 SNPs in GCKR, C2orf16, APOA5, ZPR1, and BUD13 were associated with high triglyceride (TG). 14 SNPs in APOA5, ALDH1A2, LIPC, HERPUD1, and CETP, and 2 SNPs in MTNR1B were associated with low high density lipoprotein cholesterol (HDL-C) and high fasting blood glucose respectively. Among these SNPs, 6 TG SNPs: rs1260326, rs1260333, rs1919127, rs964184, rs2075295 and rs1558861 and 11 HDL-C SNPs: rs4775041, rs10468017, rs1800588, rs72786786, rs173539, rs247616, rs247617, rs3764261, rs4783961, rs708272, and rs7499892 were first discovered in Koreans. Additional research is needed to confirm these 17 novel SNPs in Korean population.
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Valente-Frossard TNS, Cruz NRC, Ferreira FO, Belisario AR, Pereira BM, Gomides AFDF, Resende GAD, Carlos AM, Moraes-Souza H, Velloso-Rodrigues C. Polymorphisms in genes that affect the variation of lipid levels in a Brazilian pediatric population with sickle cell disease: rs662799 APOA5 and rs964184 ZPR1. Blood Cells Mol Dis 2019; 80:102376. [PMID: 31670185 DOI: 10.1016/j.bcmd.2019.102376] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2019] [Revised: 10/10/2019] [Accepted: 10/16/2019] [Indexed: 12/31/2022]
Abstract
This cross-sectional study investigated associations between SNPs in metabolizing lipid genes, alpha-thalassemia and laboratory parameters in two forms of sickle cell disease (SCD), sickle cell anemia (SCA) and hemoglobin SC disease (HbSC) in a pediatric population. Among the groups SCA and HbSC was found a higher proportion of increased triglycerides (TG) in SCA. High levels of TG were significantly associated with lower hemoglobin (p = 0.006) and HDL-C (p = 0.037), higher white blood cell count (p = 0.027), LDH (p = 0.004) and bilirubins (p < 0.05) in SCD. Patients with HDL-C ≤40 mg/dL had higher markers hemolytic levels. Therapy of HU significantly influenced several hematological and biochemical parameters but not lipid fractions. Genotypes of the APOA5 rs662799 were not associated with lipid levels. The G-risk allele rs964184/ZPRI ZNF259/ZPR1 gene (GC + GG genotypes) was associated with increased levels of TG in children ≥10 years old (p = 0.045) and the atherogenic ratio TG/HDL-C (p = 0.032) in SCD. The use of HU improves levels of hemolysis and inflammation markers in SCD with high TG and, while not interfering with lipid levels, seems to overlap the effect of the G-risk allele in on them. This study reported for the first time that rs964184 SNP could be a genetic modifier of TG in SCD.
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Affiliation(s)
- Thaisa Netto Souza Valente-Frossard
- Departments of Basic Science of Life, Institute of Life Sciences, Federal University of Juiz de Fora, Governador Valadares, Minas Gerais, Brazil
| | - Nilcemar Rodrigues Carvalho Cruz
- Departments of Nutrition, Institute of Life Sciences, Federal University of Juiz de Fora, Governador Valadares, Minas Gerais, Brazil
| | - Fernanda Oliveira Ferreira
- Departments of Basic Science of Life, Institute of Life Sciences, Federal University of Juiz de Fora, Governador Valadares, Minas Gerais, Brazil
| | | | - Brisa Machado Pereira
- Departments of Basic Science of Life, Institute of Life Sciences, Federal University of Juiz de Fora, Governador Valadares, Minas Gerais, Brazil
| | - Antônio Frederico de Freitas Gomides
- Departments of Basic Science of Life, Institute of Life Sciences, Federal University of Juiz de Fora, Governador Valadares, Minas Gerais, Brazil
| | | | - Aline Menezes Carlos
- Federal University of Triangulo Mineiro, Uberaba, Minas Gerais, Brazil; Uberaba Regional Blood Center, Uberaba, Minas Gerais, Brazil
| | - Helio Moraes-Souza
- Federal University of Triangulo Mineiro, Uberaba, Minas Gerais, Brazil; Uberaba Regional Blood Center, Uberaba, Minas Gerais, Brazil
| | - Cibele Velloso-Rodrigues
- Departments of Basic Science of Life, Institute of Life Sciences, Federal University of Juiz de Fora, Governador Valadares, Minas Gerais, Brazil.
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Nebert DW, Liu Z. SLC39A8 gene encoding a metal ion transporter: discovery and bench to bedside. Hum Genomics 2019; 13:51. [PMID: 31521203 PMCID: PMC6744627 DOI: 10.1186/s40246-019-0233-3] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 08/14/2019] [Indexed: 01/08/2023] Open
Abstract
SLC39A8 is an evolutionarily highly conserved gene that encodes the ZIP8 metal cation transporter in all vertebrates. SLC39A8 is ubiquitously expressed, including pluripotent embryonic stem cells; SLC39A8 expression occurs in every cell type examined. Uptake of ZIP8-mediated Mn2+, Zn2+, Fe2+, Se4+, and Co2+ represents endogenous functions-moving these cations into the cell. By way of mouse genetic differences, the phenotype of "subcutaneous cadmium-induced testicular necrosis" was assigned to the Cdm locus in the 1970s. This led to identification of the mouse Slc39a8 gene, its most closely related Slc39a14 gene, and creation of Slc39a8-overexpressing, Slc39a8(neo/neo) knockdown, and cell type-specific conditional knockout mouse lines; the Slc39a8(-/-) global knockout mouse is early-embryolethal. Slc39a8(neo/neo) hypomorphs die between gestational day 16.5 and postnatal day 1-exhibiting severe anemia, dysregulated hematopoiesis, hypoplastic spleen, dysorganogenesis, stunted growth, and hypomorphic limbs. Not surprisingly, genome-wide association studies subsequently revealed human SLC39A8-deficiency variants exhibiting striking pleiotropy-defects correlated with clinical disorders in virtually every organ, tissue, and cell-type: numerous developmental and congenital disorders, the immune system, cardiovascular system, kidney, lung, liver, coagulation system, central nervous system, musculoskeletal system, eye, and gastrointestinal tract. Traits with which SLC39A8-deficiency variants are currently associated include Mn2+-deficient hypoglycosylation; numerous birth defects; Leigh syndrome-like mitochondrial redox deficiency; decreased serum high-density lipoprotein-cholesterol levels; increased body mass index; greater risk of coronary artery disease, hypotension, cardiovascular death, allergy, ischemic stroke, schizophrenia, Parkinson disease, inflammatory bowel disease, Crohn disease, myopia, and adolescent idiopathic scoliosis; systemic lupus erythematosus with primary Sjögren syndrome; decreased height; and inadvertent participation in the inflammatory progression of osteoarthritis.
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Affiliation(s)
- Daniel W Nebert
- Department of Environmental Health and Center for Environmental Genetics, University of Cincinnati Medical Center, Cincinnati, OH, 45267-0056, USA.
- Division of Human Genetics, Department of Pediatrics & Molecular Developmental Biology, Cincinnati Children's Hospital, Cincinnati, OH, 45229-2899, USA.
| | - Zijuan Liu
- Department of Biological Sciences, Oakland University, Rochester, MI, 48309, USA
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Li Z, Zhao T, Tan X, Lei S, Huang L, Yang L. Polymorphisms in PCSK9, LDLR, BCMO1, SLC12A3, and KCNJ1 are Associated with Serum Lipid Profile in Chinese Han Population. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:E3207. [PMID: 31480784 PMCID: PMC6747169 DOI: 10.3390/ijerph16173207] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Revised: 08/30/2019] [Accepted: 08/31/2019] [Indexed: 12/14/2022]
Abstract
Unfavorable serum lipid levels are the most important risk factors for coronary artery disease (CAD), cerebral infarction, and other cardiovascular and cerebrovascular diseases. This study included 2323 Han Chinese in southern China. We collected medical reports, lifestyle details, and blood samples of individuals and used the polymerase chain reaction-ligase detection reaction method to genotype single-nucleotide polymorphisms (SNPs). Two SNPs showed a strong evidence of association with total cholesterol (TC): rs1003723 and rs6413504 in the low-density lipoproteins receptor (LDLR). Two SNPs in LDLR showed a strong evidence of association with low-density lipoprotein cholesterol (LDL-C), rs1003723 and rs6413504. Two SNPs showed a strong evidence of association with triglycerides (TG), namely, rs662145 in pro-protein convertase subtilisin-kexin type 9 (PCSK9) and rs11643718 in the solute carrier family 12 member 3 (SLC12A3). For the TC, LDL-C, and TG levels, these SNPs generated strong combined effects on these lipid levels. For each additional dangerous gene, TC increased by 0.085 mmol/L (p = 7.00 × 10-6), and LDL-C increased by 0.075 mmol/L (p = 9.00 × 10-6). The TG increased by 0.096 mmol/L (p = 2.90 × 10-5). Compared with those bearing no risk alleles, the risk of hypertriglyceridemia, hypercholesterolemia, and dyslipidemia increased in those with two or more risk alleles and one risk gene. Polymorphisms of PCSK9, LDLR, and SLC12A3 were associated with the plasma lipid levels in people in southern China. These results provide a theoretical basis for gene screening and the prevention of dyslipidemia.
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Affiliation(s)
- Zheng Li
- Medical School, Hangzhou Normal University, Hangzhou 310000, China
| | - Tianyu Zhao
- Medical School, Hangzhou Normal University, Hangzhou 310000, China
- Medical School, Shihezi University, Shihezi 832000, China
| | - Xiaohua Tan
- Medical School, Hangzhou Normal University, Hangzhou 310000, China
| | - Song Lei
- Medical School, Hangzhou Normal University, Hangzhou 310000, China
- Medical School, Shihezi University, Shihezi 832000, China
| | - Liu Huang
- Medical School, Hangzhou Normal University, Hangzhou 310000, China
| | - Lei Yang
- Medical School, Hangzhou Normal University, Hangzhou 310000, China.
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Impact of the Apolipoprotein E (epsilon) Genotype on Cardiometabolic Risk Markers and Responsiveness to Acute and Chronic Dietary Fat Manipulation. Nutrients 2019; 11:nu11092044. [PMID: 31480637 PMCID: PMC6770634 DOI: 10.3390/nu11092044] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 08/19/2019] [Accepted: 08/20/2019] [Indexed: 02/01/2023] Open
Abstract
Apolipoprotein (APO) E (ε) genotype is considered to play an important role in lipid responses to dietary fat manipulation but the impact on novel cardiometabolic risk markers is unclear. To address this knowledge gap, we investigated the relationship between the APOE genotype and cardiometabolic risk markers in response to acute and chronic dietary fat intakes. Associations with fasting (baseline) outcome measures (n = 218) were determined using data from the chronic DIVAS (n = 191/195 adults at moderate cardiovascular disease risk) and acute DIVAS-2 (n = 27/32 postmenopausal women) studies examining the effects of diets/meals varying in saturated, polyunsaturated and monounsaturated (MUFA) fatty acid composition. Participants were retrospectively genotyped for APOE (rs429358, rs7412). For baseline cardiometabolic outcomes, E4 carriers had higher fasting total and low-density lipoprotein-cholesterol (LDL-C), total cholesterol: high-density lipoprotein-cholesterol (HDL-C) and LDL-C: HDL-C ratios, but lower C-reactive protein (CRP) than E3/E3 and E2 carriers (p ≤ 0.003). Digital volume pulse stiffness index was higher in E2 carriers than the E3/E3 group (p = 0.011). Following chronic dietary fat intake, the significant diet × genotype interaction was found for fasting triacylglycerol (p = 0.010), with indication of a differential responsiveness to MUFA intake between the E3/E3 and E4 carriers (p = 0.006). Test fat × genotype interactions were observed for the incremental area under the curve for the postprandial apolipoprotein B (apoB; p = 0.022) and digital volume pulse reflection index (DVP-RI; p = 0.030) responses after the MUFA-rich meals, with a reduction in E4 carriers and increase in the E3/E3 group for the apoB response, but an increase in E4 carriers and decrease in the E3/E3 group for the DVP-RI response. In conclusion, baseline associations between the APOE genotype and fasting lipids and CRP confirm previous findings, although a novel interaction with digital volume pulse arterial stiffness was observed in the fasted state and differential postprandial apoB and DVP-RI responses after the MUFA-rich meals. The reported differential impact of the APOE genotype on cardiometabolic markers in the acute and chronic state requires confirmation.
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ZRANB3 is an African-specific type 2 diabetes locus associated with beta-cell mass and insulin response. Nat Commun 2019; 10:3195. [PMID: 31324766 PMCID: PMC6642147 DOI: 10.1038/s41467-019-10967-7] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Accepted: 06/11/2019] [Indexed: 12/13/2022] Open
Abstract
Genome analysis of diverse human populations has contributed to the identification of novel genomic loci for diseases of major clinical and public health impact. Here, we report a genome-wide analysis of type 2 diabetes (T2D) in sub-Saharan Africans, an understudied ancestral group. We analyze ~18 million autosomal SNPs in 5,231 individuals from Nigeria, Ghana and Kenya. We identify a previously-unreported genome-wide significant locus: ZRANB3 (Zinc Finger RANBP2-Type Containing 3, lead SNP p = 2.831 × 10−9). Knockdown or genomic knockout of the zebrafish ortholog results in reduction in pancreatic β-cell number which we demonstrate to be due to increased apoptosis in islets. siRNA transfection of murine Zranb3 in MIN6 β-cells results in impaired insulin secretion in response to high glucose, implicating Zranb3 in β-cell functional response to high glucose conditions. We also show transferability in our study of 32 established T2D loci. Our findings advance understanding of the genetics of T2D in non-European ancestry populations. Type 2 diabetes (T2D) is prevalent in populations worldwide, however, mostly studied in European and mixed-ancestry populations. Here, the authors perform a genome-wide association study for T2D in over 5,000 sub-Saharan Africans and identify a locus, ZRANB3, that is specific for this population.
