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Armstrong ND, Srinivasasainagendra V, Ammous F, Assimes TL, Beitelshees AL, Brody J, Cade BE, Ida Chen YD, Chen H, de Vries PS, Floyd JS, Franceschini N, Guo X, Hellwege JN, House JS, Hwu CM, Kardia SLR, Lange EM, Lange LA, McDonough CW, Montasser ME, O’Connell JR, Shuey MM, Sun X, Tanner RM, Wang Z, Zhao W, Carson AP, Edwards TL, Kelly TN, Kenny EE, Kooperberg C, Loos RJF, Morrison AC, Motsinger-Reif A, Psaty BM, Rao DC, Redline S, Rich SS, Rotter JI, Smith JA, Smith AV, Irvin MR, Arnett DK. Whole genome sequence analysis of apparent treatment resistant hypertension status in participants from the Trans-Omics for Precision Medicine program. Front Genet 2023; 14:1278215. [PMID: 38162683 PMCID: PMC10755672 DOI: 10.3389/fgene.2023.1278215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 11/24/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction: Apparent treatment-resistant hypertension (aTRH) is characterized by the use of four or more antihypertensive (AHT) classes to achieve blood pressure (BP) control. In the current study, we conducted single-variant and gene-based analyses of aTRH among individuals from 12 Trans-Omics for Precision Medicine cohorts with whole-genome sequencing data. Methods: Cases were defined as individuals treated for hypertension (HTN) taking three different AHT classes, with average systolic BP ≥ 140 or diastolic BP ≥ 90 mmHg, or four or more medications regardless of BP (n = 1,705). A normotensive control group was defined as individuals with BP < 140/90 mmHg (n = 22,079), not on AHT medication. A second control group comprised individuals who were treatment responsive on one AHT medication with BP < 140/ 90 mmHg (n = 5,424). Logistic regression with kinship adjustment using the Scalable and Accurate Implementation of Generalized mixed models (SAIGE) was performed, adjusting for age, sex, and genetic ancestry. We assessed variants using SKAT-O in rare-variant analyses. Single-variant and gene-based tests were conducted in a pooled multi-ethnicity stratum, as well as self-reported ethnic/racial strata (European and African American). Results: One variant in the known HTN locus, KCNK3, was a top finding in the multi-ethnic analysis (p = 8.23E-07) for the normotensive control group [rs12476527, odds ratio (95% confidence interval) = 0.80 (0.74-0.88)]. This variant was replicated in the Vanderbilt University Medical Center's DNA repository data. Aggregate gene-based signals included the genes AGTPBP, MYL4, PDCD4, BBS9, ERG, and IER3. Discussion: Additional work validating these loci in larger, more diverse populations, is warranted to determine whether these regions influence the pathobiology of aTRH.
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Affiliation(s)
- Nicole D. Armstrong
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, United States
| | | | - Farah Ammous
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
- Survey Research Center, Institute for Social Research, Ann Arbor, MI, United States
| | - Themistocles L. Assimes
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Amber L. Beitelshees
- Division of Endocrinology, Diabetes, and Nutrition, Program for Personalized and Genomic Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Jennifer Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States
| | - Brian E. Cade
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Paul S. de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - James S. Floyd
- Department of Medicine, University of Washington, Seattle, WA, United States
- Department of Epidemiology, University of Washington, Seattle, WA, United States
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States
| | - Jacklyn N. Hellwege
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States
| | - John S. House
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, United States
| | - Chii-Min Hwu
- Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Sharon L. R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
| | - Ethan M. Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Leslie A. Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Caitrin W. McDonough
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, United States
| | - May E. Montasser
- Division of Endocrinology, Diabetes, and Nutrition, Program for Personalized and Genomic Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
| | | | - Megan M. Shuey
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Xiao Sun
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States
| | - Rikki M. Tanner
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
- Survey Research Center, Institute for Social Research, Ann Arbor, MI, United States
| | - April P. Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, United States
| | - Todd L. Edwards
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Tanika N. Kelly
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago, Chicago, IL, United States
| | - Eimear E. Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Alanna C. Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Alison Motsinger-Reif
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, United States
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States
- Department of Medicine, University of Washington, Seattle, WA, United States
- Department of Epidemiology, University of Washington, Seattle, WA, United States
| | - Dabeeru C. Rao
- Division of Biostatistics, School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Stephen S. Rich
- Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
- Survey Research Center, Institute for Social Research, Ann Arbor, MI, United States
| | - Albert V. Smith
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, United States
| | - Marguerite R. Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Donna K. Arnett
- Office of the Provost, University of South Carolina, Columbia, SC, United States
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Daly AK, Bjornsson ES, Lucena MI, Andrade RJ, Aithal GP. Drug-induced liver injury due to nitrofurantoin: Similar clinical features, but different HLA risk alleles in an independent cohort. J Hepatol 2022; 78:e165-e166. [PMID: 36460164 DOI: 10.1016/j.jhep.2022.11.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 11/23/2022] [Indexed: 12/03/2022]
Affiliation(s)
- Ann K Daly
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.
