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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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Linder JE, Tao R, Chung WK, Kiryluk K, Liu C, Weng C, Connolly JJ, Hakonarson H, Harr M, Leppig KA, Jarvik GP, Veenstra DL, Aufox S, Chisholm RL, Gordon AS, Hoell C, Rasmussen-Torvik LJ, Smith ME, Holm IA, Miller EM, Prows CA, Elskeally O, Kullo IJ, Lee C, Jose S, Manolio TA, Rowley R, Padi-Adjirackor NA, Wilmayani NK, City B, Wei WQ, Wiesner GL, Rahm AK, Williams JL, Williams MS, Peterson JF. Prospective, multi-site study of healthcare utilization after actionable monogenic findings from clinical sequencing. Am J Hum Genet 2023; 110:1950-1958. [PMID: 37883979 PMCID: PMC10645563 DOI: 10.1016/j.ajhg.2023.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/05/2023] [Accepted: 10/08/2023] [Indexed: 10/28/2023] Open
Abstract
As large-scale genomic screening becomes increasingly prevalent, understanding the influence of actionable results on healthcare utilization is key to estimating the potential long-term clinical impact. The eMERGE network sequenced individuals for actionable genes in multiple genetic conditions and returned results to individuals, providers, and the electronic health record. Differences in recommended health services (laboratory, imaging, and procedural testing) delivered within 12 months of return were compared among individuals with pathogenic or likely pathogenic (P/LP) findings to matched individuals with negative findings before and after return of results. Of 16,218 adults, 477 unselected individuals were found to have a monogenic risk for arrhythmia (n = 95), breast cancer (n = 96), cardiomyopathy (n = 95), colorectal cancer (n = 105), or familial hypercholesterolemia (n = 86). Individuals with P/LP results more frequently received services after return (43.8%) compared to before return (25.6%) of results and compared to individuals with negative findings (24.9%; p < 0.0001). The annual cost of qualifying healthcare services increased from an average of $162 before return to $343 after return of results among the P/LP group (p < 0.0001); differences in the negative group were non-significant. The mean difference-in-differences was $149 (p < 0.0001), which describes the increased cost within the P/LP group corrected for cost changes in the negative group. When stratified by individual conditions, significant cost differences were observed for arrhythmia, breast cancer, and cardiomyopathy. In conclusion, less than half of individuals received billed health services after monogenic return, which modestly increased healthcare costs for payors in the year following return.
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Affiliation(s)
- Jodell E Linder
- Vanderbilt University Medical Center, Nashville, TN 37203, USA.
| | - Ran Tao
- Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | | | | | - Cong Liu
- Columbia University, New York, NY 10032, USA
| | | | - John J Connolly
- Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Hakon Hakonarson
- Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Margaret Harr
- Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Kathleen A Leppig
- Genetic Services, Kaiser Permanente of Washington, Seattle, WA 98195, USA
| | - Gail P Jarvik
- University of Washington Medical Center, Departments of Medicine (Medical Genetics) and Genome Sciences, Seattle, WA 98195, USA
| | - David L Veenstra
- University of Washington, Department of Pharmacy, Seattle, WA 98195, USA
| | - Sharon Aufox
- Northwestern University, Center for Genetic Medicine, Chicago, IL 60611, USA
| | - Rex L Chisholm
- Northwestern University, Center for Genetic Medicine, Chicago, IL 60611, USA
| | - Adam S Gordon
- Northwestern University, Center for Genetic Medicine, Chicago, IL 60611, USA
| | - Christin Hoell
- Northwestern University, Center for Genetic Medicine, Chicago, IL 60611, USA
| | | | - Maureen E Smith
- Northwestern University, Center for Genetic Medicine, Chicago, IL 60611, USA
| | | | - Erin M Miller
- Division of Cardiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA
| | - Cynthia A Prows
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | | | | | | | - Sheethal Jose
- National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Teri A Manolio
- National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Robb Rowley
- National Human Genome Research Institute, Bethesda, MD 20892, USA
| | | | | | - Brittany City
- Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Wei-Qi Wei
- Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | | | | | - Janet L Williams
- Department of Genomic Health, Geisinger, Danville, PA 17822, USA
| | - Marc S Williams
- Department of Genomic Health, Geisinger, Danville, PA 17822, USA
| | - Josh F Peterson
- Vanderbilt University Medical Center, Nashville, TN 37203, USA
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Singhal P, Tan ALM, Drivas TG, Johnson KB, Ritchie MD, Beaulieu-Jones BK. Opportunities and challenges for biomarker discovery using electronic health record data. Trends Mol Med 2023; 29:765-776. [PMID: 37474378 PMCID: PMC10530198 DOI: 10.1016/j.molmed.2023.06.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/16/2023] [Accepted: 06/22/2023] [Indexed: 07/22/2023]
Abstract
Electronic health records (EHRs) have become increasingly relied upon as a source for biomedical research. One important research application of EHRs is the identification of biomarkers associated with specific patient states, especially within complex conditions. However, using EHRs for biomarker identification can be challenging because the EHR was not designed with research as the primary focus. Despite this challenge, the EHR offers huge potential for biomarker discovery research to transform our understanding of disease etiology and treatment and generate biological insights informing precision medicine initiatives. This review paper provides an in-depth analysis of how EHR data is currently used for phenotyping and identifying molecular biomarkers, current challenges and limitations, and strategies we can take to mitigate challenges going forward.
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Affiliation(s)
- P Singhal
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - A L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - T G Drivas
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - K B Johnson
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA; Department of Pediatrics, University of Pennsylvania, Philadelphia, PA, USA
| | - M D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
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4
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Cruz LA, Cooke Bailey JN, Crawford DC. Importance of Diversity in Precision Medicine: Generalizability of Genetic Associations Across Ancestry Groups Toward Better Identification of Disease Susceptibility Variants. Annu Rev Biomed Data Sci 2023; 6:339-356. [PMID: 37196357 PMCID: PMC10720270 DOI: 10.1146/annurev-biodatasci-122220-113250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Genome-wide association studies (GWAS) revolutionized our understanding of common genetic variation and its impact on common human disease and traits. Developed and adopted in the mid-2000s, GWAS led to searchable genotype-phenotype catalogs and genome-wide datasets available for further data mining and analysis for the eventual development of translational applications. The GWAS revolution was swift and specific, including almost exclusively populations of European descent, to the neglect of the majority of the world's genetic diversity. In this narrative review, we recount the GWAS landscape of the early years that established a genotype-phenotype catalog that is now universally understood to be inadequate for a complete understanding of complex human genetics. We then describe approaches taken to augment the genotype-phenotype catalog, including the study populations, collaborative consortia, and study design approaches aimed to generalize and then ultimately discover genome-wide associations in non-European descent populations. The collaborations and data resources established in the efforts to diversify genomic findings undoubtedly provide the foundations of the next chapters of genetic association studies with the advent of budget-friendly whole-genome sequencing.
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Affiliation(s)
- Lauren A Cruz
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA;
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Jessica N Cooke Bailey
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA;
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, USA
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dana C Crawford
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA;
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, USA
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, USA
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Yamazaki K, Terao C, Takahashi A, Kamatani Y, Matsuda K, Asai S, Takahashi Y. Genome-wide Association Studies Categorized by Class of Antihypertensive Drugs Reveal Complex Pathogenesis of Hypertension with Drug Resistance. Clin Pharmacol Ther 2023; 114:393-403. [PMID: 37151119 DOI: 10.1002/cpt.2934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 05/02/2023] [Indexed: 05/09/2023]
Abstract
Resistant hypertension is defined as uncontrolled blood pressure (BP) despite the use of three or more antihypertensive drugs of different classes. Although genetic factors may greatly contribute to hypertension with resistance to multiple drug classes, more than for general hypertension, its pathogenesis remains unknown. To reveal the genetic background of resistant hypertension, we categorized 32,239 patients whose data were obtained from the BioBank Japan Project, by prescription of 7 classes of antihypertensive drugs and performed genome-wide association studies (GWAS). Our GWAS identified four loci with significant association (P < 5 × 10-8 ): rs6445583 in CACNA1D and rs12308051 in the intergenic region on chromosome 12 for angiotensin II receptor blockers, rs35497065 in FOXA3 for calcium channel blockers, and rs11066280 in HECTD4 for αβ-blockers. Because these loci are known to be susceptibility loci for hypertension and/or BP, our results indicate that resistant hypertension is caused by a combination of excessive BP and drug resistance to each antihypertensive pharmacological class. Furthermore, to investigate the genetic difference between BP traits and the treatment effectiveness of antihypertensive drugs, we performed gene-set analysis and calculated the genetic correlation continuously. Most of the genetic factors were in common between BP traits and antihypertensive effectiveness, but it seems that the genetic architecture of the drug response to antihypertensive treatment is more complicated than BP traits. This corresponds to the well-known mosaic theory of hypertension. Our findings reveal the complex pathogenesis of hypertension with resistance to multiple classes of antihypertensive drugs.