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RAD51B (rs8017304 and rs2588809), TRIB1 (rs6987702, rs4351379, and rs4351376), COL8A1 (rs13095226), and COL10A1 (rs1064583) Gene Variants with Predisposition to Age-Related Macular Degeneration. DISEASE MARKERS 2019; 2019:5631083. [PMID: 31191752 PMCID: PMC6525907 DOI: 10.1155/2019/5631083] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 03/27/2019] [Indexed: 12/16/2022]
Abstract
Background Age-related macular degeneration (AMD) is a progressive neurodegenerative disease of a central part of the neural retina (macula) and a leading cause of blindness in elderly people. While it is known that the AMD is a multifactorial disease, genetic factors involved in lipid metabolism, inflammation, and neovascularization are currently being widely studied in genome-wide association studies (GWAS). The aim of our study was to evaluate the impact of new single nucleotide polymorphisms (SNPs) in RAD51B, TRIB1, COL8A1, and COL10A1 genes on AMD development. Methods Case-control study involved 254 patients diagnosed with early AMD, 244 patients with exudative AMD, and 942 control subjects. The genotyping of RAD51B (rs8017304 and rs2588809), TRIB1 (rs6987702, rs4351379, and rs4351376), COL8A1 (rs13095226), and COL10A1 (rs1064583) was carried out using TaqMan assays by a real-time polymerase chain reaction (RT-PCR) method. Results Statistically significant difference was found in genotype (TT, TC, and CC) distribution of COL8A1 rs13095226 between exudative AMD and control groups (60.2%, 33.6%, and 6.1% vs. 64.9%, 32.3%, and 2.9%, respectively, p = 0.036). Also, comparing with TT+TC, rs13095226 CC genotype was associated with 3.5-fold increased odds of exudative AMD development (OR = 3.540; 95% CI: 1.415-8.856; p = 0.007). Conclusion Our study revealed a strong association between a variant in COL8A1 (rs13095226) and exudative AMD development.
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van Loon NM, Lindholm D, Zelcer N. The E3 ubiquitin ligase inducible degrader of the LDL receptor/myosin light chain interacting protein in health and disease. Curr Opin Lipidol 2019; 30:192-197. [PMID: 30896554 DOI: 10.1097/mol.0000000000000593] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
PURPOSE OF REVIEW The RING E3 ubiquitin ligase inducible degrader of the LDL receptor (IDOL, also known as MYLIP) promotes ubiquitylation and subsequent lysosomal degradation of the LDL receptor (LDLR), thus acting to limit uptake of lipoprotein-derived cholesterol into cells. Next to the LDLR, IDOL also promotes degradation of two related receptors, the very LDL receptor (VLDLR) and apolipoprotein E receptor 2 (APOER2), which have important signaling functions in the brain. We review here the emerging role of IDOL in lipoprotein and energy metabolism, neurodegenerative diseases, and the potential for therapeutic targeting of IDOL. RECENT FINDINGS Genetic studies suggest an association between IDOL and lipoprotein metabolism in humans. Studies in rodents and nonhuman primates support an in-vivo role for IDOL in lipoprotein metabolism, and also uncovered an unexpected role in whole-body energy metabolism. Recent evaluation of IDOL function in the brain revealed a role in memory formation and progression of Alzheimer's disease. The report of the first IDOL inhibitor may facilitate further investigations on therapeutic strategies to target IDOL. SUMMARY IDOL is emerging as an important determinant of lipid and energy metabolism in metabolic disease as well as in Alzheimer's disease. IDOL targeting may be beneficial in treating these conditions.
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Affiliation(s)
- Nienke M van Loon
- Department of Medical Biochemistry, Academic Medical Center, University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - Dan Lindholm
- Medicum, Department of Biochemistry and Developmental Biology, Medical Faculty, University of Helsinki
- Minerva Foundation Institute for Medical Research, Biomedicum-2, Helsinki, Finland
| | - Noam Zelcer
- Department of Medical Biochemistry, Academic Medical Center, University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
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Teng MS, Wu S, Hsu LA, Tzeng IS, Chou HH, Su CW, Ko YL. Pleiotropic association of LIPC variants with lipid and urinary 8-hydroxy deoxyguanosine levels in a Taiwanese population. Lipids Health Dis 2019; 18:111. [PMID: 31077211 PMCID: PMC6511151 DOI: 10.1186/s12944-019-1057-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 04/24/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Hepatic lipase (HL, encoded by LIPC) is a glycoprotein primarily synthesized and secreted by hepatocytes. Previous studies had demonstrated that HL is crucial for reverse cholesterol transport and affects the metabolism, composition, and level of several lipoproteins. In current study, we investigated the association of LIPC (Lipase C, Hepatic Type) variants with circulating and urinary biomarker levels by using subgroup and mediation analyses. METHODS A total of 572 participants from Taiwan were genotyped for three LIPC single nucleotide polymorphisms (SNPs) by using TaqMan assay. Fasting levels of glucose, lipid profile, inflammation markers, urine creatinine and 8-hydroxy deoxyguanosine (8-OHdG) were measured. The chi-square test, 2-sample t test and Analysis of variance (ANOVA) were used to examine differences among variables and genotype frequencies. RESULTS SNPs rs2043085 and rs1532085 were significantly associated with urinary 8-OHdG levels, whereas all three SNPs were more significantly associated with Triglycerides (TG) or HDL-cholesterol (HDL-C) levels after additional adjustment for HDL-C or TG levels, respectively. Subgroup analyses revealed that the association of the LIPC SNPs with the levels of serum TG, HDL-C, and urinary 8-OHdG were predominantly observed in the men but not in the women. Differential associations of the LIPC SNPs with various lipid levels were observed in participants with different adiposity statuses. Mediation analyses indicated that TG levels acted as a suppressor masking the association of the LIPC genotypes with HDL-C levels, particularly in the men (Sobel test, all P < 0.01). CONCLUSION Our data revealed that interaction and suppression effects mediated the pleiotropic association of the LIPC variants. The effects of the LIPC SNPs depended on sex, adiposity status, and TG levels. Thus, our findings can provide a method for identifying high-risk populations of cardiovascular diseases for clinical diagnosis.
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Affiliation(s)
- Ming-Sheng Teng
- Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei city, Taiwan
| | - Semon Wu
- Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei city, Taiwan.,Department of Life Science, Chinese Culture University, Taipei, Taiwan
| | - Lung-An Hsu
- The First Cardiovascular Division, Department of Internal Medicine, Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - I-Shiang Tzeng
- Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei city, Taiwan
| | - Hsin-Hua Chou
- The Division of Cardiology, Department of Internal Medicine and Cardiovascular Center, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei city, Taiwan
| | - Cheng-Wen Su
- Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei city, Taiwan
| | - Yu-Lin Ko
- Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei city, Taiwan. .,The Division of Cardiology, Department of Internal Medicine and Cardiovascular Center, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei city, Taiwan. .,School of Medicine, Tzu Chi University, Hualien, Taiwan.
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72
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Impact of gender and age on the association of the BUD13-ZNF259 rs964184 polymorphism with coronary heart disease. Anatol J Cardiol 2019; 19:42-49. [PMID: 29339699 PMCID: PMC5864789 DOI: 10.14744/anatoljcardiol.2017.8002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVE Coronary heart disease (CHD) is the most common cause of death worldwide. This study aimed to validate the association of the rs964184 polymorphism with the CHD risk and included 874 CHD patients and 776 controls. METHODS rs964184 polymorphism genotyping was performed using Tm-shift polymerase chain reaction. RESULTS A strong association of the rs964184 polymorphism with CHD was found (genotype: X2=14.365, p=0.001; allele: X2=14.191, p=1.67x10-4; power=0.965). Gender analysis revealed a significant association only in males (genotype: X2=12.387, p=0.002; allele: X2=12.404, p=4.32x10-4; OR=1.467, 95% CI=1.185-1.817, power=0.945). Age and gender analyses revealed significant associations of the rs964184 polymorphism with CHD in males between the ages of 55 and 65 years (genotype: X2=10.070, p=0.007; allele: X2=10.077, p=0.002; OR=1.706, 95% CI=1.224-2.377, power=0.996) and in females older than 65 years (genotype: X2=9.462, p=0.009; allele: X2=9.560, p=0.002; OR=2.112, 95% CI=1.308-3.412, power=0.994). Further subgroup analysis suggested that rs964184 genotypes were significantly associated with TG levels in the patients (r=0.191, adjusted p=1.05x10-5) and controls (r=0.101, adjusted p=0.026). CONCLUSION Our results indicate that both gender and age have great impacts on the association of the rs964184 polymorphism with CHD among Chinese.
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Zhao Q, Sun D, Li Y, Qin J, Yan J. Integrated analyses of lncRNAs microarray profiles and mRNA-lncRNA coexpression in smooth muscle cells under hypoxic and normoxic conditions. Biosci Rep 2019; 39:BSR20181783. [PMID: 30850398 PMCID: PMC6443952 DOI: 10.1042/bsr20181783] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Revised: 03/03/2019] [Accepted: 03/06/2019] [Indexed: 01/23/2023] Open
Abstract
Hypoxia may cause abnormal proliferation and migration of the vascular smooth muscle cells (VSMCs) from the media to the intima. This contributes to vessel narrowing and accelerates the process of atherosclerosis. The association of the aberrant expression of long noncoding RNAs (lncRNAs) with the development and progression of atherosclerosis is well known; however, it is not well investigated in hypoxic VSMCs. Using a microarray approach, we identified 1056 and 2804 differentially expressed lncRNAs and mRNAs, respectively, in hypoxic and normoxic mouse aorta smooth muscle (MOVAS) cells. Of them, we randomly chose several lncRNAs and validated the microarray data using the quantitative PCR (qPCR) assay. Advanced bioinformatics analyses indicated that the up-regulated mRNAs were mainly involved in inflammatory responses, lipid metabolism, clearance of amyloid-β peptide, citrate cycle (TCA cycle), TGF-β signaling, and chemokine signaling. The down-regulated mRNAs were mainly involved in the apoptosis pathway, glycerolipid metabolism, Wnt signaling pathway, and MAPK signaling pathway. The constructed coexpression network indicated interactions between 87 lncRNAs and ten mRNAs. In addition, we demonstrated that the silence of lncRNA NONMMUT002434 expression could abrogate the migration and proliferation of smooth muscle cells dramatically. Our data provide comprehensive evidence on the differential expression of lncRNAs and mRNAs in hypoxic MOVAS cells, which may be valuable biomarkers for atherosclerotic diseases, and thereby facilitating diagnosis of atherosclerosis.
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Affiliation(s)
- Qinshuo Zhao
- Division of Cardiology, Departments of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, People's Republic of China
- Genetic Diagnosis Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, People's Republic of China
| | - Dating Sun
- Division of Cardiology, Departments of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, People's Republic of China
- Genetic Diagnosis Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, People's Republic of China
| | - Yuanyuan Li
- Division of Cardiology, Departments of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, People's Republic of China
- Genetic Diagnosis Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, People's Republic of China
| | - Jin Qin
- Division of Cardiology, Departments of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, People's Republic of China
- Genetic Diagnosis Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, People's Republic of China
| | - JiangTao Yan
- Division of Cardiology, Departments of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, People's Republic of China
- Genetic Diagnosis Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, People's Republic of China
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Howard AD, Wang X, Prasad M, Sahu AD, Aniba R, Miller M, Hannenhalli S, Chang YPC. Allele-specific enhancers mediate associations between LCAT and ABCA1 polymorphisms and HDL metabolism. PLoS One 2019; 14:e0215911. [PMID: 31039173 PMCID: PMC6490890 DOI: 10.1371/journal.pone.0215911] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Accepted: 04/10/2019] [Indexed: 11/19/2022] Open
Abstract
For most complex traits, the majority of SNPs identified through genome-wide association studies (GWAS) reside within noncoding regions that have no known function. However, these regions are enriched for the regulatory enhancers specific to the cells relevant to the specific trait. Indeed, many of the GWAS loci that have been functionally characterized lie within enhancers that regulate expression levels of key genes. In order to identify polymorphisms with potential allele-specific regulatory effects, we developed a bioinformatics pipeline that harnesses epigenetic signatures as well as transcription factor (TF) binding motifs to identify putative enhancers containing a SNP with potential allele-specific TF binding in linkage disequilibrium (LD) with a GWAS-identified SNP. We applied the approach to GWAS findings for blood lipids, revealing 7 putative enhancers harboring associated SNPs, 3 of which lie within the introns of LCAT and ABCA1, genes that play crucial roles in cholesterol biogenesis and lipoprotein metabolism. All 3 enhancers demonstrated allele-specific in vitro regulatory activity in liver-derived cell lines. We demonstrated that these putative enhancers are in close physical proximity to the promoters of their respective genes, in situ, likely through chromatin looping. In addition, the associated alleles altered the likelihood of transcription activator STAT3 binding. Our results demonstrate that through our approach, the LD blocks that contain GWAS signals, often hundreds of kilobases in size with multiple SNPs serving as statistical proxies to the true functional site, can provide an experimentally testable hypothesis for the underlying regulatory mechanism linking genetic variants to complex traits.
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Affiliation(s)
- Alicia D. Howard
- Division of Endocrinology, Nutrition, and Diabetes, Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Xiaochun Wang
- Division of Endocrinology, Nutrition, and Diabetes, Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Megana Prasad
- Division of Endocrinology, Nutrition, and Diabetes, Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Avinash Das Sahu
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America
| | - Radhouane Aniba
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America
| | - Michael Miller
- Center for Preventive Cardiology, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States of America
| | - Sridhar Hannenhalli
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America
| | - Yen-Pei Christy Chang
- Division of Endocrinology, Nutrition, and Diabetes, Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
- * E-mail:
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75
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Jia X, Hou Y, Xu M, Zhao Z, Xuan L, Wang T, Li M, Xu Y, Lu J, Bi Y, Wang W, Chen Y. Mendelian Randomization Analysis Support Causal Associations of HbA1c with Circulating Triglyceride, Total and Low-density Lipoprotein Cholesterol in a Chinese Population. Sci Rep 2019; 9:5525. [PMID: 30940890 PMCID: PMC6445078 DOI: 10.1038/s41598-019-41076-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 02/25/2019] [Indexed: 01/06/2023] Open
Abstract
Previous observational studies supported a positive association of glycated hemoglobin A1c (HbA1c) level with serum triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). However, the causal relationship between HbA1c and either one of them was unclear in the East Asians. We performed a Mendelian Randomization (MR) analysis in a community-based study sample in Shanghai, China (n = 11,935). To clarify the cause-and-effect relationships of HbA1c with the four interested lipids, an Expanded HbA1c genetic risk score (GRS) with 17 HbA1c-related common variants and a Conservative score by excluding 11 variants were built and adopted as the Instrumental Variables (IVs), respectively. The Expanded HbA1c-GRS was associated with 0.19 unit increment in log-TG (P = 0.009), 0.42 mmol/L TC (P = 0.01), and 0.33 mmol/L LDL-C (P = 0.01); while the Conservative HbA1c-GRS was associated with 0.22 unit in log-TG (P = 0.03), 0.60 mmol/L TC (P = 0.01), and 0.51 mmol/L LDL-C (P = 0.007). No causal relationship was detected for HDL-C. Sensitivity analysis supported the above findings. In conclusions, MR analysis supports a causal role of increased HbA1c level in increment of circulating TG, TC, and LDL-C in a Chinese population.
