1
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Raghunath S, Pfeifer JM, Kelsey CR, Nemani A, Ruhl JA, Hartzel DN, Ulloa Cerna AE, Jing L, vanMaanen DP, Leader JB, Schneider G, Morland TB, Chen R, Zimmerman N, Fornwalt BK, Haggerty CM. An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk. J Electrocardiol 2023; 76:61-65. [PMID: 36436476 DOI: 10.1016/j.jelectrocard.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 10/11/2022] [Accepted: 11/02/2022] [Indexed: 11/09/2022]
Abstract
BACKGROUND Several large trials have employed age or clinical features to select patients for atrial fibrillation (AF) screening to reduce strokes. We hypothesized that a machine learning (ML) model trained to predict AF risk from 12‑lead electrocardiogram (ECG) would be more efficient than criteria based on clinical variables in indicating a population for AF screening to potentially prevent AF-related stroke. METHODS We retrospectively included all patients with clinical encounters in Geisinger without a prior history of AF. Incidence of AF within 1 year and AF-related strokes within 3 years of the encounter were identified. AF-related stroke was defined as a stroke where AF was diagnosed at the time of stroke or within a year after the stroke. The efficiency of five methods was evaluated for selecting a cohort for AF screening. The methods were selected from four clinical trials (mSToPS, GUARD-AF, SCREEN-AF and STROKESTOP) and the ECG-based ML model. We simulated patient selection for the five methods between the years 2011 and 2014 and evaluated outcomes for 1 year intervals between 2012 and 2015, resulting in a total of twenty 1-year periods. Patients were considered eligible if they met the criteria before the start of the given 1-year period or within that period. The primary outcomes were numbers needed to screen (NNS) for AF and AF-associated stroke. RESULTS The clinical trial models indicated large proportions of the population with a prior ECG for AF screening (up to 31%), coinciding with NNS ranging from 14 to 18 for AF and 249-359 for AF-associated stroke. At comparable sensitivity, the ECG ML model indicated a modest number of patients for screening (14%) and had the highest efficiency in NNS for AF (7.3; up to 60% reduction) and AF-associated stroke (223; up to 38% reduction). CONCLUSIONS An ECG-based ML risk prediction model is more efficient than contemporary AF-screening criteria based on age alone or age and clinical features at indicating a population for AF screening to potentially prevent AF-related strokes.
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Affiliation(s)
| | - John M Pfeifer
- Geisinger, Danville, PA, USA; Tempus Labs Inc., Chicago, IL, USA
| | | | | | | | | | | | | | | | - Joseph B Leader
- Geisinger, Danville, PA, USA; Tempus Labs Inc., Chicago, IL, USA
| | | | | | - Ruijun Chen
- Geisinger, Danville, PA, USA; Tempus Labs Inc., Chicago, IL, USA
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2
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Jones LK, Chen N, Hassen D, McMinn M, Klinger T, Hartzel DN, Veenstra D, Spencer S, Snyder SR, Peterson JF, Schlieder V, Sturm AC, Gidding SS, Williams MS, Hao J. Impact of a Population Genomic Screening Program on Health Behaviors Related to Familial Hypercholesterolemia Risk Reduction. Circ Genom Precis Med 2022; 15:e003549. [PMID: 35862023 PMCID: PMC9584046 DOI: 10.1161/circgen.121.003549] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Limited information is available regarding clinician and participant behaviors after disclosure of genomic risk variants for familial hypercholesterolemia (FH) from a population genomic screening program.
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Affiliation(s)
- Laney K. Jones
- Geisinger, Danville, PA (L.K.J., N.C., D.H., M.M., T.K., D.N.H., V.S., A.C.S., S.S.G., M.S.W., J.H.)
| | - Nan Chen
- Geisinger, Danville, PA (L.K.J., N.C., D.H., M.M., T.K., D.N.H., V.S., A.C.S., S.S.G., M.S.W., J.H.)
| | - Dina Hassen
- Geisinger, Danville, PA (L.K.J., N.C., D.H., M.M., T.K., D.N.H., V.S., A.C.S., S.S.G., M.S.W., J.H.)
| | - Megan McMinn
- Geisinger, Danville, PA (L.K.J., N.C., D.H., M.M., T.K., D.N.H., V.S., A.C.S., S.S.G., M.S.W., J.H.)
| | - Tracey Klinger
- Geisinger, Danville, PA (L.K.J., N.C., D.H., M.M., T.K., D.N.H., V.S., A.C.S., S.S.G., M.S.W., J.H.)
| | - Dustin N. Hartzel
- Geisinger, Danville, PA (L.K.J., N.C., D.H., M.M., T.K., D.N.H., V.S., A.C.S., S.S.G., M.S.W., J.H.)
| | | | | | | | | | - Victoria Schlieder
- Geisinger, Danville, PA (L.K.J., N.C., D.H., M.M., T.K., D.N.H., V.S., A.C.S., S.S.G., M.S.W., J.H.)
| | - Amy C. Sturm
- Geisinger, Danville, PA (L.K.J., N.C., D.H., M.M., T.K., D.N.H., V.S., A.C.S., S.S.G., M.S.W., J.H.)
| | - Samuel S. Gidding
- Geisinger, Danville, PA (L.K.J., N.C., D.H., M.M., T.K., D.N.H., V.S., A.C.S., S.S.G., M.S.W., J.H.)
| | - Marc S. Williams
- Geisinger, Danville, PA (L.K.J., N.C., D.H., M.M., T.K., D.N.H., V.S., A.C.S., S.S.G., M.S.W., J.H.)
| | - Jing Hao
- Geisinger, Danville, PA (L.K.J., N.C., D.H., M.M., T.K., D.N.H., V.S., A.C.S., S.S.G., M.S.W., J.H.)
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3
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Khurshid S, Mars N, Haggerty CM, Huang Q, Weng LC, Hartzel DN, Lunetta KL, Ashburner JM, Anderson CD, Benjamin EJ, Salomaa V, Ellinor PT, Fornwalt BK, Ripatti S, Trinquart L, Lubitz SA. Predictive Accuracy of a Clinical and Genetic Risk Model for Atrial Fibrillation. Circ Genom Precis Med 2021; 14:e003355. [PMID: 34463125 PMCID: PMC8530935 DOI: 10.1161/circgen.121.003355] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Atrial fibrillation (AF) risk estimation using clinical factors with or without genetic information may identify AF screening candidates more accurately than the guideline-based age threshold of ≥65 years. METHODS We analyzed 4 samples across the United States and Europe (derivation: UK Biobank; validation: FINRISK, Geisinger MyCode Initiative, and Framingham Heart Study). We estimated AF risk using the CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology AF) score and a combination of CHARGE-AF and a 1168-variant polygenic score (Predict-AF). We compared the utility of age, CHARGE-AF, and Predict-AF for predicting 5-year AF by quantifying discrimination and calibration. RESULTS Among 543 093 individuals, 8940 developed AF within 5 years. In the validation sets, CHARGE-AF (C index range, 0.720-0.824) and Predict-AF (0.749-0.831) had largely comparable discrimination, both favorable to continuous age (0.675-0.801). Calibration was similar using CHARGE-AF (slope range, 0.67-0.87) and Predict-AF (0.65-0.83). Net reclassification improvement using Predict-AF versus CHARGE-AF was modest (net reclassification improvement range, 0.024-0.057) but more favorable among individuals aged <65 years (0.062-0.11). Using Predict-AF among 99 530 individuals aged ≥65 years across each sample, 70 849 had AF risk <5%, of whom 69 067 (97.5%) did not develop AF, whereas 28 681 had AF risk ≥5%, of whom 2264 (7.9%) developed AF. Of 11 379 individuals aged <65 years with AF risk ≥5%, 435 (3.8%) developed AF before age 65 years, with roughly half (46.9%) meeting anticoagulation criteria. CONCLUSIONS AF risk estimation using clinical factors may prioritize individuals for AF screening more precisely than the age threshold endorsed in current guidelines. The additional value of genetic predisposition is modest but greatest among younger individuals.
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Affiliation(s)
- Shaan Khurshid
- Division of Cardiology, Massachusetts General Hospital, Boston
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA
| | - Nina Mars
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Christopher M. Haggerty
- Heart Institute, Geisinger, Danville, PA
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA
| | - Qiuxi Huang
- Department of Biostatistics, Boston University School of Public Health, Boston
- Boston University and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA
| | - Lu-Chen Weng
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA
| | - Dustin N. Hartzel
- Phenomic Analytics and Clinical Data Core, Geisinger Health, Danville, PA
| | | | - Kathryn L. Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston
- Boston University and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA
| | | | - Christopher D. Anderson
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston
- Center for Genomic Medicine, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA
| | - Emelia J. Benjamin
- Sections of Preventive Medicine and Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Department of Epidemiology, Boston University School of Public Health, Boston
- Boston University and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA
| | - Veikko Salomaa
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Patrick T. Ellinor
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
- Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA
| | - Brandon K. Fornwalt
- Heart Institute, Geisinger, Danville, PA
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA
| | - Ludovic Trinquart
- Department of Biostatistics, Boston University School of Public Health, Boston
- Boston University and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA
| | - Steven A. Lubitz
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
- Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA
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4
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Ulloa Cerna AE, Jing L, Good CW, vanMaanen DP, Raghunath S, Suever JD, Nevius CD, Wehner GJ, Hartzel DN, Leader JB, Alsaid A, Patel AA, Kirchner HL, Pfeifer JM, Carry BJ, Pattichis MS, Haggerty CM, Fornwalt BK. Deep-learning-assisted analysis of echocardiographic videos improves predictions of all-cause mortality. Nat Biomed Eng 2021; 5:546-554. [PMID: 33558735 DOI: 10.1038/s41551-020-00667-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 11/24/2020] [Indexed: 01/30/2023]
Abstract
Machine learning promises to assist physicians with predictions of mortality and of other future clinical events by learning complex patterns from historical data, such as longitudinal electronic health records. Here we show that a convolutional neural network trained on raw pixel data in 812,278 echocardiographic videos from 34,362 individuals provides superior predictions of one-year all-cause mortality. The model's predictions outperformed the widely used pooled cohort equations, the Seattle Heart Failure score (measured in an independent dataset of 2,404 patients with heart failure who underwent 3,384 echocardiograms), and a machine learning model involving 58 human-derived variables from echocardiograms and 100 clinical variables derived from electronic health records. We also show that cardiologists assisted by the model substantially improved the sensitivity of their predictions of one-year all-cause mortality by 13% while maintaining prediction specificity. Large unstructured datasets may enable deep learning to improve a wide range of clinical prediction models.