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Affiliation(s)
- Keiko Yamazaki
- Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Tokyo, Japan
- Department of Public Health, Graduate School of Medicine, Chiba University, Chiba, Japan
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Chikashi Terao
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Atsushi Takahashi
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Genomic Medicine, Research Institute, National Cerebral and Cardiovascular Center, Osaka, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Satoshi Asai
- Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Tokyo, Japan
- Division of Pharmacology, Nihon University School of Medicine, Tokyo, Japan
| | - Yasuo Takahashi
- Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Tokyo, Japan
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La Cava WG, Lee PC, Ajmal I, Ding X, Solanki P, Cohen JB, Moore JH, Herman DS. A flexible symbolic regression method for constructing interpretable clinical prediction models. NPJ Digit Med 2023; 6:107. [PMID: 37277550 DOI: 10.1038/s41746-023-00833-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 05/05/2023] [Indexed: 06/07/2023] Open
Abstract
Machine learning (ML) models trained for triggering clinical decision support (CDS) are typically either accurate or interpretable but not both. Scaling CDS to the panoply of clinical use cases while mitigating risks to patients will require many ML models be intuitively interpretable for clinicians. To this end, we adapted a symbolic regression method, coined the feature engineering automation tool (FEAT), to train concise and accurate models from high-dimensional electronic health record (EHR) data. We first present an in-depth application of FEAT to classify hypertension, hypertension with unexplained hypokalemia, and apparent treatment-resistant hypertension (aTRH) using EHR data for 1200 subjects receiving longitudinal care in a large healthcare system. FEAT models trained to predict phenotypes adjudicated by chart review had equivalent or higher discriminative performance (p < 0.001) and were at least three times smaller (p < 1 × 10-6) than other potentially interpretable models. For aTRH, FEAT generated a six-feature, highly discriminative (positive predictive value = 0.70, sensitivity = 0.62), and clinically intuitive model. To assess the generalizability of the approach, we tested FEAT on 25 benchmark clinical phenotyping tasks using the MIMIC-III critical care database. Under comparable dimensionality constraints, FEAT's models exhibited higher area under the receiver-operating curve scores than penalized linear models across tasks (p < 6 × 10-6). In summary, FEAT can train EHR prediction models that are both intuitively interpretable and accurate, which should facilitate safe and effective scaling of ML-triggered CDS to the panoply of potential clinical use cases and healthcare practices.
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Affiliation(s)
- William G La Cava
- Computational Health Informatics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Paul C Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Imran Ajmal
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Xiruo Ding
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Priyanka Solanki
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jordana B Cohen
- Division of Renal-Electrolyte and Hypertension, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Jason H Moore
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel S Herman
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Xiao X, Li R, Wu C, Yan Y, Yuan M, Cui B, Zhang Y, Zhang C, Zhang X, Zhang W, Hui R, Wang Y. A genome-wide association study identifies a novel association between SDC3 and apparent treatment-resistant hypertension. BMC Med 2022; 20:463. [PMID: 36447229 PMCID: PMC9710180 DOI: 10.1186/s12916-022-02665-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 11/14/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Compared with patients who require fewer antihypertensive agents, those with apparent treatment-resistant hypertension (aTRH) are at increased risk for cardiovascular and all-cause mortality, independent of blood pressure control. However, the etiopathogenesis of aTRH is still poorly elucidated. METHODS We performed a genome-wide association study (GWAS) in first cohort including 586 aTRHs and 871 healthy controls. Next, expression quantitative trait locus (eQTL) analysis was used to identify genes that are regulated by single nucleotide polymorphisms (SNPs) derived from the GWAS. Then, we verified the genes obtained from the eQTL analysis in the validation cohort including 65 aTRHs, 96 hypertensives, and 100 healthy controls through gene expression profiling analysis and real-time quantitative polymerase chain reaction (RT-qPCR) assay. RESULTS The GWAS in first cohort revealed four suggestive loci (1p35, 4q13.2-21.1, 5q22-23.2, and 15q11.1-q12) represented by 23 SNPs. The 23 significant SNPs were in or near LAPTM5, SDC3, UGT2A1, FTMT, and NIPA1. eQTL analysis uncovered 14 SNPs in 1p35 locus all had same regulation directions for SDC3 and LAPTM5. The disease susceptible alleles of SNPs in 1p35 locus were associated with lower gene expression for SDC3 and higher gene expression for LAPTM5. The disease susceptible alleles of SNPs in 4q13.2-21.1 were associated with higher gene expression for UGT2B4. GTEx database did not show any statistically significant eQTLs between the SNPs in 5q22-23.2 and 15q11.1-q12 loci and their influenced genes. Then, gene expression profiling analysis in the validation cohort confirmed lower expression of SDC3 in aTRH but no significant differences on LAPTM5 and UGT2B4, when compared with controls and hypertensives, respectively. RT-qPCR assay further verified the lower expression of SDC3 in aTRH. CONCLUSIONS Our study identified a novel association of SDC3 with aTRH, which contributes to the elucidation of its etiopathogenesis and provides a promising therapeutic target.
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Affiliation(s)
- Xiao Xiao
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China
| | - Rui Li
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China
| | - Cunjin Wu
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China
| | - Yupeng Yan
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China
| | - Mengmeng Yuan
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China
| | - Bing Cui
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China
| | - Yu Zhang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China
| | - Channa Zhang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China
| | - Xiaoxia Zhang
- Department of Pharmacy, The First Affiliated Hospital, Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, Shaanxi, China
| | - Weili Zhang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China
| | - Rutai Hui
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China
| | - Yibo Wang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Rd, Beijing, China.
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8
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Guo J, Guo X, Sun Y, Li Z, Jia P. Application of omics in hypertension and resistant hypertension. Hypertens Res 2022; 45:775-788. [PMID: 35264783 DOI: 10.1038/s41440-022-00885-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/11/2022] [Accepted: 01/29/2022] [Indexed: 12/12/2022]
Abstract
Hypertension is a major modifiable risk factor that affects the global health burden. Despite the availability of multiple antihypertensive drugs, blood pressure is often not optimally controlled. The prevalence of true resistant hypertension in treated hypertensive patients is ~2-20%, and these patients are at higher risk for adverse events and poor clinical outcomes. Therefore, an in-depth dissection of the pathophysiological mechanisms of hypertension and resistant hypertension is needed to identify more effective targets for regulating blood pressure. Omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, and microbiomics, can accurately present the characteristics of organisms at varying molecular levels. Integrative omics can further reveal the network of interactions between molecular levels and provide a complete dynamic view of the organism. In this review, we describe the applications, progress, and challenges of omics technologies in hypertension. Specifically, we discuss the application of omics in resistant hypertension. We believe that omics approaches will produce a better understanding of the pathogenesis of hypertension and resistant hypertension and improve diagnostic and therapeutic strategies, thus increasing rates of blood pressure control and reducing the public health burden of hypertension.
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Affiliation(s)
- Jiuqi Guo
- Department of Cardiology, The First Hospital of China Medical University, Shenyang, 110001, China
| | - Xiaofan Guo
- Department of Cardiology, The First Hospital of China Medical University, Shenyang, 110001, China
| | - Yingxian Sun
- Department of Cardiology, The First Hospital of China Medical University, Shenyang, 110001, China
| | - Zhao Li
- Department of Cardiology, The First Hospital of China Medical University, Shenyang, 110001, China.
| | - Pengyu Jia
- Department of Cardiology, The First Hospital of China Medical University, Shenyang, 110001, China.
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9
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Kaur H, Crawford DC, Liang J, Benchek P, Zhu X, Kallianpur AR, Bush WS. Replication of European hypertension associations in a case-control study of 9,534 African Americans. PLoS One 2021; 16:e0259962. [PMID: 34793544 PMCID: PMC8601554 DOI: 10.1371/journal.pone.0259962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 10/29/2021] [Indexed: 12/01/2022] Open
Abstract
Objective Hypertension is more prevalent in African Americans (AA) than other ethnic groups. Genome-wide association studies (GWAS) have identified loci associated with hypertension and other cardio-metabolic traits like type 2 diabetes, coronary artery disease, and body mass index (BMI), however the AA population is underrepresented in these studies. In this study, we examined a large AA cohort for the generalizability of 14 Metabochip array SNPs with previously reported European hypertension associations. Methods To evaluate associations, we analyzed genotype data of 14 SNPs for their associations with a diagnosis of hypertension, systolic blood pressure (SBP), and diastolic blood pressure (DBP) in a case-control study of an AA population (N = 9,534). We also performed an age-stratified analysis (>30, 30≥59 and ≥60 years) following the hypertension definition described by the 8th Joint National Committee (JNC). Associations were adjusted for BMI, age, age2, sex, clinical confounders, and genetic ancestry using multivariable regression models to estimate odds ratios (ORs) and beta-coefficients. Analyses stratified by sex were also conducted. Meta-analyses (including both BioVU and COGENT-BP cohorts) were performed using a random-effects model. Results We found rs880315 to be associated with systolic hypertension (SBP≥140 mmHg) in the entire cohort (OR = 1.14, p = 0.003) and within women only (OR = 1.16, p = 0.012). Variant rs17080093 associated with lower SBP and DBP (β = -2.99, p = 0.0352 and - β = 1.69, p = 0.0184) among younger individuals, particularly in younger women (β = -3.92, p = 0.0025 and β = -1.87, p = 0.0241 for SBP and DBP respectively). SNP rs1530440 associated with higher SBP and DBP measurements (younger individuals β = 4.1, p = 0.039 and β = 2.5, p = 0.043 for SBP and DBP; (younger women β = 4.5, p = 0.025 and β = 2.9, p = 0.028 for SBP and DBP), and hypertension risk in older women (OR = 1.4, p = 0.050). rs16948048 increases hypertension risk in younger individuals (OR = 1.31, p = 0.011). Among mid-age women rs880315 associated with higher risk of hypertension (OR = 1.20, p = 0.027). rs1361831 associated with DBP (β = -1.96, p = 0.02) among individuals older than 60 years. rs3096277 increases hypertension risk among older individuals (OR = 1.26 p = 0.0015), however, this variant also reduces SBP among younger women (β = -2.63, p = 0.0102). Conclusion These findings suggest that European-descent and AA populations share genetic loci that contribute to blood pressure traits and hypertension. However, the OR and beta-coefficient estimates differ, and some are age-dependent. Additional genetic studies of hypertension in AA are warranted to identify new loci associated with hypertension and blood pressure traits in this population.