| | - Einar S Bjornsson
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, The National University Hospital of Iceland, Reykjavik, Iceland
| | - M Isabel Lucena
- UGC Digestivo y Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga (IBIMA_Plataforma Bionand), Hospital Universitario Virgen de la Victoria, Universidad de Málaga, Málaga, Spain; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain
| | - Raul J Andrade
- UGC Digestivo y Servicio de Farmacología Clínica, Instituto de Investigación Biomédica de Málaga (IBIMA_Plataforma Bionand), Hospital Universitario Virgen de la Victoria, Universidad de Málaga, Málaga, Spain; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain
| | - Guruprasad P Aithal
- NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and University of Nottingham, UK
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McDonough CW, Warren HR, Jack JR, Motsinger-Reif AA, Armstrong ND, Bis JC, House JS, Singh S, El Rouby NM, Gong Y, Mychaleckyj JC, Rotroff DM, Benavente OR, Caulfield MJ, Doria A, Pepine CJ, Psaty BM, Glorioso V, Glorioso N, Hiltunen TP, Kontula KK, Arnett DK, Buse JB, Irvin MR, Johnson JA, Munroe PB, Wagner MJ, Cooper-DeHoff RM. Adverse Cardiovascular Outcomes and Antihypertensive Treatment: A Genome-Wide Interaction Meta-Analysis in the International Consortium for Antihypertensive Pharmacogenomics Studies. Clin Pharmacol Ther 2021; 110:723-732. [PMID: 34231218 PMCID: PMC8672325 DOI: 10.1002/cpt.2355] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 06/11/2021] [Indexed: 01/01/2023]
Abstract
We sought to identify genome-wide variants influencing antihypertensive drug response and adverse cardiovascular outcomes, utilizing data from four randomized controlled trials in the International Consortium for Antihypertensive Pharmacogenomics Studies (ICAPS). Genome-wide antihypertensive drug-single nucleotide polymorphism (SNP) interaction tests for four drug classes (β-blockers, n = 9,195; calcium channel blockers (CCBs), n = 10,511; thiazide/thiazide-like diuretics, n = 3,516; ACE-inhibitors/ARBs, n = 2,559) and cardiovascular outcomes (incident myocardial infarction, stroke, or death) were analyzed among patients with hypertension of European ancestry. Top SNPs from the meta-analyses were tested for replication of cardiovascular outcomes in an independent Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) study (n = 21,267), blood pressure (BP) response in independent ICAPS studies (n = 1,552), and ethnic validation in African Americans from the Genetics of Hypertension Associated Treatment study (GenHAT; n = 5,115). One signal reached genome-wide significance in the β-blocker-SNP interaction analysis (rs139945292, Interaction P = 1.56 × 10-8 ). rs139945292 was validated through BP response to β-blockers, with the T-allele associated with less BP reduction (systolic BP response P = 6 × 10-4 , Beta = 3.09, diastolic BP response P = 5 × 10-3 , Beta = 1.53). The T-allele was also associated with increased adverse cardiovascular risk within the β-blocker treated patients' subgroup (P = 2.35 × 10-4 , odds ratio = 1.57, 95% confidence interval = 1.23-1.99). The locus showed nominal replication in CHARGE, and consistent directional trends in β-blocker treated African Americans. rs139945292 is an expression quantitative trait locus for the 50 kb upstream gene NTM (neurotrimin). No SNPs attained genome-wide significance for any other drugs classes. Top SNPs were located near CALB1 (CCB), FLJ367777 (ACE-inhibitor), and CES5AP1 (thiazide). The NTM region is associated with increased risk for adverse cardiovascular outcomes and less BP reduction in β-blocker treated patients. Further investigation into this region is warranted.
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Affiliation(s)
- Caitrin W. McDonough
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, Florida, USA
| | - Helen R. Warren
- Clinical Pharmacology Department, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - John R. Jack
- Bioinformatics Research Center, Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| | - Alison A. Motsinger-Reif
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, North Carolina, USA
| | - Nicole D. Armstrong
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, USA
| | - John S. House
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, North Carolina, USA
| | - Sonal Singh
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, Florida, USA
| | - Nihal M. El Rouby
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, Florida, USA
| | - Yan Gong
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, Florida, USA
| | - Joesyf C. Mychaleckyj
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Daniel M. Rotroff
- Bioinformatics Research Center, Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| | - Oscar R. Benavente
- Department of Neurology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Mark J. Caulfield
- National Institute for Health Research, Barts Cardiovascular Biomedical Research Center, Queen Mary University of London, London, UK
| | - Alessandrio Doria
- Research Division, Joslin Diabetes Center; and Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Carl J. Pepine
- Division of Cardiovascular Medicine, Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, USA
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Valeria Glorioso
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milano, Italy
| | - Nicola Glorioso
- Department of Clinical, Surgical and Experimental Science, University of Sassari, Medical School, Sassari, Italy
| | - Timo P. Hiltunen
- Department of Medicine and Research Program for Clinical and Molecular Metabolism, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Kimmo K. Kontula
- Department of Medicine and Research Program for Clinical and Molecular Metabolism, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Donna K. Arnett
- College of Public Health, Dean’s Office, University of Kentucky, Lexington, Kentucky, USA
| | - John B. Buse
- Division of Endocrinology, Department of Medicine, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Marguerite R. Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Julie A. Johnson
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, Florida, USA
- Division of Cardiovascular Medicine, Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Patricia B. Munroe
- Clinical Pharmacology Department, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Michael J. Wagner
- Center for Pharmacogenomics and Individualized Therapy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Rhonda M. Cooper-DeHoff
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, Florida, USA
- Division of Cardiovascular Medicine, Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida, USA
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Tafazoli A, Wawrusiewicz-Kurylonek N, Posmyk R, Miltyk W. Pharmacogenomics, How to Deal with Different Types of Variants in Next Generation Sequencing Data in the Personalized Medicine Area. J Clin Med 2020; 10:jcm10010034. [PMID: 33374421 PMCID: PMC7796098 DOI: 10.3390/jcm10010034] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 12/21/2020] [Accepted: 12/22/2020] [Indexed: 12/15/2022] Open
Abstract
Pharmacogenomics (PGx) is the knowledge of diverse drug responses and effects in people, based on their genomic profiles. Such information is considered as one of the main directions to reach personalized medicine in future clinical practices. Since the start of applying next generation sequencing (NGS) methods in drug related clinical investigations, many common medicines found their genetic data for the related metabolizing/shipping proteins in the human body. Yet, the employing of technology is accompanied by big obtained data, which most of them have no clear guidelines for consideration in routine treatment decisions for patients. This review article talks about different types of NGS derived PGx variants in clinical studies and try to display the current and newly developed approaches to deal with pharmacogenetic data with/without clear guidelines for considering in clinical settings.
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Affiliation(s)
- Alireza Tafazoli
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy with the Division of Laboratory Medicine, Medical University of Białystok, 15-089 Białystok, Poland;
- Clinical Research Centre, Medical University of Białystok, 15-276 Bialystok, Poland
| | | | - Renata Posmyk
- Department of Clinical Genetics, Medical University of Białystok, 15-089 Białystok, Poland; (N.W.-K.); (R.P.)
| | - Wojciech Miltyk
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy with the Division of Laboratory Medicine, Medical University of Białystok, 15-089 Białystok, Poland;
- Correspondence: ; Tel.: +48-857485845
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Lund JB, Li S, Baumbach J, Christensen K, Li W, Mohammadnejad A, Pattie A, Marioni RE, Deary IJ, Tan Q. Weighted Gene Coregulation Network Analysis of Promoter DNA Methylation on All-Cause Mortality in Old-Aged Birth Cohorts Finds Modules of High-Risk Associated Biomarkers. J Gerontol A Biol Sci Med Sci 2020; 75:2249-2257. [PMID: 32154558 DOI: 10.1093/gerona/glaa066] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Indexed: 02/07/2023] Open
Abstract
Overall or all-cause mortality is a key measure of health in a population. Multiple epigenome-wide association studies have been conducted on all-cause mortality with limited significant findings and low replication. To elucidate the coregulated DNA methylation patterns associated with all-cause mortality, we conducted a weighted DNA methylation coregulation network analysis on whole-blood samples of 1,425 older individuals from the Lothian Birth Cohorts of 1921 and 1936. Our network-based analysis defined coregulated DNA methylation patterns in gene promoters into clusters or modules whose correlation with all-cause mortality was assessed by survival analysis. We found two significant modules or gene clusters associated with all-cause mortality in LBC1921 based on their eigengenes; one negatively correlated (p = 8.14E-03, 698 genes) and one positively correlated (p = 4.26E-02, 1,431 genes) with the risk of death. The two modules were replicated in LBC1936 with the same directions of correlation (p = 6.35E-02 and p = 3.64E-02, respectively). Furthermore, the modules revealed 32 genes associated with all-cause mortality (FDR < 0.05) linked to various diseases, including cancer and diabetes. Additionally, we performed pathway analysis and found 22 pathways (FDR < 0.05), including a pathway for taste transduction, which has been shown to be associated with poor prognosis in acutely hospitalized patients, and several pathways were linked to different types of cancer. The results from our network analysis show that DNA methylation of multiple genes could have been coregulated in an association with the overall risk of death. The identified epigenetic markers might help with our understanding of the molecular basis of all-cause mortality and general health.