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Affiliation(s)
- Alvaro E Ulloa Cerna
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA, USA.,Electrical and Computer Engineering Department, University of New Mexico, Albuquerque, NM, USA
| | - Linyuan Jing
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA, USA
| | | | - David P vanMaanen
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA, USA
| | - Sushravya Raghunath
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA, USA
| | - Jonathan D Suever
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA, USA
| | - Christopher D Nevius
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA, USA
| | - Gregory J Wehner
- Department of Biomedical Engineering, University of Kentucky, Lexington, KY, USA
| | - Dustin N Hartzel
- Phenomic Analytics and Clinical Data Core, Geisinger, Danville, PA, USA
| | - Joseph B Leader
- Phenomic Analytics and Clinical Data Core, Geisinger, Danville, PA, USA
| | - Amro Alsaid
- Heart Institute, Geisinger, Danville, PA, USA
| | | | - H Lester Kirchner
- Department of Population Health Sciences, Geisinger, Danville, PA, USA
| | - John M Pfeifer
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA, USA.,Heart and Vascular Center, Evangelical Hospital, Lewisburg, PA, USA
| | | | - Marios S Pattichis
- Electrical and Computer Engineering Department, University of New Mexico, Albuquerque, NM, USA
| | - Christopher M Haggerty
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA, USA.,Heart Institute, Geisinger, Danville, PA, USA
| | - Brandon K Fornwalt
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA, USA. .,Heart Institute, Geisinger, Danville, PA, USA. .,Department of Radiology, Geisinger, Danville, PA, USA.
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5
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Wehner GJ, Jing L, Haggerty CM, Suever JD, Leader JB, Hartzel DN, Kirchner HL, Manus JNA, James N, Ayar Z, Gladding P, Good CW, Cleland JGF, Fornwalt BK. Routinely reported ejection fraction and mortality in clinical practice: where does the nadir of risk lie? Eur Heart J 2021; 41:1249-1257. [PMID: 31386109 DOI: 10.1093/eurheartj/ehz550] [Citation(s) in RCA: 140] [Impact Index Per Article: 46.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 05/07/2019] [Accepted: 07/20/2019] [Indexed: 12/14/2022] Open
Abstract
AIMS We investigated the relationship between clinically assessed left ventricular ejection fraction (LVEF) and survival in a large, heterogeneous clinical cohort. METHODS AND RESULTS Physician-reported LVEF on 403 977 echocardiograms from 203 135 patients were linked to all-cause mortality using electronic health records (1998-2018) from US regional healthcare system. Cox proportional hazards regression was used for analyses while adjusting for many patient characteristics including age, sex, and relevant comorbidities. A dataset including 45 531 echocardiograms and 35 976 patients from New Zealand was used to provide independent validation of analyses. During follow-up of the US cohort, 46 258 (23%) patients who had undergone 108 578 (27%) echocardiograms died. Overall, adjusted hazard ratios (HR) for mortality showed a u-shaped relationship for LVEF with a nadir of risk at an LVEF of 60-65%, a HR of 1.71 [95% confidence interval (CI) 1.64-1.77] when ≥70% and a HR of 1.73 (95% CI 1.66-1.80) at LVEF of 35-40%. Similar relationships with a nadir at 60-65% were observed in the validation dataset as well as for each age group and both sexes. The results were similar after further adjustments for conditions associated with an elevated LVEF, including mitral regurgitation, increased wall thickness, and anaemia and when restricted to patients reported to have heart failure at the time of the echocardiogram. CONCLUSION Deviation of LVEF from 60% to 65% is associated with poorer survival regardless of age, sex, or other relevant comorbidities such as heart failure. These results may herald the recognition of a new phenotype characterized by supra-normal LVEF.
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Affiliation(s)
- Gregory J Wehner
- Department of Biomedical Engineering, University of Kentucky, Lexington, KY, USA
| | - Linyuan Jing
- Department of Imaging Science and Innovation, Geisinger, Danville, PA, USA.,Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, USA
| | - Christopher M Haggerty
- Department of Imaging Science and Innovation, Geisinger, Danville, PA, USA.,Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, USA
| | - Jonathan D Suever
- Department of Imaging Science and Innovation, Geisinger, Danville, PA, USA.,Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, USA
| | - Joseph B Leader
- Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, USA
| | - Dustin N Hartzel
- Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, USA
| | - H Lester Kirchner
- Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, USA
| | - Joseph N A Manus
- Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, USA
| | - Nick James
- Department of Cardiology, Waitemata District Health Board, Auckland, New Zealand
| | - Zina Ayar
- Clinical Informatics Service, Waitemata District Health Board, Auckland, New Zealand
| | - Patrick Gladding
- Department of Cardiology, Waitemata District Health Board, Auckland, New Zealand
| | | | - John G F Cleland
- Robertson Centre for Biostatistics and Clinical Trials, University of Glasgow and National Heart & Lung Institute, Imperial College London, London, UK
| | - Brandon K Fornwalt
- Department of Imaging Science and Innovation, Geisinger, Danville, PA, USA.,Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, USA.,Heart Institute, Geisinger, Danville, PA, USA.,Department of Radiology, Geisinger, 100 North Academy Avenue, Danville 17822-4400, PA, USA
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6
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Raghunath S, Pfeifer JM, Ulloa-Cerna AE, Nemani A, Carbonati T, Jing L, vanMaanen DP, Hartzel DN, Ruhl JA, Lagerman BF, Rocha DB, Stoudt NJ, Schneider G, Johnson KW, Zimmerman N, Leader JB, Kirchner HL, Griessenauer CJ, Hafez A, Good CW, Fornwalt BK, Haggerty CM. Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke. Circulation 2021; 143:1287-1298. [PMID: 33588584 PMCID: PMC7996054 DOI: 10.1161/circulationaha.120.047829] [Citation(s) in RCA: 93] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Supplemental Digital Content is available in the text. Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We hypothesized that a deep neural network could predict new-onset AF from the resting 12-lead ECG and that this prediction may help identify those at risk of AF-related stroke.
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Affiliation(s)
- Sushravya Raghunath
- Department of Translational Data Science and Informatics (S.R., A.E.U.-C., L.J., D.P.v.M, J.A.R., N.J.S., G.S., B.K.F., C.M.H.), Geisinger, Danville, PA
| | - John M Pfeifer
- Heart and Vascular Center, Evangelical Hospital, Lewisburg, PA (J.M.P.)
| | - Alvaro E Ulloa-Cerna
- Department of Translational Data Science and Informatics (S.R., A.E.U.-C., L.J., D.P.v.M, J.A.R., N.J.S., G.S., B.K.F., C.M.H.), Geisinger, Danville, PA
| | - Arun Nemani
- Tempus Labs Inc, Chicago, IL (A.N., T.C., K.W.J., N.Z., A.H.)
| | | | - Linyuan Jing
- Department of Translational Data Science and Informatics (S.R., A.E.U.-C., L.J., D.P.v.M, J.A.R., N.J.S., G.S., B.K.F., C.M.H.), Geisinger, Danville, PA
| | - David P vanMaanen
- Department of Translational Data Science and Informatics (S.R., A.E.U.-C., L.J., D.P.v.M, J.A.R., N.J.S., G.S., B.K.F., C.M.H.), Geisinger, Danville, PA
| | - Dustin N Hartzel
- Phenomic Analytics and Clinical Data Core (D.N.H., B.F.L., D.B.R., J.B.L.), Geisinger, Danville, PA
| | - Jeffery A Ruhl
- Department of Translational Data Science and Informatics (S.R., A.E.U.-C., L.J., D.P.v.M, J.A.R., N.J.S., G.S., B.K.F., C.M.H.), Geisinger, Danville, PA
| | - Braxton F Lagerman
- Phenomic Analytics and Clinical Data Core (D.N.H., B.F.L., D.B.R., J.B.L.), Geisinger, Danville, PA
| | - Daniel B Rocha
- Phenomic Analytics and Clinical Data Core (D.N.H., B.F.L., D.B.R., J.B.L.), Geisinger, Danville, PA
| | - Nathan J Stoudt
- Department of Translational Data Science and Informatics (S.R., A.E.U.-C., L.J., D.P.v.M, J.A.R., N.J.S., G.S., B.K.F., C.M.H.), Geisinger, Danville, PA
| | - Gargi Schneider
- Department of Translational Data Science and Informatics (S.R., A.E.U.-C., L.J., D.P.v.M, J.A.R., N.J.S., G.S., B.K.F., C.M.H.), Geisinger, Danville, PA
| | - Kipp W Johnson
- Tempus Labs Inc, Chicago, IL (A.N., T.C., K.W.J., N.Z., A.H.)
| | - Noah Zimmerman
- Tempus Labs Inc, Chicago, IL (A.N., T.C., K.W.J., N.Z., A.H.)
| | - Joseph B Leader
- Phenomic Analytics and Clinical Data Core (D.N.H., B.F.L., D.B.R., J.B.L.), Geisinger, Danville, PA
| | - H Lester Kirchner
- Department of Population Health Sciences (H.L.K.), Geisinger, Danville, PA
| | - Christoph J Griessenauer
- Department of Vascular and Endovascular Neurosurgery (C.J.G.), Geisinger, Danville, PA.,Research Institute of Neurointervention, Paracelsus Medical University, Salzburg, Austria (C.J.G.)
| | - Ashraf Hafez
- Tempus Labs Inc, Chicago, IL (A.N., T.C., K.W.J., N.Z., A.H.)
| | - Christopher W Good
- Heart Institute (C.W.G., B.K.F., C.M.H.), Geisinger, Danville, PA.,Heart and Vascular Institute at University of Pittsburgh Medical Center Hamot, Erie, PA (C.W.G.)
| | - Brandon K Fornwalt
- Department of Translational Data Science and Informatics (S.R., A.E.U.-C., L.J., D.P.v.M, J.A.R., N.J.S., G.S., B.K.F., C.M.H.), Geisinger, Danville, PA.,Heart Institute (C.W.G., B.K.F., C.M.H.), Geisinger, Danville, PA.,Department of Radiology (B.K.F.), Geisinger, Danville, PA
| | - Christopher M Haggerty
- Department of Translational Data Science and Informatics (S.R., A.E.U.-C., L.J., D.P.v.M, J.A.R., N.J.S., G.S., B.K.F., C.M.H.), Geisinger, Danville, PA.,Heart Institute (C.W.G., B.K.F., C.M.H.), Geisinger, Danville, PA
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7
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Oetjens MT, Luo JZ, Chang A, Leader JB, Hartzel DN, Moore BS, Strande NT, Kirchner HL, Ledbetter DH, Justice AE, Carey DJ, Mirshahi T. Electronic health record analysis identifies kidney disease as the leading risk factor for hospitalization in confirmed COVID-19 patients. PLoS One 2020; 15:e0242182. [PMID: 33180868 PMCID: PMC7660530 DOI: 10.1371/journal.pone.0242182] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 10/28/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Empirical data on conditions that increase risk of coronavirus disease 2019 (COVID-19) progression are needed to identify high risk individuals. We performed a comprehensive quantitative assessment of pre-existing clinical phenotypes associated with COVID-19-related hospitalization. METHODS Phenome-wide association study (PheWAS) of SARS-CoV-2-positive patients from an integrated health system (Geisinger) with system-level outpatient/inpatient COVID-19 testing capacity and retrospective electronic health record (EHR) data to assess pre-COVID-19 pandemic clinical phenotypes associated with hospital admission (hospitalization). RESULTS Of 12,971 individuals tested for SARS-CoV-2 with sufficient pre-COVID-19 pandemic EHR data at Geisinger, 1604 were SARS-CoV-2 positive and 354 required hospitalization. We identified 21 clinical phenotypes in 5 disease categories meeting phenome-wide significance (P<1.60x10-4), including: six kidney phenotypes, e.g. end stage renal disease or stage 5 CKD (OR = 11.07, p = 1.96x10-8), six cardiovascular phenotypes, e.g. congestive heart failure (OR = 3.8, p = 3.24x10-5), five respiratory phenotypes, e.g. chronic airway obstruction (OR = 2.54, p = 3.71x10-5), and three metabolic phenotypes, e.g. type 2 diabetes (OR = 1.80, p = 7.51x10-5). Additional analyses defining CKD based on estimated glomerular filtration rate, confirmed high risk of hospitalization associated with pre-existing stage 4 CKD (OR 2.90, 95% CI: 1.47, 5.74), stage 5 CKD/dialysis (OR 8.83, 95% CI: 2.76, 28.27), and kidney transplant (OR 14.98, 95% CI: 2.77, 80.8) but not stage 3 CKD (OR 1.03, 95% CI: 0.71, 1.48). CONCLUSIONS This study provides quantitative estimates of the contribution of pre-existing clinical phenotypes to COVID-19 hospitalization and highlights kidney disorders as the strongest factors associated with hospitalization in an integrated US healthcare system.