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Affiliation(s)
- Harpreet Kaur
- Genomic Medicine Institute, Cleveland Clinic/Lerner Research Institute, Cleveland, OH, United States of America
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, United States of America
| | - Dana C. Crawford
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, United States of America
| | - Jingjing Liang
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, United States of America
| | - Penelope Benchek
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, United States of America
| | | | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, United States of America
| | - Asha R. Kallianpur
- Genomic Medicine Institute, Cleveland Clinic/Lerner Research Institute, Cleveland, OH, United States of America
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, United States of America
| | - William S. Bush
- Genomic Medicine Institute, Cleveland Clinic/Lerner Research Institute, Cleveland, OH, United States of America
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, United States of America
- * E-mail:
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10
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Takahashi Y, Yamazaki K, Kamatani Y, Kubo M, Matsuda K, Asai S. A genome-wide association study identifies a novel candidate locus at the DLGAP1 gene with susceptibility to resistant hypertension in the Japanese population. Sci Rep 2021; 11:19497. [PMID: 34593835 PMCID: PMC8484335 DOI: 10.1038/s41598-021-98144-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 09/03/2021] [Indexed: 01/11/2023] Open
Abstract
Numerous genetic variants associated with hypertension and blood pressure are known, but there is a paucity of evidence from genetic studies of resistant hypertension, especially in Asian populations. To identify novel genetic loci associated with resistant hypertension in the Japanese population, we conducted a genome-wide association study with 2705 resistant hypertension cases and 21,296 mild hypertension controls, all from BioBank Japan. We identified one novel susceptibility candidate locus, rs1442386 on chromosome 18p11.3 (DLGAP1), achieving genome-wide significance (odds ratio (95% CI) = 0.85 (0.81–0.90), P = 3.75 × 10−8) and 18 loci showing suggestive association, including rs62525059 of 8q24.3 (CYP11B2) and rs3774427 of 3p21.1 (CACNA1D). We further detected biological processes associated with resistant hypertension, including chemical synaptic transmission, regulation of transmembrane transport, neuron development and neurological system processes, highlighting the importance of the nervous system. This study provides insights into the etiology of resistant hypertension in the Japanese population.
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Affiliation(s)
- Yasuo Takahashi
- Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, 30-1 Oyaguchi-Kami Machi, Itabashi-ku, Tokyo, 173-8610, Japan.
| | - Keiko Yamazaki
- Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, 30-1 Oyaguchi-Kami Machi, Itabashi-ku, Tokyo, 173-8610, Japan.,Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoichiro Kamatani
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Koichi Matsuda
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Satoshi Asai
- Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, 30-1 Oyaguchi-Kami Machi, Itabashi-ku, Tokyo, 173-8610, Japan. .,Division of Pharmacology, Department of Biomedical Sciences, Nihon University School of Medicine, 30-1 Oyaguchi-Kami Machi, Itabashi-ku, Tokyo, 173-8610, Japan.
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11
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Linder JE, Bastarache L, Hughey JJ, Peterson JF. The Role of Electronic Health Records in Advancing Genomic Medicine. Annu Rev Genomics Hum Genet 2021; 22:219-238. [PMID: 34038146 PMCID: PMC9297710 DOI: 10.1146/annurev-genom-121120-125204] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Recent advances in genomic technology and widespread adoption of electronic health records (EHRs) have accelerated the development of genomic medicine, bringing promising research findings from genome science into clinical practice. Genomic and phenomic data, accrued across large populations through biobanks linked to EHRs, have enabled the study of genetic variation at a phenome-wide scale. Through new quantitative techniques, pleiotropy can be explored with phenome-wide association studies, the occurrence of common complex diseases can be predicted using the cumulative influence of many genetic variants (polygenic risk scores), and undiagnosed Mendelian syndromes can be identified using EHR-based phenotypic signatures (phenotype risk scores). In this review, we trace the role of EHRs from the development of genome-wide analytic techniques to translational efforts to test these new interventions to the clinic. Throughout, we describe the challenges that remain when combining EHRs with genetics to improve clinical care.
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Affiliation(s)
- Jodell E Linder
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA;
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA; , ,
| | - Jacob J Hughey
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA; , ,
| | - Josh F Peterson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA; , ,
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
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12
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Chandler PD, Clark CR, Zhou G, Noel NL, Achilike C, Mendez L, Ramirez AH, Loperena-Cortes R, Mayo K, Cohn E, Ohno-Machado L, Boerwinkle E, Cicek M, Qian J, Schully S, Ratsimbazafy F, Mockrin S, Gebo K, Dedier JJ, Murphy SN, Smoller JW, Karlson EW. Hypertension prevalence in the All of Us Research Program among groups traditionally underrepresented in medical research. Sci Rep 2021; 11:12849. [PMID: 34158555 PMCID: PMC8219813 DOI: 10.1038/s41598-021-92143-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 06/04/2021] [Indexed: 11/18/2022] Open
Abstract
The All of Us Research Program was designed to enable broad-based precision medicine research in a cohort of unprecedented scale and diversity. Hypertension (HTN) is a major public health concern. The validity of HTN data and definition of hypertension cases in the All of Us (AoU) Research Program for use in rule-based algorithms is unknown. In this cross-sectional, population-based study, we compare HTN prevalence in the AoU Research Program to HTN prevalence in the 2015-2016 National Health and Nutrition Examination Survey (NHANES). We used AoU baseline data from patient (age ≥ 18) measurements (PM), surveys, and electronic health record (EHR) blood pressure measurements. We retrospectively examined the prevalence of HTN in the EHR cohort using Systemized Nomenclature of Medicine (SNOMED) codes and blood pressure medications recorded in the EHR. We defined HTN as the participant having at least 2 HTN diagnosis/billing codes on separate dates in the EHR data AND at least one HTN medication. We calculated an age-standardized HTN prevalence according to the age distribution of the U.S. Census, using 3 groups (18-39, 40-59, and ≥ 60). Among the 185,770 participants enrolled in the AoU Cohort (mean age at enrollment = 51.2 years) available in a Researcher Workbench as of October 2019, EHR data was available for at least one SNOMED code from 112,805 participants, medications for 104,230 participants, and 103,490 participants had both medication and SNOMED data. The total number of persons with SNOMED codes on at least two distinct dates and at least one antihypertensive medication was 33,310 for a crude prevalence of HTN of 32.2%. AoU age-adjusted HTN prevalence was 27.9% using 3 groups compared to 29.6% in NHANES. The AoU cohort is a growing source of diverse longitudinal data to study hypertension nationwide and develop precision rule-based algorithms for use in hypertension treatment and prevention research. The prevalence of hypertension in this cohort is similar to that in prior population-based surveys.
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Affiliation(s)
- Paulette D Chandler
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
| | - Cheryl R Clark
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Guohai Zhou
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Nyia L Noel
- Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
| | - Confidence Achilike
- Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
| | - Lizette Mendez
- Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
| | | | | | - Kelsey Mayo
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | - Eric Boerwinkle
- University of Texas Health Science Center School of Public Health, Houston, TX, USA
| | | | - Jun Qian
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | | | - Kelly Gebo
- Johns Hopkins University, Baltimore, MD, USA
| | - Julien J Dedier
- Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
| | - Shawn N Murphy
- Research Information Science and Computing, Mass General Brigham, Boston, MA, USA
| | - Jordan W Smoller
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Elizabeth W Karlson
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
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13
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Wenric S, Jeff JM, Joseph T, Yee MC, Belbin GM, Owusu Obeng A, Ellis SB, Bottinger EP, Gottesman O, Levin MA, Kenny EE. Rapid response to the alpha-1 adrenergic agent phenylephrine in the perioperative period is impacted by genomics and ancestry. THE PHARMACOGENOMICS JOURNAL 2021; 21:174-189. [PMID: 33168928 PMCID: PMC7997806 DOI: 10.1038/s41397-020-00194-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Revised: 08/21/2020] [Accepted: 10/05/2020] [Indexed: 11/10/2022]
Abstract
The emergence of genomic data in biobanks and health systems offers new ways to derive medically important phenotypes, including acute phenotypes occurring during inpatient clinical care. Here we study the genetic underpinnings of the rapid response to phenylephrine, an α1-adrenergic receptor agonist commonly used to treat hypotension during anesthesia and surgery. We quantified this response by extracting blood pressure (BP) measurements 5 min before and after the administration of phenylephrine. Based on this derived phenotype, we show that systematic differences exist between self-reported ancestry groups: European-Americans (EA; n = 1387) have a significantly higher systolic response to phenylephrine than African-Americans (AA; n = 1217) and Hispanic/Latinos (HA; n = 1713) (31.3% increase, p value < 6e-08 and 22.9% increase, p value < 5e-05 respectively), after adjusting for genetic ancestry, demographics, and relevant clinical covariates. We performed a genome-wide association study to investigate genetic factors underlying individual differences in this derived phenotype. We discovered genome-wide significant association signals in loci and genes previously associated with BP measured in ambulatory settings, and a general enrichment of association in these genes. Finally, we discovered two low frequency variants, present at ~1% in EAs and AAs, respectively, where patients carrying one copy of these variants show no phenylephrine response. This work demonstrates our ability to derive a quantitative phenotype suited for comparative statistics and genome-wide association studies from dense clinical and physiological measures captured for managing patients during surgery. We identify genetic variants underlying non response to phenylephrine, with implications for preemptive pharmacogenomic screening to improve safety during surgery.