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Affiliation(s)
- Jesper B Lund
- Epidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Shuxia Li
- Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Jan Baumbach
- Experimental Bioinformatics, Department of Informatics, TUM School of Life Sciences Weihenstephan, Germany
| | - Kaare Christensen
- Epidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark.,Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Weilong Li
- Epidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Afsaneh Mohammadnejad
- Epidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Alison Pattie
- Department of Psychology, Centre for Cognitive Aging and Cognitive Epidemiology, University of Edinburgh, UK
| | - Riccardo E Marioni
- Department of Psychology, Centre for Cognitive Aging and Cognitive Epidemiology, University of Edinburgh, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, Scotland, UK
| | - Ian J Deary
- Department of Psychology, Centre for Cognitive Aging and Cognitive Epidemiology, University of Edinburgh, UK.,Department of Psychology, University of Edinburgh, UK
| | - Qihua Tan
- Epidemiology and Biostatistics, Department of Public Health, University of Southern Denmark, Odense, Denmark.,Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
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Rivera MA, Fahey TD, López-Taylor JR, Martínez JL. The Association of Aquaporin-1 Gene with Marathon Running Performance Level: a Confirmatory Study Conducted in Male Hispanic Marathon Runners. SPORTS MEDICINE-OPEN 2020; 6:16. [PMID: 32198675 PMCID: PMC7083975 DOI: 10.1186/s40798-020-00243-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 02/19/2020] [Indexed: 01/10/2023]
Abstract
Background Replication studies are essential for identifying credible associations between alleles and phenotypes. Validation of genotype-phenotype associations in the sports and exercise field is rare. An initial genetic association study suggested that rs1049305 (C > G) in the 3′ untranslated region (3′UTR) of the aquaporin-1 (AQP1) gene was associated with marathon running (MR) performance level in Hispanic males. To validate this finding, we conducted a replication analysis in an independent case-control sample of Hispanic male marathon runners (n = 1430; cases n = 713 and controls n = 717). A meta-analysis was utilized to test the extent of the association between the initial results and the present report. It also provided to test the heterogeneity (variation) between the two studies. Results The replication study showed a statistically significant (p ≤ 0.05) association between rs1049305 (C > G) of the AQP1 gene and MR performance level. Association test results using a fixed effect model for the combined, original study and the present report, yielded an odds ratio = 1.28, 95% confidence interval = 1.13–1.45, p = 0.0001. The extent of the measures of heterogeneity was Tau-squared = 0, H statistic = 1, I2 statistic = 0, and Cochran’s Q test (Q = 0.29; p value 0.59), indicated the variation between studies were due to chance and not to differences in heterogeneity between the two studies. Within the limitations of the present replication, contrast of two studies and its effects on meta-analysis, the findings were robust. Conclusion This study successfully replicated the results of Martínez et al. (Med Sportiva 13:251-5, 2009). The meta-analysis provided further epidemiological credibility for the hypothesis of association between the DNA rs1049305 (C > G) variation in the 3′UTR of the AQP1 gene and MR running performance level in Hispanics male marathon runners. It is not precluded that a linked DNA structure in the surrounding molecular neighborhood could be of influence by been part of the overly complex phenotype of MR performance level.
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Affiliation(s)
- Miguel A Rivera
- Department of Physical Medicine, Rehabilitation & Sports Medicine, School of Medicine, University of Puerto Rico, Main Building Office A204, San Juan, PR, 00936, USA.
| | - Thomas D Fahey
- Department of Kinesiology, California State University, Chico, CA, USA
| | - Juan R López-Taylor
- Physical Activity and Applied Sport Sciences Institute, Universidad de Guadalajara, Guadalajara, Jalisco, México
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Cresci S, Pereira NL, Ahmad F, Byku M, de las Fuentes L, Lanfear DE, Reilly CM, Owens AT, Wolf MJ. Heart Failure in the Era of Precision Medicine: A Scientific Statement From the American Heart Association. CIRCULATION-GENOMIC AND PRECISION MEDICINE 2019; 12:458-485. [DOI: 10.1161/hcg.0000000000000058] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
One of 5 people will develop heart failure over his or her lifetime. Early diagnosis and better understanding of the pathophysiology of this disease are critical to optimal treatment. The “omics”—genomics, pharmacogenomics, epigenomics, proteomics, metabolomics, and microbiomics— of heart failure represent rapidly expanding fields of science that have, to date, not been integrated into a single body of work. The goals of this statement are to provide a comprehensive overview of the current state of these omics as they relate to the development and progression of heart failure and to consider the current and potential future applications of these data for precision medicine with respect to prevention, diagnosis, and therapy.