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Affiliation(s)
- Xu Jia
- State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, Shanghai National Clinical Research Center for Metabolic Diseases, and Collaborative Innovation Center of Systems Biomedicine, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200025, China.,Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yanan Hou
- State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, Shanghai National Clinical Research Center for Metabolic Diseases, and Collaborative Innovation Center of Systems Biomedicine, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200025, China.,Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Min Xu
- State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, Shanghai National Clinical Research Center for Metabolic Diseases, and Collaborative Innovation Center of Systems Biomedicine, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200025, China.,Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Zhiyun Zhao
- State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, Shanghai National Clinical Research Center for Metabolic Diseases, and Collaborative Innovation Center of Systems Biomedicine, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200025, China.,Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Liping Xuan
- State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, Shanghai National Clinical Research Center for Metabolic Diseases, and Collaborative Innovation Center of Systems Biomedicine, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200025, China.,Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Tiange Wang
- State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, Shanghai National Clinical Research Center for Metabolic Diseases, and Collaborative Innovation Center of Systems Biomedicine, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200025, China.,Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Mian Li
- State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, Shanghai National Clinical Research Center for Metabolic Diseases, and Collaborative Innovation Center of Systems Biomedicine, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200025, China.,Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yu Xu
- State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, Shanghai National Clinical Research Center for Metabolic Diseases, and Collaborative Innovation Center of Systems Biomedicine, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200025, China.,Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jieli Lu
- State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, Shanghai National Clinical Research Center for Metabolic Diseases, and Collaborative Innovation Center of Systems Biomedicine, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200025, China.,Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yufang Bi
- State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, Shanghai National Clinical Research Center for Metabolic Diseases, and Collaborative Innovation Center of Systems Biomedicine, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200025, China.,Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Weiqing Wang
- State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, Shanghai National Clinical Research Center for Metabolic Diseases, and Collaborative Innovation Center of Systems Biomedicine, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200025, China.,Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yuhong Chen
- State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, Shanghai National Clinical Research Center for Metabolic Diseases, and Collaborative Innovation Center of Systems Biomedicine, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200025, China. .,Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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Ma Y, Wei P. FunSPU: A versatile and adaptive multiple functional annotation-based association test of whole-genome sequencing data. PLoS Genet 2019; 15:e1008081. [PMID: 31034468 PMCID: PMC6508749 DOI: 10.1371/journal.pgen.1008081] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 05/09/2019] [Accepted: 03/11/2019] [Indexed: 11/19/2022] Open
Abstract
Despite ongoing large-scale population-based whole-genome sequencing (WGS) projects such as the NIH NHLBI TOPMed program and the NHGRI Genome Sequencing Program, WGS-based association analysis of complex traits remains a tremendous challenge due to the large number of rare variants, many of which are non-trait-associated neutral variants. External biological knowledge, such as functional annotations based on the ENCODE, Epigenomics Roadmap and GTEx projects, may be helpful in distinguishing causal rare variants from neutral ones; however, each functional annotation can only provide certain aspects of the biological functions. Our knowledge for selecting informative annotations a priori is limited, and incorporating non-informative annotations will introduce noise and lose power. We propose FunSPU, a versatile and adaptive test that incorporates multiple biological annotations and is adaptive at both the annotation and variant levels and thus maintains high power even in the presence of noninformative annotations. In addition to extensive simulations, we illustrate our proposed test using the TWINSUK cohort (n = 1,752) of UK10K WGS data based on six functional annotations: CADD, RegulomeDB, FunSeq, Funseq2, GERP++, and GenoSkyline. We identified genome-wide significant genetic loci on chromosome 19 near gene TOMM40 and APOC4-APOC2 associated with low-density lipoprotein (LDL), which are replicated in the UK10K ALSPAC cohort (n = 1,497). These replicated LDL-associated loci were missed by existing rare variant association tests that either ignore external biological information or rely on a single source of biological knowledge. We have implemented the proposed test in an R package "FunSPU".
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Affiliation(s)
- Yiding Ma
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center, Houston, Texas, United States of America
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
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Pirim D, Radwan ZH, Wang X, Niemsiri V, Hokanson JE, Hamman RF, Feingold E, Bunker CH, Demirci FY, Kamboh MI. Apolipoprotein E-C1-C4-C2 gene cluster region and inter-individual variation in plasma lipoprotein levels: a comprehensive genetic association study in two ethnic groups. PLoS One 2019; 14:e0214060. [PMID: 30913229 PMCID: PMC6435132 DOI: 10.1371/journal.pone.0214060] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 02/12/2019] [Indexed: 01/15/2023] Open
Abstract
The apolipoprotein E-C1-C4-C2 gene cluster at 19q13.32 encodes four amphipathic apolipoproteins. The influence of APOE common polymorphisms on plasma lipid/lipoprotein profile, especially on LDL-related traits, is well recognized; however, little is known about the role of other genes/variants in this gene cluster. In this study, we evaluated the role of common and uncommon/rare genetic variation in this gene region on inter-individual variation in plasma lipoprotein levels in non-Hispanic Whites (NHWs) and African blacks (ABs). In the variant discovery step, the APOE, APOC1, APOC4, APOC2 genes were sequenced along with their flanking and hepatic control regions (HCR1 and HCR2) in 190 subjects with extreme HDL-C/TG levels. The next step involved the genotyping of 623 NHWs and 788 ABs for the identified uncommon/rare variants and common tagSNPs along with additional relevant SNPs selected from public resources, followed by association analyses with lipid traits. A total of 230 sequence variants, including 15 indels were identified, of which 65 were novel. A total of 70 QC-passed variants in NHWs and 108 QC-passed variants in ABs were included in the final association analyses. Single-site association analysis of SNPs with MAF>1% revealed 20 variants in NHWs and 24 variants in ABs showing evidence of association with at least one lipid trait, including several variants exhibiting independent associations from the established APOE polymorphism even after multiple-testing correction. Overall, our study has confirmed known associations and also identified novel associations in this genomic region with various lipid traits. Our data also support the contribution of both common and uncommon/rare variation in this gene region in affecting plasma lipid profile in the general population.
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Affiliation(s)
- Dilek Pirim
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Molecular Biology and Genetics, Faculty of Arts&Science, Uludag University, Gorukle, Bursa, Turkey
| | - Zaheda H. Radwan
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Xingbin Wang
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Vipavee Niemsiri
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - John E. Hokanson
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Aurora, Colorado, United States of America
| | - Richard F. Hamman
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Aurora, Colorado, United States of America
| | - Eleanor Feingold
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Clareann H. Bunker
- Department of Epidemiology, Graduate School of Public Health, University Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - F. Yesim Demirci
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- * E-mail: (MIK); (FYD)
| | - M. Ilyas Kamboh
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- * E-mail: (MIK); (FYD)
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78
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There is an association between a genetic polymorphism in the ZNF259 gene involved in lipid metabolism and coronary artery disease. Gene 2019; 704:80-85. [PMID: 30902787 DOI: 10.1016/j.gene.2019.02.101] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2018] [Revised: 02/09/2019] [Accepted: 02/22/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Recent genome-wide association studies (GWAS) have identified several genetic variants that influence the risk of dyslipidemia and coronary artery disease (CAD). In this study, we have examined the potential association of five SNPs variants related to lipid pathway, previously identified in GWAS studies (ZNF259 C>G, CETP I405VA/G, LPA C>T, LPLS447X and PSRC1 A>G) with CAD. METHODS Two hundred and ninety subjects including 194 patients with coronary artery disease and 96 controls were enrolled, followed by the analyses of anthropometric/biochemical parameters. Genotyping was carried out using Taq-Man real-time PCR based method. The association of the genetic polymorphisms with CAD was determined using univariate and multivariate analyses. RESULTS CAD patients had a higher (p < 0.05) fasting blood glucose (FBG), total cholesterol (TC), high sensitivity C-reactive protein (hs-CRP), low-density lipoprotein cholesterol (LDL-C) and waist circumference. Results showed that subjects with CETP rs5882 genetic variant, AA&AG genotypes, had a higher risk of developing Coronary artery disease [OR: 2.1, 95% CI (1.2-4.1), p value = 0.015]. Also subjects who carried the G allele of the ZNF259 polymorphism were at an increased the risk of developing CAD [OR 1.86, 95% CI: 1.06-3.25, p value = 0.029] and had an increased TC, LDL and TG levels (p < 0.05). Furthermore, no statistically significant association was found between genetic polymorphisms of PSRC1 A>G, LPL S447X and LPA C>T and CAD. CONCLUSION We identified a relationship between a genetic variant in CETP and ZNF259 gene with CAD and CAD and lipid profile, respectively. Further investigation in a larger population may help to investigate the value of emerging marker as a risk stratification marker in CAD and its risk factors.
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Metabolome-based signature of disease pathology in MS. Mult Scler Relat Disord 2019; 31:12-21. [PMID: 30877925 DOI: 10.1016/j.msard.2019.03.006] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Revised: 02/06/2019] [Accepted: 03/05/2019] [Indexed: 11/21/2022]
Abstract
BACKGROUND Diagnostic delays are common for multiple sclerosis (MS) since diagnosis typically depends on the presentation of nonspecific clinical symptoms together with radiologically-determined central nervous system (CNS) lesions. It is important to reduce diagnostic delays as earlier initiation of disease modifying therapies mitigates long-term disability. Developing a metabolomic blood-based MS biomarker is attractive, but prior efforts have largely focused on specific subsets of metabolite classes or analytical platforms. Thus, there are opportunities to interrogate metabolite profiles using more expansive and comprehensive approaches for developing MS biomarkers and for advancing our understanding of MS pathogenesis. METHODS To identify putative blood-based MS biomarkers, we comprehensively interrogated the metabolite profiles in 12 non-Hispanic white, non-smoking, male MS cases who were drug naïve for 3 months prior to biospecimen collection and 13 non-Hispanic white, non-smoking male controls who were frequency matched to cases by age and body mass index. We performed untargeted two-dimensional gas chromatography and time-of-flight mass spectrometry (GCxGC-TOFMS) and targeted lipidomic and amino acid analysis on serum. 325 metabolites met quality control and supervised machine learning was used to identify metabolites most informative for MS status. The discrimination potential of these select metabolites were assessed using receiver operator characteristic curves based on logistic models; top candidate metabolites were defined as having area under the curves (AUC) >80%. The associations between whole-genome expression data and the top candidate metabolites were examined, followed by pathway enrichment analyses. Similar associations were examined for 175 putative MS risk variants and the top candidate metabolites. RESULTS 12 metabolites were determined to be informative for MS status, of which 6 had AUCs >80%: pyroglutamate, laurate, acylcarnitine C14:1, N-methylmaleimide, and 2 phosphatidylcholines (PC ae 40:5, PC ae 42:5). These metabolites participate in glutathione metabolism, fatty acid metabolism/oxidation, cellular membrane composition, and transient receptor potential channel signaling. Pathway analyses based on the gene expression association for each metabolite suggested enrichment for pathways associated with apoptosis and mitochondrial dysfunction. Interestingly, the predominant MS genetic risk allele HLA-DRB1×15:01 was associated with one of the 6 top metabolites. CONCLUSION Our analysis represents the most comprehensive description of metabolic changes associated with MS in serum, to date, with the inclusion of genomic and genetic information. We identified atypical metabolic processes that differed between MS patients and controls, which may enable the development of biological targets for diagnosis and treatment.
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Abstract
After more than 10 years of accumulated efforts, genome-wide association studies (GWAS) have led to many findings, most of which have been deposited into the GWAS Catalog. Between GWAS's inception and March 2017, the GWAS Catalog has collected 2429 studies, 1818 phenotypes, and 28,462 associated SNPs. We reclassified the psychology-related phenotypes into 217 reclassified phenotypes, which accounted for 514 studies and 7052 SNPs. In total, 1223 of the SNPs reached genome-wide significance. Of these, 147 were replicated for the same psychological trait in different studies. Another 305 SNPs were replicated within one original study. The SNPs rs2075650 and rs4420638 were linked to the most replications within a single reclassified phenotype or very similar reclassified phenotypes; both were associated with Alzheimer's disease (AD). Schizophrenia was associated with 74 within-phenotype SNPs reported in independents studies. Alzheimer's disease and schizophrenia were both linked to some physical phenotypes, including cholesterol and body mass index, through common GWAS signals. Alzheimer's disease also shared risk SNPs with age-related phenotypes such as age-related macular degeneration and longevity. Smoking-related SNPs were linked to lung cancer and respiratory function. Alcohol-related SNPs were associated with cardiovascular and digestive system phenotypes and disorders. Two separate studies also identified a shared risk SNP for bipolar disorder and educational attainment. This review revealed a list of reproducible SNPs worthy of future functional investigation. Additionally, by identifying SNPs associated with multiple phenotypes, we illustrated the importance of studying the relationships among phenotypes to resolve the nature of their causal links. The insights within this review will hopefully pave the way for future evidence-based genetic studies.
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Zhang QH, Yin RX, Chen WX, Cao XL, Wu JZ. TRIB1 and TRPS1 variants, G × G and G × E interactions on serum lipid levels, the risk of coronary heart disease and ischemic stroke. Sci Rep 2019; 9:2376. [PMID: 30787327 PMCID: PMC6382757 DOI: 10.1038/s41598-019-38765-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 01/09/2019] [Indexed: 02/07/2023] Open
Abstract
This study aimed to assess the association of the tribbles pseudokinase 1 (TRIB1) and transcriptional repressor GATA binding 1 (TRPS1) single nucleotide polymorphisms (SNPs) and the gene-gene (G × G) and gene-environment (G × E) interactions with serum lipid levels, the risk of coronary heart disease (CHD) and ischemic stroke (IS) in the Guangxi Han population. Genotyping of the rs2954029, rs2980880, rs10808546, rs231150, rs2737229 and rs10505248 SNPs was performed in 625 controls and 1146 unrelated patients (CHD, 593 and IS, 553). The genotypic and allelic frequencies of some SNPs were different between controls and patients (CHD, rs2954029 and rs231150; IS, rs2954029 and rs2980880; P < 0.05-0.01). Two SNPs were associated with increased risk of CHD (rs2954029 and rs231150) and IS (rs2954029) in different genetic models. Several SNPs in controls were associated with total cholesterol (rs2954029, rs2980880 and rs2737229), triglyceride (rs2954029 and rs10808546), low-density lipoprotein cholesterol (rs2954029), high-density lipoprotein cholesterol (rs2980880 and rs231150) and apolipoprotein A1 (rs2737229) levels. The rs2954029TA/AA-age (>60 year) interaction increased the risk of CHD, whereas the rs10808546CT/TT-drinking interaction decreased the risk of IS. The rs2954029A-rs2980880C-rs10808546C haplotype was associated with increased risk of CHD and IS. The rs2954029A-rs2980880T-rs10808546C haplotype was associated with increased risk of CHD. The rs2954029-rs231150 interactions had an increased risk of both CHD and IS. These results suggest that several TRIB1 and TRPS1 SNPs were associated with dyslipidemia and increased risk of CHD and IS in our study population. The G × G and G × E interactions on serum lipid levels, and the risk of CHD and IS were also observed.