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Affiliation(s)
- Stephane Wenric
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Janina M Jeff
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Thomas Joseph
- Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Muh-Ching Yee
- Stanford Functional Genomics Facility, Stanford, CA, USA
- Invitae Corporation, San Francisco, CA, USA
| | - Gillian M Belbin
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Aniwaa Owusu Obeng
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Pharmacy Department, The Mount Sinai Hospital, New York, NY, USA
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stephen B Ellis
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Erwin P Bottinger
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Omri Gottesman
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matthew A Levin
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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14
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15
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McDonough CW, Babcock K, Chucri K, Crawford DC, Bian J, Modave F, Cooper-DeHoff RM, Hogan WR. Optimizing identification of resistant hypertension: Computable phenotype development and validation. Pharmacoepidemiol Drug Saf 2020; 29:1393-1401. [PMID: 32844549 DOI: 10.1002/pds.5095] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 07/21/2020] [Accepted: 07/22/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE Computable phenotypes are constructed to utilize data within the electronic health record (EHR) to identify patients with specific characteristics; a necessary step for researching a complex disease state. We developed computable phenotypes for resistant hypertension (RHTN) and stable controlled hypertension (HTN) based on the National Patient-Centered Clinical Research Network (PCORnet) common data model (CDM). The computable phenotypes were validated through manual chart review. METHODS We adapted and refined existing computable phenotype algorithms for RHTN and stable controlled HTN to the PCORnet CDM in an adult HTN population from the OneFlorida Clinical Research Consortium (2015-2017). Two independent reviewers validated the computable phenotypes through manual chart review of 425 patient records. We assessed precision of our computable phenotypes through positive predictive value (PPV) and test validity through interrater reliability (IRR). RESULTS Among the 156 730 HTN patients in our final dataset, the final computable phenotype algorithms identified 24 926 patients with RHTN and 19 100 with stable controlled HTN. The PPV for RHTN in patients randomly selected for validation of the final algorithm was 99.1% (n = 113, CI: 95.2%-99.9%). The PPV for stable controlled HTN in patients randomly selected for validation of the final algorithm was 96.5% (n = 113, CI: 91.2%-99.0%). IRR analysis revealed a raw percent agreement of 91% (152/167) with Cohen's kappa statistic = 0.87. CONCLUSIONS We constructed and validated a RHTN computable phenotype algorithm and a stable controlled HTN computable phenotype algorithm. Both algorithms are based on the PCORnet CDM, allowing for future application to epidemiological and drug utilization based research.
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Affiliation(s)
- Caitrin W McDonough
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, Florida, USA
| | - Kyle Babcock
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, Florida, USA
| | - Kristen Chucri
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, Florida, USA
| | - Dana C Crawford
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Jiang Bian
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - François Modave
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Rhonda M Cooper-DeHoff
- Department of Pharmacotherapy and Translational Research, 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
| | - William R Hogan
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
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16
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Dikilitas O, Schaid DJ, Kosel ML, Carroll RJ, Chute CG, Denny JA, Fedotov A, Feng Q, Hakonarson H, Jarvik GP, Lee MTM, Pacheco JA, Rowley R, Sleiman PM, Stein CM, Sturm AC, Wei WQ, Wiesner GL, Williams MS, Zhang Y, Manolio TA, Kullo IJ. Predictive Utility of Polygenic Risk Scores for Coronary Heart Disease in Three Major Racial and Ethnic Groups. Am J Hum Genet 2020; 106:707-716. [PMID: 32386537 DOI: 10.1016/j.ajhg.2020.04.002] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 03/31/2020] [Indexed: 12/28/2022] Open
Abstract
Because polygenic risk scores (PRSs) for coronary heart disease (CHD) are derived from mainly European ancestry (EA) cohorts, their validity in African ancestry (AA) and Hispanic ethnicity (HE) individuals is unclear. We investigated associations of "restricted" and genome-wide PRSs with CHD in three major racial and ethnic groups in the U.S. The eMERGE cohort (mean age 48 ± 14 years, 58% female) included 45,645 EA, 7,597 AA, and 2,493 HE individuals. We assessed two restricted PRSs (PRSTikkanen and PRSTada; 28 and 50 variants, respectively) and two genome-wide PRSs (PRSmetaGRS and PRSLDPred; 1.7 M and 6.6 M variants, respectively) derived from EA cohorts. Over a median follow-up of 11.1 years, 2,652 incident CHD events occurred. Hazard and odds ratios for the association of PRSs with CHD were similar in EA and HE cohorts but lower in AA cohorts. Genome-wide PRSs were more strongly associated with CHD than restricted PRSs were. PRSmetaGRS, the best performing PRS, was associated with CHD in all three cohorts; hazard ratios (95% CI) per 1 SD increase were 1.53 (1.46-1.60), 1.53 (1.23-1.90), and 1.27 (1.13-1.43) for incident CHD in EA, HE, and AA individuals, respectively. The hazard ratios were comparable in the EA and HE cohorts (pinteraction = 0.77) but were significantly attenuated in AA individuals (pinteraction= 2.9 × 10-3). These results highlight the potential clinical utility of PRSs for CHD as well as the need to assemble diverse cohorts to generate ancestry- and ethnicity PRSs.
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Affiliation(s)
- Ozan Dikilitas
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Daniel J Schaid
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Matthew L Kosel
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Robert J Carroll
- Department of Biomedical Informatics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Joshua A Denny
- Department of Biomedical Informatics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Alex Fedotov
- Irving Institute for Clinical and Translational Research, Columbia University Medical Center, New York, NY 10032, USA
| | - QiPing Feng
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Gail P Jarvik
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | | | - Jennifer A Pacheco
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Robb Rowley
- National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Patrick M Sleiman
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - C Michael Stein
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | | | - Wei-Qi Wei
- Department of Biomedical Informatics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Georgia L Wiesner
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | | | | | - Teri A Manolio
- National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA.
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17
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Li R, Chen Y, Ritchie MD, Moore JH. Electronic health records and polygenic risk scores for predicting disease risk. Nat Rev Genet 2020; 21:493-502. [PMID: 32235907 DOI: 10.1038/s41576-020-0224-1] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/02/2020] [Indexed: 01/03/2023]
Abstract
Accurate prediction of disease risk based on the genetic make-up of an individual is essential for effective prevention and personalized treatment. Nevertheless, to date, individual genetic variants from genome-wide association studies have achieved only moderate prediction of disease risk. The aggregation of genetic variants under a polygenic model shows promising improvements in prediction accuracies. Increasingly, electronic health records (EHRs) are being linked to patient genetic data in biobanks, which provides new opportunities for developing and applying polygenic risk scores in the clinic, to systematically examine and evaluate patient susceptibilities to disease. However, the heterogeneous nature of EHR data brings forth many practical challenges along every step of designing and implementing risk prediction strategies. In this Review, we present the unique considerations for using genotype and phenotype data from biobank-linked EHRs for polygenic risk prediction.
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Affiliation(s)
- Ruowang Li
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Jason H Moore
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA.
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18
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McDonough CW, Smith SM, Cooper-DeHoff RM, Hogan WR. Optimizing Antihypertensive Medication Classification in Electronic Health Record-Based Data: Classification System Development and Methodological Comparison. JMIR Med Inform 2020; 8:e14777. [PMID: 32130152 PMCID: PMC7068459 DOI: 10.2196/14777] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 10/18/2019] [Accepted: 12/15/2019] [Indexed: 01/16/2023] Open
Abstract
Background Computable phenotypes have the ability to utilize data within the electronic health record (EHR) to identify patients with certain characteristics. Many computable phenotypes rely on multiple types of data within the EHR including prescription drug information. Hypertension (HTN)-related computable phenotypes are particularly dependent on the correct classification of antihypertensive prescription drug information, as well as corresponding diagnoses and blood pressure information. Objective This study aimed to create an antihypertensive drug classification system to be utilized with EHR-based data as part of HTN-related computable phenotypes. Methods We compared 4 different antihypertensive drug classification systems based off of 4 different methodologies and terminologies, including 3 RxNorm Concept Unique Identifier (RxCUI)–based classifications and 1 medication name–based classification. The RxCUI-based classifications utilized data from (1) the Drug Ontology, (2) the new Medication Reference Terminology, and (3) the Anatomical Therapeutic Chemical Classification System and DrugBank, whereas the medication name–based classification relied on antihypertensive drug names. Each classification system was applied to EHR-based prescription drug data from hypertensive patients in the OneFlorida Data Trust. Results There were 13,627 unique RxCUIs and 8025 unique medication names from the 13,879,046 prescriptions. We observed a broad overlap between the 4 methods, with 84.1% (691/822) to 95.3% (695/729) of terms overlapping pairwise between the different classification methods. Key differences arose from drug products with multiple dosage forms, drug products with an indication of benign prostatic hyperplasia, drug products that contain more than 1 ingredient (combination products), and terms within the classification systems corresponding to retired or obsolete RxCUIs. Conclusions In total, 2 antihypertensive drug classifications were constructed, one based on RxCUIs and one based on medication name, that can be used in future computable phenotypes that require antihypertensive drug classifications.