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Gruber PJ. Genetic association studies: Is non-replication failure or progress? J Thorac Cardiovasc Surg 2019; 157:e399-e400. [PMID: 30885625 DOI: 10.1016/j.jtcvs.2019.02.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Accepted: 02/05/2019] [Indexed: 11/24/2022]
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Lund JB, Li S, Baumbach J, Svane AM, Hjelmborg J, Christiansen L, Christensen K, Redmond P, Marioni RE, Deary IJ, Tan Q. DNA methylome profiling of all-cause mortality in comparison with age-associated methylation patterns. Clin Epigenetics 2019; 11:23. [PMID: 30736859 PMCID: PMC6368749 DOI: 10.1186/s13148-019-0622-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Accepted: 01/24/2019] [Indexed: 01/05/2023] Open
Abstract
Background Multiple epigenome-wide association studies have been performed to identify DNA methylation patterns regulated by aging or correlated with risk of death. However, the inter-relatedness of the epigenetic basis of aging and mortality has not been well investigated. Methods Using genome-wide DNA methylation data from the Lothian Birth Cohorts, we conducted a genome-wide association analysis of all-cause mortality and compared this with age-associated methylation patterns reported on the same samples. Results Survival analysis using the Cox regression model identified 2552 CpG sites with genome-wide significance (false discovery rate < 0.05) for all-cause mortality. CpGs whose methylation levels are associated with increased mortality appear more distributed from the gene body to the intergenic regions whereas CpGs whose methylation levels are associated with decreased mortality is more concentrated at the promoter regions. In comparison with reported CpGs displaying significant age-dependent methylation patterns in the same samples, we observed a limited but highly significant overlap between mortality-associated and age-associated CpGs (p value 2.52e−06). Most importantly, the overlapping CpGs are dominated by those whose overall age-related methylation patterns reduce the risk of death. Conclusion All-cause mortality is significantly associated with altered methylation at multiple genomic sites with differential distribution in gene regions for CpGs correlated with increased or decreased risk of death. The age-dependent methylation changes could reflect an active response to the aging process that contributes to maintain individual survival. Electronic supplementary material The online version of this article (10.1186/s13148-019-0622-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jesper Beltoft Lund
- Epidemiology and Biostatistics, Department of Public Health, Faculty of Health Science, University of Southern Denmark, J. B. Winsløws Vej 9B, DK-5000, Odense, Denmark
| | - Shuxia Li
- Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Jan Baumbach
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.,Chair of Experimental Bioinformatics, School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Anne Marie Svane
- Epidemiology and Biostatistics, Department of Public Health, Faculty of Health Science, University of Southern Denmark, J. B. Winsløws Vej 9B, DK-5000, Odense, Denmark
| | - Jacob Hjelmborg
- Epidemiology and Biostatistics, Department of Public Health, Faculty of Health Science, University of Southern Denmark, J. B. Winsløws Vej 9B, DK-5000, Odense, Denmark
| | - Lene Christiansen
- Epidemiology and Biostatistics, Department of Public Health, Faculty of Health Science, University of Southern Denmark, J. B. Winsløws Vej 9B, DK-5000, Odense, Denmark
| | - Kaare Christensen
- Epidemiology and Biostatistics, Department of Public Health, Faculty of Health Science, University of Southern Denmark, J. B. Winsløws Vej 9B, DK-5000, Odense, Denmark.,Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Paul Redmond
- Department of Psychology, University of Edinburgh, Edinburgh, Scotland, UK
| | - Riccardo E Marioni
- Center for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, Scotland, UK.,Center for Cognitive Aging and Cognitive Epidemiology, University of Edinburgh, Edinburgh, Scotland, UK
| | - Ian J Deary
- Department of Psychology, University of Edinburgh, Edinburgh, Scotland, UK.,Center for Cognitive Aging and Cognitive Epidemiology, University of Edinburgh, Edinburgh, Scotland, UK
| | - Qihua Tan
- Epidemiology and Biostatistics, Department of Public Health, Faculty of Health Science, University of Southern Denmark, J. B. Winsløws Vej 9B, DK-5000, Odense, Denmark. .,Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
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10
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Luizon MR, Pereira DA, Tanus-Santos JE. Pharmacogenetic relevance of endothelial nitric oxide synthase polymorphisms and gene interactions. Pharmacogenomics 2018; 19:1423-1435. [PMID: 30398085 DOI: 10.2217/pgs-2018-0098] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Endothelial nitric oxide synthase (NOS3) is a key enzyme responsible for nitric oxide (NO) generation in the vascular endothelium. Endothelial dysfunction is characterized by reduced NO production, and is a hallmark of cardiovascular diseases. Drugs with cardiovascular action may activate NOS3 and result in NO release and vasodilation. Moreover, genetic variations affect NOS3 expression and activity, and may partially explain the variability in the responses to cardiovascular drugs. We reviewed NO signaling and genetic effects on NO formation, and the effects of NOS3 polymorphisms, haplotypes and gene-gene interactions within NO signaling pathways on the responses to cardiovascular drugs. We discuss the role of rare NOS3 variants and further gene-gene interactions analysis for the development of novel therapies for cardiovascular diseases.
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Affiliation(s)
- Marcelo R Luizon
- Department of General Biology, Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31270-901, Brazil.,UFMG Graduate Program in Genetics, Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Daniela A Pereira
- UFMG Graduate Program in Genetics, Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Jose E Tanus-Santos
- Department of Pharmacology, Ribeirao Preto Medical School, University of Sao Paulo, Ribeirao Preto, Sao Paulo 14049-900, Brazil
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11
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Wittkowski KM, Dadurian C, Seybold MP, Kim HS, Hoshino A, Lyden D. Complex polymorphisms in endocytosis genes suggest alpha-cyclodextrin as a treatment for breast cancer. PLoS One 2018; 13:e0199012. [PMID: 29965997 PMCID: PMC6028090 DOI: 10.1371/journal.pone.0199012] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Accepted: 05/17/2018] [Indexed: 02/06/2023] Open
Abstract
Most breast cancer deaths are caused by metastasis and treatment options beyond radiation and cytotoxic drugs, which have severe side effects, and hormonal treatments, which are or become ineffective for many patients, are urgently needed. This study reanalyzed existing data from three genome-wide association studies (GWAS) using a novel computational biostatistics approach (muGWAS), which had been validated in studies of 600-2000 subjects in epilepsy and autism. MuGWAS jointly analyzes several neighboring single nucleotide polymorphisms while incorporating knowledge about genetics of heritable diseases into the statistical method and about GWAS into the rules for determining adaptive genome-wide significance. Results from three independent GWAS of 1000-2000 subjects each, which were made available under the National Institute of Health's "Up For A Challenge" (U4C) project, not only confirmed cell-cycle control and receptor/AKT signaling, but, for the first time in breast cancer GWAS, also consistently identified many genes involved in endo-/exocytosis (EEC), most of which had already been observed in functional and expression studies of breast cancer. In particular, the findings include genes that translocate (ATP8A1, ATP8B1, ANO4, ABCA1) and metabolize (AGPAT3, AGPAT4, DGKQ, LPPR1) phospholipids entering the phosphatidylinositol cycle, which controls EEC. These novel findings suggest scavenging phospholipids as a novel intervention to control local spread of cancer, packaging of exosomes (which prepare distant microenvironment for organ-specific metastases), and endocytosis of β1 integrins (which are required for spread of metastatic phenotype and mesenchymal migration of tumor cells). Beta-cyclodextrins (βCD) have already been shown to be effective in in vitro and animal studies of breast cancer, but exhibits cholesterol-related ototoxicity. The smaller alpha-cyclodextrins (αCD) also scavenges phospholipids, but cannot fit cholesterol. An in-vitro study presented here confirms hydroxypropyl (HP)-αCD to be twice as effective as HPβCD against migration of human cells of both receptor negative and estrogen-receptor positive breast cancer. If the previous successful animal studies with βCDs are replicated with the safer and more effective αCDs, clinical trials of adjuvant treatment with αCDs are warranted. Ultimately, all breast cancer are expected to benefit from treatment with HPαCD, but women with triple-negative breast cancer (TNBC) will benefit most, because they have fewer treatment options and their cancer advances more aggressively.