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Affiliation(s)
- Qing-Hui Zhang
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China
| | - Rui-Xing Yin
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China.
| | - Wu-Xian Chen
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China
| | - Xiao-Li Cao
- Department of Neurology, The First Affiliated Hospital, Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China
| | - Jin-Zhen Wu
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China
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Yang S, Yin RX, Miao L, Zhang QH, Zhou YG, Wu J. Association between the LIPG polymorphisms and serum lipid levels in the Maonan and Han populations. J Gene Med 2019; 21:e3071. [PMID: 30657227 PMCID: PMC6590183 DOI: 10.1002/jgm.3071] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 12/21/2018] [Accepted: 12/24/2018] [Indexed: 01/19/2023] Open
Abstract
Introduction The Maonan population is a relatively isolated minority in China. Little is known about endothelial lipase gene (LIPG) single nucleotide polymorphisms (SNPs) and serum lipid levels in the Chinese populations. The present study aimed to detect the association of several LIPG SNPs and environmental factors with serum lipid levels in the Chinese Maonan and Han populations. Methods In total, 773 subjects of Maonan ethnicity and 710 participants of Han ethnicity were randomly selected from our previous stratified randomized samples. Genotypes of the LIPG rs2156552, rs4939883 and rs7241918 SNPs were determined by polymerase chain reaction‐restriction fragment length polymorphism, and then confirmed by direct sequencing. Results The allelic (rs2156552, rs4939883 and rs7241918) and genotypic (rs2156552 and rs4939883) frequencies were different between the two ethnic groups (p < 0.05–0.01). The minor allele carriers had lower apolipoprotein (Apo)A1/ApoB ratio (rs2156552 and rs7241918), high‐density lipoprotein cholesterol (HDL‐C) and apolipoprotein (Apo)A1 (rs2156552) levels and higher ApoB levels (rs4939883) in the Han population, and lower HDL‐C (rs2156552, rs4939883 and rs7241918) levels in the Maonan minority than the minor allele non‐carriers (p < 0.0167 after Bonferroni correction). Subgroup analyses according to sex showed that the minor allele carriers had a lower ApoA1/ApoB ratio (rs2156552 and rs7241918) and higher ApoB levels (rs7241918) in Han males, and lower ApoA1 and HDL‐C levels in Maonan females than the minor allele non‐carriers (p < 0.0167–0.001). Conclusions The present study demonstrates the association between the LIPG polymorphsims and serum lipid levels in the two ethnic groups. These associations might have an ethnic‐ and or/sex‐specificity.
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Affiliation(s)
- Shuo Yang
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, China
| | - Rui-Xing Yin
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, China
| | - Liu Miao
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, China
| | - Qing-Hui Zhang
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, China
| | - Yong-Gang Zhou
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, China
| | - Jie Wu
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, China
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Yang S, Yin RX, Miao L, Zhou YG, Wu J, Zhang QH. LIPG SNPs, their haplotypes and gene-environment interactions on serum lipid levels. Lipids Health Dis 2019; 18:10. [PMID: 30621702 PMCID: PMC6325827 DOI: 10.1186/s12944-018-0942-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 12/04/2018] [Indexed: 02/06/2023] Open
Abstract
Background Maonan nationality is a relatively conservative and isolated minority in the Southwest of China. Little is known about the association of endothelial lipase gene (LIPG) single nucleotide polymorphisms (SNPs) and serum lipid levels in the Chinese populations. Methods A total of 1280 subjects of Maonan nationality and 1218 participants of Han nationality were randomly selected from our previous stratified randomized samples. Genotypes of the four LIPG SNPs were determined by polymerase chain reaction-restriction fragment length polymorphism, and then confirmed by direct sequencing. Results Several SNPs were associated with high-density lipoprotein cholesterol (rs3813082, rs2000813 and rs2097055) in the both ethnic groups; total cholesterol and apolipoprotein (Apo) A1 (rs2000813) in Han nationality; and low-density lipoprotein cholesterol, ApoB, triglyceride (rs2097055) and ApoA1 (rs3819166) in Maonan minority (P < 0.0125 for all after Bonferroni correction). The commonest haplotype was rs3813082T-rs2000813C-rs2097055T-rs3819166A (Han, 44.2% and Maonan, 48.7%). The frequencies of the T-C-T-A, T-C-T-G, T-T-C-G and G-T-C-G haplotypes were different between the Maonan and Han populations (P < 0.05–0.001). The associations between haplotypes and dyslipidemia were also different in the Han and/or Maonan populations (P < 0.05–0.001). Conclusions The differences in serum lipid profiles between the two ethnic groups might partly be attributed to these LIPG SNPs, their haplotypes and gene-environmental interactions. Trial registration Retrospectively registered.
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Affiliation(s)
- Shuo Yang
- Department of Cardiology, Institute of Cardiovascular Diseases, the First Affiliated Hospital, Guangxi Medical University, 22 Shuangyong Road, Nanning, 530021, Guangxi, People's Republic of China
| | - Rui-Xing Yin
- Department of Cardiology, Institute of Cardiovascular Diseases, the First Affiliated Hospital, Guangxi Medical University, 22 Shuangyong Road, Nanning, 530021, Guangxi, People's Republic of China.
| | - Liu Miao
- Department of Cardiology, Institute of Cardiovascular Diseases, the First Affiliated Hospital, Guangxi Medical University, 22 Shuangyong Road, Nanning, 530021, Guangxi, People's Republic of China
| | - Yong-Gang Zhou
- Department of Cardiology, Institute of Cardiovascular Diseases, the First Affiliated Hospital, Guangxi Medical University, 22 Shuangyong Road, Nanning, 530021, Guangxi, People's Republic of China
| | - Jie Wu
- Department of Cardiology, Institute of Cardiovascular Diseases, the First Affiliated Hospital, Guangxi Medical University, 22 Shuangyong Road, Nanning, 530021, Guangxi, People's Republic of China
| | - Qing-Hui Zhang
- Department of Cardiology, Institute of Cardiovascular Diseases, the First Affiliated Hospital, Guangxi Medical University, 22 Shuangyong Road, Nanning, 530021, Guangxi, People's Republic of China
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Developing a Modified Low-Density Lipoprotein (M-LDL-C) Friedewald's Equation as a Substitute for Direct LDL-C Measure in a Ghanaian Population: A Comparative Study. J Lipids 2018; 2018:7078409. [PMID: 30693111 PMCID: PMC6332996 DOI: 10.1155/2018/7078409] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 11/24/2018] [Accepted: 12/20/2018] [Indexed: 01/09/2023] Open
Abstract
Despite the availability of several homogenous LDL-C assays, calculated Friedewald's LDL-C equation remains the widely used formula in clinical practice. Several novel formulas developed in different populations have been reported to outperform the Friedewald formula. This study validated the existing LDL-C formulas and derived a modified LDL-C formula specific to a Ghanaian population. In this comparative study, we recruited 1518 participants, derived a new modified Friedewald's LDL-C (M-LDL-C) equation, evaluated LDL-C by Friedewald's formula (F-LDL-C), Martin's formula (N-LDL-C), Anandaraja's formula (A-LDL-C), and compared them to direct measurement of LDL-C (D-LDL-C). The mean D-LDL-C (2.47±0.71 mmol/L) was significantly lower compared to F-LDL-C (2.76±1.05 mmol/L), N-LDL-C (2.74±1.04 mmol/L), A-LDL-C (2.99±1.02 mmol/L), and M-LDL-C (2.97±1.08 mmol/L) p < 0.001. There was a significantly positive correlation between D-LDL-C and A-LDL-C (r=0.658, p<0.0001), N-LDL-C (r=0.693, p<0.0001), and M-LDL-C (r=0.693, p<0.0001). M-LDL-c yielded a better diagnostic performance [(area under the curve (AUC)=0.81; sensitivity (SE) (60%) and specificity (SP) (88%)] followed by N-LDL-C [(AUC=0.81; SE (63%) and SP (85%)], F-LDL-C [(AUC=0.80; SE (63%) and SP (84%)], and A-LDL-C (AUC=0.77; SE (68%) and SP (78%)] using D-LDL-C as gold standard. Bland-Altman plots showed a definite agreement between means and differences of D-LDL-C and the calculated formulas with 95% of values lying within ±0.50 SD limits. The modified LDL-C (M-LDL-C) formula derived by this study yielded a better diagnostic accuracy compared to A-LDL-C and F-LDL-C equations and thus could serve as a substitute for D-LDL-C and F-LDL-C equations in the Ghanaian population.
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Vitamin E Metabolic Effects and Genetic Variants: A Challenge for Precision Nutrition in Obesity and Associated Disturbances. Nutrients 2018; 10:nu10121919. [PMID: 30518135 PMCID: PMC6316334 DOI: 10.3390/nu10121919] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 11/27/2018] [Accepted: 11/30/2018] [Indexed: 02/07/2023] Open
Abstract
Vitamin E (VE) has a recognized leading role as a contributor to the protection of cell constituents from oxidative damage. However, evidence suggests that the health benefits of VE go far beyond that of an antioxidant acting in lipophilic environments. In humans, VE is channeled toward pathways dealing with lipoproteins and cholesterol, underlining its relevance in lipid handling and metabolism. In this context, both VE intake and status may be relevant in physiopathological conditions associated with disturbances in lipid metabolism or concomitant with oxidative stress, such as obesity. However, dietary reference values for VE in obese populations have not yet been defined, and VE supplementation trials show contradictory results. Therefore, a better understanding of the role of genetic variants in genes involved in VE metabolism may be crucial to exert dietary recommendations with a higher degree of precision. In particular, genetic variability should be taken into account in targets concerning VE bioavailability per se or concomitant with impaired lipoprotein transport. Genetic variants associated with impaired VE liver balance, and the handling/resolution of oxidative stress might also be relevant, but the core information that exists at present is insufficient to deliver precise recommendations.
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Pikó P, Fiatal S, Kósa Z, Sándor J, Ádány R. Generalizability and applicability of results obtained from populations of European descent regarding the effect direction and size of HDL-C level-associated genetic variants to the Hungarian general and Roma populations. Gene 2018; 686:187-193. [PMID: 30468910 DOI: 10.1016/j.gene.2018.11.067] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 09/28/2018] [Accepted: 11/19/2018] [Indexed: 12/13/2022]
Abstract
OBJECTIVES Large-scale association studies that mainly involve European populations identified many genetic loci related to high-density lipoprotein cholesterol (HDL-C) levels, one of the most important indicators of the risk for cardiovascular diseases. The question with intense speculation of whether the effect estimates obtained from European populations for different HDL-C level-related SNPs are applicable to the Roma ethnicity, the largest minority group in Europe with a South Asian origin, was addressed in the present study. DESIGN The associations between 21 SNPs (in the genes LIPC(G), CETP, GALNT2, HMGCP, ABCA1, KCTD10 and WWOX) and HDL-C levels were examined separately in adults of the Hungarian general (N = 1542) and Roma (N = 646) populations by linear regression. Individual effects (direction and size) of single SNPs on HDL-C levels were computed and compared between the study groups and with data published in the literature. RESULTS Significant associations between SNPs and HDL-C levels were more frequently found in general subjects than in Roma subjects (11 SNPs in general vs. 4 SNPs in Roma). The CETP gene variants rs1532624, rs708272 and rs7499892 consistently showed significant associations with HDL-C levels across the study groups (p ˂ 0.05), indicating a possible causal variant(s) in this region. Although nominally significant differences in effect size were found for three SNPs (rs693 in gene APOB, rs9989419 in gene CETP, and rs2548861 in gene WWOX) by comparing the general and Roma populations, most of these SNPs did not have a significant effect on HDL-C levels. The β coefficients for SNPs in the Roma population were found to be identical both in direction and magnitude to the effect obtained previously in large-scale studies on European populations. CONCLUSIONS The effect of the vast majority of the SNPs on HDL-C levels could be replicated in the Hungarian general and Roma populations, which indicates that the effect size measurements obtained from the literature can be used for risk estimation for both populations.
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Affiliation(s)
- Péter Pikó
- MTA-DE Public Health Research Group of the Hungarian Academy of Sciences, Faculty of Public Health, University of Debrecen, Debrecen 4028, Hungary; Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen 4028, Hungary
| | - Szilvia Fiatal
- Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen 4028, Hungary; WHO Collaborating Centre on Vulnerability and Health, Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen 4028, Hungary
| | - Zsigmond Kósa
- Department of Health Visitor Methodology and Public Health, Faculty of Health, University of Debrecen, Nyíregyháza 4400, Hungary
| | - János Sándor
- Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen 4028, Hungary; WHO Collaborating Centre on Vulnerability and Health, Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen 4028, Hungary
| | - Róza Ádány
- MTA-DE Public Health Research Group of the Hungarian Academy of Sciences, Faculty of Public Health, University of Debrecen, Debrecen 4028, Hungary; Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen 4028, Hungary; WHO Collaborating Centre on Vulnerability and Health, Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen 4028, Hungary.
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Role of ZIP8 in regulation of cisplatin sensitivity through Bcl-2. Toxicol Appl Pharmacol 2018; 362:52-58. [PMID: 30342059 DOI: 10.1016/j.taap.2018.10.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Revised: 09/12/2018] [Accepted: 10/16/2018] [Indexed: 02/06/2023]
Abstract
ZIP8 is a membrane transporter that facilitates the uptake of divalent metals (e.g., Zn, Mn, Fe, Cd) and the mineral selenite in anionic form. ZIP8 functionality has been recently reported to regulate cell proliferation, migration and cytoskeleton arrangement, exhibiting an essential role for normal physiology. In this study, we report a ZIP8 role in chemotherapy response. We show ZIP8 regulates cell sensitivity to the anti-cancer drug cisplatin. Overexpression of ZIP8 in mouse embryonic fibroblast (MEF) cells induces cisplatin sensitivity, while knockout of ZIP8 in leukemia HAP1 cells leads to cisplatin resistance. In ZIP8 altered cells and transgenic mice, we show cisplatin is not a direct ZIP8 substrate. Further studies demonstrate that ZIP8 regulates anti-apoptotic protein Bcl-2. ZIP8 overexpression decreases Bcl-2 levels in cultured cells, mice lung and liver tissue while loss of ZIP8 elevates Bcl-2 expression in HAP1 cells and liver tissue. We also observe that ZIP8 overexpression modulates cisplatin-induced cell apoptosis, manifested by the increased protein level of cleaved Caspase-3. Since Bcl-2 elevation was previously discovered to induce cisplatin drug resistance, our results suggest ZIP8 may modulate cisplatin drug responses as well as apoptosis through Bcl-2. We therefore conclude ZIP8 is a new molecule to be involved in cisplatin drug responses and is predicted as a genetic factor to be considered in cisplatin therapy.