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Affiliation(s)
- Caitrin W McDonough
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, United States
| | - Steven M Smith
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, United States
| | - Rhonda M Cooper-DeHoff
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, United States.,Division of Cardiovascular Medicine, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
| | - William R Hogan
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
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19
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Loganathan L, Gopinath K, Sankaranarayanan VM, Kukreti R, Rajendran K, Lee JK, Muthusamy K. Computational and Pharmacogenomic Insights on Hypertension Treatment: Rational Drug Design and Optimization Strategies. Curr Drug Targets 2019; 21:18-33. [DOI: 10.2174/1389450120666190808101356] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 07/15/2019] [Accepted: 07/16/2019] [Indexed: 02/07/2023]
Abstract
Background::
Hypertension is a prevalent cardiovascular complication caused by genetic
and nongenetic factors. Blood pressure (BP) management is difficult because most patients become
resistant to monotherapy soon after treatment initiation. Although many antihypertensive drugs are
available, some patients do not respond to multiple drugs. Identification of personalized antihypertensive
treatments is a key for better BP management.
Objective::
This review aimed to elucidate aspects of rational drug design and other methods to develop
better hypertension management.
Results::
Among hypertension-related signaling mechanisms, the renin-angiotensin-aldosterone system
is the leading genetic target for hypertension treatment. Identifying a single drug that acts on multiple
targets is an emerging strategy for hypertension treatment, and could be achieved by discovering new
drug targets with less mutated and highly conserved regions. Extending pharmacogenomics research
to include patients with hypertension receiving multiple antihypertensive drugs could help identify the
genetic markers of hypertension. However, available evidence on the role of pharmacogenomics in
hypertension is limited and primarily focused on candidate genes. Studies on hypertension pharmacogenomics
aim to identify the genetic causes of response variations to antihypertensive drugs. Genetic
association studies have identified single nucleotide polymorphisms affecting drug responses. To understand
how genetic traits alter drug responses, computational screening of mutagenesis can be utilized
to observe drug response variations at the protein level, which can help identify new inhibitors
and drug targets to manage hypertension.
Conclusions::
Rational drug design facilitates the discovery and design of potent inhibitors. However,
further research and clinical validation are required before novel inhibitors can be clinically used as
antihypertensive therapies.
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Affiliation(s)
| | - Krishnasamy Gopinath
- Department of Chemical Engineering, Konkuk University, 1 Hwayang-Dong, Gwangjin-Gu, Seoul, Korea
| | | | - Ritushree Kukreti
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology, Council of Scientific and Industrial Research, New Delhi, India
| | - Kannan Rajendran
- Department of General Medicine, Saveetha Medical College and Hospital, Chennai, Tamil Nadu, India
| | - Jung-Kul Lee
- Department of Chemical Engineering, Konkuk University, 1 Hwayang-Dong, Gwangjin-Gu, Seoul, Korea
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20
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Irvin MR, Sitlani CM, Floyd JS, Psaty BM, Bis JC, Wiggins KL, Whitsel EA, Sturmer T, Stewart J, Raffield L, Sun F, Liu CT, Xu H, Cupples AL, Tanner RM, Rossing P, Smith A, Zilhão NR, Launer LJ, Noordam R, Rotter JI, Yao J, Li X, Guo X, Limdi N, Sundaresan A, Lange L, Correa A, Stott DJ, Ford I, Jukema JW, Gudnason V, Mook-Kanamori DO, Trompet S, Palmas W, Warren HR, Hellwege JN, Giri A, O'donnell C, Hung AM, Edwards TL, Ahluwalia TS, Arnett DK, Avery CL. Genome-Wide Association Study of Apparent Treatment-Resistant Hypertension in the CHARGE Consortium: The CHARGE Pharmacogenetics Working Group. Am J Hypertens 2019; 32:1146-1153. [PMID: 31545351 DOI: 10.1093/ajh/hpz150] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 09/10/2019] [Accepted: 09/13/2019] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Only a handful of genetic discovery efforts in apparent treatment-resistant hypertension (aTRH) have been described. METHODS We conducted a case-control genome-wide association study of aTRH among persons treated for hypertension, using data from 10 cohorts of European ancestry (EA) and 5 cohorts of African ancestry (AA). Cases were treated with 3 different antihypertensive medication classes and had blood pressure (BP) above goal (systolic BP ≥ 140 mm Hg and/or diastolic BP ≥ 90 mm Hg) or 4 or more medication classes regardless of BP control (nEA = 931, nAA = 228). Both a normotensive control group and a treatment-responsive control group were considered in separate analyses. Normotensive controls were untreated (nEA = 14,210, nAA = 2,480) and had systolic BP/diastolic BP < 140/90 mm Hg. Treatment-responsive controls (nEA = 5,266, nAA = 1,817) had BP at goal (<140/90 mm Hg), while treated with one antihypertensive medication class. Individual cohorts used logistic regression with adjustment for age, sex, study site, and principal components for ancestry to examine the association of single-nucleotide polymorphisms with case-control status. Inverse variance-weighted fixed-effects meta-analyses were carried out using METAL. RESULTS The known hypertension locus, CASZ1, was a top finding among EAs (P = 1.1 × 10-8) and in the race-combined analysis (P = 1.5 × 10-9) using the normotensive control group (rs12046278, odds ratio = 0.71 (95% confidence interval: 0.6-0.8)). Single-nucleotide polymorphisms in this locus were robustly replicated in the Million Veterans Program (MVP) study in consideration of a treatment-responsive control group. There were no statistically significant findings for the discovery analyses including treatment-responsive controls. CONCLUSION This genomic discovery effort for aTRH identified CASZ1 as an aTRH risk locus.
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Affiliation(s)
- Marguerite R Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Colleen M Sitlani
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, USA
| | - James S Floyd
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, 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
- Department of Health Services, University of Washington, Seattle, Washington, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Kerri L Wiggins
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Eric A Whitsel
- Department of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Til Sturmer
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - James Stewart
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Laura Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Fangui Sun
- Department of Biostatistics, Boston University, Boston, Maryland, USA
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University, Boston, Maryland, USA
| | - Hanfei Xu
- Department of Biostatistics, Boston University, Boston, Maryland, USA
| | | | - Rikki M Tanner
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | | | - Albert Smith
- Icelandic Heart Association, Kopavogur, Iceland
- University of Iceland, Reykjavik, Iceland
| | | | - Lenore J Launer
- Laboratory of Epidemiology and Population Science, Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Departments of Pediatrics and Medicine, LABioMed at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Jie Yao
- Institute for Translational Genomics and Population Sciences, Departments of Pediatrics and Medicine, LABioMed at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Xiaohui Li
- Institute for Translational Genomics and Population Sciences, Departments of Pediatrics and Medicine, LABioMed at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Departments of Pediatrics and Medicine, LABioMed at Harbor-UCLA Medical Center, Torrance, California, USA
| | - Nita Limdi
- Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Aishwarya Sundaresan
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Leslie Lange
- Department of Medicine, University of Colorado–Denver, Aurora, Colorado, USA
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - David J Stott
- Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Ian Ford
- Robertson Center for Biostatistics, University of Glasgow, Glasgow, UK
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Vilmundur Gudnason
- 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
| | - Dennis O Mook-Kanamori
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Stella Trompet
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Walter Palmas
- Department of Medicine, Columbia University Medical Center, New York, New York, USA
| | - Helen R Warren
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- National Institute for Health Research Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, UK
| | - Jacklyn N Hellwege
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, Tennessee, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Ayush Giri
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, Tennessee, USA
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt Genetics Institute, Vanderbilt Epidemiology Center, Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Christopher O'donnell
- VA Boston Health Care System, Boston, Massachusetts, USA
- Section of Cardiology and Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Adriana M Hung
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, Tennessee, USA
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Todd L Edwards
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, Tennessee, USA
- Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Tarunveer S Ahluwalia
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Donna K Arnett
- Deans Office, School of Public Health, University of Kentucky, Lexington, Kentucky, USA
| | - Christy L Avery
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA
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21
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Bottinor WJ, Shuey MM, Manouchehri A, Farber-Eger EH, Xu M, Nair D, Salem JE, Wang TJ, Brittain EL. Renin-Angiotensin-Aldosterone System Modulates Blood Pressure Response During Vascular Endothelial Growth Factor Receptor Inhibition. JACC: CARDIOONCOLOGY 2019; 1:14-23. [PMID: 32984850 PMCID: PMC7513950 DOI: 10.1016/j.jaccao.2019.07.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Objectives This study postulated that antihypertensive therapy with renin-angiotensin-aldosterone system (RAAS) inhibition may mitigate vascular endothelial growth factor inhibitor (VEGFi)–mediated increases in blood pressure more effectively than other antihypertensive medications in patients receiving VEGFi therapy. Background VEGFi therapy is commonly used in the treatment of cancer. One common side effect of VEGFi therapy is elevated blood pressure. Evidence suggests that the RAAS may be involved in VEGFi-mediated increases in blood pressure. Methods This retrospective cohort analysis was performed using a de-identified version of the electronic health record at Vanderbilt University Medical Center in Nashville, Tennessee. Subjects with cancer who were exposed to VEGFi therapy were identified, and blood pressure and medication data were extracted. Changes in mean systolic and diastolic blood pressure in response to VEGFi therapy in patients receiving RAAS inhibitor (RAASi) therapy before VEGFi initiation were compared with changes in mean systolic and diastolic blood pressure in patients not receiving RAASi therapy before VEGFi initiation. Results Mean systolic and diastolic blood pressure rose in both groups after VEGFi use; however, patients who had RAASi therapy before VEGFi initiation had a significantly lower increase in systolic blood pressure as compared with patients with no RAASi therapy (2.46 mm Hg [95% confidence interval: 0.7 to 4.2] compared with 4.56 mm Hg [95% confidence interval: 3.5 to 5.6], respectively; p = 0.034). Conclusions In a real-world clinical population, RAASi therapy before VEGFi initiation may ameliorate VEGFi-mediated increases in blood pressure. Randomized clinical trials are needed to further our understanding of the role of RAASi therapy in VEGFi-mediated increases in blood pressure.