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Affiliation(s)
- Knut M. Wittkowski
- Center for Clinical and Translational Science, The Rockefeller University, New York, New York, United States of America
| | - Christina Dadurian
- Center for Clinical and Translational Science, The Rockefeller University, New York, New York, United States of America
| | - Martin P. Seybold
- Institut für Formale Methoden der Informatik, Universität Stuttgart, Stuttgart, Germany
| | - Han Sang Kim
- Department of Pediatrics, and Cell and Developmental Biology Weill Medical College of Cornell University, New York, New York, United States of America
| | - Ayuko Hoshino
- Department of Pediatrics, and Cell and Developmental Biology Weill Medical College of Cornell University, New York, New York, United States of America
| | - David Lyden
- Department of Pediatrics, and Cell and Developmental Biology Weill Medical College of Cornell University, New York, New York, United States of America
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12
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Schielzeth H, Rios Villamil A, Burri R. Success and failure in replication of genotype-phenotype associations: How does replication help in understanding the genetic basis of phenotypic variation in outbred populations? Mol Ecol Resour 2018; 18:739-754. [PMID: 29575806 DOI: 10.1111/1755-0998.12780] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 03/09/2018] [Accepted: 03/09/2018] [Indexed: 12/29/2022]
Abstract
Recent developments in sequencing technologies have facilitated genomewide mapping of phenotypic variation in natural populations. Such mapping efforts face a number of challenges potentially leading to low reproducibility. However, reproducible research forms the basis of scientific progress. We here discuss the options for replication and the reasons for potential nonreproducibility. We then review the evidence for reproducible quantitative trait loci (QTL) with a focus on natural animal populations. Existing case studies of replication fall into three categories: (i) traits that have been mapped to major effect loci (including chromosomal inversion and supergenes) by independent research teams; (ii) QTL fine-mapped in discovery populations; and (iii) attempts to replicate QTL across multiple populations. Major effect loci, in particular those associated with inversions, have been successfully replicated in several cases within and across populations. Beyond such major effect variants, replication has been more successful within than across populations, suggesting that QTL discovered in natural populations may often be population-specific. This suggests that biological causes (differences in linkage patterns, allele frequencies or context-dependencies of QTL) contribute to nonreproducibility. Evidence from other fields, notably animal breeding and QTL mapping in humans, suggests that a significant fraction of QTL is indeed reproducible in direction and magnitude at least within populations. However, there is also a large number of QTL that cannot be easily reproduced. We put forward that more studies should explicitly address the causes and context-dependencies of QTL signals, in particular to disentangle linkage differences, allele frequency differences and gene-by-environment interactions as biological causes of nonreproducibility of QTL, especially between populations.
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Affiliation(s)
- Holger Schielzeth
- Population Ecology Group, Institute of Ecology and Evolution, Friedrich Schiller University, Jena, Germany
| | - Alejandro Rios Villamil
- Population Ecology Group, Institute of Ecology and Evolution, Friedrich Schiller University, Jena, Germany
| | - Reto Burri
- Population Ecology Group, Institute of Ecology and Evolution, Friedrich Schiller University, Jena, Germany
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13
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Floyd JS, Sitlani CM, Avery CL, Noordam R, Li X, Smith AV, Gogarten SM, Li J, Broer L, Evans DS, Trompet S, Brody JA, Stewart JD, Eicher JD, Seyerle AA, Roach J, Lange LA, Lin HJ, Kors JA, Harris TB, Li-Gao R, Sattar N, Cummings SR, Wiggins KL, Napier MD, Stürmer T, Bis JC, Kerr KF, Uitterlinden AG, Taylor KD, Stott DJ, de Mutsert R, Launer LJ, Busch EL, Méndez-Giráldez R, Sotoodehnia N, Soliman EZ, Li Y, Duan Q, Rosendaal FR, Slagboom PE, Wilhelmsen KC, Reiner AP, Chen YDI, Heckbert SR, Kaplan RC, Rice KM, Jukema JW, Johnson AD, Liu Y, Mook-Kanamori DO, Gudnason V, Wilson JG, Rotter JI, Laurie CC, Psaty BM, Whitsel EA, Cupples LA, Stricker BH. Large-scale pharmacogenomic study of sulfonylureas and the QT, JT and QRS intervals: CHARGE Pharmacogenomics Working Group. THE PHARMACOGENOMICS JOURNAL 2018; 18:127-135. [PMID: 27958378 PMCID: PMC5468495 DOI: 10.1038/tpj.2016.90] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Revised: 10/25/2016] [Accepted: 11/14/2016] [Indexed: 12/17/2022]
Abstract
Sulfonylureas, a commonly used class of medication used to treat type 2 diabetes, have been associated with an increased risk of cardiovascular disease. Their effects on QT interval duration and related electrocardiographic phenotypes are potential mechanisms for this adverse effect. In 11 ethnically diverse cohorts that included 71 857 European, African-American and Hispanic/Latino ancestry individuals with repeated measures of medication use and electrocardiogram (ECG) measurements, we conducted a pharmacogenomic genome-wide association study of sulfonylurea use and three ECG phenotypes: QT, JT and QRS intervals. In ancestry-specific meta-analyses, eight novel pharmacogenomic loci met the threshold for genome-wide significance (P<5 × 10-8), and a pharmacokinetic variant in CYP2C9 (rs1057910) that has been associated with sulfonylurea-related treatment effects and other adverse drug reactions in previous studies was replicated. Additional research is needed to replicate the novel findings and to understand their biological basis.