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Mo MQ, Pan L, Lu QM, Li QL, Liao YH. The association of the CMIP rs16955379 polymorphism with dyslipidemia and the clinicopathological features of IgA nephropathy. INTERNATIONAL JOURNAL OF CLINICAL AND EXPERIMENTAL PATHOLOGY 2018; 11:5008-5023. [PMID: 31949578 PMCID: PMC6962923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 08/23/2018] [Indexed: 06/10/2023]
Abstract
Immunoglobulin A nephropathy (IgAN) is among the most common primary glomerular diseases. The prognosis in IgAN is affected by dyslipidemia, a risk factor for cardiovascular disease. The c-Maf inducing protein (CMIP) gene has been found to be associated with lipid metabolism. But the association between the CMIP rs16955379 single nucleotide polymorphism (SNP) and dyslipidemia or the related clinicopathological features in IgAN have not been reported thus far. The present study investigated the correlation between them. The CMIP rs16955379 SNP genotypes of 300 subjects with IgAN recruited from the First Affiliated Hospital of Guangxi Medical University were identified by polymerase chain reaction and direct sequencing. Compared with the control (normal lipid) group, the dyslipidemia group with IgAN had higher blood uric acid, serum creatinine, blood urea nitrogen and urinary protein quantity, higher proportions of mesangial cell proliferation and renal tubular atrophy/interstitial fibrosis (IFTA), and a lower estimated glomerular filtration rate and serum albumin. The frequencies of the CMIP rs16955379 SNP TT genotype and T allele in the dyslipidemia group were higher than in the control group. Triglyceride, apolipoprotein A1 (ApoA1), ApoA1/B, incidences of mesangial cell proliferation, and IFTA were higher in TT genotype carriers than in CC/CT genotype carriers. Serum lipid profiles and dyslipidemia were significantly associated with renal dysfunction and IFTA. IgAN patients with the TT genotype were more likely to have dyslipidemia, renal dysfunction and IFTA (P < 0.05 for all above). These results indicate that CMIP rs16955379 SNP may be a genetic susceptibility gene for dyslipidemia and poor renal outcome in IgAN.
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Affiliation(s)
- Man-Qiu Mo
- Department of Nephrology, Institute of Urology, The First Affiliated Hospital, Guangxi Medical UniversityNanning, Guangxi, People’s Republic of China
| | - Ling Pan
- Department of Nephrology, Institute of Urology, The First Affiliated Hospital, Guangxi Medical UniversityNanning, Guangxi, People’s Republic of China
| | - Qing-Mei Lu
- First Clinical Medical College, Guangxi Medical UniversityNanning, Guangxi, People’s Republic of China
| | - Qiu-Lin Li
- Department of Nephrology, Institute of Urology, The First Affiliated Hospital, Guangxi Medical UniversityNanning, Guangxi, People’s Republic of China
| | - Yun-Hua Liao
- Department of Nephrology, Institute of Urology, The First Affiliated Hospital, Guangxi Medical UniversityNanning, Guangxi, People’s Republic of China
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Exome-wide analysis identifies three low-frequency missense variants associated with pancreatic cancer risk in Chinese populations. Nat Commun 2018; 9:3688. [PMID: 30206226 PMCID: PMC6134090 DOI: 10.1038/s41467-018-06136-x] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Accepted: 07/30/2018] [Indexed: 12/15/2022] Open
Abstract
Germline coding variants have not been systematically investigated for pancreatic ductal adenocarcinoma (PDAC). Here we report an exome-wide investigation using the Illumina Human Exome Beadchip with 943 PDAC cases and 3908 controls in the Chinese population, followed by two independent replicate samples including 2142 cases and 4697 controls. We identify three low-frequency missense variants associated with the PDAC risk: rs34309238 in PKN1 (OR = 1.77, 95% CI: 1.48–2.12, P = 5.35 × 10−10), rs2242241 in DOK2 (OR = 1.85, 95% CI: 1.50–2.27, P = 4.34 × 10−9), and rs183117027 in APOB (OR = 2.34, 95% CI: 1.72–3.16, P = 4.21 × 10−8). Functional analyses show that the PKN1 rs34309238 variant significantly increases the level of phosphorylated PKN1 and thus enhances PDAC cells' proliferation by phosphorylating and activating the FAK/PI3K/AKT pathway. These findings highlight the significance of coding variants in the development of PDAC and provide more insights into the prevention of this disease. Pancreatic ductal adenocarcinoma is a lethal human cancer with a poor 5-year overall survival rate. Here the authors perform an exome-wide analysis in a cohort of PDAC patients to identify three novel missense variants in PKN1, DOK2, and APOB genes, that are associated with PDAC risk.
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90
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Chen J, Gálvez-Peralta M, Zhang X, Deng J, Liu Z, Nebert DW. In utero gene expression in the Slc39a8(neo/neo) knockdown mouse. Sci Rep 2018; 8:10703. [PMID: 30013175 PMCID: PMC6048144 DOI: 10.1038/s41598-018-29109-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Accepted: 07/05/2018] [Indexed: 12/13/2022] Open
Abstract
Slc39a8 encodes ZIP8, a divalent cation/bicarbonate symporter expressed in pluripotent mouse embryonic stem cells, and therefore ubiquitous in adult tissues; ZIP8 influxes Zn2+, Mn2+ and Fe2+. Slc39a8(neo/neo) knockdown mice exhibit 10-15% of wild-type ZIP8 mRNA and protein levels, and show pleiotropic phenotype of stunted growth, neonatal lethality, multi-organ dysmorphogenesis, and dysregulated hematopoiesis manifested as severe anemia. Herein we performed RNA-seq analysis of gestational day (GD)13.5 yolk sac and placenta, and GD16.5 liver, kidney, lung, heart and cerebellum, comparing Slc39a8(neo/neo) with Slc39a8(+/+) wild-type. Meta-data analysis of differentially-expressed genes revealed 29 unique genes from all tissues - having enriched GO categories associated with hematopoiesis and hypoxia and KEGG categories of complement, response to infection, and coagulation cascade - consistent with dysregulated hematopoietic stem cell fate. Based on transcription factor (TF) profiles in the JASPAR database, and searching for TF-binding sites enriched by Pscan, we identified numerous genes encoding zinc-finger and other TFs associated with hematopoietic stem cell functions. We conclude that, in this mouse model, deficient ZIP8-mediated divalent cation transport affects zinc-finger (e.g. GATA proteins) and other TFs interacting with GATA proteins (e.g. TAL1), predominantly in yolk sac. These data strongly support the phenotype of dysmorphogenesis and anemia seen in Slc39a8(neo/neo) mice in utero.
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Affiliation(s)
- Jing Chen
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, 45229, USA
| | - Marina Gálvez-Peralta
- Department of Environmental Health, University of Cincinnati College of Medicine, Cincinnati, Ohio, 45267, USA.,Department of Pharmaceutical Sciences, West Virginia University Medical Center, Morgantown, WV, 26506, USA
| | - Xiang Zhang
- Department of Environmental Health, University of Cincinnati College of Medicine, Cincinnati, Ohio, 45267, USA
| | - Jingyuan Deng
- Department of Environmental Health, University of Cincinnati College of Medicine, Cincinnati, Ohio, 45267, USA.,Amazon.com, Inc., Seattle, WA, 98101, USA
| | - Zijuan Liu
- Department of Biological Sciences, Oakland University, Rochester, MI, 48309, USA
| | - Daniel W Nebert
- Department of Environmental Health, University of Cincinnati College of Medicine, Cincinnati, Ohio, 45267, USA.
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91
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Liu T, Jiang F, Liu X, Zhang H, Wang L, Liu W, Wang X. Association of
ABCG
2
polymorphisms with ischemic stroke in a Chinese population. Ann Hum Genet 2018; 82:325-330. [PMID: 29900524 DOI: 10.1111/ahg.12258] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Revised: 03/28/2018] [Accepted: 05/08/2018] [Indexed: 11/28/2022]
Affiliation(s)
- Tonghanyu Liu
- Laboratory of Human Genetics Beijing Hypertension League Institute Beijing China
- Affiliated Zhongshan Hospital of Dalian University Dalian China
| | - Feng Jiang
- Heart Center & Beijing Key Laboratory of Hypertension Beijing Chaoyang Hospital, Capital Medical University Beijing 100020 China
| | - Xin Liu
- Laboratory of Human Genetics Beijing Hypertension League Institute Beijing China
- National Human Genetics Resources Center Beijing China
| | - Hongye Zhang
- Laboratory of Human Genetics Beijing Hypertension League Institute Beijing China
| | - Lefeng Wang
- Heart Center & Beijing Key Laboratory of Hypertension Beijing Chaoyang Hospital, Capital Medical University Beijing 100020 China
| | - Wenzhi Liu
- Affiliated Zhongshan Hospital of Dalian University Dalian China
| | - Xingyu Wang
- Laboratory of Human Genetics Beijing Hypertension League Institute Beijing China
- National Human Genetics Resources Center Beijing China
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92
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Li WJ, Yin RX, Huang JH, Bin Y, Chen WX, Cao XL. Association between the PPP1R3B polymorphisms and serum lipid traits, the risk of coronary artery disease and ischemic stroke in a southern Chinese Han population. Nutr Metab (Lond) 2018; 15:27. [PMID: 29681992 PMCID: PMC5898016 DOI: 10.1186/s12986-018-0266-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Accepted: 04/09/2018] [Indexed: 12/11/2022] Open
Abstract
Background Little is known about the association of the protein phosphatase 1 regulatory subunit 3B gene (PPP1R3B) single nucleotide polymorphisms (SNPs) and serum lipid levels, the risk of coronary artery disease (CAD) and ischemic stroke (IS) in the Chinese populations. This study detected such association in a Southern Chinese Han population. Methods Genotypes of 4 novel PPP1R3B SNPs (rs12785, rs330910, rs330915 and rs9949) in 1704 Han Chinese (CAD, 556; IS, 531 and control, 617) were determined by the Snapshot technology. Results The rs12785A and rs9949A allele frequency was higher in both CAD/IS patients than in controls. The rs330910T and rs330915T allele frequency was also higher in CAD patients than in controls. The rs330910T allele carriers in controls had lower serum low-density lipoprotein cholesterol (LDL-C) levels than the rs330910T allele non-carriers (P < 0.0014). The rs12785A, rs9949A and rs330910T allele carriers were associated with an increased risk of CAD (P = 0.008–0.004). There was strong linkage disequilibrium among the 4 SNPs in the controls and CAD/IS patients. The T-A-A-G haplotype was associated with a decreased risk of CAD and IS, whereas the A-A-T-A haplotype was associated with an increased risk for IS. Haplotype-environment interactions on the risk of CAD and IS were also observed. Conclusions Several PPP1R3B polymorphisms were associated with serum LDL-C levels, the risk of CAD and IS in the Southern Chinese Han population. But these findings still need to be confirmed in the other populations with larger sample sizes. Electronic supplementary material The online version of this article (10.1186/s12986-018-0266-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Wei-Jun Li
- 1Department of Cardiology, Institute of Cardiovascular Diseases, the First Affiliated Hospital, Guangxi Medical University, Nanning, 530021 Guangxi People's Republic of China
| | - Rui-Xing Yin
- 1Department of Cardiology, Institute of Cardiovascular Diseases, the First Affiliated Hospital, Guangxi Medical University, Nanning, 530021 Guangxi People's Republic of China
| | - Jian-Hua Huang
- 1Department of Cardiology, Institute of Cardiovascular Diseases, the First Affiliated Hospital, Guangxi Medical University, Nanning, 530021 Guangxi People's Republic of China
| | - Yuan Bin
- 1Department of Cardiology, Institute of Cardiovascular Diseases, the First Affiliated Hospital, Guangxi Medical University, Nanning, 530021 Guangxi People's Republic of China
| | - Wu-Xian Chen
- 1Department of Cardiology, Institute of Cardiovascular Diseases, the First Affiliated Hospital, Guangxi Medical University, Nanning, 530021 Guangxi People's Republic of China
| | - Xiao-Li Cao
- 2Department of Neurology, the First Affiliated Hospital, Guangxi Medical University, Nanning, 530021 Guangxi People's Republic of China
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93
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Reynolds LM, Howard TD, Ruczinski I, Kanchan K, Seeds MC, Mathias RA, Chilton FH. Tissue-specific impact of FADS cluster variants on FADS1 and FADS2 gene expression. PLoS One 2018; 13:e0194610. [PMID: 29590160 PMCID: PMC5874031 DOI: 10.1371/journal.pone.0194610] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 03/06/2018] [Indexed: 12/31/2022] Open
Abstract
Omega-6 (n-6) and omega-3 (n-3) long (≥ 20 carbon) chain polyunsaturated fatty acids (LC-PUFAs) play a critical role in human health and disease. Biosynthesis of LC-PUFAs from dietary 18 carbon PUFAs in tissues such as the liver is highly associated with genetic variation within the fatty acid desaturase (FADS) gene cluster, containing FADS1 and FADS2 that encode the rate-limiting desaturation enzymes in the LC-PUFA biosynthesis pathway. However, the molecular mechanisms by which FADS genetic variants affect LC-PUFA biosynthesis, and in which tissues, are unclear. The current study examined associations between common single nucleotide polymorphisms (SNPs) within the FADS gene cluster and FADS1 and FADS2 gene expression in 44 different human tissues (sample sizes ranging 70-361) from the Genotype-Tissue Expression (GTEx) Project. FADS1 and FADS2 expression were detected in all 44 tissues. Significant cis-eQTLs (within 1 megabase of each gene, False Discovery Rate, FDR<0.05, as defined by GTEx) were identified in 12 tissues for FADS1 gene expression and 23 tissues for FADS2 gene expression. Six tissues had significant (FDR< 0.05) eQTLs associated with both FADS1 and FADS2 (including artery, esophagus, heart, muscle, nerve, and thyroid). Interestingly, the identified eQTLs were consistently found to be associated in opposite directions for FADS1 and FADS2 expression. Taken together, findings from this study suggest common SNPs within the FADS gene cluster impact the transcription of FADS1 and FADS2 in numerous tissues and raise important questions about how the inverse expression of these two genes impact intermediate molecular (such a LC-PUFA and LC-PUFA-containing glycerolipid levels) and ultimately clinical phenotypes associated with inflammatory diseases and brain health.