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Affiliation(s)
| | | | | | | | | | - Bryn S. Moore
- Geisinger, Danville, Pennsylvania, United States of America
| | | | | | | | | | - David J. Carey
- Geisinger, Danville, Pennsylvania, United States of America
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8
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Jing L, Ulloa Cerna AE, Good CW, Sauers NM, Schneider G, Hartzel DN, Leader JB, Kirchner HL, Hu Y, Riviello DM, Stough JV, Gazes S, Haggerty A, Raghunath S, Carry BJ, Haggerty CM, Fornwalt BK. A Machine Learning Approach to Management of Heart Failure Populations. JACC Heart Fail 2020; 8:578-587. [PMID: 32387064 DOI: 10.1016/j.jchf.2020.01.012] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 01/02/2020] [Accepted: 01/02/2020] [Indexed: 01/19/2023]
Abstract
BACKGROUND Heart failure is a prevalent, costly disease for which new value-based payment models demand optimized population management strategies. OBJECTIVES This study sought to generate a strategy for managing populations of patients with heart failure by leveraging large clinical datasets and machine learning. METHODS Geisinger electronic health record data were used to train machine learning models to predict 1-year all-cause mortality in 26,971 patients with heart failure who underwent 276,819 clinical episodes. There were 26 clinical variables (demographics, laboratory test results, medications), 90 diagnostic codes, 41 electrocardiogram measurements and patterns, 44 echocardiographic measurements, and 8 evidence-based "care gaps": flu vaccine, blood pressure of <130/80 mm Hg, A1c of <8%, cardiac resynchronization therapy, and active medications (active angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker/angiotensin receptor-neprilysin inhibitor, aldosterone receptor antagonist, hydralazine, and evidence-based beta-blocker) were collected. Care gaps represented actionable variables for which associations with all-cause mortality were modeled from retrospective data and then used to predict the benefit of prospective interventions in 13,238 currently living patients. RESULTS Machine learning models achieved areas under the receiver-operating characteristic curve (AUCs) of 0.74 to 0.77 in a split-by-year training/test scheme, with the nonlinear XGBoost model (AUC: 0.77) outperforming linear logistic regression (AUC: 0.74). Out of 13,238 currently living patients, 2,844 were predicted to die within a year, and closing all care gaps was predicted to save 231 of these lives. Prioritizing patients for intervention by using the predicted reduction in 1-year mortality risk outperformed all other priority rankings (e.g., random selection or Seattle Heart Failure risk score). CONCLUSIONS Machine learning can be used to priority-rank patients most likely to benefit from interventions to optimize evidence-based therapies. This approach may prove useful for optimizing heart failure population health management teams within value-based payment models.
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Affiliation(s)
- Linyuan Jing
- Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania
| | - Alvaro E Ulloa Cerna
- Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania
| | | | - Nathan M Sauers
- Center for Pharmacy Innovation and Outcomes, Geisinger, Danville, Pennsylvania
| | | | - Dustin N Hartzel
- Phenomic Analytics and Clinical Data Core, Geisinger, Danville, Pennsylvania
| | - Joseph B Leader
- Phenomic Analytics and Clinical Data Core, Geisinger, Danville, Pennsylvania
| | - H Lester Kirchner
- Department of Population Health Sciences, Geisinger, Danville, Pennsylvania
| | - Yirui Hu
- Department of Population Health Sciences, Geisinger, Danville, Pennsylvania
| | - David M Riviello
- Steele Institute for Health Innovation, Geisinger, Danville, Pennsylvania
| | - Joshua V Stough
- Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania; Department of Computer Science, Bucknell University, Lewisburg, Pennsylvania
| | - Seth Gazes
- Center for Pharmacy Innovation and Outcomes, Geisinger, Danville, Pennsylvania
| | - Allyson Haggerty
- Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania
| | - Sushravya Raghunath
- Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania
| | | | - Christopher M Haggerty
- Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania; Heart Institute, Geisinger, Danville, Pennsylvania
| | - Brandon K Fornwalt
- Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania; Heart Institute, Geisinger, Danville, Pennsylvania; Department of Radiology, Geisinger, Danville, Pennsylvania.
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9
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Hao J, Hassen D, Manickam K, Murray MF, Hartzel DN, Hu Y, Liu K, Rahm AK, Williams MS, Lazzeri A, Buchanan A, Sturm A, Snyder SR. Healthcare Utilization and Costs after Receiving a Positive BRCA1/2 Result from a Genomic Screening Program. J Pers Med 2020; 10:jpm10010007. [PMID: 32028596 PMCID: PMC7151600 DOI: 10.3390/jpm10010007] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 01/21/2020] [Accepted: 01/28/2020] [Indexed: 01/08/2023] Open
Abstract
Population genomic screening has been demonstrated to detect at-risk individuals who would not be clinically identified otherwise. However, there are concerns about the increased utilization of unnecessary services and the associated increase in costs. The objectives of this study are twofold: (1) determine whether there is a difference in healthcare utilization and costs following disclosure of a pathogenic/likely pathogenic (P/LP) BRCA1/2 variant via a genomic screening program, and (2) measure the post-disclosure uptake of National Comprehensive Cancer Network (NCCN) guideline-recommended risk management. We retrospectively reviewed electronic health record (EHR) and billing data from a female population of BRCA1/2 P/LP variant carriers without a personal history of breast or ovarian cancer enrolled in Geisinger’s MyCode genomic screening program with at least a one-year post-disclosure observation period. We identified 59 women for the study cohort out of 50,726 MyCode participants. We found no statistically significant differences in inpatient and outpatient utilization and average total costs between one-year pre- and one-year post-disclosure periods ($18,821 vs. $19,359, p = 0.76). During the first year post-disclosure, 49.2% of women had a genetic counseling visit, 45.8% had a mammography and 32.2% had an MRI. The uptake of mastectomy and oophorectomy was 3.5% and 11.8%, respectively, and 5% of patients received chemoprevention.
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Affiliation(s)
- Jing Hao
- Department of Population Health Sciences, Geisinger, Danville, PA 17822, USA
| | - Dina Hassen
- Department of Population Health Sciences, Geisinger, Danville, PA 17822, USA
| | - Kandamurugu Manickam
- Division of Genetic and Genomic Medicine, Nationwide Children's Hospital, Columbus, OH 43205, USA
| | - Michael F Murray
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA
| | - Dustin N Hartzel
- Phenomic Analytics and Clinical Data Core, Geisinger, Danville, PA 17822, USA
| | - Yirui Hu
- Department of Population Health Sciences, Geisinger, Danville, PA 17822, USA
| | - Kunpeng Liu
- Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA
| | | | - Marc S Williams
- Genomic Medicine Institute, Geisinger, Danville, PA 17822, USA
| | - Amanda Lazzeri
- Genomic Medicine Institute, Geisinger, Danville, PA 17822, USA
| | - Adam Buchanan
- Genomic Medicine Institute, Geisinger, Danville, PA 17822, USA
| | - Amy Sturm
- Genomic Medicine Institute, Geisinger, Danville, PA 17822, USA
| | - Susan R Snyder
- Department of Health Policy and Behavioral Science, School of Public Health, Georgia State University, Atlanta, GA 30302, USA
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10
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Raghunath S, Ulloa Cerna AE, Jing L, van Maanen DP, Hartzel DN, Good CW, Patel AA, Delisle BP, Haggerty CM, Fornwalt BK. Deep neural networks can predict one-year mortality and incident atrial fibrillation from raw 12-lead electrocardiogram voltage data. J Electrocardiol 2019. [DOI: 10.1016/j.jelectrocard.2019.08.033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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11
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Carruth ED, Young W, Beer D, James CA, Calkins H, Jing L, Raghunath S, Hartzel DN, Leader JB, Kirchner HL, Smelser DT, Carey DJ, Kelly MA, Sturm AC, Alsaid A, Fornwalt BK, Haggerty CM. Prevalence and Electronic Health Record-Based Phenotype of Loss-of-Function Genetic Variants in Arrhythmogenic Right Ventricular Cardiomyopathy-Associated Genes. Circ Genom Precis Med 2019; 12:e002579. [PMID: 31638835 DOI: 10.1161/circgen.119.002579] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
BACKGROUND Arrhythmogenic right ventricular cardiomyopathy (ARVC) is associated with variants in desmosome genes. Secondary findings of pathogenic/likely pathogenic variants, primarily loss-of-function (LOF) variants, are recommended for clinical reporting; however, their prevalence and associated phenotype in a general clinical population are not fully characterized. METHODS From whole-exome sequencing of 61 019 individuals in the DiscovEHR cohort, we screened for putative loss-of-function variants in PKP2, DSC2, DSG2, and DSP. We evaluated measures from prior clinical ECG and echocardiograms, manually over-read to evaluate ARVC diagnostic criteria, and performed a PheWAS (phenome-wide association study). Finally, we estimated expected penetrance using Bayesian inference. RESULTS One hundred forty individuals (0.23%; 59±18 years old at last encounter; 33% male) had an ARVC variant (G+). None had an existing diagnosis of ARVC in the electronic health record, nor significant differences in prior ECG or echocardiogram findings compared with matched controls without variants. Several G+ individuals satisfied major repolarization (n=4) and ventricular function (n=5) criteria, but this prevalence matched controls. PheWAS showed no significant associations of other heart disease diagnoses. Combining our best genetic and disease prevalence estimates yields an estimated penetrance of 6.0%. CONCLUSIONS The prevalence of ARVC loss-of-function variants is ≈1:435 in a general clinical population of predominantly European descent, but with limited electronic health record-based evidence of phenotypic association in our population, consistent with a low penetrance estimate. Prospective deep phenotyping and longitudinal follow-up of a large sequenced cohort is needed to determine the true clinical relevance of an incidentally identified ARVC loss-of-function variant.