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Affiliation(s)
- James S Floyd
- Deparments of Epidemiology and Medicine, University of Washington, Seattle, WA, USA
| | | | - Christy L Avery
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Raymond Noordam
- Department of Epidemiology, Erasmus MC - University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Xiaohui Li
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Albert V Smith
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykavik, Iceland
| | | | - Jin Li
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Linda Broer
- Department of Internal Medicine, Erasmus MC - University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Daniel S Evans
- California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - Stella Trompet
- Department of Cardiology and Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Jennifer A Brody
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - James D Stewart
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA
| | - John D Eicher
- Population Sciences Branch, National Heart Lung and Blood Institute, National Institutes of Health, Framingham, MA USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Amanda A Seyerle
- Department of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Jeffrey Roach
- Research Computing Center, University of North Carolina, Chapel Hill, NC
| | - Leslie A Lange
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Henry J Lin
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, USA
- Division of Medical Genetics, Harbor-UCLA Medical Center, Torrance, California, USA
| | - Jan A Kors
- Department of Medical Informatics, Erasmus MC - University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Tamara B Harris
- Laboratory of Epidemiology, Demography, and Biometry, National Institue on Aging, Bethesda, MD, USA
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Naveed Sattar
- BHF Glasgow Cardiovascular Research Centre, Faculty of Medicine, Glasgow, United Kingdom
| | - Steven R Cummings
- California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - Kerri L Wiggins
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Melanie D Napier
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Til Stürmer
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
- Center for Pharmacoepidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Joshua C Bis
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Kathleen F Kerr
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus MC - University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, USA
| | - David J Stott
- Institute of Cardiovascular and Medical Sciences, Faculty of Medicine, University of Glasgow, Scotland, United Kingdom
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Lenore J Launer
- Laboratory of Epidemiology, Demography, and Biometry, National Institue on Aging, Bethesda, MD, USA
| | - Evan L Busch
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Nona Sotoodehnia
- Deparments of Epidemiology and Medicine, University of Washington, Seattle, WA, USA
| | - Elsayed Z Soliman
- Epidemiological Cardiology Research Center (EPICARE), Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Yun Li
- Department of Biostatistics, Computer Science, and Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Qing Duan
- Research Computing Center, University of North Carolina, Chapel Hill, NC
| | - Frits R Rosendaal
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - P Eline Slagboom
- Department of Medical Statistics and Bioinformatics, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Kirk C Wilhelmsen
- Research Computing Center, University of North Carolina, Chapel Hill, NC
- The Renaissance Computing Institute, Chapel Hill, NC, USA
| | - Alexander P Reiner
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Yii-Der I Chen
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Susan R Heckbert
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands
- Interuniversity Cardiology Institute of the Netherlands, Utrecht, The Netherlands
| | - Andrew D Johnson
- Population Sciences Branch, National Heart Lung and Blood Institute, National Institutes of Health, Framingham, MA USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University, Winston-Salem, NC, USA
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykavik, Iceland
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Cathy C Laurie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Bruce M Psaty
- Departments of Epidemiology, Health Services, and Medicine, University of Washington, Seattle, WA, USA
- Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA
| | - Eric A Whitsel
- Departments of Epidemiology and Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - L Adrienne Cupples
- The Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Bruno H Stricker
- Department of Epidemiology, Erasmus MC - University Medical Center Rotterdam, Rotterdam, the Netherlands
- Inspectorate of Health Care, Utrecht, the Netherlands
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14
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Abstract
PURPOSE OF REVIEW Pharmacogenetics is an important component of precision medicine. Even within the genomic era, several challenges lie ahead in the road towards clinical implementation of pharmacogenetics in the clinic. This review will summarize the current state of knowledge regarding pharmacogenetics of cardiovascular drugs, focusing on those with the most evidence supporting clinical implementation- clopidogrel, warfarin and simvastatin. RECENT FINDINGS There is limited translation of pharmacogenetics into clinical practice primarily due to the absence of outcomes data from prospective, randomized, genotype-directed clinical trials. There are several ongoing randomized controlled trials that will provide some answers as to the clinical utility of genotype-directed strategies. Several academic medical centers have pushed towards clinical implementation where the clinical validity data are strong. Their experiences will inform operational requirements of a clinical pharmacogenetics testing including the timing of testing, incorporation of test results into the electronic health record, reimbursement and ethical issues. SUMMARY Pharmacogenetics of clopidogrel, warfarin and simvastatin are three examples where pharmacogenetics testing may provide added clinical value. Continued accumulation of evidence surrounding clinical utility of pharmacogenetics markers is imperative as this will inform reimbursement policy and drive adoption of pharamcogenetics into routine care.
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Affiliation(s)
- Sony Tuteja
- Department of Medicine, University of Pennsylvania Perelman School of Medicine
| | - Nita Limdi
- Department of Neurology, University of Alabama at Birmingham
- Hugh Kaul Personalized Medicine Institute
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15
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Abstract
There is a great deal of interest in personalized, individualized, or precision interventions for disease and health-risk mitigation. This is as true of nutrition-based intervention and prevention strategies as it is for pharmacotherapies and pharmaceutical-oriented prevention strategies. Essentially, technological breakthroughs have enabled researchers to probe an individual's unique genetic, biochemical, physiological, behavioral, and exposure profile, allowing them to identify very specific and often nuanced factors that an individual might possess, which may make it more or less likely that he or she responds favorably to a particular intervention (e.g., nutrient supplementation) or disease prevention strategy (e.g., specific diet). However, as compelling and intuitive as personalized nutrition might be in the current era in which data-intensive biomedical characterization of individuals is possible, appropriately and objectively vetting personalized nutrition strategies is not trivial and requires novel study designs and data analytical methods. These designs and methods must consider a very integrated use of the multiple contemporary biomedical assays and technologies that motivate them, which adds to their complexity. Single-subject or N-of-1 trials can be used to assess the utility of personalized interventions and, in addition, can be crafted in such a way as to accommodate the necessarily integrated use of many emerging biomedical technologies and assays. In this review, we consider the motivation, design, and implementation of N-of-1 trials in translational nutrition research that are meant to assess the utility of personalized nutritional strategies. We provide a number of example studies, discuss appropriate analytical methods given the complex data they generate and require, and consider how such studies could leverage integration of various biomarker assays and clinical end points. Importantly, we also consider the development of strategies and algorithms for matching nutritional needs to individual biomedical profiles and the issues surrounding them. Finally, we discuss the limitations of personalized nutrition studies, possible extensions of N-of-1 nutritional intervention studies, and areas of future research.