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Affiliation(s)
- Wendy J Bottinor
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Megan M Shuey
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Ali Manouchehri
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Eric H Farber-Eger
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Meng Xu
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee
| | - Devika Nair
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Joe-Elie Salem
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee.,Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, Tennessee.,Department of Clinical Pharmacology, University of the Sorbonne, Assistance Publique Hôpitaux de Paris, Institut National de la Santé et de la Recherche Médicale CIC 14-21, Pitié-Salpêtrière Hospital, Paris, France
| | - Thomas J Wang
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Evan L Brittain
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee
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22
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Kolifarhood G, Daneshpour MS, Khayat BS, Saadati HM, Guity K, Khosravi N, Akbarzadeh M, Sabour S. Generality of genomic findings on blood pressure traits and its usefulness in precision medicine in diverse populations: A systematic review. Clin Genet 2019; 96:17-27. [PMID: 30820929 DOI: 10.1111/cge.13527] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 02/14/2019] [Accepted: 02/21/2019] [Indexed: 01/01/2023]
Abstract
Remarkable findings from genome-wide association studies (GWAS) on blood pressure (BP) traits have made new insights for developing precision medicine toward more effective screening measures. However, generality of GWAS findings in diverse populations is hampered by some technical limitations. There is no comprehensive study to evaluate source(s) of the non-generality of GWAS results on BP traits, so to fill the gap, this systematic review study was carried out. Using MeSH terms, 1545 records were detected through searching in five databases and 49 relevant full-text articles were included in our review. Overall, 749 unique variants were reported, of those, majority of variants have been detected in Europeans and were associated to systolic and diastolic BP traits. Frequency of genetic variants with same position was low in European and non-European populations (n = 38). However, more than 200 (>25%) single nucleotide polymorphisms were found on same loci or linkage disequilibrium blocks (r2 ≥ 80%). Investigating for locus position and linkage disequilibrium of infrequent unique variants showed modest to high reproducibility of findings in Europeans that in some extent was generalizable in other populations. Beyond theoretical limitations, our study addressed other possible sources of non-generality of GWAS findings for BP traits in the same and different origins.
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Affiliation(s)
- Goodarz Kolifarhood
- Department of Epidemiology, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maryam S Daneshpour
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Bahareh S Khayat
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hossein M Saadati
- Department of Epidemiology, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Kamran Guity
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Nasim Khosravi
- Department of Community Health Nursing, School of Nursing and Midwifery, Iran University of Medical Sciences, Tehran, Iran
| | - Mahdi Akbarzadeh
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Siamak Sabour
- Department of Epidemiology, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Safety Promotion and Injury Prevention Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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23
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Pendergrass SA, Crawford DC. Using Electronic Health Records To Generate Phenotypes For Research. CURRENT PROTOCOLS IN HUMAN GENETICS 2019; 100:e80. [PMID: 30516347 PMCID: PMC6318047 DOI: 10.1002/cphg.80] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Electronic health records contain patient-level data collected during and for clinical care. Data within the electronic health record include diagnostic billing codes, procedure codes, vital signs, laboratory test results, clinical imaging, and physician notes. With repeated clinic visits, these data are longitudinal, providing important information on disease development, progression, and response to treatment or intervention strategies. The near universal adoption of electronic health records nationally has the potential to provide population-scale real-world clinical data accessible for biomedical research, including genetic association studies. For this research potential to be realized, high-quality research-grade variables must be extracted from these clinical data warehouses. We describe here common and emerging electronic phenotyping approaches applied to electronic health records, as well as current limitations of both the approaches and the biases associated with these clinically collected data that impact their use in research. © 2018 by John Wiley & Sons, Inc.
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Affiliation(s)
- Sarah A. Pendergrass
- Biomedical and Translational Informatics Institute,
Geisinger Research, Rockville MD
| | - Dana C. Crawford
- Institute for Computational Biology, Department of
Population and Quantitative Health Sciences, Case Western Reserve University,
Cleveland, OH
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24
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Pacheco JA, Rasmussen LV, Kiefer RC, Campion TR, Speltz P, Carroll RJ, Stallings SC, Mo H, Ahuja M, Jiang G, LaRose ER, Peissig PL, Shang N, Benoit B, Gainer VS, Borthwick K, Jackson KL, Sharma A, Wu AY, Kho AN, Roden DM, Pathak J, Denny JC, Thompson WK. A case study evaluating the portability of an executable computable phenotype algorithm across multiple institutions and electronic health record environments. J Am Med Inform Assoc 2018; 25:1540-1546. [PMID: 30124903 PMCID: PMC6213083 DOI: 10.1093/jamia/ocy101] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 06/13/2018] [Accepted: 07/10/2018] [Indexed: 12/12/2022] Open
Abstract
Electronic health record (EHR) algorithms for defining patient cohorts are commonly shared as free-text descriptions that require human intervention both to interpret and implement. We developed the Phenotype Execution and Modeling Architecture (PhEMA, http://projectphema.org) to author and execute standardized computable phenotype algorithms. With PhEMA, we converted an algorithm for benign prostatic hyperplasia, developed for the electronic Medical Records and Genomics network (eMERGE), into a standards-based computable format. Eight sites (7 within eMERGE) received the computable algorithm, and 6 successfully executed it against local data warehouses and/or i2b2 instances. Blinded random chart review of cases selected by the computable algorithm shows PPV ≥90%, and 3 out of 5 sites had >90% overlap of selected cases when comparing the computable algorithm to their original eMERGE implementation. This case study demonstrates potential use of PhEMA computable representations to automate phenotyping across different EHR systems, but also highlights some ongoing challenges.
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Affiliation(s)
- Jennifer A Pacheco
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Luke V Rasmussen
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Richard C Kiefer
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Thomas R Campion
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Peter Speltz
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Sarah C Stallings
- Meharry-Vanderbilt Alliance, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Huan Mo
- Department of Pathology, Loma Linda University Health, Loma Linda, California, USA
| | - Monika Ahuja
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Guoqian Jiang
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Eric R LaRose
- Department of Biomedical Informatics, Marshfield Clinic Research Institute, Marshfield, Wisconsin, USA
| | - Peggy L Peissig
- Department of Biomedical Informatics, Marshfield Clinic Research Institute, Marshfield, Wisconsin, USA
| | - Ning Shang
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Barbara Benoit
- Research IS and Computing, Partners HealthCare, Harvard University, Somerville, Massachusetts, USA
| | - Vivian S Gainer
- Research IS and Computing, Partners HealthCare, Harvard University, Somerville, Massachusetts, USA
| | - Kenneth Borthwick
- Henry Hood Center for Health Research, Geisinger, Danville, Pennsylvania, USA
| | - Kathryn L Jackson
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Ambrish Sharma
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Andy Yizhou Wu
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Abel N Kho
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Dan M Roden
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jyotishman Pathak
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - William K Thompson
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
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25
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Stanaway IB, Hall TO, Rosenthal EA, Palmer M, Naranbhai V, Knevel R, Namjou-Khales B, Carroll RJ, Kiryluk K, Gordon AS, Linder J, Howell KM, Mapes BM, Lin FTJ, Joo YY, Hayes MG, Gharavi AG, Pendergrass SA, Ritchie MD, de Andrade M, Croteau-Chonka DC, Raychaudhuri S, Weiss ST, Lebo M, Amr SS, Carrell D, Larson EB, Chute CG, Rasmussen-Torvik LJ, Roy-Puckelwartz MJ, Sleiman P, Hakonarson H, Li R, Karlson EW, Peterson JF, Kullo IJ, Chisholm R, Denny JC, Jarvik GP, Crosslin DR. The eMERGE genotype set of 83,717 subjects imputed to ~40 million variants genome wide and association with the herpes zoster medical record phenotype. Genet Epidemiol 2018; 43:63-81. [PMID: 30298529 PMCID: PMC6375696 DOI: 10.1002/gepi.22167] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 08/10/2018] [Accepted: 08/28/2018] [Indexed: 12/30/2022]
Abstract
The Electronic Medical Records and Genomics (eMERGE) network is a network of medical centers with electronic medical records linked to existing biorepository samples for genomic discovery and genomic medicine research. The network sought to unify the genetic results from 78 Illumina and Affymetrix genotype array batches from 12 contributing medical centers for joint association analysis of 83,717 human participants. In this report, we describe the imputation of eMERGE results and methods to create the unified imputed merged set of genome‐wide variant genotype data. We imputed the data using the Michigan Imputation Server, which provides a missing single‐nucleotide variant genotype imputation service using the minimac3 imputation algorithm with the Haplotype Reference Consortium genotype reference set. We describe the quality control and filtering steps used in the generation of this data set and suggest generalizable quality thresholds for imputation and phenotype association studies. To test the merged imputed genotype set, we replicated a previously reported chromosome 6 HLA‐B herpes zoster (shingles) association and discovered a novel zoster‐associated loci in an epigenetic binding site near the terminus of chromosome 3 (3p29).