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Affiliation(s)
- Lindsay M. Reynolds
- Department of Epidemiology & Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Timothy D. Howard
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Ingo Ruczinski
- Division of Allergy and Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Kanika Kanchan
- Division of Allergy and Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Michael C. Seeds
- Department of Internal Medicine/Section on Molecular Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Rasika A. Mathias
- Division of Allergy and Clinical Immunology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Floyd H. Chilton
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
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94
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Sung YJ, Winkler TW, de Las Fuentes L, Bentley AR, Brown MR, Kraja AT, Schwander K, Ntalla I, Guo X, Franceschini N, Lu Y, Cheng CY, Sim X, Vojinovic D, Marten J, Musani SK, Li C, Feitosa MF, Kilpeläinen TO, Richard MA, Noordam R, Aslibekyan S, Aschard H, Bartz TM, Dorajoo R, Liu Y, Manning AK, Rankinen T, Smith AV, Tajuddin SM, Tayo BO, Warren HR, Zhao W, Zhou Y, Matoba N, Sofer T, Alver M, Amini M, Boissel M, Chai JF, Chen X, Divers J, Gandin I, Gao C, Giulianini F, Goel A, Harris SE, Hartwig FP, Horimoto ARVR, Hsu FC, Jackson AU, Kähönen M, Kasturiratne A, Kühnel B, Leander K, Lee WJ, Lin KH, 'an Luan J, McKenzie CA, Meian H, Nelson CP, Rauramaa R, Schupf N, Scott RA, Sheu WHH, Stančáková A, Takeuchi F, van der Most PJ, Varga TV, Wang H, Wang Y, Ware EB, Weiss S, Wen W, Yanek LR, Zhang W, Zhao JH, Afaq S, Alfred T, Amin N, Arking D, Aung T, Barr RG, Bielak LF, Boerwinkle E, Bottinger EP, Braund PS, Brody JA, Broeckel U, Cabrera CP, Cade B, Caizheng Y, Campbell A, Canouil M, Chakravarti A, Chauhan G, Christensen K, Cocca M, Collins FS, Connell JM, de Mutsert R, de Silva HJ, Debette S, Dörr M, Duan Q, Eaton CB, Ehret G, Evangelou E, Faul JD, Fisher VA, Forouhi NG, Franco OH, Friedlander Y, Gao H, Gigante B, Graff M, Gu CC, Gu D, Gupta P, Hagenaars SP, Harris TB, He J, Heikkinen S, Heng CK, Hirata M, Hofman A, Howard BV, Hunt S, Irvin MR, Jia Y, Joehanes R, Justice AE, Katsuya T, Kaufman J, Kerrison ND, Khor CC, Koh WP, Koistinen HA, Komulainen P, Kooperberg C, Krieger JE, Kubo M, Kuusisto J, Langefeld CD, Langenberg C, Launer LJ, Lehne B, Lewis CE, Li Y, Lim SH, Lin S, Liu CT, Liu J, Liu J, Liu K, Liu Y, Loh M, Lohman KK, Long J, Louie T, Mägi R, Mahajan A, Meitinger T, Metspalu A, Milani L, Momozawa Y, Morris AP, Mosley TH, Munson P, Murray AD, Nalls MA, Nasri U, Norris JM, North K, Ogunniyi A, Padmanabhan S, Palmas WR, Palmer ND, Pankow JS, Pedersen NL, Peters A, Peyser PA, Polasek O, Raitakari OT, Renström F, Rice TK, Ridker PM, Robino A, Robinson JG, Rose LM, Rudan I, Sabanayagam C, Salako BL, Sandow K, Schmidt CO, Schreiner PJ, Scott WR, Seshadri S, Sever P, Sitlani CM, Smith JA, Snieder H, Starr JM, Strauch K, Tang H, Taylor KD, Teo YY, Tham YC, Uitterlinden AG, Waldenberger M, Wang L, Wang YX, Wei WB, Williams C, Wilson G, Wojczynski MK, Yao J, Yuan JM, Zonderman AB, Becker DM, Boehnke M, Bowden DW, Chambers JC, Chen YDI, de Faire U, Deary IJ, Esko T, Farrall M, Forrester T, Franks PW, Freedman BI, Froguel P, Gasparini P, Gieger C, Horta BL, Hung YJ, Jonas JB, Kato N, Kooner JS, Laakso M, Lehtimäki T, Liang KW, Magnusson PKE, Newman AB, Oldehinkel AJ, Pereira AC, Redline S, Rettig R, Samani NJ, Scott J, Shu XO, van der Harst P, Wagenknecht LE, Wareham NJ, Watkins H, Weir DR, Wickremasinghe AR, Wu T, Zheng W, Kamatani Y, Laurie CC, Bouchard C, Cooper RS, Evans MK, Gudnason V, Kardia SLR, Kritchevsky SB, Levy D, O'Connell JR, Psaty BM, van Dam RM, Sims M, Arnett DK, Mook-Kanamori DO, Kelly TN, Fox ER, Hayward C, Fornage M, Rotimi CN, Province MA, van Duijn CM, Tai ES, Wong TY, Loos RJF, Reiner AP, Rotter JI, Zhu X, Bierut LJ, Gauderman WJ, Caulfield MJ, Elliott P, Rice K, Munroe PB, Morrison AC, Cupples LA, Rao DC, Chasman DI. A Large-Scale Multi-ancestry Genome-wide Study Accounting for Smoking Behavior Identifies Multiple Significant Loci for Blood Pressure. Am J Hum Genet 2018; 102:375-400. [PMID: 29455858 PMCID: PMC5985266 DOI: 10.1016/j.ajhg.2018.01.015] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Accepted: 01/18/2018] [Indexed: 12/18/2022] Open
Abstract
Genome-wide association analysis advanced understanding of blood pressure (BP), a major risk factor for vascular conditions such as coronary heart disease and stroke. Accounting for smoking behavior may help identify BP loci and extend our knowledge of its genetic architecture. We performed genome-wide association meta-analyses of systolic and diastolic BP incorporating gene-smoking interactions in 610,091 individuals. Stage 1 analysis examined ∼18.8 million SNPs and small insertion/deletion variants in 129,913 individuals from four ancestries (European, African, Asian, and Hispanic) with follow-up analysis of promising variants in 480,178 additional individuals from five ancestries. We identified 15 loci that were genome-wide significant (p < 5 × 10-8) in stage 1 and formally replicated in stage 2. A combined stage 1 and 2 meta-analysis identified 66 additional genome-wide significant loci (13, 35, and 18 loci in European, African, and trans-ancestry, respectively). A total of 56 known BP loci were also identified by our results (p < 5 × 10-8). Of the newly identified loci, ten showed significant interaction with smoking status, but none of them were replicated in stage 2. Several loci were identified in African ancestry, highlighting the importance of genetic studies in diverse populations. The identified loci show strong evidence for regulatory features and support shared pathophysiology with cardiometabolic and addiction traits. They also highlight a role in BP regulation for biological candidates such as modulators of vascular structure and function (CDKN1B, BCAR1-CFDP1, PXDN, EEA1), ciliopathies (SDCCAG8, RPGRIP1L), telomere maintenance (TNKS, PINX1, AKTIP), and central dopaminergic signaling (MSRA, EBF2).
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Affiliation(s)
- Yun J Sung
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO 63110, USA.
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg 93051, Germany
| | - Lisa de Las Fuentes
- Cardiovascular Division, Department of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Michael R Brown
- Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas School of Public Health, Houston, TX 77030, USA
| | - Aldi T Kraja
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Karen Schwander
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Ioanna Ntalla
- William Harvey Research Institute, Clinical Pharmacology, Queen Mary University of London, London EC1M 6BQ, UK
| | - Xiuqing Guo
- Genomic Outcomes, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC 27514, USA
| | - Yingchang Lu
- Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, NY 10029, USA
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore 169856, Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore 169857, Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore 117597, Singapore
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore 117549, Singapore
| | - Dina Vojinovic
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Jonathan Marten
- Medical Research Council Human Genetics Unit, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Solomon K Musani
- Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39213, USA
| | - Changwei Li
- Department of Epidemiology and Biostatistics, University of Giorgia at Athens College of Public Health, Athens, GA 30602, USA
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Tuomas O Kilpeläinen
- Section of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2100, Denmark; Department of Environmental Medicine and Public Health, The Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Melissa A Richard
- Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden 2300RC, the Netherlands
| | - Stella Aslibekyan
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Hugues Aschard
- Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA; Centre de Bioinformatique Biostatistique et Biologie Integrative (C3BI), Institut Pasteur, Paris 75015, France
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Biostatistics and Medicine, University of Washington, Seattle, WA 98101, USA
| | - Rajkumar Dorajoo
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore 138672, Singapore
| | - Yongmei Liu
- Division of Biostatistical Sciences, Department of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Alisa K Manning
- Center for Human Genetics Research, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Tuomo Rankinen
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA 70808, USA
| | - Albert Vernon Smith
- Icelandic Heart Association, Kopavogur 201, Iceland; Faculty of Medicine, University of Iceland, Reykjavik 101, Iceland
| | - Salman M Tajuddin
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - Bamidele O Tayo
- Department of Public Health Sciences, Loyola University Chicago, Maywood, IL 60153, USA
| | - Helen R Warren
- William Harvey Research Institute, Clinical Pharmacology, Queen Mary University of London, London EC1M 6BQ, UK; NIHR Cardiovascular Biomedical Research Unit, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Wei Zhao
- School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yanhua Zhou
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Nana Matoba
- Laboratory for Statistical Analysis, Center for Integrative Medical Sciences, RIKEN, Yokohama 230-0045, Japan
| | - Tamar Sofer
- Department of Biostatistics, University of Washington, Seattle, WA 98105, USA
| | - Maris Alver
- Estonian Genome Center, University of Tartu, Tartu 51010, Estonia
| | - Marzyeh Amini
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, the Netherlands
| | - Mathilde Boissel
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille 59000, France
| | - Jin Fang Chai
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117549, Singapore
| | - Xu Chen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 17177, Sweden
| | - Jasmin Divers
- Division of Biostatistical Sciences, Department of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Ilaria Gandin
- Department of Medical Sciences, University of Trieste, Trieste 34137, Italy
| | - Chuan Gao
- Department of Molecular Genetics and Genomics Program, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Franco Giulianini
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02215, USA
| | - Anuj Goel
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, Oxfordshire OX3 9DU, UK; Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Sarah E Harris
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh EH8 9JZ, UK; Medical Genetics Section, University of Edinburgh Centre for Genomic and Experimental Medicine and MRC Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Fernando Pires Hartwig
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, RS 96020220, Brazil
| | - Andrea R V R Horimoto
- Lab Genetics and Molecular Cardiology, Department of Cardiology, Heart Institute, University of Sao Paulo, Sao Paulo, Brazil
| | - Fang-Chi Hsu
- Division of Biostatistical Sciences, Department of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Mika Kähönen
- Department of Clinical Physiology, Faculty of Medicine and Life Sciences, University of Tampere, Tampere 33014, Finland; Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
| | | | - Brigitte Kühnel
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg 85764, Germany; Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg 85764, Germany
| | - Karin Leander
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm 17177, Sweden
| | - Wen-Jane Lee
- Department of Medical Research, Taichung Veterans General Hospital, Department of Social Work, Tunghai University, Taichung 40705, Taiwan
| | - Keng-Hung Lin
- Department of Opthalmology, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Jian 'an Luan
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Colin A McKenzie
- Tropical Metabolism Research Unit, Tropical Medicine Research Institute, University of the West Indies, Mona JMAAW15, Jamaica
| | - He Meian
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College Huazhong University of Science and Technology, Wuhan, China
| | - Christopher P Nelson
- Department of Cardiovascular Sciences, University of Leicester, Leicester LE3 9QP, UK; NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester LE3 9QP, UK
| | - Rainer Rauramaa
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio 70100, Finland
| | - Nicole Schupf
- Taub Institute for Research on Alzheimer disease and the Aging Brain, Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY 10032, USA
| | - Robert A Scott
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Wayne H H Sheu
- Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan; School of Medicine, National Yang-ming University, Taipei, Taiwan; School of Medicine, National Defense Medical Center, Taipei, Taiwan; Institute of Medical Technology, National Chung-Hsing University, Taichung 40705, Taiwan
| | - Alena Stančáková
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio 70210, Finland
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo 1628655, Japan
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, the Netherlands
| | - Tibor V Varga
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Malmö, Skåne 205 02, Sweden
| | - Heming Wang
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Yajuan Wang
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Erin B Ware
- School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109, USA; Institute for Social Research, Research Center for Group Dynamics, University of Michigan, Ann Arbor, MI 48104, USA
| | - Stefan Weiss
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst-Moritz Arndt University Greifswald, Greifswald 17487, Germany; DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald 17475, Germany
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Lisa R Yanek
- General Internal Medicine, GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK; Department of Cardiology, Ealing Hospital, Middlesex UB1 3HW, UK
| | - Jing Hua Zhao
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Saima Afaq
- Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK
| | - Tamuno Alfred
- Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, NY 10029, USA
| | - Najaf Amin
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Dan Arking
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore 169856, Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore 169857, Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore 117597, Singapore
| | - R Graham Barr
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Lawrence F Bielak
- School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Eric Boerwinkle
- Human Genetics Center, The University of Texas School of Public Health, Houston, TX 77030, USA; Institute of Molecular Medicine, The University of Texas Health Science Center, Houston, TX 77030, USA
| | - Erwin P Bottinger
- Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, NY 10029, USA
| | - Peter S Braund
- Department of Cardiovascular Sciences, University of Leicester, Leicester LE3 9QP, UK; NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester LE3 9QP, UK
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Medicine, University of Washington, Seattle, WA 98101, USA
| | - Ulrich Broeckel
- Section of Genomic Pediatrics, Department of Pediatrics, Medicine and Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Claudia P Cabrera
- William Harvey Research Institute, Clinical Pharmacology, Queen Mary University of London, London EC1M 6BQ, UK; NIHR Cardiovascular Biomedical Research Unit, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Brian Cade
- Sleep Medicine and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Yu Caizheng
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College Huazhong University of Science and Technology, Wuhan, China
| | - Archie Campbell
- Centre for Genomic & Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Mickaël Canouil
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille 59000, France
| | - Aravinda Chakravarti
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Ganesh Chauhan
- Centre for Brain Research, Indian Institute of Schience, Bangalore 560012, India
| | - Kaare Christensen
- The Danish Aging Research Center, Institute of Public Health, University of Southern Denmark, Odense, Denmark
| | - Massimiliano Cocca
- Department of Medical Sciences, University of Trieste, Trieste 34137, Italy
| | - Francis S Collins
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - John M Connell
- Ninewells Hospital & Medical School, University of Dundee, Dundee DD1 9SY, UK
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden 2300RC, the Netherlands
| | | | - Stephanie Debette
- Inserm U1219 Neuroepidemiology, University of Bordeaux, Bordeaux, France; Department of Neurology, University Hospital, Bordeaux, France; Boston University School of Medicine, Boston, MA 02118, USA
| | - Marcus Dörr
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald 17475, Germany; Department of Internal Medicine B, University Medicine Greifswald, Greifswald 17475, Germany
| | - Qing Duan
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27514, USA
| | - Charles B Eaton
- Department of Family Medicine and Epidemiology, Alpert Medical School of Brown University, Providence, RI 02860, USA
| | - Georg Ehret
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Division of Cardiology, Department of Specialties of Medicine, Geneva University Hospital, Geneva 1211, Switzerland
| | - Evangelos Evangelou
- Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK; Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina 45110, Greece
| | - Jessica D Faul
- Institute for Social Research, Survey Research Center, University of Michigan, Ann Arbor, MI 48104, USA
| | - Virginia A Fisher
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Nita G Forouhi
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Oscar H Franco
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Yechiel Friedlander
- Braun School of Public Health, Hebrew University-Hadassah Medical Center, Jerusalem 91120, Israel
| | - He Gao
- Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK; MRC-PHE Centre for Environment and Health, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Bruna Gigante
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm 17177, Sweden
| | - Misa Graff
- Department of Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC 27514, USA
| | - C Charles Gu
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Dongfeng Gu
- Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Preeti Gupta
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore 169856, Singapore
| | - Saskia P Hagenaars
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh EH8 9JZ, UK; Department of Psychology, The University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Tamara B Harris