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Affiliation(s)
- Eric D Carruth
- Department of Imaging Science and Innovation (E.D.C., L.J., S.R., B.K.F., C.M.H.), Geisinger, Danville, PA.,Biomedical and Translational Informatics Institute (E.D.C., L.J., S.R., D.N.H., J.B.L., H.L.K., B.K.F., C.M.H.), Geisinger, Danville, PA
| | - Wilson Young
- The Heart Institute (W.Y., D.B., A.A., B.K.F., C.M.H.), Geisinger, Danville, PA
| | - Dominik Beer
- The Heart Institute (W.Y., D.B., A.A., B.K.F., C.M.H.), Geisinger, Danville, PA
| | - Cynthia A James
- Department of Medicine, Division of Cardiology, Johns Hopkins Medical Center, Baltimore, MD (C.A.J., H.C.)
| | - Hugh Calkins
- Department of Medicine, Division of Cardiology, Johns Hopkins Medical Center, Baltimore, MD (C.A.J., H.C.)
| | - Linyuan Jing
- Department of Imaging Science and Innovation (E.D.C., L.J., S.R., B.K.F., C.M.H.), Geisinger, Danville, PA.,Biomedical and Translational Informatics Institute (E.D.C., L.J., S.R., D.N.H., J.B.L., H.L.K., B.K.F., C.M.H.), Geisinger, Danville, PA
| | - Sushravya Raghunath
- Department of Imaging Science and Innovation (E.D.C., L.J., S.R., B.K.F., C.M.H.), Geisinger, Danville, PA.,Biomedical and Translational Informatics Institute (E.D.C., L.J., S.R., D.N.H., J.B.L., H.L.K., B.K.F., C.M.H.), Geisinger, Danville, PA
| | - Dustin N Hartzel
- Biomedical and Translational Informatics Institute (E.D.C., L.J., S.R., D.N.H., J.B.L., H.L.K., B.K.F., C.M.H.), Geisinger, Danville, PA
| | - Joseph B Leader
- Biomedical and Translational Informatics Institute (E.D.C., L.J., S.R., D.N.H., J.B.L., H.L.K., B.K.F., C.M.H.), Geisinger, Danville, PA
| | - H Lester Kirchner
- Biomedical and Translational Informatics Institute (E.D.C., L.J., S.R., D.N.H., J.B.L., H.L.K., B.K.F., C.M.H.), Geisinger, Danville, PA
| | - Diane T Smelser
- Department of Molecular and Functional Genomics (D.T.S., D.J.C.), Geisinger, Danville, PA
| | - David J Carey
- Department of Molecular and Functional Genomics (D.T.S., D.J.C.), Geisinger, Danville, PA
| | - Melissa A Kelly
- Genomic Medicine Institute (M.A.K., A.C.S.), Geisinger, Danville, PA
| | - Amy C Sturm
- Genomic Medicine Institute (M.A.K., A.C.S.), Geisinger, Danville, PA
| | - Amro Alsaid
- The Heart Institute (W.Y., D.B., A.A., B.K.F., C.M.H.), Geisinger, Danville, PA
| | - Brandon K Fornwalt
- Department of Imaging Science and Innovation (E.D.C., L.J., S.R., B.K.F., C.M.H.), Geisinger, Danville, PA.,Biomedical and Translational Informatics Institute (E.D.C., L.J., S.R., D.N.H., J.B.L., H.L.K., B.K.F., C.M.H.), Geisinger, Danville, PA.,The Heart Institute (W.Y., D.B., A.A., B.K.F., C.M.H.), Geisinger, Danville, PA.,Department of Radiology (B.K.F.), Geisinger, Danville, PA
| | - Christopher M Haggerty
- Department of Imaging Science and Innovation (E.D.C., L.J., S.R., B.K.F., C.M.H.), Geisinger, Danville, PA.,Biomedical and Translational Informatics Institute (E.D.C., L.J., S.R., D.N.H., J.B.L., H.L.K., B.K.F., C.M.H.), Geisinger, Danville, PA.,The Heart Institute (W.Y., D.B., A.A., B.K.F., C.M.H.), Geisinger, Danville, PA
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12
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Zhang Y, Zafar W, Hartzel DN, Williams MS, Tin A, Chang AR, Lee MTM. GSTM1 Copy Number Is Not Associated With Risk of Kidney Failure in a Large Cohort. Front Genet 2019; 10:765. [PMID: 31555322 PMCID: PMC6728412 DOI: 10.3389/fgene.2019.00765] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 07/19/2019] [Indexed: 12/15/2022] Open
Abstract
Deletion of glutathione S-transferase µ1 (GSTM1) is common in populations and has been asserted to associate with chronic kidney disease progression in some research studies. The association needs to be validated. We estimated GSTM1 copy number using whole exome sequencing data in the DiscovEHR cohort. Kidney failure was defined as requiring dialysis or receiving kidney transplant using data from the electronic health record and linkage to the United States Renal Data System, or the most recent eGFR < 15 ml/min/1.73 m2. In a cohort of 46,983 unrelated participants, 28.8% of blacks and 52.1% of whites had 0 copies of GSTM1. Over a mean of 9.2 years follow-up, 645 kidney failure events were observed in 46,187 white participants, and 28 in 796 black participants. No significant association was observed between GSTM1 copy number and kidney failure in Cox regression adjusting for age, sex, BMI, smoking status, genetic principal components, or comorbid conditions (hypertension, diabetes, heart failure, coronary artery disease, and stroke), whether using a genotypic, dominant, or recessive model. In sensitivity analyses, GSTM1 copy number was not associated with kidney failure in participants that were 45 years or older at baseline, had baseline eGFR < 60 ml/min/1.73 m2, or with baseline year between 1996 and 2002. In conclusion, we found no association between GSTM1 copy number and kidney failure in a large cohort study.
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Affiliation(s)
- Yanfei Zhang
- Genomic Medicine Institute, Geisinger, Danville, PA, United States
| | - Waleed Zafar
- Kidney Institute, Geisinger, Danville, PA, United States
| | - Dustin N Hartzel
- Phenomic Analytics & Clinical Data Core, Geisinger, Danville, PA, United States
| | - Marc S Williams
- Genomic Medicine Institute, Geisinger, Danville, PA, United States
| | - Adrienne Tin
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States
| | - Alex R Chang
- Kidney Institute, Geisinger, Danville, PA, United States
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13
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Besse W, Chang AR, Luo JZ, Triffo WJ, Moore BS, Gulati A, Hartzel DN, Mane S, Torres VE, Somlo S, Mirshahi T. ALG9 Mutation Carriers Develop Kidney and Liver Cysts. J Am Soc Nephrol 2019; 30:2091-2102. [PMID: 31395617 DOI: 10.1681/asn.2019030298] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 06/26/2019] [Indexed: 11/03/2022] Open
Abstract
BACKGROUND Mutations in PKD1 or PKD2 cause typical autosomal dominant polycystic kidney disease (ADPKD), the most common monogenic kidney disease. Dominantly inherited polycystic kidney and liver diseases on the ADPKD spectrum are also caused by mutations in at least six other genes required for protein biogenesis in the endoplasmic reticulum, the loss of which results in defective production of the PKD1 gene product, the membrane protein polycystin-1 (PC1). METHODS We used whole-exome sequencing in a cohort of 122 patients with genetically unresolved clinical diagnosis of ADPKD or polycystic liver disease to identify a candidate gene, ALG9, and in vitro cell-based assays of PC1 protein maturation to functionally validate it. For further validation, we identified carriers of ALG9 loss-of-function mutations and noncarrier matched controls in a large exome-sequenced population-based cohort and evaluated the occurrence of polycystic phenotypes in both groups. RESULTS Two patients in the clinically defined cohort had rare loss-of-function variants in ALG9, which encodes a protein required for addition of specific mannose molecules to the assembling N-glycan precursors in the endoplasmic reticulum lumen. In vitro assays showed that inactivation of Alg9 results in impaired maturation and defective glycosylation of PC1. Seven of the eight (88%) cases selected from the population-based cohort based on ALG9 mutation carrier state who had abdominal imaging after age 50; seven (88%) had at least four kidney cysts, compared with none in matched controls without ALG9 mutations. CONCLUSIONS ALG9 is a novel disease gene in the genetically heterogeneous ADPKD spectrum. This study supports the utility of phenotype characterization in genetically-defined cohorts to validate novel disease genes, and provide much-needed genotype-phenotype correlations.
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Affiliation(s)
| | | | | | | | | | | | - Dustin N Hartzel
- Biomedical and Translational Informatics, Geisinger Clinic, Danville, Pennsylvania; and
| | - Shrikant Mane
- Genetics, Yale University School of Medicine, New Haven, Connecticut
| | | | - Vicente E Torres
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Stefan Somlo
- Departments of Internal Medicine (Nephrology) and .,Genetics, Yale University School of Medicine, New Haven, Connecticut
| | - Tooraj Mirshahi
- Biomedical and Translational Informatics, Geisinger Clinic, Danville, Pennsylvania; and
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14
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Park J, Levin MG, Haggerty CM, Hartzel DN, Judy R, Kember RL, Reza N, Ritchie MD, Owens AT, Damrauer SM, Rader DJ. A genome-first approach to aggregating rare genetic variants in LMNA for association with electronic health record phenotypes. Genet Med 2019; 22:102-111. [PMID: 31383942 DOI: 10.1038/s41436-019-0625-8] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 07/18/2019] [Indexed: 01/21/2023] Open
Abstract
PURPOSE "Genome-first" approaches, in which genetic sequencing is agnostically linked to associated phenotypes, can enhance our understanding of rare variants' contributions to disease. Loss-of-function variants in LMNA cause a range of rare diseases, including cardiomyopathy. METHODS We leveraged exome sequencing from 11,451 unselected individuals in the Penn Medicine Biobank to associate rare variants in LMNA with diverse electronic health record (EHR)-derived phenotypes. We used Rare Exome Variant Ensemble Learner (REVEL) to annotate rare missense variants, clustered predicted deleterious and loss-of-function variants into a "gene burden" (N = 72 individuals), and performed a phenome-wide association study (PheWAS). Major findings were replicated in DiscovEHR. RESULTS The LMNA gene burden was significantly associated with primary cardiomyopathy (p = 1.78E-11) and cardiac conduction disorders (p = 5.27E-07). Most patients had not been clinically diagnosed with LMNA cardiomyopathy. We also noted an association with chronic kidney disease (p = 1.13E-06). Regression analyses on echocardiography and serum labs revealed that LMNA variant carriers had dilated cardiomyopathy and primary renal disease. CONCLUSION Pathogenic LMNA variants are an underdiagnosed cause of cardiomyopathy. We also find that LMNA loss of function may be a primary cause of renal disease. Finally, we show the value of aggregating rare, annotated variants into a gene burden and using PheWAS to identify novel ontologies for pleiotropic human genes.