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Affiliation(s)
- Nicholas J Schork
- Translational Genomics Research Institute, Phoenix, Arizona 85004; .,J. Craig Venter Institute, La Jolla, California 92037; .,Departments of Psychiatry and Family Medicine and Public Health, University of California, San Diego, La Jolla, California 92037
| | - Laura H Goetz
- J. Craig Venter Institute, La Jolla, California 92037; .,Department of Surgery, Scripps Clinic Medical Group, La Jolla, California 92037.,Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, California 92037
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16
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Gondalia R, Avery CL, Napier MD, Méndez-Giráldez R, Stewart JD, Sitlani CM, Li Y, Wilhelmsen KC, Duan Q, Roach J, North KE, Reiner AP, Zhang ZM, Tinker LF, Yanosky JD, Liao D, Whitsel EA. Genome-wide Association Study of Susceptibility to Particulate Matter-Associated QT Prolongation. ENVIRONMENTAL HEALTH PERSPECTIVES 2017; 125:067002. [PMID: 28749367 PMCID: PMC5714283 DOI: 10.1289/ehp347] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Revised: 09/07/2016] [Accepted: 09/19/2016] [Indexed: 05/02/2023]
Abstract
BACKGROUND Ambient particulate matter (PM) air pollution exposure has been associated with increases in QT interval duration (QT). However, innate susceptibility to PM-associated QT prolongation has not been characterized. OBJECTIVE To characterize genetic susceptibility to PM-associated QT prolongation in a multi-racial/ethnic, genome-wide association study (GWAS). METHODS Using repeated electrocardiograms (1986–2004), longitudinal data on PM<10 μm in diameter (PM10), and generalized estimating equations methods adapted for low-prevalence exposure, we estimated approximately 2.5×106 SNP×PM10 interactions among nine Women’s Health Initiative clinical trials and Atherosclerosis Risk in Communities Study subpopulations (n=22,158), then combined subpopulation-specific results in a fixed-effects, inverse variance-weighted meta-analysis. RESULTS A common variant (rs1619661; coded allele: T) significantly modified the QT-PM10 association (p=2.11×10−8). At PM10 concentrations >90th percentile, QT increased 7 ms across the CC and TT genotypes: 397 (95% confidence interval: 396, 399) to 404 (403, 404) ms. However, QT changed minimally across rs1619661 genotypes at lower PM10 concentrations. The rs1619661 variant is on chromosome 10, 132 kilobase (kb) downstream from CXCL12, which encodes a chemokine, stromal cell-derived factor 1, that is expressed in cardiomyocytes and decreases calcium influx across the L-type Ca2+ channel. CONCLUSIONS The findings suggest that biologically plausible genetic factors may alter susceptibility to PM10-associated QT prolongation in populations protected by the U.S. Environmental Protection Agency’s National Ambient Air Quality Standards. Independent replication and functional characterization are necessary to validate our findings. https://doi.org/10.1289/EHP347
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Affiliation(s)
- Rahul Gondalia
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Christy L Avery
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Melanie D Napier
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Raúl Méndez-Giráldez
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - James D Stewart
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA
- Carolina Population Center, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Colleen M Sitlani
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, USA
| | - Yun Li
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Kirk C Wilhelmsen
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA
- The Renaissance Computing Institute, Chapel Hill, North Carolina, USA
| | - Qing Duan
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Jeffrey Roach
- Research Computing Center, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA
- Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Alexander P Reiner
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Zhu-Ming Zhang
- Epidemiologic Cardiology Research Center, Dept. of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Lesley F Tinker
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Jeff D Yanosky
- Division of Epidemiology, Dept. of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Duanping Liao
- Division of Epidemiology, Dept. of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Eric A Whitsel
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
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Lynch SM, Mitra N, Ross M, Newcomb C, Dailey K, Jackson T, Zeigler-Johnson CM, Riethman H, Branas CC, Rebbeck TR. A Neighborhood-Wide Association Study (NWAS): Example of prostate cancer aggressiveness. PLoS One 2017; 12:e0174548. [PMID: 28346484 PMCID: PMC5367705 DOI: 10.1371/journal.pone.0174548] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Accepted: 03/11/2017] [Indexed: 12/31/2022] Open
Abstract
PURPOSE Cancer results from complex interactions of multiple variables at the biologic, individual, and social levels. Compared to other levels, social effects that occur geospatially in neighborhoods are not as well-studied, and empiric methods to assess these effects are limited. We propose a novel Neighborhood-Wide Association Study(NWAS), analogous to genome-wide association studies(GWAS), that utilizes high-dimensional computing approaches from biology to comprehensively and empirically identify neighborhood factors associated with disease. METHODS Pennsylvania Cancer Registry data were linked to U.S. Census data. In a successively more stringent multiphase approach, we evaluated the association between neighborhood (n = 14,663 census variables) and prostate cancer aggressiveness(PCA) with n = 6,416 aggressive (Stage≥3/Gleason grade≥7 cases) vs. n = 70,670 non-aggressive (Stage<3/Gleason grade<7) cases in White men. Analyses accounted for age, year of diagnosis, spatial correlation, and multiple-testing. We used generalized estimating equations in Phase 1 and Bayesian mixed effects models in Phase 2 to calculate odds ratios(OR) and confidence/credible intervals(CI). In Phase 3, principal components analysis grouped correlated variables. RESULTS We identified 17 new neighborhood variables associated with PCA. These variables represented income, housing, employment, immigration, access to care, and social support. The top hits or most significant variables related to transportation (OR = 1.05;CI = 1.001-1.09) and poverty (OR = 1.07;CI = 1.01-1.12). CONCLUSIONS This study introduces the application of high-dimensional, computational methods to large-scale, publically-available geospatial data. Although NWAS requires further testing, it is hypothesis-generating and addresses gaps in geospatial analysis related to empiric assessment. Further, NWAS could have broad implications for many diseases and future precision medicine studies focused on multilevel risk factors of disease.
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Affiliation(s)
- Shannon M. Lynch
- Fox Chase Cancer Center, Cancer Prevention and Control, Philadelphia, Pennsylvania, United States of America
- * E-mail:
| | - Nandita Mitra
- University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Michelle Ross
- University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Craig Newcomb
- University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Karl Dailey
- University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Tara Jackson
- University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | | | - Harold Riethman
- Old Dominion University, Norfolk, Virginia, United States of America
| | - Charles C. Branas
- University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Columbia University, Mailman School of Public Health, New York, New York, United States of America
| | - Timothy R. Rebbeck
- Dana Farber Cancer Institute and Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
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Floyd JS, Psaty BM. The Application of Genomics in Diabetes: Barriers to Discovery and Implementation. Diabetes Care 2016; 39:1858-1869. [PMID: 27926887 PMCID: PMC5079615 DOI: 10.2337/dc16-0738] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Accepted: 08/16/2016] [Indexed: 02/03/2023]
Abstract
The emerging availability of genomic and electronic health data in large populations is a powerful tool for research that has drawn interest in bringing precision medicine to diabetes. In this article, we discuss the potential application of genomics to the prediction, prevention, and treatment of diabetes, and we use examples from other areas of medicine to illustrate some of the challenges involved in conducting genomics research in human populations and implementing findings in practice. At this time, a major barrier to the application of genomics in diabetes care is the lack of actionable genomic findings. Whether genomic information should be used in clinical practice requires a framework for evaluating the validity and clinical utility of this approach, an improved integration of genomic data into electronic health records, and the clinical decision support and educational resources for clinicians to use these data. Efforts to identify optimal approaches in all of these domains are in progress and may help to bring diabetes into the era of genomic medicine.