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIH, Bethesda, MD 20892, USA
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA; Department of Medicine, Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Sami Heikkinen
- University of Eastern Finland, Institute of Biomedicine, Kuopio 70211, Finland
| | - Chew-Kiat Heng
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore; Khoo Teck Puat - National University Children's Medical Institute, National University Health System, Singapore 119228, Singapore
| | - Makoto Hirata
- Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Minato-ku 108-8639, Japan
| | - Albert Hofman
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Barbara V Howard
- MedStar Health Research Institute, Hyattsville, MD 20782, USA; Center for Clinical and Translational Sciences and Department of Medicine, Georgetown-Howard Universities, Washington, DC 20057, USA
| | - Steven Hunt
- Cardiovascular Genetics, Department of Internal Medicine, University of Utah, Salt Lake City, UT 84108, USA; Weill Cornell Medicine in Qatar, Doha, Qatar
| | - Marguerite R Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Yucheng Jia
- Genomic Outcomes, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Roby Joehanes
- Hebrew SeniorLife, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02131, USA; Framingham Heart Study, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD 20982, USA
| | - Anne E Justice
- Department of Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC 27514, USA
| | - Tomohiro Katsuya
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita 5650871, Japan; Department of Geriatric and General Medicine, Osaka University Graduate School of Medicine, Suita 5650871, Japan
| | - Joel Kaufman
- Epidemiology, Department of Occupational and Environmental Medicine Program, University of Washington, Seattle, WA 98105, USA
| | - Nicola D Kerrison
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Chiea Chuen Khor
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore 138672, Singapore; Department of Biochemistry, National University of Singapore, Singapore 117596, Singapore
| | - Woon-Puay Koh
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117549, Singapore; Duke-NUS Medical School, Singapore 169857, Singapore
| | - Heikki A Koistinen
- Department of Health, National Institute for Health and Welfare, Helsinki 00271, Finland; Department of Medicine and Abdominal Center: Endocrinology, University of Helsinki and Helsinki University Central Hospital, Helsinki 00029, Finland
| | - Pirjo Komulainen
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio 70100, Finland
| | - Charles Kooperberg
- Fred Hutchinson Cancer Research Center, University of Washington School of Public Health, Seattle, WA 98109, USA
| | - Jose E Krieger
- Lab Genetics and Molecular Cardiology, Department of Cardiology, Heart Institute, University of Sao Paulo, Sao Paulo, Brazil
| | - Michiaki Kubo
- Center for Integrative Medical Sciences, RIKEN, Yokohama 230-0045, Japan
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland
| | - Carl D Langefeld
- Division of Biostatistical Sciences, Department of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | | | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIH, Bethesda, MD 20892, USA
| | - Benjamin Lehne
- Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK
| | - Cora E Lewis
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL 35205, USA
| | - Yize Li
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Sing Hui Lim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore 169856, Singapore
| | - Shiow Lin
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Jianjun Liu
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore 117549, Singapore; Genome Institute of Singapore, Agency for Science Technology and Research, Singapore 138672, Singapore
| | - Jingmin Liu
- WHI CCC, Fred Hutchinson Cancer Research Center, Seattle, WA 98115, USA
| | - Kiang Liu
- Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Yeheng Liu
- Genomic Outcomes, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Marie Loh
- Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK; Translational Laboratory in Genetic Medicine, Agency for Science, Technology and Research 138648, Singapore
| | - Kurt K Lohman
- Division of Biostatistical Sciences, Department of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Tin Louie
- Department of Biostatistics, University of Washington, Seattle, WA 98105, USA
| | - Reedik Mägi
- Estonian Genome Center, University of Tartu, Tartu 51010, Estonia
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg 85764, Germany; Institute of Human Genetics, Technische Universität München, Munich 80333, Germany
| | - Andres Metspalu
- Estonian Genome Center, University of Tartu, Tartu 51010, Estonia
| | - Lili Milani
- Estonian Genome Center, University of Tartu, Tartu 51010, Estonia
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, Center for Integrative Medical Sciences, RIKEN, Yokohama 230-0045, Japan
| | - Andrew P Morris
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK; Department of Biostatistics, University of Liverpool, Liverpool L69 3GL, UK
| | - Thomas H Mosley
- Geriatrics, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Peter Munson
- Mathematical and Statistical Computing Laboratory, Center for Information Technology, NIH, Bethesda, MD 20892, USA
| | - Alison D Murray
- The Institute of Medical Sciences, Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen AB25 2ZD, UK
| | - Mike A Nalls
- Data Tecnica International, Glen Echo, MD 20812, USA; Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD 20892, USA
| | - Ubaydah Nasri
- Genomic Outcomes, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Jill M Norris
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO 80045, USA
| | - Kari North
- Department of Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC 27514, USA
| | | | - Sandosh Padmanabhan
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow G12 8TA, UK
| | - Walter R Palmas
- Internal Medicine, Department of Medicine, Columbia University, New York, NY 10032, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - James S Pankow
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN 55454, USA
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 17177, Sweden
| | - Annette Peters
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg 85764, Germany; DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Neuherberg 85764, Germany
| | - Patricia A Peyser
- School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ozren Polasek
- Faculty of Medicine, University of Split, Split, Croatia
| | - Olli T Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku 20521, Finland; Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku 20520, Finland
| | - Frida Renström
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Malmö, Skåne 205 02, Sweden; Department of Biobank Research, Umeå University, Umeå, Västerbotten 901 87, Sweden
| | - Treva K Rice
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Paul M Ridker
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02215, USA
| | | | - Jennifer G Robinson
- Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Lynda M Rose
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02215, USA
| | - Igor Rudan
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore 169856, Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore 169857, Singapore
| | | | - Kevin Sandow
- Genomic Outcomes, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Carsten O Schmidt
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald 17475, Germany; Institute for Community Medicine, University Medicine Greifswald, Greifswald 17475, Germany
| | - Pamela J Schreiner
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN 55454, USA
| | - William R Scott
- Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK; National Heart and Lung Institute, Imperial College London, London W12 0NN, UK
| | - Sudha Seshadri
- Framingham Heart Study, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD 20982, USA; Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
| | - Peter Sever
- International Centre for Circulatory Health, Imperial College London, London W2 1PG, UK
| | - Colleen M Sitlani
- Cardiovascular Health Research Unit, Medicine, University of Washington, Seattle, WA 98101, USA
| | - Jennifer A Smith
- School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, the Netherlands
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh EH8 9JZ, UK; Alzheimer Scotland Dementia Research Centre, The University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg 85764, Germany; Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich 81377, Germany
| | - Hua Tang
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Kent D Taylor
- Genomic Outcomes, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Yik Ying Teo
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore 117549, Singapore; Genome Institute of Singapore, Agency for Science Technology and Research, Singapore 138672, Singapore; Life Sciences Institute, National University of Singapore, Singapore, Singapore 117456, Singapore; NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore 117456, Singapore; Department of Statistics and Applied Probability, National University of Singapore, Singapore 117546, Singapore
| | - Yih Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore 169856, Singapore
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg 85764, Germany; Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg 85764, Germany
| | - Lihua Wang
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Ya X Wang
- Beijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Capital Medical University, Beijing, China 100730, China
| | - Wen Bin Wei
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
| | - Christine Williams
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Gregory Wilson
- Jackson Heart Study, Department of Public Health, Jackson State University, Jackson, MS 39213, USA
| | - Mary K Wojczynski
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Jie Yao
- Genomic Outcomes, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Jian-Min Yuan
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA; Division of Cancer Control and Population Sciences, University of Pittsburgh Cancer Institute, Pittsburgh, PA 15232, USA
| | - Alan B Zonderman
- Behavioral Epidemiology Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - Diane M Becker
- General Internal Medicine, GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - John C Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK; Department of Cardiology, Ealing Hospital, Middlesex UB1 3HW, UK
| | - Yii-Der Ida Chen
- Genomic Outcomes, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Ulf de Faire
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm 17177, Sweden
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh EH8 9JZ, UK; Department of Psychology, The University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Tõnu Esko
- Estonian Genome Center, University of Tartu, Tartu 51010, Estonia; Broad Institute of the Massachusetts Institute of Technology and Harvard University, Boston, MA 02142, USA
| | - Martin Farrall
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, Oxfordshire OX3 9DU, UK; Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Terrence Forrester
- Tropical Metabolism Research Unit, Tropical Medicine Research Institute, University of the West Indies, Mona JMAAW15, Jamaica
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Malmö, Skåne 205 02, Sweden; Harvard T.H. Chan School of Public Health, Department of Nutrition, Harvard University, Boston, MA 02115, USA; Department of Public Health & Clinical Medicine, Umeå University, Umeå, Västerbotten 901 85, Sweden
| | - Barry I Freedman
- Division of Nephrology, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Philippe Froguel
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille 59000, France; Department of Genomics of Common Disease, Imperial College London, London W12 0NN, UK
| | - Paolo Gasparini
- Department of Medical Sciences, University of Trieste, Trieste 34137, Italy; Division Experimental Genetics, Sidra, Doha 26999, Qatar
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg 85764, Germany; German Center for Diabetes Research (DZD e.V.), Neuherberg 85764, Germany
| | - Bernardo Lessa Horta
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, RS 96020220, Brazil
| | - Yi-Jen Hung
- Endocrinology and Metabolism, Tri-Service General Hospital, National Defense Medical Center, Taipei City, Taipei 11490, Taiwan
| | - Jost B Jonas
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China; Department of Ophthalmology, Medical Faculty Mannheim, University Heidelberg, Mannheim 68167, Germany
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo 1628655, Japan
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, Middlesex UB1 3HW, UK; National Heart and Lung Institute, Imperial College London, London W12 0NN, UK
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland; Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Lifes Sciences, University of Tampere, Tampere 33014, Finland
| | - Kae-Woei Liang
- School of Medicine, National Yang-ming University, Taipei, Taiwan; Cardiovascular Center, Taichung Veterans General Hospital, Taichung 40705, Taiwan; Department of Medicine, China Medical University, Taichung 40705, Taiwan
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 17177, Sweden
| | - Anne B Newman
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Albertine J Oldehinkel
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, the Netherlands
| | - Alexandre C Pereira
- Lab Genetics and Molecular Cardiology, Department of Cardiology, Heart Institute, University of Sao Paulo, Sao Paulo, Brazil; Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Susan Redline
- Sleep Medicine and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Rainer Rettig
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald 17475, Germany; Institute of Physiology, University Medicine Greifswald, Greifswald 17495, Germany
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester LE3 9QP, UK; NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester LE3 9QP, UK
| | - James Scott
- National Heart and Lung Institute, Imperial College London, London W12 0NN, UK
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, the Netherlands
| | - Lynne E Wagenknecht
- Department of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | | | - Hugh Watkins
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, Oxfordshire OX3 9DU, UK; Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - David R Weir
- Institute for Social Research, Survey Research Center, University of Michigan, Ann Arbor, MI 48104, USA
| | | | - Tangchun Wu
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College Huazhong University of Science and Technology, Wuhan, China
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, Center for Integrative Medical Sciences, RIKEN, Yokohama 230-0045, Japan
| | - Cathy C Laurie
- Department of Biostatistics, University of Washington, Seattle, WA 98105, USA
| | - Claude Bouchard
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA 70808, USA
| | - Richard S Cooper
- Department of Public Health Sciences, Loyola University Chicago, Maywood, IL 60153, USA
| | - Michele K Evans
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur 201, Iceland; Faculty of Medicine, University of Iceland, Reykjavik 101, Iceland
| | - Sharon L R Kardia
- School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Stephen B Kritchevsky
- Sticht Center for Health Aging and Alzheimer's Prevention, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Daniel Levy
- Framingham Heart Study, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD 20982, USA; Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD 20892, USA
| | - Jeff R O'Connell
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD 21201, USA; Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Epidemiology, Medicine and Health Services, University of Washington, Seattle, WA 98101, USA; Kaiser Permanente Washington, Health Research Institute, Seattle, WA 98101, USA
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117549, Singapore; Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
| | - Mario Sims
- Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39213, USA
| | - Donna K Arnett
- Dean's Office, University of Kentucky College of Public Health, Lexington, KY 40536, USA
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden 2300RC, the Netherlands; Department of Public Health and Primary Care, Leiden University Medical Center, Leiden 2300RC, the Netherlands
| | - Tanika N Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA
| | - Ervin R Fox
- Cardiology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Michael A Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore 117549, Singapore; Duke-NUS Medical School, Singapore 169857, Singapore; Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore 169856, Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore 169857, Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore 117597, Singapore
| | - Ruth J F Loos
- Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, NY 10029, USA; Icahn School of Medicine at Mount Sinai, The Mindich Child Health and Development Institute, New York, NY 10029, USA
| | - Alex P Reiner
- Fred Hutchinson Cancer Research Center, University of Washington School of Public Health, Seattle, WA 98109, USA
| | - Jerome I Rotter
- Genomic Outcomes, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA 90502, USA; Genomic Outcomes, Department of Medicine, LABioMed at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Xiaofeng Zhu
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Laura J Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - W James Gauderman
- Division of Biostatistics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90032, USA
| | - Mark J Caulfield
- William Harvey Research Institute, Clinical Pharmacology, Queen Mary University of London, London EC1M 6BQ, UK; NIHR Cardiovascular Biomedical Research Unit, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK; MRC-PHE Centre for Environment and Health, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Kenneth Rice
- Department of Biostatistics, University of Washington, Seattle, WA 98105, USA
| | - Patricia B Munroe
- William Harvey Research Institute, Clinical Pharmacology, Queen Mary University of London, London EC1M 6BQ, UK; NIHR Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London EC1M 6BQ, UK
| | - Alanna C Morrison
- Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas School of Public Health, Houston, TX 77030, USA
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA; Framingham Heart Study, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD 20982, USA
| | - Dabeeru C Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Daniel I Chasman
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02215, USA.