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Affiliation(s)
- Joseph Park
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael G Levin
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher M Haggerty
- Department of Imaging Science and Innovation and The Heart Institute, Geisinger, Danville, PA, USA.,Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, USA
| | - Dustin N Hartzel
- Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, USA
| | - Renae Judy
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rachel L Kember
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nosheen Reza
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Center for Inherited Cardiovascular Disease, Division of Cardiovascular Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | | | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anjali T Owens
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Center for Inherited Cardiovascular Disease, Division of Cardiovascular Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Scott M Damrauer
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel J Rader
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. .,Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. .,Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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15
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Haggerty CM, Damrauer SM, Levin MG, Birtwell D, Carey DJ, Golden AM, Hartzel DN, Hu Y, Judy R, Kelly MA, Kember RL, Lester Kirchner H, Leader JB, Liang L, McDermott-Roe C, Babu A, Morley M, Nealy Z, Person TN, Pulenthiran A, Small A, Smelser DT, Stahl RC, Sturm AC, Williams H, Baras A, Margulies KB, Cappola TP, Dewey FE, Verma A, Zhang X, Correa A, Hall ME, Wilson JG, Ritchie MD, Rader DJ, Murray MF, Fornwalt BK, Arany Z. Genomics-First Evaluation of Heart Disease Associated With Titin-Truncating Variants. Circulation 2019; 140:42-54. [PMID: 31216868 DOI: 10.1161/circulationaha.119.039573] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
BACKGROUND Truncating variants in the Titin gene (TTNtvs) are common in individuals with idiopathic dilated cardiomyopathy (DCM). However, a comprehensive genomics-first evaluation of the impact of TTNtvs in different clinical contexts, and the evaluation of modifiers such as genetic ancestry, has not been performed. METHODS We reviewed whole exome sequence data for >71 000 individuals (61 040 from the Geisinger MyCode Community Health Initiative (2007 to present) and 10 273 from the PennMedicine BioBank (2013 to present) to identify anyone with TTNtvs. We further selected individuals with TTNtvs in exons highly expressed in the heart (proportion spliced in [PSI] >0.9). Using linked electronic health records, we evaluated associations of TTNtvs with diagnoses and quantitative echocardiographic measures, including subanalyses for individuals with and without DCM diagnoses. We also reviewed data from the Jackson Heart Study to validate specific analyses for individuals of African ancestry. RESULTS Identified with a TTNtv in a highly expressed exon (hiPSI) were 1.2% individuals in PennMedicine BioBank and 0.6% at Geisinger. The presence of a hiPSI TTNtv was associated with increased odds of DCM in individuals of European ancestry (odds ratio [95% CI]: 18.7 [9.1-39.4] {PennMedicine BioBank} and 10.8 [7.0-16.0] {Geisinger}). hiPSI TTNtvs were not associated with DCM in individuals of African ancestry, despite a high DCM prevalence (odds ratio, 1.8 [0.2-13.7]; P=0.57). Among 244 individuals of European ancestry with DCM in PennMedicine BioBank, hiPSI TTNtv carriers had lower left ventricular ejection fraction (β=-12%, P=3×10-7), and increased left ventricular diameter (β=0.65 cm, P=9×10-3). In the Geisinger cohort, hiPSI TTNtv carriers without a cardiomyopathy diagnosis had more atrial fibrillation (odds ratio, 2.4 [1.6-3.6]) and heart failure (odds ratio, 3.8 [2.4-6.0]), and lower left ventricular ejection fraction (β=-3.4%, P=1×10-7). CONCLUSIONS Individuals of European ancestry with hiPSI TTNtv have an abnormal cardiac phenotype characterized by lower left ventricular ejection fraction, irrespective of the clinical manifestation of cardiomyopathy. Associations with arrhythmias, including atrial fibrillation, were observed even when controlling for cardiomyopathy diagnosis. In contrast, no association between hiPSI TTNtvs and DCM was discerned among individuals of African ancestry. Given these findings, clinical identification of hiPSI TTNtv carriers may alter clinical management strategies.
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Affiliation(s)
- Christopher M Haggerty
- Geisinger, Danville, PA (C.M.H., D.J.C., A.M.G., D.N.H., Y.H., M.A.K., H.L.K., J.B.L., Z.N., T.N.P., A.P., D.T.S., R.C.S., A.C.S., M.F.M., B.K.F.)
| | - Scott M Damrauer
- Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D., M.G.L., D.B., R.J., R.L.K., L.L., C.M.-R., A. Babu, M.M., A.S., H.W., K.B.M., T.P.C., A.V., X.Z., M.D.R., D.J.R., Z.A.).,Corporal Michael Crescenz VA Medical Center, Philadelphia, PA (S.M.D.)
| | - Michael G Levin
- Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D., M.G.L., D.B., R.J., R.L.K., L.L., C.M.-R., A. Babu, M.M., A.S., H.W., K.B.M., T.P.C., A.V., X.Z., M.D.R., D.J.R., Z.A.)
| | - David Birtwell
- Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D., M.G.L., D.B., R.J., R.L.K., L.L., C.M.-R., A. Babu, M.M., A.S., H.W., K.B.M., T.P.C., A.V., X.Z., M.D.R., D.J.R., Z.A.)
| | - David J Carey
- Geisinger, Danville, PA (C.M.H., D.J.C., A.M.G., D.N.H., Y.H., M.A.K., H.L.K., J.B.L., Z.N., T.N.P., A.P., D.T.S., R.C.S., A.C.S., M.F.M., B.K.F.)
| | - Alicia M Golden
- Geisinger, Danville, PA (C.M.H., D.J.C., A.M.G., D.N.H., Y.H., M.A.K., H.L.K., J.B.L., Z.N., T.N.P., A.P., D.T.S., R.C.S., A.C.S., M.F.M., B.K.F.)
| | - Dustin N Hartzel
- Geisinger, Danville, PA (C.M.H., D.J.C., A.M.G., D.N.H., Y.H., M.A.K., H.L.K., J.B.L., Z.N., T.N.P., A.P., D.T.S., R.C.S., A.C.S., M.F.M., B.K.F.)
| | - Yirui Hu
- Geisinger, Danville, PA (C.M.H., D.J.C., A.M.G., D.N.H., Y.H., M.A.K., H.L.K., J.B.L., Z.N., T.N.P., A.P., D.T.S., R.C.S., A.C.S., M.F.M., B.K.F.)
| | - Renae Judy
- Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D., M.G.L., D.B., R.J., R.L.K., L.L., C.M.-R., A. Babu, M.M., A.S., H.W., K.B.M., T.P.C., A.V., X.Z., M.D.R., D.J.R., Z.A.)
| | - Melissa A Kelly
- Geisinger, Danville, PA (C.M.H., D.J.C., A.M.G., D.N.H., Y.H., M.A.K., H.L.K., J.B.L., Z.N., T.N.P., A.P., D.T.S., R.C.S., A.C.S., M.F.M., B.K.F.)
| | - Rachel L Kember
- Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D., M.G.L., D.B., R.J., R.L.K., L.L., C.M.-R., A. Babu, M.M., A.S., H.W., K.B.M., T.P.C., A.V., X.Z., M.D.R., D.J.R., Z.A.)
| | - H Lester Kirchner
- Geisinger, Danville, PA (C.M.H., D.J.C., A.M.G., D.N.H., Y.H., M.A.K., H.L.K., J.B.L., Z.N., T.N.P., A.P., D.T.S., R.C.S., A.C.S., M.F.M., B.K.F.)
| | - Joseph B Leader
- Geisinger, Danville, PA (C.M.H., D.J.C., A.M.G., D.N.H., Y.H., M.A.K., H.L.K., J.B.L., Z.N., T.N.P., A.P., D.T.S., R.C.S., A.C.S., M.F.M., B.K.F.)
| | - Lusha Liang
- Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D., M.G.L., D.B., R.J., R.L.K., L.L., C.M.-R., A. Babu, M.M., A.S., H.W., K.B.M., T.P.C., A.V., X.Z., M.D.R., D.J.R., Z.A.)
| | - Chris McDermott-Roe
- Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D., M.G.L., D.B., R.J., R.L.K., L.L., C.M.-R., A. Babu, M.M., A.S., H.W., K.B.M., T.P.C., A.V., X.Z., M.D.R., D.J.R., Z.A.)
| | - Apoorva Babu
- Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D., M.G.L., D.B., R.J., R.L.K., L.L., C.M.-R., A. Babu, M.M., A.S., H.W., K.B.M., T.P.C., A.V., X.Z., M.D.R., D.J.R., Z.A.)
| | - Michael Morley
- Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D., M.G.L., D.B., R.J., R.L.K., L.L., C.M.-R., A. Babu, M.M., A.S., H.W., K.B.M., T.P.C., A.V., X.Z., M.D.R., D.J.R., Z.A.)
| | - Zachariah Nealy
- Geisinger, Danville, PA (C.M.H., D.J.C., A.M.G., D.N.H., Y.H., M.A.K., H.L.K., J.B.L., Z.N., T.N.P., A.P., D.T.S., R.C.S., A.C.S., M.F.M., B.K.F.)
| | - Thomas N Person
- Geisinger, Danville, PA (C.M.H., D.J.C., A.M.G., D.N.H., Y.H., M.A.K., H.L.K., J.B.L., Z.N., T.N.P., A.P., D.T.S., R.C.S., A.C.S., M.F.M., B.K.F.)
| | - Arichanah Pulenthiran
- Geisinger, Danville, PA (C.M.H., D.J.C., A.M.G., D.N.H., Y.H., M.A.K., H.L.K., J.B.L., Z.N., T.N.P., A.P., D.T.S., R.C.S., A.C.S., M.F.M., B.K.F.)
| | - Aeron Small
- Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D., M.G.L., D.B., R.J., R.L.K., L.L., C.M.-R., A. Babu, M.M., A.S., H.W., K.B.M., T.P.C., A.V., X.Z., M.D.R., D.J.R., Z.A.)
| | - Diane T Smelser
- Geisinger, Danville, PA (C.M.H., D.J.C., A.M.G., D.N.H., Y.H., M.A.K., H.L.K., J.B.L., Z.N., T.N.P., A.P., D.T.S., R.C.S., A.C.S., M.F.M., B.K.F.)
| | - Richard C Stahl
- Geisinger, Danville, PA (C.M.H., D.J.C., A.M.G., D.N.H., Y.H., M.A.K., H.L.K., J.B.L., Z.N., T.N.P., A.P., D.T.S., R.C.S., A.C.S., M.F.M., B.K.F.)
| | - Amy C Sturm
- Geisinger, Danville, PA (C.M.H., D.J.C., A.M.G., D.N.H., Y.H., M.A.K., H.L.K., J.B.L., Z.N., T.N.P., A.P., D.T.S., R.C.S., A.C.S., M.F.M., B.K.F.)
| | - Heather Williams
- Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D., M.G.L., D.B., R.J., R.L.K., L.L., C.M.-R., A. Babu, M.M., A.S., H.W., K.B.M., T.P.C., A.V., X.Z., M.D.R., D.J.R., Z.A.)
| | - Aris Baras
- Regeneron Genetics Center, Tarrytown, NY (A. Baras, F.E.D.)
| | - Kenneth B Margulies
- Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D., M.G.L., D.B., R.J., R.L.K., L.L., C.M.-R., A. Babu, M.M., A.S., H.W., K.B.M., T.P.C., A.V., X.Z., M.D.R., D.J.R., Z.A.)
| | - Thomas P Cappola
- Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D., M.G.L., D.B., R.J., R.L.K., L.L., C.M.-R., A. Babu, M.M., A.S., H.W., K.B.M., T.P.C., A.V., X.Z., M.D.R., D.J.R., Z.A.)
| | | | - Anurag Verma
- Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D., M.G.L., D.B., R.J., R.L.K., L.L., C.M.-R., A. Babu, M.M., A.S., H.W., K.B.M., T.P.C., A.V., X.Z., M.D.R., D.J.R., Z.A.)
| | - Xinyuang Zhang
- Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D., M.G.L., D.B., R.J., R.L.K., L.L., C.M.-R., A. Babu, M.M., A.S., H.W., K.B.M., T.P.C., A.V., X.Z., M.D.R., D.J.R., Z.A.)