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Affiliation(s)
- Ian B Stanaway
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington
| | - Taryn O Hall
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington
| | - Elisabeth A Rosenthal
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington
| | - Melody Palmer
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington
| | - Vivek Naranbhai
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington.,Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - Rachel Knevel
- Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - Bahram Namjou-Khales
- Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Robert J Carroll
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, Tennessee
| | - Krzysztof Kiryluk
- Department of Medicine, Columbia University, New York City, New York
| | - Adam S Gordon
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington
| | - Jodell Linder
- Vanderbilt Institute for Clinical and Translational Research, School of Medicine, Vanderbilt University, Nashville, Tennessee
| | - Kayla Marie Howell
- Vanderbilt Institute for Clinical and Translational Research, School of Medicine, Vanderbilt University, Nashville, Tennessee
| | - Brandy M Mapes
- Vanderbilt Institute for Clinical and Translational Research, School of Medicine, Vanderbilt University, Nashville, Tennessee
| | - Frederick T J Lin
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | | | - M Geoffrey Hayes
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Ali G Gharavi
- Department of Medicine, Columbia University, New York City, New York
| | | | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania
| | | | | | - Soumya Raychaudhuri
- Harvard Medical School, Harvard University, Cambridge, Massachusetts.,Program in Medical and Population Genetics, Broad Institute of Massachusetts Technical Institute and Harvard University, Cambridge, Massachusetts
| | - Scott T Weiss
- Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - Matt Lebo
- Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - Sami S Amr
- Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - David Carrell
- Kaiser Permanente Washington Health Research Institute (Formerly Group Health Cooperative-Seattle), Kaiser Permanente, Seattle, Washington
| | - Eric B Larson
- Kaiser Permanente Washington Health Research Institute (Formerly Group Health Cooperative-Seattle), Kaiser Permanente, Seattle, Washington
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland
| | | | | | - Patrick Sleiman
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | | | - Rongling Li
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - Elizabeth W Karlson
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Josh F Peterson
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, Tennessee
| | | | - Rex Chisholm
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Joshua Charles Denny
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, Tennessee
| | - Gail P Jarvik
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington
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- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - David R Crosslin
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington
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El Rouby N, McDonough CW, Gong Y, McClure LA, Mitchell BD, Horenstein RB, Talbert RL, Crawford DC, Gitzendanner MA, Takahashi A, Tanaka T, Kubo M, Pepine CJ, Cooper-DeHoff RM, Benavente OR, Shuldiner AR, Johnson JA. Genome-wide association analysis of common genetic variants of resistant hypertension. THE PHARMACOGENOMICS JOURNAL 2018; 19:295-304. [PMID: 30237584 DOI: 10.1038/s41397-018-0049-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Revised: 08/02/2018] [Accepted: 08/10/2018] [Indexed: 12/24/2022]
Abstract
Resistant hypertension (RHTN), defined as uncontrolled blood pressure (BP) ≥ 140/90 using three or more drugs or controlled BP (<140/90) using four or more drugs, is associated with adverse outcomes, including decline in kidney function. We conducted a genome-wide association analysis in 1194 White and Hispanic participants with hypertension and coronary artery disease from the INternational VErapamil-SR Trandolapril STudy-GENEtic Substudy (INVEST-GENES). Top variants associated with RHTN at p < 10-4 were tested for replication in 585 White and Hispanic participants with hypertension and subcortical strokes from the Secondary Prevention of Subcortical Strokes GENEtic Substudy (SPS3-GENES). A genetic risk score for RHTN was created by summing the risk alleles of replicated RHTN signals. rs11749255 in MSX2 was associated with RHTN in INVEST (odds ratio (OR) (95% CI) = 1.50 (1.2-1.8), p = 7.3 × 10-5) and replicated in SPS3 (OR = 2.0 (1.4-2.8), p = 4.3 × 10-5), with genome-wide significance in meta-analysis (OR = 1.60 (1.3-1.9), p = 3.8 × 10-8). Other replicated signals were in IFLTD1 and PTPRD. IFLTD1 rs6487504 was associated with RHTN in INVEST (OR = 1.90 (1.4-2.5), p = 1.1 × 10-5) and SPS3 (OR = 1.70 (1.2-2.5), p = 4 × 10-3). PTPRD rs324498, a previously reported RHTN signal, was among the top signals in INVEST (OR = 1.60 (1.3-2.0), p = 3.4 × 10-5) and replicated in SPS3 (OR = 1.60 (1.1-2.4), one-sided p = 0.005). Participants with the highest number of risk alleles were at increased risk of RHTN compared to participants with a lower number (p-trend = 1.8 × 10-15). Overall, we identified and replicated associations with RHTN in the MSX2, IFLTD1, and PTPRD regions, and combined these associations to create a genetic risk score.
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Affiliation(s)
- Nihal El Rouby
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, College of Pharmacy, University of Florida, Gainesville, FL, USA
| | - Caitrin W McDonough
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, College of Pharmacy, University of Florida, Gainesville, FL, USA
| | - Yan Gong
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, College of Pharmacy, University of Florida, Gainesville, FL, USA
| | - Leslie A McClure
- Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA
| | - Braxton D Mitchell
- Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA.,Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, MD, USA
| | - Richard B Horenstein
- Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA.,Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Robert L Talbert
- College of Pharmacy, University of Texas at Austin, Austin, TX, USA
| | - Dana C Crawford
- Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | | | | | | | | | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Carl J Pepine
- Division of Cardiovascular Medicine, Department of Medicine, University of Florida, Gainesville, FL, USA
| | - Rhonda M Cooper-DeHoff
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, College of Pharmacy, University of Florida, Gainesville, FL, USA.,Division of Cardiovascular Medicine, Department of Medicine, University of Florida, Gainesville, FL, USA
| | - Oscar R Benavente
- Department of Neurology, University of British Columbia, Vancouver, BC, Canada
| | - Alan R Shuldiner
- Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA.,Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, MD, USA.,Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Julie A Johnson
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, College of Pharmacy, University of Florida, Gainesville, FL, USA. .,Division of Cardiovascular Medicine, Department of Medicine, University of Florida, Gainesville, FL, USA.
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27
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Abstract
Biomedical data science has experienced an explosion of new data over the past decade. Abundant genetic and genomic data are increasingly available in large, diverse data sets due to the maturation of modern molecular technologies. Along with these molecular data, dense, rich phenotypic data are also available on comprehensive clinical data sets from health care provider organizations, clinical trials, population health registries, and epidemiologic studies. The methods and approaches for interrogating these large genetic/genomic and clinical data sets continue to evolve rapidly, as our understanding of the questions and challenges continue to emerge. In this review, the state-of-the-art methodologies for genetic/genomic analysis along with complex phenomics will be discussed. This field is changing and adapting to the novel data types made available, as well as technological advances in computation and machine learning. Thus, I will also discuss the future challenges in this exciting and innovative space. The promises of precision medicine rely heavily on the ability to marry complex genetic/genomic data with clinical phenotypes in meaningful ways.
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Affiliation(s)
- Marylyn D. Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
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28
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Shuey MM, Gandelman JS, Chung CP, Nian H, Yu C, Denny JC, Brown NJ. Characteristics and treatment of African-American and European-American patients with resistant hypertension identified using the electronic health record in an academic health centre: a case-control study. BMJ Open 2018; 8:e021640. [PMID: 29950471 PMCID: PMC6020960 DOI: 10.1136/bmjopen-2018-021640] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVE To identify patients with hypertension with resistant and controlled blood pressure (BP) using electronic health records (EHRs) in order to elucidate practices in the real-world clinical treatment of hypertension and to enable future genetic studies. DESIGN Using EHRs, we developed and validated algorithms to identify patients with resistant and controlled hypertension. SETTING An academic medical centre in Nashville, Tennessee. POPULATION European-American (EA) and African-American (AA) patients with hypertension. MAIN OUTCOME MEASURES Demographic characteristics: race, age, gender, body mass index, outpatient BPs and the history of diabetes mellitus, chronic kidney disease stage 3, ischaemic heart disease, transient ischaemic attack, atrial fibrillation and sleep apnoea. MEDICATION TREATMENT All antihypertensive medication classes prescribed to a patient at the time of classification and ever prescribed following classification. RESULTS The algorithms had performance metrics exceeding 92%. The prevalence of resistant hypertension in the total hypertensive population was 7.3% in EA and 10.5% in AA. At diagnosis, AA were younger, heavier, more often female and had a higher incidence of type 2 diabetes and higher BPs than EA. AA with resistant hypertension were more likely to be treated with vasodilators, dihydropyridine calcium channel blockers and alpha-2 agonists while EA were more likely to be treated with angiotensin receptor blockers, renin inhibitors and beta blockers. Mineralocorticoid receptor antagonists use was increased in patients treated with more than four antihypertensive medications compared with patients treated with three (12.4% vs 2.6% in EA, p<0.001; 12.3% vs 2.8% in AA, p<0.001). The number of patients treated with a mineralocorticoid receptor antagonist increased to 37.4% in EA and 41.2% in AA over a mean follow-up period of 7.4 and 8.7 years, respectively. CONCLUSIONS Clinical treatment of resistant hypertension differs in EA and AA patients. These results demonstrate the feasibility of identifying resistant hypertension using an EHR.