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Hovsepian S, Javanmard SH, Mansourian M, Tajadini M, Hashemipour M, Kelishadi R. Relationship of lipid regulatory gene polymorphisms and dyslipidemia in a pediatric population: the CASPIAN III study. Hormones (Athens) 2018; 17:97-105. [PMID: 29858861 DOI: 10.1007/s42000-018-0020-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Accepted: 02/23/2018] [Indexed: 01/10/2023]
Abstract
OBJECTIVE In this study, we aimed to assess the association between four variants in three genes whose association has been reported in adults but not in children. We evaluated the relationship of the GCKR (rs780094), GCKR (rs1260333), FADS (rs174547), and MLXIPL (rs3812316) polymorphisms with serum lipid levels in Iranian children. DESIGN This cross-sectional study was conducted in a subpopulation of the CASPIAN III study. During this study, 550 frozen whole blood samples were selected randomly. Using the recorded information of selected cases, those with and without abnormal lipid levels were determined. Allelic and genotypic frequencies of GCKR (rs780094), GCKR (rs1260333), MLXIPL (rs3812316), and FADS (rs174547) polymorphisms were determined and compared in dyslipidemic and normal children. The association between the studied polymorphisms and lipid profiles was determined using logistic regression analysis. RESULTS Prevalence of hypercholesterolemia, hypertriglyceridemia, high low-density lipoprotein cholesterol (LDL-C), and low high-density lipoprotein cholesterol (HDL-C) were 24.9, 34.5, 19.0, and 40.7%, respectively. Significant correlations were found between GCKR (rs780094) and GCKR (rs1260333) polymorphisms and cholesterol and triglyceride levels, between FADS (rs174547) polymorphism and level of triglyceride, and also between MLXIPL (rs3812316) and levels of HDL-C. CONCLUSIONS The results of this population-based study provide evidence for a relationship between lipid regulatory gene polymorphisms including GCKR (rs780094), GCKR (rs1260333), FADS (rs174547), and MLXIPL (rs3812316) with dyslipidemia in an Iranian population. These results could provide baseline information on as well as further insight into the genetic makeup of lipid profiles in Iranian children, which could be used for preventative strategies.
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Affiliation(s)
- Silva Hovsepian
- Pediatrics Department, Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Emam Hossein Children's Hospital, Isfahan University of Medical Sciences, Isfahan, Iran
| | | | - Marjan Mansourian
- Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mohamadhasan Tajadini
- Applied Physiology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mahin Hashemipour
- Pediatrics Department, Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Roya Kelishadi
- Pediatrics Department, Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Hezar-Jarib Ave, Isfahan, Iran.
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96
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Hovsepian S, Javanmard SH, Mansourian M, Hashemipour M, Tajadini M, Kelishadi R. Lipid regulatory genes polymorphism in children with and without obesity and cardiometabolic risk factors: The CASPIAN-III study. JOURNAL OF RESEARCH IN MEDICAL SCIENCES 2018. [PMID: 29531563 PMCID: PMC5842446 DOI: 10.4103/jrms.jrms_911_17] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background: Genetically, predisposed children are considered as at-risk individuals for cardiovascular disease. In this study, we aimed to compare the frequency of four-lipid regulatory polymorphism in obese and normal-weight children with and without cardiometabolic risk factors. Materials and Methods: In this nested case–control study, 600 samples of four groups of participants consisted of those with normal weight with and without cardiometabolic risk factors and obese with and without cardiometabolic risk factors. Allelic and genotypic frequencies of GCKR (rs780094), GCKR (rs1260333), MLXIPL (rs3812316), and FADS (rs174547) polymorphisms were compared in the four studied groups. Results: Data of 528 samples were complete and included in this study. The mean (standard deviation) age of participants was 15.01 (2.21) years. Frequency of tt allele (minor allele) of GCKR (rs1260333) polymorphism was significantly lower in normal weight metabolically healthy participants than metabolically unhealthy normal weight (MUHNW) and obese children with and without cardiometabolic risk factor (P = 0.01). Frequency of ga allele of GCKR (rs780094) polymorphism was significantly higher in normal weight children with cardiometabolic risk factor than in their obese counterparts with cardiometabolic risk factor (P = 0.04). Frequency of cg and gg alleles (minor type) of MLXIPL (rs3812316) polymorphism in normal weight metabolically healthy participants was significantly higher than MUHNW (P = 0.04) and metabolically healthy obese children (P = 0.04). Conclusion: The findings of our study indicated that the minor allele of GCKR (rs1260333) single nucleotide polymorphisms (SNPs) could have pathogenic effect for obesity and cardiometabolic risk factors. Ga allele of GCKR (rs780094) SNPs had a protective effect on obesity. Minor alleles of MLXIPL (rs3812316) could have a protective effect for obesity and cardiometabolic risk factors.
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Affiliation(s)
- Silva Hovsepian
- Department of Pediatrics, Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Emam Hossein Children's Hospital, Isfahan, Iran
| | - Shaghayegh Haghjooy Javanmard
- Applied Physiology Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Marjan Mansourian
- Department of Biostatistics and Epidemiology, School of Health, Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mahin Hashemipour
- Isfahan Endocrine and Metabolism Research Center, Department of Pediatrics, Emam Hossein Children's Hospital, Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mohamadhasan Tajadini
- Applied Physiology Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Roya Kelishadi
- Department of Pediatrics, Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
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97
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McDermott JR, Geng X, Jiang L, Gálvez-Peralta M, Chen F, Nebert DW, Liu Z. Zinc- and bicarbonate-dependent ZIP8 transporter mediates selenite uptake. Oncotarget 2018; 7:35327-40. [PMID: 27166256 PMCID: PMC5085232 DOI: 10.18632/oncotarget.9205] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2015] [Accepted: 04/11/2016] [Indexed: 12/26/2022] Open
Abstract
Selenite (HSeO3−) is a monovalent anion of the essential trace element and micronutrient selenium (Se). In therapeutic concentrations, HSeO3− has been studied for treating certain cancers, serious inflammatory disorders, and septic shock. Little is known, however, about HSeO3− uptake into mammalian cells; until now, no mammalian HSeO3− uptake transporter has been identified. The ubiquitous mammalian ZIP8 divalent cation transporter (encoded by the SLC39A8 gene) is bicarbonate-dependent, moving endogenous substrates (Zn2+, Mn2+, Fe2+ or Co2+) and nonessential metals such as Cd2+ into the cell. Herein we studied HSeO3− uptake in: human and mouse cell cultures, shRNA-knockdown experiments, Xenopus oocytes, wild-type mice and two transgenic mouse lines having genetically altered ZIP8 expression, and mouse erythrocytes ex vivo. In mammalian cell culture, excess Zn2+ levels and/or ZIP8 over-expression can be associated with diminished viability in selenite-treated cells. Intraperitoneal HSeO3− causes the largest ZIP8-dependent increases in intracellular Se content in liver, followed by kidney, heart, lung and spleen. In every model system studied, HSeO3− uptake is tightly associated with ZIP8 protein levels and sufficient Zn2+ and HCO3− concentrations, suggesting that the ZIP8-mediated electroneutral complex transported contains three ions: Zn2+/(HCO3−)(HSeO3−). Transporters having three different ions in their transport complex are not without precedent. Although there might be other HSeO3− influx transporters as yet undiscovered, data herein suggest that mammalian ZIP8 plays a major role in HSeO3− uptake.
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Affiliation(s)
- Joseph R McDermott
- Department of Biological Sciences, Oakland University, Rochester, MI 48309, USA
| | - Xiangrong Geng
- Department of Biological Sciences, Oakland University, Rochester, MI 48309, USA
| | - Lan Jiang
- Department of Biological Sciences, Oakland University, Rochester, MI 48309, USA
| | - Marina Gálvez-Peralta
- Department of Environmental Health and Center for Environmental Genetics, University of Cincinnati Medical Center, Cincinnati, OH 45267, USA
| | - Fei Chen
- Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, Wayne State University, Detroit, MI 48201, USA
| | - Daniel W Nebert
- Department of Environmental Health and Center for Environmental Genetics, University of Cincinnati Medical Center, Cincinnati, OH 45267, USA
| | - Zijuan Liu
- Department of Biological Sciences, Oakland University, Rochester, MI 48309, USA
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98
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Plasma inducible degrader of the LDLR, soluble low-density lipoprotein receptor, and proprotein convertase subtilisin/kexin type 9 levels as potential biomarkers of familial hypercholesterolemia in children. J Clin Lipidol 2018; 12:211-218. [DOI: 10.1016/j.jacl.2017.10.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Revised: 09/28/2017] [Accepted: 10/03/2017] [Indexed: 02/02/2023]
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99
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Zhang GM, Wang MY, Liu YN, Zhu Y, Wan FN, Wei QY, Ye DW. Functional variants in the low-density lipoprotein receptor gene are associated with clear cell renal cell carcinoma susceptibility. Carcinogenesis 2017; 38:1241-1248. [PMID: 29029037 DOI: 10.1093/carcin/bgx098] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Accepted: 09/21/2017] [Indexed: 12/17/2022] Open
Abstract
Recent studies indicate that abnormal levels of low-density lipoprotein (LDL), which is an important component of dyslipidaemia, are associated with alterations to cancer risk, including that of renal cell carcinoma (RCC). Single nucleotide polymorphisms at microRNA-binding sites contribute to cancer susceptibility and progression by affecting the messenger RNA (mRNA) function of target genes. In this case-control study, we examined the frequency of six potentially functional single nucleotide polymorphisms in the LDL receptor gene (LDLR) in 1004 clear cell RCC (ccRCC) patients and 1065 cancer-free subjects. Logistic regression analyses estimated odds ratios (ORs) and 95% confidence intervals (CIs). The association between genetic variants and levels of LDLR mRNA and protein was also evaluated. Compared with the CC genotype, multivariate logistic regression analysis showed that the LDLR rs2738464 variant GG genotype was associated with a significantly decreased ccRCC risk (P = 0.002, OR: 0.605, 95% CI: 0.439-0.833). Further functional experiments showed that the rs2738464 variant G allele affected miR-330 regulation of the LDLR 3'-untranslated region (UTR), increasing LDLR mRNA levels in patient kidney tissues. These findings suggest that LDLR rs2738464 may affect the affinity of miR-330 binding to the LDLR 3'-UTR, thus regulating LDLR expression and contributing to ccRCC risk.
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Affiliation(s)
- Gui-Ming Zhang
- Department of Urology, The Affiliated Hospital of Qingdao University, China.,Department of Urology, Fudan University Shanghai Cancer Center, China.,Department of Oncology, Shanghai Medical College, Fudan University, China
| | - Meng-Yun Wang
- Department of Oncology, Shanghai Medical College, Fudan University, China.,Cancer Institute, Fudan University Shanghai Cancer Center, China
| | - Ya-Nan Liu
- Department of Urology, The Affiliated Hospital of Qingdao University, China
| | - Yao Zhu
- Department of Urology, Fudan University Shanghai Cancer Center, China.,Department of Oncology, Shanghai Medical College, Fudan University, China
| | - Fang-Ning Wan
- Department of Urology, Fudan University Shanghai Cancer Center, China.,Department of Oncology, Shanghai Medical College, Fudan University, China
| | - Qing-Yi Wei
- Cancer Institute, Fudan University Shanghai Cancer Center, China.,Duke Cancer Institute, Duke University Medical Center, USA
| | - Ding-Wei Ye
- Department of Urology, Fudan University Shanghai Cancer Center, China.,Department of Oncology, Shanghai Medical College, Fudan University, China
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100
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Wu Z, Su X, Sheng H, Chen Y, Gao X, Bao L, Jin W. Conditional Inference Tree for Multiple Gene-Environment Interactions on Myocardial Infarction. Arch Med Res 2017; 48:546-552. [PMID: 29258680 DOI: 10.1016/j.arcmed.2017.12.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 12/06/2017] [Indexed: 01/29/2023]
Abstract
BACKGROUND AND AIMS Identifying gene-environment interaction in the context of multiple environmental factors has been a challenging task. We aimed to use conditional inference tree (CTREE) to strata myocardial infarction (MI) risk synthesizing information from both genetic and environmental factors. METHODS We conducted a case-control study including 1440 Chinese men (730 MI patients and 710 controls). We first calculated a weighted genetic risk score (GRS) by combining 25 single nucleotide polymorphisms (SNPs) that had been identified to be associated with coronary artery diseases in previous genome wide association studies. We then developed a CTREE model to interpret the gene-environment interaction network in predicting MI. RESULTS We detected high-order interactions between dyslipidemia, GRS, smoking status, age and diabetes. Of all the variables examined, high density lipoprotein cholesterol (HDL-C) of 1.25 mmlo/L was identified as the key discriminator. The subsequent splits of MI were low density lipoprotein cholesterol (LDL-C) of 4.01 mmol/L and GRS of 20.9. We found that individuals with HDL-C ≤1.25 mmol/L, GRS >20.9 and lipoprotein (a) > 0.09 g/L had a higher risk of MI than those who at the lowest risk group (OR: 5.89, 95% CI: 3.99-8.69). This magnitude of MI risk was similar to the combination of HDL-C ≤1.25 mmol/L, GRS ≤20.9, smoking and lipoprotein (a) > 0.15 g/L (OR: 5.49, 95% CI: 3.51-8.58). CONCLUSIONS The multiple interactions between genetic and environmental factors can be visually present via the CTREE approach. The tree diagram also simplifies the decision making procedure by answering a sequence of questions along the branches.
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Affiliation(s)
- Zhijun Wu
- Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiuxiu Su
- Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haihui Sheng
- National Engineering Center for Biochip at Shanghai, Shanghai, China
| | - Yanjia Chen
- Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiang Gao
- Department of Nutritional Sciences, Pennsylvania State University, State college, Pennsylvania
| | - Le Bao
- Department of Statistics, Pennsylvania State University, State college, Pennsylvania
| | - Wei Jin
- Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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