| | - Adolfo Correa
- Department of Medicine (A.C., M.E.H.), University of Mississippi Medical Center, Jackson
| | - Michael E Hall
- Department of Medicine (A.C., M.E.H.), University of Mississippi Medical Center, Jackson.,Department of Physiology and Biophysics (M.E.H., J.G.W.), University of Mississippi Medical Center, Jackson
| | - James G Wilson
- Department of Physiology and Biophysics (M.E.H., J.G.W.), University of Mississippi Medical Center, Jackson
| | - Marylyn D Ritchie
- Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D., M.G.L., D.B., R.J., R.L.K., L.L., C.M.-R., A. Babu, M.M., A.S., H.W., K.B.M., T.P.C., A.V., X.Z., M.D.R., D.J.R., Z.A.)
| | - Daniel J Rader
- Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D., M.G.L., D.B., R.J., R.L.K., L.L., C.M.-R., A. Babu, M.M., A.S., H.W., K.B.M., T.P.C., A.V., X.Z., M.D.R., D.J.R., Z.A.)
| | - Michael F Murray
- Geisinger, Danville, PA (C.M.H., D.J.C., A.M.G., D.N.H., Y.H., M.A.K., H.L.K., J.B.L., Z.N., T.N.P., A.P., D.T.S., R.C.S., A.C.S., M.F.M., B.K.F.)
| | - Brandon K Fornwalt
- Geisinger, Danville, PA (C.M.H., D.J.C., A.M.G., D.N.H., Y.H., M.A.K., H.L.K., J.B.L., Z.N., T.N.P., A.P., D.T.S., R.C.S., A.C.S., M.F.M., B.K.F.)
| | - Zoltan Arany
- Perelman School of Medicine, University of Pennsylvania, Philadelphia (S.M.D., M.G.L., D.B., R.J., R.L.K., L.L., C.M.-R., A. Babu, M.M., A.S., H.W., K.B.M., T.P.C., A.V., X.Z., M.D.R., D.J.R., Z.A.)
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Verma SS, Josyula N, Verma A, Zhang X, Veturi Y, Dewey FE, Hartzel DN, Lavage DR, Leader J, Ritchie MD, Pendergrass SA. Author Correction: Rare variants in drug target genes contributing to complex diseases, phenome-wide. Sci Rep 2018; 8:15911. [PMID: 30353015 PMCID: PMC6199295 DOI: 10.1038/s41598-018-27936-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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Manickam K, Buchanan AH, Schwartz MLB, Hallquist MLG, Williams JL, Rahm AK, Rocha H, Savatt JM, Evans AE, Butry LM, Lazzeri AL, Lindbuchler DM, Flansburg CN, Leeming R, Vogel VG, Lebo MS, Mason-Suares HM, Hoskinson DC, Abul-Husn NS, Dewey FE, Overton JD, Reid JG, Baras A, Willard HF, McCormick CZ, Krishnamurthy SB, Hartzel DN, Kost KA, Lavage DR, Sturm AC, Frisbie LR, Person TN, Metpally RP, Giovanni MA, Lowry LE, Leader JB, Ritchie MD, Carey DJ, Justice AE, Kirchner HL, Faucett WA, Williams MS, Ledbetter DH, Murray MF. Exome Sequencing-Based Screening for BRCA1/2 Expected Pathogenic Variants Among Adult Biobank Participants. JAMA Netw Open 2018; 1:e182140. [PMID: 30646163 PMCID: PMC6324494 DOI: 10.1001/jamanetworkopen.2018.2140] [Citation(s) in RCA: 140] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
IMPORTANCE Detection of disease-associated variants in the BRCA1 and BRCA2 (BRCA1/2) genes allows for cancer prevention and early diagnosis in high-risk individuals. OBJECTIVES To identify pathogenic and likely pathogenic (P/LP) BRCA1/2 variants in an unselected research cohort, and to characterize the features associated with P/LP variants. DESIGN, SETTING, AND PARTICIPANTS This is a cross-sectional study of adult volunteers (n = 50 726) who underwent exome sequencing at a single health care system (Geisinger Health System, Danville, Pennsylvania) from January 1, 2014, to March 1, 2016. Participants are part of the DiscovEHR cohort and were identified through the Geisinger MyCode Community Health Initiative. They consented to a research protocol that included sequencing and return of actionable test results. Clinical data from electronic health records and clinical visits were correlated with variants. Comparisons were made between those with (cases) and those without (controls) P/LP variants in BRCA1/2. MAIN OUTCOMES Prevalence of P/LP BRCA1/2 variants in cohort, proportion of variant carriers not previously ascertained through clinical testing, and personal and family history of relevant cancers among BRCA1/2 variant carriers and noncarriers. RESULTS Of the 50 726 health system patients who underwent exome sequencing, 50 459 (99.5%) had no expected pathogenic BRCA1/2 variants and 267 (0.5%) were BRCA1/2 carriers. Of the 267 cases (148 [55.4%] were women and 119 [44.6%] were men with a mean [range] age of 58.9 [23-90] years), 183 (68.5%) received clinically confirmed results in their electronic health record. Among the 267 participants with P/LP BRCA1/2 variants, 219 (82.0%) had no prior clinical testing, 95 (35.6%) had BRCA1 variants, and 172 (64.4%) had BRCA2 variants. Syndromic cancer diagnoses were present in 11 (47.8%) of the 23 deceased BRCA1/2 carriers and in 56 (20.9%) of all 267 BRCA1/2 carriers. Among women, 31 (20.9%) of 148 variant carriers had a personal history of breast cancer, compared with 1554 (5.2%) of 29 880 noncarriers (odds ratio [OR], 5.95; 95% CI, 3.88-9.13; P < .001). Ovarian cancer history was present in 15 (10.1%) of 148 variant carriers and in 195 (0.6%) of 29 880 variant noncarriers (OR, 18.30; 95% CI, 10.48-31.4; P < .001). Among 89 BRCA1/2 carriers without prior testing but with comprehensive personal and family history data, 44 (49.4%) did not meet published guidelines for clinical testing. CONCLUSIONS AND RELEVANCE This study found that compared with previous clinical care, exome sequencing-based screening identified 5 times as many individuals with P/LP BRCA1/2 variants. These findings suggest that genomic screening may identify BRCA1/2-associated cancer risk that might otherwise remain undetected within health care systems and may provide opportunities to reduce morbidity and mortality in patients.
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Affiliation(s)
- Kandamurugu Manickam
- Molecular and Human Genetics Department, Nationwide Children’s Hospital, Columbus, Ohio
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
| | | | | | | | | | | | - Heather Rocha
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
| | | | - Alyson E. Evans
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
| | - Loren M. Butry
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
| | | | | | | | | | - Victor G. Vogel
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
| | - Matthew S. Lebo
- Laboratory for Molecular Medicine, Partners HealthCare, Cambridge, Massachusetts
| | | | - Derick C. Hoskinson
- Laboratory for Molecular Medicine, Partners HealthCare, Cambridge, Massachusetts
| | | | | | | | | | - Aris Baras
- Regeneron Genetics Center, Tarrytown, New York
| | | | | | | | | | - Korey A. Kost
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
| | | | - Amy C. Sturm
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
| | | | - T. Nate Person
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
| | | | | | - Lacy E. Lowry
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
| | | | - Marylyn D. Ritchie
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
- Center for Translational Bioinformatics, University of Pennsylvania, Philadelphia
| | - David J. Carey
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
| | - Anne E. Justice
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
| | | | | | | | | | - Michael F. Murray
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
- Department of Genetics, Yale School of Medicine, New Haven, Connecticut
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Smith JL, Tester DJ, Hall AR, Burgess DE, Hsu CC, Elayi SC, Anderson CL, January CT, Luo JZ, Hartzel DN, Mirshahi UL, Murray MF, Mirshahi T, Ackerman MJ, Delisle BP. Functional Invalidation of Putative Sudden Infant Death Syndrome-Associated Variants in the KCNH2-Encoded Kv11.1 Channel. Circ Arrhythm Electrophysiol 2018; 11:e005859. [PMID: 29752375 PMCID: PMC11081002 DOI: 10.1161/circep.117.005859] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Accepted: 03/12/2018] [Indexed: 12/14/2022]
Abstract
BACKGROUND Heterologous functional validation studies of putative long-QT syndrome subtype 2-associated variants clarify their pathological potential and identify disease mechanism(s) for most variants studied. The purpose of this study is to clarify the pathological potential for rare nonsynonymous KCNH2 variants seemingly associated with sudden infant death syndrome. METHODS Genetic testing of 292 sudden infant death syndrome cases identified 9 KCNH2 variants: E90K, R181Q, A190T, G294V, R791W, P967L, R1005W, R1047L, and Q1068R. Previous studies show R181Q-, P967L-, and R1047L-Kv11.1 channels function similar to wild-type Kv11.1 channels, whereas Q1068R-Kv11.1 channels accelerate inactivation gating. We studied the biochemical and biophysical properties for E90K-, G294V-, R791W-, and R1005W-Kv11.1 channels expressed in human embryonic kidney 293 cells; examined the electronic health records of patients who were genotype positive for the sudden infant death syndrome-linked KCNH2 variants; and simulated their functional impact using computational models of the human ventricular action potential. RESULTS Western blot and voltage-clamping analyses of cells expressing E90K-, G294V-, R791W-, and R1005W-Kv11.1 channels demonstrated these variants express and generate peak Kv11.1 current levels similar to cells expressing wild-type-Kv11.1 channels, but R791W- and R1005W-Kv11.1 channels accelerated deactivation and activation gating, respectively. Electronic health records of patients with the sudden infant death syndrome-linked KCNH2 variants showed that the patients had median heart rate-corrected QT intervals <480 ms and none had been diagnosed with long-QT syndrome or experienced cardiac arrest. Simulating the impact of dysfunctional gating variants predicted that they have little impact on ventricular action potential duration. CONCLUSIONS We conclude that these rare Kv11.1 missense variants are not long-QT syndrome subtype 2-causative variants and therefore do not represent the pathogenic substrate for sudden infant death syndrome in the variant-positive infants.