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Affiliation(s)
- Megan M Shuey
- Department of Pharmacology, Vanderbilt University Medical Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Jocelyn S Gandelman
- Department of Medicine, Vanderbilt University Medical Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Cecilia P Chung
- Department of Medicine, Vanderbilt University Medical Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Hui Nian
- Department of Biostatistics, Vanderbilt University Medical Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Chang Yu
- Department of Biostatistics, Vanderbilt University Medical Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Nancy J Brown
- Department of Pharmacology, Vanderbilt University Medical Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
- Department of Medicine, Vanderbilt University Medical Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
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29
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Teixeira SK, Pereira AC, Krieger JE. Genetics of Resistant Hypertension: the Missing Heritability and Opportunities. Curr Hypertens Rep 2018; 20:48. [PMID: 29779058 DOI: 10.1007/s11906-018-0852-4] [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] [Indexed: 12/27/2022]
Abstract
PURPOSE OF THE REVIEW Blood pressure regulation in humans has long been known to be a genetically determined trait. The identification of causal genetic modulators for this trait has been unfulfilling at the least. Despite the recent advances of genome-wide genetic studies, loci associated with hypertension or blood pressure still explain a very low percentage of the overall variation of blood pressure in the general population. This has precluded the translation of discoveries in the genetics of human hypertension to clinical use. Here, we propose the combined use of resistant hypertension as a trait for mapping genetic determinants in humans and the integration of new large-scale technologies to approach in model systems the multidimensional nature of the problem. RECENT FINDINGS New large-scale efforts in the genetic and genomic arenas are paving the way for an increased and granular understanding of genetic determinants of hypertension. New technologies for whole genome sequence and large-scale forward genetic screens can help prioritize gene and gene-pathways for downstream characterization and large-scale population studies, and guided pharmacological design can be used to drive discoveries to the translational application through better risk stratification and new therapeutic approaches. Although significant challenges remain in the mapping and identification of genetic determinants of hypertension, new large-scale technological approaches have been proposed to surpass some of the shortcomings that have limited progress in the area for the last three decades. The incorporation of these technologies to hypertension research may significantly help in the understanding of inter-individual blood pressure variation and the deployment of new phenotyping and treatment approaches for the condition.
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Affiliation(s)
- Samantha K Teixeira
- Laboratorio de Genetica e Cardiologia Molecular, Faculdade Medicina da Universidade de São Paulo, Instituto do Coracao (InCor) HC.FMUSP, Av Dr Eneas C Aguiar 44, São Paulo, SP, 05403-000, Brazil
| | - Alexandre C Pereira
- Laboratorio de Genetica e Cardiologia Molecular, Faculdade Medicina da Universidade de São Paulo, Instituto do Coracao (InCor) HC.FMUSP, Av Dr Eneas C Aguiar 44, São Paulo, SP, 05403-000, Brazil
| | - Jose E Krieger
- Laboratorio de Genetica e Cardiologia Molecular, Faculdade Medicina da Universidade de São Paulo, Instituto do Coracao (InCor) HC.FMUSP, Av Dr Eneas C Aguiar 44, São Paulo, SP, 05403-000, Brazil.
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30
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Abstract
Technologies such as next-generation sequencing and chromosomal microarray have advanced the understanding of the molecular pathogenesis of a variety of renal disorders. Genetic findings are increasingly used to inform the clinical management of many nephropathies, enabling targeted disease surveillance, choice of therapy, and family counselling. Genetic analysis has excellent diagnostic utility in paediatric nephrology, as illustrated by sequencing studies of patients with congenital anomalies of the kidney and urinary tract and steroid-resistant nephrotic syndrome. Although additional investigation is needed, pilot studies suggest that genetic testing can also provide similar diagnostic insight among adult patients. Reaching a genetic diagnosis first involves choosing the appropriate testing modality, as guided by the clinical presentation of the patient and the number of potential genes associated with the suspected nephropathy. Genome-wide sequencing increases diagnostic sensitivity relative to targeted panels, but holds the challenges of identifying causal variants in the vast amount of data generated and interpreting secondary findings. In order to realize the promise of genomic medicine for kidney disease, many technical, logistical, and ethical questions that accompany the implementation of genetic testing in nephrology must be addressed. The creation of evidence-based guidelines for the utilization and implementation of genetic testing in nephrology will help to translate genetic knowledge into improved clinical outcomes for patients with kidney disease.
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Affiliation(s)
- Emily E Groopman
- Division of Nephrology, Columbia University College of Physicians and Surgeons, 1150 Saint Nicholas Avenue, Russ Berrie Pavilion #412C, New York, New York 10032, USA
| | - Hila Milo Rasouly
- Division of Nephrology, Columbia University College of Physicians and Surgeons, 1150 Saint Nicholas Avenue, Russ Berrie Pavilion #412C, New York, New York 10032, USA
| | - Ali G Gharavi
- Division of Nephrology, Columbia University College of Physicians and Surgeons, 1150 Saint Nicholas Avenue, Russ Berrie Pavilion #412C, New York, New York 10032, USA
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31
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Magvanjav O, Gong Y, McDonough CW, Chapman AB, Turner ST, Gums JG, Bailey KR, Boerwinkle E, Beitelshees AL, Tanaka T, Kubo M, Pepine CJ, Cooper-DeHoff RM, Johnson JA. Genetic Variants Associated With Uncontrolled Blood Pressure on Thiazide Diuretic/β-Blocker Combination Therapy in the PEAR (Pharmacogenomic Evaluation of Antihypertensive Responses) and INVEST (International Verapamil-SR Trandolapril Study) Trials. J Am Heart Assoc 2017; 6:e006522. [PMID: 29097388 PMCID: PMC5721751 DOI: 10.1161/jaha.117.006522] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Accepted: 09/11/2017] [Indexed: 12/29/2022]
Abstract
BACKGROUND The majority of hypertensive individuals require combination antihypertensive therapy to achieve adequate blood pressure (BP) control. This study aimed to identify genetic variants associated with uncontrolled BP on combination therapy with a thiazide diuretic and a β-blocker. METHODS AND RESULTS A genome-wide association study of uncontrolled BP on combination therapy was conducted among 314 white participants of the PEAR (Pharmacogenomic Evaluation of Antihypertensive Responses) trial. Multivariable logistic regression analysis was used. Genetic variants meeting a suggestive level of significance (P<1.0E-05) were tested for replication in an external cohort, INVEST (International Verapamil-SR Trandolapril study). We also examined genome-wide variant associations with systolic and diastolic BP response on combination therapy and tested for replication. We discovered a single nucleotide polymorphism, the rs261316 major allele, at chromosome 15 in the gene ALDH1A2 associated with an increased odds of having uncontrolled BP on combination therapy (odds ratio: 2.56, 95% confidence interval, 1.69-3.88, P=8.64E-06). This single nucleotide polymorphism replicated (odds ratio: 1.86, 95% confidence interval, 1.35-2.57, P=0.001) and approached genome-wide significance in the meta-analysis between discovery and replication cohorts (odds ratio: 2.16, 95% confidence interval, 1.63-2.86, P=8.60E-08). Other genes in the region surrounding rs261316 (ALDH1A2) include AQP9 and LIPC. CONCLUSIONS A single nucleotide polymorphism in the gene ALDH1A2 may be associated with uncontrolled BP following treatment with a thiazide diuretic/β-blocker combination. CLINICAL TRIAL REGISTRATION URL: https://www.clinicaltrials.gov. Unique identifier: NCT00246519.
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Affiliation(s)
- Oyunbileg Magvanjav
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida College of Pharmacy, Gainesville, FL
- College of Medicine, University of Florida, Gainesville, FL
| | - Yan Gong
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida College of Pharmacy, Gainesville, FL
| | - Caitrin W McDonough
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida College of Pharmacy, Gainesville, FL
| | - Arlene B Chapman
- Section of Nephrology, Department of Medicine, University of Chicago, Chicago, IL
| | - Stephen T Turner
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN
| | - John G Gums
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida College of Pharmacy, Gainesville, FL
| | - Kent R Bailey
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN
| | - Eric Boerwinkle
- Human Genetics Center and Institute of Molecular Medicine, University of Texas Health Science Center, Houston, TX
| | - Amber L Beitelshees
- Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD
| | | | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Carl J Pepine
- Division of Cardiovascular Medicine, Department of Medicine, University of Florida College of Medicine, Gainesville, FL
| | - Rhonda M Cooper-DeHoff
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida College of Pharmacy, Gainesville, FL
- Division of Cardiovascular Medicine, Department of Medicine, University of Florida College of Medicine, Gainesville, FL
| | - Julie A Johnson
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida College of Pharmacy, Gainesville, FL
- Division of Cardiovascular Medicine, Department of Medicine, University of Florida College of Medicine, Gainesville, FL
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