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Affiliation(s)
- James S Floyd
- Cardiovascular Health Research Unit and Departments of Epidemiology and Medicine, University of Washington, Seattle, WA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit and Departments of Epidemiology and Medicine, University of Washington, Seattle, WA
- Department of Health Services, University of Washington, Seattle, WA
- Group Health Research Institute, Seattle, WA
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Mitchel K, Theusch E, Cubitt C, Dosé AC, Stevens K, Naidoo D, Medina MW. RP1-13D10.2 Is a Novel Modulator of Statin-Induced Changes in Cholesterol. ACTA ACUST UNITED AC 2016; 9:223-30. [PMID: 27071970 DOI: 10.1161/circgenetics.115.001274] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 03/30/2016] [Indexed: 12/16/2022]
Abstract
BACKGROUND Numerous genetic contributors to cardiovascular disease risk have been identified through genome-wide association studies; however, identifying the molecular mechanism underlying these associations is not straightforward. The Justification for the Use of Statins in Primary Prevention: An Intervention Trial Evaluating Rosuvastatin (JUPITER) trial of rosuvastatin users identified a sub-genome-wide association of rs6924995, a single-nucleotide polymorphism ≈10 kb downstream of myosin regulatory light chain interacting protein (MYLIP, aka IDOL and inducible degrader of low-density lipoprotein receptor [LDLR]), with LDL cholesterol statin response. Interestingly, although this signal was initially attributed to MYLIP, rs6924995 lies within RP1-13D10.2, an uncharacterized long noncoding RNA. METHODS AND RESULTS Using simvastatin and sham incubated lymphoblastoid cell lines from participants of the Cholesterol and Pharmacogenetics (CAP) simvastatin clinical trial, we found that statin-induced change in RP1-13D10.2 levels differed between cell lines from the tails of the white and black low-density lipoprotein cholesterol response distributions, whereas no difference in MYLIP was observed. RP1-13D10.2 overexpression in Huh7 and HepG2 increased LDLR transcript levels, increased LDL uptake, and decreased media levels of apolipoprotein B. In addition, we found a trend of slight differences in the effects of RP1-13D10.2 overexpression on LDLR transcript levels between hepatoma cells transfected with the rs6924995 A versus G allele and a suggestion of an association between rs6924995 and RP1-10D13.2 expression levels in the CAP lymphoblastoid cell lines. Finally, RP1-13D10.2 expression levels seem to be sterol regulated, consistent with its potential role as a novel lipid regulator. CONCLUSIONS RP1-13D10.2 is a long noncoding RNA that regulates LDLR and may contribute to low-density lipoprotein cholesterol response to statin treatment. These findings highlight the potential role of noncoding RNAs as determinants of interindividual variation in drug response.
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Affiliation(s)
| | | | - Celia Cubitt
- From the Children's Hospital Oakland Research Institute, CA
| | - Andréa C Dosé
- From the Children's Hospital Oakland Research Institute, CA
| | | | - Devesh Naidoo
- From the Children's Hospital Oakland Research Institute, CA
| | - Marisa W Medina
- From the Children's Hospital Oakland Research Institute, CA.
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Abstract
Heart disease is a leading cause of death in the United States, and hypertension is a predominant risk factor. Thus, effective blood pressure control is important to prevent adverse sequelae of hypertension, including heart failure, coronary artery disease, atrial fibrillation, and ischemic stroke. Over half of Americans have uncontrolled blood pressure, which may in part be explained by interpatient variability in drug response secondary to genetic polymorphism. As such, pharmacogenetic testing may be a supplementary tool to guide treatment. This review highlights the pharmacogenetics of antihypertensive response and response to drugs that treat adverse hypertension-related sequelae, particularly coronary artery disease and atrial fibrillation. While pharmacogenetic evidence may be more robust for the latter with respect to clinical implementation, there is increasing evidence of genetic variants that may help predict antihypertensive response. However, additional research and validation are needed before clinical implementation guidelines for antihypertensive therapy can become a reality.
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Abstract
Consensus practice guidelines and the implementation of clinical therapeutic advances are usually based on the results of large, randomized clinical trials (RCTs). However, RCTs generally inform us on an average treatment effect for a presumably homogeneous population, but therapeutic interventions rarely benefit the entire population targeted. Indeed, multiple RCTs have demonstrated that interindividual variability exists both in drug response and in the development of adverse effects. The field of pharmacogenomics promises to deliver the right drug to the right patient. Substantial progress has been made in this field, with advances in technology, statistical and computational methods, and the use of cell and animal model systems. However, clinical implementation of pharmacogenetic principles has been difficult because RCTs demonstrating benefit are lacking. For patients, the potential benefits of performing such trials include the individualization of therapy to maximize efficacy and minimize adverse effects. These trials would also enable investigators to reduce sample size and hence contain costs for trial sponsors. Multiple ethical, legal, and practical issues need to be considered for the conduct of genotype-based RCTs. Whether pre-emptive genotyping embedded in electronic health records will preclude the need for performing genotype-based RCTs remains to be seen.
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Affiliation(s)
- Naveen L Pereira
- Division of Cardiovascular Diseases, Department of Internal Medicine, 200 First Street SW, Rochester, MN 55905, USA
| | - Daniel J Sargent
- Department of Biomedical Statistics and Informatics, Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Michael E Farkouh
- Peter Munk Cardiac Centre and Heart and Stroke Richard Lewer Centre, University of Toronto, 585 University Avenue, Toronto, ON M5G 2N2, Canada
| | - Charanjit S Rihal
- Division of Cardiovascular Diseases, Department of Internal Medicine, 200 First Street SW, Rochester, MN 55905, USA
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Abstract
Heart failure is becoming increasingly prevalent in the United States and is a significant cause of morbidity and mortality. Several therapies are currently available to treat this chronic illness; however, clinical response to these treatment options exhibit significant interpatient variation. It is now clearly understood that genetics is a key contributor to diversity in therapeutic response, and evidence that genetic polymorphisms alter the pharmacokinetics, pharmacodynamics, and clinical response of heart failure drugs continues to accumulate. This suggests that pharmacogenomics has the potential to help clinicians improve the management of heart failure by choosing the safest and most effective medications and doses. Unfortunately, despite much supportive data, pharmacogenetic optimization of heart failure treatment regimens is not yet a reality. In order to attenuate the rising burden of heart failure, particularly in the context of the recent paucity of new effective interventions, there is an urgent need to extend pharmacogenetic knowledge and leverage these associations in order to enhance the effectiveness of existing heart failure therapies. This review focuses on the current state of pharmacogenomics in heart failure and provides a glimpse of the aforementioned future needs.
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Affiliation(s)
- Akinyemi Oni-Orisan
- University of North Carolina at Chapel Hill, UNC Eshelman School of Pharmacy, Center for Pharmacogenomics and Individualized Therapy
| | - David Lanfear
- Section Head, Advanced Heart Failure and Cardiac Transplantation, Research Scientist, Center for Health Services Research, Henry Ford Hospital, 2799 W. Grand Boulevard Detroit, MI 48202, Phone: 313-916-6375, Fax: 313-916-8799
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