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Affiliation(s)
- Jennifer L Smith
- Department of Physiology, Cardiovascular Research Center, Center for Muscle Biology, University of Kentucky, Lexington (J.L.S., A.R.H., D.E.B., B.P.D.)
| | - David J Tester
- Departments of Cardiovascular Diseases, Pediatrics, and Molecular Pharmacology & Experimental Therapeutics, Divisions of Heart Rhythm Services and Pediatric Cardiology, Windland Smith Rice Sudden Death Genomics Laboratory, Mayo Clinic, Rochester, MN (D.J.T., M.J.A.)
| | - Allison R Hall
- Department of Physiology, Cardiovascular Research Center, Center for Muscle Biology, University of Kentucky, Lexington (J.L.S., A.R.H., D.E.B., B.P.D.)
| | - Don E Burgess
- Department of Physiology, Cardiovascular Research Center, Center for Muscle Biology, University of Kentucky, Lexington (J.L.S., A.R.H., D.E.B., B.P.D.)
| | - Chun-Chun Hsu
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taiwan (C.-C.H.)
| | - Samy Claude Elayi
- University of Kentucky, Gill Heart Institute and VAMC, Cardiology, Lexington (S.C.E.)
| | - Corey L Anderson
- Cellular and Molecular Arrhythmias Research Program, Department of Medicine, University of Wisconsin, Madison (C.L.A., C.T.J.)
| | - Craig T January
- Cellular and Molecular Arrhythmias Research Program, Department of Medicine, University of Wisconsin, Madison (C.L.A., C.T.J.)
| | - Jonathan Z Luo
- Department of Molecular and Functional Genomics and Genomic Medicine Institute, Geisinger Clinic, Danville, PA (J.Z.L., D.N.H., U.L.M., M.F.M., T.M.)
| | - Dustin N Hartzel
- Department of Molecular and Functional Genomics and Genomic Medicine Institute, Geisinger Clinic, Danville, PA (J.Z.L., D.N.H., U.L.M., M.F.M., T.M.)
| | - Uyenlinh L Mirshahi
- Department of Molecular and Functional Genomics and Genomic Medicine Institute, Geisinger Clinic, Danville, PA (J.Z.L., D.N.H., U.L.M., M.F.M., T.M.)
| | - Michael F Murray
- Department of Molecular and Functional Genomics and Genomic Medicine Institute, Geisinger Clinic, Danville, PA (J.Z.L., D.N.H., U.L.M., M.F.M., T.M.)
| | - Tooraj Mirshahi
- Department of Molecular and Functional Genomics and Genomic Medicine Institute, Geisinger Clinic, Danville, PA (J.Z.L., D.N.H., U.L.M., M.F.M., T.M.)
| | - Michael J Ackerman
- Departments of Cardiovascular Diseases, Pediatrics, and Molecular Pharmacology & Experimental Therapeutics, Divisions of Heart Rhythm Services and Pediatric Cardiology, Windland Smith Rice Sudden Death Genomics Laboratory, Mayo Clinic, Rochester, MN (D.J.T., M.J.A.)
| | - Brian P Delisle
- Department of Physiology, Cardiovascular Research Center, Center for Muscle Biology, University of Kentucky, Lexington (J.L.S., A.R.H., D.E.B., B.P.D.).
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Verma A, Lucas A, Verma SS, Zhang Y, Josyula N, Khan A, Hartzel DN, Lavage DR, Leader J, Ritchie MD, Pendergrass SA. PheWAS and Beyond: The Landscape of Associations with Medical Diagnoses and Clinical Measures across 38,662 Individuals from Geisinger. Am J Hum Genet 2018; 102:592-608. [PMID: 29606303 PMCID: PMC5985339 DOI: 10.1016/j.ajhg.2018.02.017] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 02/20/2018] [Indexed: 01/23/2023] Open
Abstract
Most phenome-wide association studies (PheWASs) to date have used a small to moderate number of SNPs for association with phenotypic data. We performed a large-scale single-cohort PheWAS, using electronic health record (EHR)-derived case-control status for 541 diagnoses using International Classification of Disease version 9 (ICD-9) codes and 25 median clinical laboratory measures. We calculated associations between these diagnoses and traits with ∼630,000 common frequency SNPs with minor allele frequency > 0.01 for 38,662 individuals. In this landscape PheWAS, we explored results within diseases and traits, comparing results to those previously reported in genome-wide association studies (GWASs), as well as previously published PheWASs. We further leveraged the context of functional impact from protein-coding to regulatory regions, providing a deeper interpretation of these associations. The comprehensive nature of this PheWAS allows for novel hypothesis generation, the identification of phenotypes for further study for future phenotypic algorithm development, and identification of cross-phenotype associations.
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Affiliation(s)
- Anurag Verma
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA; The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Anastasia Lucas
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shefali S Verma
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA; The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Yu Zhang
- Department of Statistics, The Pennsylvania State University, University Park, PA 16802, USA
| | - Navya Josyula
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA 17822, USA
| | - Anqa Khan
- Mount Holyoke College, South Hadley, MA 01075, USA
| | - Dustin N Hartzel
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA 17822, USA
| | - Daniel R Lavage
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA 17822, USA
| | - Joseph Leader
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA 17822, USA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA; The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA; Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA 16802, USA
| | - Sarah A Pendergrass
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA 17822, USA.
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Verma SS, Josyula N, Verma A, Zhang X, Veturi Y, Dewey FE, Hartzel DN, Lavage DR, Leader J, Ritchie MD, Pendergrass SA. Rare variants in drug target genes contributing to complex diseases, phenome-wide. Sci Rep 2018; 8:4624. [PMID: 29545597 PMCID: PMC5854600 DOI: 10.1038/s41598-018-22834-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 03/01/2018] [Indexed: 12/30/2022] Open
Abstract
The DrugBank database consists of ~800 genes that are well characterized drug targets. This list of genes is a useful resource for association testing. For example, loss of function (LOF) genetic variation has the potential to mimic the effect of drugs, and high impact variation in these genes can impact downstream traits. Identifying novel associations between genetic variation in these genes and a range of diseases can also uncover new uses for the drugs that target these genes. Phenome Wide Association Studies (PheWAS) have been successful in identifying genetic associations across hundreds of thousands of diseases. We have conducted a novel gene based PheWAS to test the effect of rare variants in DrugBank genes, evaluating associations between these genes and more than 500 quantitative and dichotomous phenotypes. We used whole exome sequencing data from 38,568 samples in Geisinger MyCode Community Health Initiative. We evaluated the results of this study when binning rare variants using various filters based on potential functional impact. We identified multiple novel associations, and the majority of the significant associations were driven by functionally annotated variation. Overall, this study provides a sweeping exploration of rare variant associations within functionally relevant genes across a wide range of diagnoses.
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Affiliation(s)
- Shefali Setia Verma
- Perelman School of Medicine, Department of Genetics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Navya Josyula
- Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, 17221, USA
| | - Anurag Verma
- Perelman School of Medicine, Department of Genetics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Xinyuan Zhang
- Perelman School of Medicine, Department of Genetics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yogasudha Veturi
- Perelman School of Medicine, Department of Genetics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | - Dustin N Hartzel
- Phenomic Analytics and Clinical Data Core, Geisinger, Danville, PA, USA
| | - Daniel R Lavage
- Phenomic Analytics and Clinical Data Core, Geisinger, Danville, PA, USA
| | - Joe Leader
- Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, 17221, USA.,Phenomic Analytics and Clinical Data Core, Geisinger, Danville, PA, USA
| | - Marylyn D Ritchie
- Perelman School of Medicine, Department of Genetics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sarah A Pendergrass
- Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, 17221, USA.
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21
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Hartle CM, Luo JZ, Stepanchick AN, Mirshahi UL, Hartzel DN, Manickam K, Murray MF, Mirshahi T. Combining Population Whole Exome Sequencing and Functional Analysis to Detect LQT1. Biophys J 2018. [DOI: 10.1016/j.bpj.2017.11.701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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22
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Dewey FE, Murray MF, Overton JD, Habegger L, Leader JB, Fetterolf SN, O'Dushlaine C, Van Hout CV, Staples J, Gonzaga-Jauregui C, Metpally R, Pendergrass SA, Giovanni MA, Kirchner HL, Balasubramanian S, Abul-Husn NS, Hartzel DN, Lavage DR, Kost KA, Packer JS, Lopez AE, Penn J, Mukherjee S, Gosalia N, Kanagaraj M, Li AH, Mitnaul LJ, Adams LJ, Person TN, Praveen K, Marcketta A, Lebo MS, Austin-Tse CA, Mason-Suares HM, Bruse S, Mellis S, Phillips R, Stahl N, Murphy A, Economides A, Skelding KA, Still CD, Elmore JR, Borecki IB, Yancopoulos GD, Davis FD, Faucett WA, Gottesman O, Ritchie MD, Shuldiner AR, Reid JG, Ledbetter DH, Baras A, Carey DJ. Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study. Science 2017; 354:354/6319/aaf6814. [PMID: 28008009 DOI: 10.1126/science.aaf6814] [Citation(s) in RCA: 364] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Accepted: 11/16/2016] [Indexed: 11/02/2022]
Abstract
The DiscovEHR collaboration between the Regeneron Genetics Center and Geisinger Health System couples high-throughput sequencing to an integrated health care system using longitudinal electronic health records (EHRs). We sequenced the exomes of 50,726 adult participants in the DiscovEHR study to identify ~4.2 million rare single-nucleotide variants and insertion/deletion events, of which ~176,000 are predicted to result in a loss of gene function. Linking these data to EHR-derived clinical phenotypes, we find clinical associations supporting therapeutic targets, including genes encoding drug targets for lipid lowering, and identify previously unidentified rare alleles associated with lipid levels and other blood level traits. About 3.5% of individuals harbor deleterious variants in 76 clinically actionable genes. The DiscovEHR data set provides a blueprint for large-scale precision medicine initiatives and genomics-guided therapeutic discovery.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Korey A Kost
- Geisinger Health System, Danville, PA 17822, USA
| | | | | | - John Penn
- Regeneron Genetics Center, Tarrytown, NY 10591, USA
| | | | | | | | | | | | | | | | | | | | - Matthew S Lebo
- Laboratory for Molecular Medicine, Cambridge, MA 02139, USA
| | | | | | | | - Scott Mellis
- Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA
| | | | - Neil Stahl
- Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Aris Baras
- Regeneron Genetics Center, Tarrytown, NY 10591, USA
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23
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Haggerty CM, James CA, Calkins H, Tichnell C, Leader JB, Hartzel DN, Nevius CD, Pendergrass SA, Person TN, Schwartz M, Ritchie MD, Carey DJ, Ledbetter DH, Williams MS, Dewey FE, Lopez A, Penn J, Overton JD, Reid JG, Lebo M, Mason-Suares H, Austin-Tse C, Rehm HL, Delisle BP, Makowski DJ, Mehra VC, Murray MF, Fornwalt BK. Electronic health record phenotype in subjects with genetic variants associated with arrhythmogenic right ventricular cardiomyopathy: a study of 30,716 subjects with exome sequencing. Genet Med 2017; 19:1245-1252. [PMID: 28471438 PMCID: PMC5671380 DOI: 10.1038/gim.2017.40] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2016] [Accepted: 03/03/2017] [Indexed: 01/24/2023] Open
Abstract
Purpose Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart disease. Clinical follow-up of incidental findings in ARVC-associated genes is recommended. We aimed to determine the prevalence of disease thus ascertained. Methods 30,716 individuals underwent exome sequencing. Variants in PKP2, DSG2, DSC2, DSP, JUP, TMEM43, or TGFβ3 that were database-listed as pathogenic or likely pathogenic were identified and evidence-reviewed. For subjects with putative loss-of-function (pLOF) variants or variants of uncertain significance (VUS), electronic health records (EHR) were reviewed for ARVC diagnosis, diagnostic criteria, and International Classification of Diseases (ICD-9) codes. Results 18 subjects had pLOF variants; none had an EHR diagnosis of ARVC. Of 14 patients with an electrocardiogram (ECG), one had a minor diagnostic criterion, 13 were normal. 184 subjects had VUSs; none had an ARVC diagnosis. In subjects with VUSs, there was no difference in the proportion with major (4%) or minor (13%) ECG diagnostic criteria compared to variant-negative controls. ICD-9 codes showed no difference in defibrillator utilization, electrophysiologic abnormalities or non-ischemic cardiomyopathies in patients with pLOF or VUSs compared to controls. Conclusion pLOF variants in an unselected cohort were not associated with ARVC phenotypes based on EHR review. The negative predictive value of EHR review remains uncertain.
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Affiliation(s)
- Christopher M Haggerty
- Department of Imaging Science and Innovation, Geisinger Health System, Danville, Pennsylvania, USA
| | - Cynthia A James
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Hugh Calkins
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Crystal Tichnell
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Joseph B Leader
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, Pennsylvania, USA
| | - Dustin N Hartzel
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, Pennsylvania, USA
| | - Christopher D Nevius
- Department of Imaging Science and Innovation, Geisinger Health System, Danville, Pennsylvania, USA
| | - Sarah A Pendergrass
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, Pennsylvania, USA
| | - Thomas N Person
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, Pennsylvania, USA
| | - Marci Schwartz
- Genomic Medicine Institute, Geisinger Health System, Danville, Pennsylvania, USA
| | - Marylyn D Ritchie
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, Pennsylvania, USA
| | - David J Carey
- Weis Center for Health Research, Geisinger Health System, Danville, Pennsylvania, USA
| | - David H Ledbetter
- Genomic Medicine Institute, Geisinger Health System, Danville, Pennsylvania, USA
| | - Marc S Williams
- Genomic Medicine Institute, Geisinger Health System, Danville, Pennsylvania, USA
| | - Frederick E Dewey
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, New York, USA
| | - Alexander Lopez
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, New York, USA
| | - John Penn
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, New York, USA
| | - John D Overton
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, New York, USA
| | - Jeffrey G Reid
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, New York, USA
| | - Matthew Lebo
- Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, Massachusetts, USA.,Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Heather Mason-Suares
- Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, Massachusetts, USA.,Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Christina Austin-Tse
- Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, Massachusetts, USA
| | - Heidi L Rehm
- Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, Massachusetts, USA.,Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Brian P Delisle
- Department of Physiology, University of Kentucky, Lexington, Kentucky, USA
| | - Daniel J Makowski
- Division of Cardiology, Geisinger Health System, Danville, Pennsylvania, USA
| | - Vishal C Mehra
- Division of Cardiology, Geisinger Health System, Danville, Pennsylvania, USA
| | - Michael F Murray
- Genomic Medicine Institute, Geisinger Health System, Danville, Pennsylvania, USA
| | - Brandon K Fornwalt
- Department of Imaging Science and Innovation, Geisinger Health System, Danville, Pennsylvania, USA
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24
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Khera AV, Won HH, Peloso GM, O'Dushlaine C, Liu D, Stitziel NO, Natarajan P, Nomura A, Emdin CA, Gupta N, Borecki IB, Asselta R, Duga S, Merlini PA, Correa A, Kessler T, Wilson JG, Bown MJ, Hall AS, Braund PS, Carey DJ, Murray MF, Kirchner HL, Leader JB, Lavage DR, Manus JN, Hartzel DN, Samani NJ, Schunkert H, Marrugat J, Elosua R, McPherson R, Farrall M, Watkins H, Lander ES, Rader DJ, Danesh J, Ardissino D, Gabriel S, Willer C, Abecasis GR, Saleheen D, Dewey FE, Kathiresan S. Association of Rare and Common Variation in the Lipoprotein Lipase Gene With Coronary Artery Disease. JAMA 2017; 317:937-946. [PMID: 28267856 PMCID: PMC5664181 DOI: 10.1001/jama.2017.0972] [Citation(s) in RCA: 124] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
IMPORTANCE The activity of lipoprotein lipase (LPL) is the rate-determining step in clearing triglyceride-rich lipoproteins from the circulation. Mutations that damage the LPL gene (LPL) lead to lifelong deficiency in enzymatic activity and can provide insight into the relationship of LPL to human disease. OBJECTIVE To determine whether rare and/or common variants in LPL are associated with early-onset coronary artery disease (CAD). DESIGN, SETTING, AND PARTICIPANTS In a cross-sectional study, LPL was sequenced in 10 CAD case-control cohorts of the multinational Myocardial Infarction Genetics Consortium and a nested CAD case-control cohort of the Geisinger Health System DiscovEHR cohort between 2010 and 2015. Common variants were genotyped in up to 305 699 individuals of the Global Lipids Genetics Consortium and up to 120 600 individuals of the CARDIoGRAM Exome Consortium between 2012 and 2014. Study-specific estimates were pooled via meta-analysis. EXPOSURES Rare damaging mutations in LPL included loss-of-function variants and missense variants annotated as pathogenic in a human genetics database or predicted to be damaging by computer prediction algorithms trained to identify mutations that impair protein function. Common variants in the LPL gene region included those independently associated with circulating triglyceride levels. MAIN OUTCOMES AND MEASURES Circulating lipid levels and CAD. RESULTS Among 46 891 individuals with LPL gene sequencing data available, the mean (SD) age was 50 (12.6) years and 51% were female. A total of 188 participants (0.40%; 95% CI, 0.35%-0.46%) carried a damaging mutation in LPL, including 105 of 32 646 control participants (0.32%) and 83 of 14 245 participants with early-onset CAD (0.58%). Compared with 46 703 noncarriers, the 188 heterozygous carriers of an LPL damaging mutation displayed higher plasma triglyceride levels (19.6 mg/dL; 95% CI, 4.6-34.6 mg/dL) and higher odds of CAD (odds ratio = 1.84; 95% CI, 1.35-2.51; P < .001). An analysis of 6 common LPL variants resulted in an odds ratio for CAD of 1.51 (95% CI, 1.39-1.64; P = 1.1 × 10-22) per 1-SD increase in triglycerides. CONCLUSIONS AND RELEVANCE The presence of rare damaging mutations in LPL was significantly associated with higher triglyceride levels and presence of coronary artery disease. However, further research is needed to assess whether there are causal mechanisms by which heterozygous lipoprotein lipase deficiency could lead to coronary artery disease.
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Affiliation(s)
- Amit V Khera
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts2Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston3Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Gina M Peloso
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts5Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | | | - Dajiang Liu
- Department of Public Health Sciences, Institute for Personalized Medicine, Penn State College of Medicine, Hershey, Pennsylvania
| | - Nathan O Stitziel
- Department of Medicine, Washington University School of Medicine, St Louis, Missouri9Department of Genetics, Washington University School of Medicine, St Louis, Missouri10McDonnell Genome Institute, Washington University School of Medicine, St Louis, Missouri
| | - Pradeep Natarajan
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts2Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston3Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston
| | - Akihiro Nomura
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts2Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston3Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston
| | - Connor A Emdin
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts2Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston3Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston
| | - Namrata Gupta
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
| | | | - Rosanna Asselta
- Department of Biomedical Sciences, Humanitas University, Milan, Italy12Humanitas Clinical and Research Center, Milan, Italy
| | - Stefano Duga
- Department of Biomedical Sciences, Humanitas University, Milan, Italy12Humanitas Clinical and Research Center, Milan, Italy
| | | | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson
| | - Thorsten Kessler
- Munich Heart Alliance, München, Germany16Deutsches Herzzentrum München, Technische Universität München, Deutsches Zentrum für Herz-Kreislauf-Forschung, München, Germany
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson
| | - Matthew J Bown
- NIHR Leicester Cardiovascular Biomedical Research Unit, Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
| | - Alistair S Hall
- Leeds Institute of Cardiovascular and Metabolic Medicine, Leeds University, Leeds, United Kingdom
| | - Peter S Braund
- NIHR Leicester Cardiovascular Biomedical Research Unit, Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
| | | | | | | | | | | | | | | | - Nilesh J Samani
- NIHR Leicester Cardiovascular Biomedical Research Unit, Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
| | - Heribert Schunkert
- Deutsches Herzzentrum München, Technische Universität München, Deutsches Zentrum für Herz-Kreislauf-Forschung, München, Germany
| | - Jaume Marrugat
- Cardiovascular Epidemiology and Genetics, Hospital del Mar Research Institute, Barcelona, Spain
| | - Roberto Elosua
- Cardiovascular Epidemiology and Genetics, Hospital del Mar Research Institute, Barcelona, Spain
| | - Ruth McPherson
- University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Martin Farrall
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom24Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Hugh Watkins
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom24Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Eric S Lander
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
| | - Daniel J Rader
- Department of Genetics, University of Pennsylvania, Philadelphia
| | - John Danesh
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom27Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom28NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom29Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Diego Ardissino
- Division of Cardiology, Azienda Ospedaliero-Universitaria di Parma, Parma, Italy31Associazione per lo Studio Della Trombosi in Cardiologia, Pavia, Italy
| | - Stacey Gabriel
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
| | - Cristen Willer
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor33Department of Human Genetics, University of Michigan, Ann Arbor34Department of Internal Medicine, University of Michigan, Ann Arbor
| | - Gonçalo R Abecasis
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor
| | - Danish Saleheen
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | | | - Sekar Kathiresan
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts2Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston3Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston
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Abul-Husn NS, Manickam K, Jones LK, Wright EA, Hartzel DN, Gonzaga-Jauregui C, O’Dushlaine C, Leader JB, Lester Kirchner H, Lindbuchler DM, Barr ML, Giovanni MA, Ritchie MD, Overton JD, Reid JG, Metpally RPR, Wardeh AH, Borecki IB, Yancopoulos GD, Baras A, Shuldiner AR, Gottesman O, Ledbetter DH, Carey DJ, Dewey FE, Murray MF. Genetic identification of familial hypercholesterolemia within a single U.S. health care system. Science 2016; 354:354/6319/aaf7000. [DOI: 10.1126/science.aaf7000] [Citation(s) in RCA: 264] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Accepted: 11/16/2016] [Indexed: 12/12/2022]
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26
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Leader JB, Pendergrass SA, Verma A, Carey DJ, Hartzel DN, Ritchie MD, Kirchner HL. Contrasting Association Results between Existing PheWAS Phenotype Definition Methods and Five Validated Electronic Phenotypes. AMIA Annu Symp Proc 2015; 2015:824-32. [PMID: 26958218 PMCID: PMC4765620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Phenome-Wide Association Studies (PheWAS) comprehensively investigate the association between genetic variation and a wide array of outcome traits. Electronic health record (EHR) based PheWAS uses various abstractions of International Classification of Diseases, Ninth Revision (ICD-9) codes to identify case/control status for diagnoses that are used as the phenotypic variables. However, there have not been comparisons within a PheWAS between results from high quality derived phenotypes and high-throughput but potentially inaccurate use of ICD-9 codes for case/control definition. For this study we first developed a group of high quality algorithms for five phenotypes. Next we evaluated the association of these "gold standard" phenotypes and 4,636,178 genetic variants with minor allele frequency > 0.01 and compared the results from high-throughput associations at the 3 digit, 5 digit, and PheWAS codes for defining case/control status. We found that certain diseases contained similar patient populations across phenotyping methods but had differences in PheWAS.
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Affiliation(s)
| | | | - Anurag Verma
- Biomedical and Translational Informatics Program, Danville, PA, USA; The Center for Systems Genomics, The Pennsylvania State University, University Park, PA USA
| | - David J Carey
- Weis Center for Research, Geisinger Health System, Danville, PA, USA
| | | | - Marylyn D Ritchie
- Biomedical and Translational Informatics Program, Danville, PA, USA; The Center for Systems Genomics, The Pennsylvania State University, University Park, PA USA
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