201
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Zhou J, Sun Y, Huang W, Ye K. Altered Blood Cell Traits Underlie a Major Genetic Locus of Severe COVID-19. J Gerontol A Biol Sci Med Sci 2021; 76:e147-e154. [PMID: 33530099 PMCID: PMC7929197 DOI: 10.1093/gerona/glab035] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Indexed: 12/29/2022] Open
Abstract
Background The genetic locus 3p21.31 has been associated with severe coronavirus disease 2019 (COVID-19), but the underlying pathophysiological mechanism is unknown. Methods To identify intermediate traits associated with the 3p21.31 locus, we first performed a phenome-wide association study (PheWAS) with 923 phenotypes in 310,999 European individuals from the UK Biobank. For genes potentially regulated by the COVID-19 risk variant, we examined associations between their expression and the polygenic score (PGS) of 1,263 complex traits in a meta-analysis of 31,684 blood samples. For the prioritized blood cell traits, we tested their associations with age and sex in the same UK Biobank sample. Results Our PheWAS highlighted multiple blood cell traits to be associated with the COVID-19 risk variant, including monocyte count and percentage (p = 1.07×10 -8, 4.09×10 -13), eosinophil count and percentage (p = 5.73×10 -3, 2.20×10 -3), and neutrophil percentage (p = 3.23×10 -3). The PGS analysis revealed positive associations between the expression of candidate genes and genetically predicted counts of specific blood cells: CCR3 with eosinophil and basophil (p = 5.73×10 -21, 5.08×10 -19); CCR2 with monocytes (p = 2.40×10 -10); and CCR1 with monocytes and neutrophil (p = 1.78×10 -6, 7.17×10 -5). Additionally, we found that almost all examined white blood cell traits are significantly different across age and sex groups. Conclusions Our findings suggest that altered blood cell traits, especially those of monocyte, eosinophil, and neutrophil, may represent the mechanistic links between the genetic locus 3p21.31 and severe COVID-19. They may also underlie the increased risk of severe COVID-19 in older adults and men.
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Affiliation(s)
- Jingqi Zhou
- Department of Genetics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, USA.,School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
| | - Yitang Sun
- Department of Genetics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, USA
| | - Weishan Huang
- Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA, USA.,Department of Microbiology and Immunology, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Kaixiong Ye
- Department of Genetics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, USA.,Institute of Bioinformatics, University of Georgia, Athens, GA, USA
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202
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Si S, Li J, Tewara MA, Xue F. Genetically Determined Chronic Low-Grade Inflammation and Hundreds of Health Outcomes in the UK Biobank and the FinnGen Population: A Phenome-Wide Mendelian Randomization Study. Front Immunol 2021; 12:720876. [PMID: 34386016 PMCID: PMC8353321 DOI: 10.3389/fimmu.2021.720876] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 07/12/2021] [Indexed: 12/17/2022] Open
Abstract
Background C-reactive protein (CRP) has been used as a biomarker of chronic low-grade inflammation in observational studies. We aimed to determine whether genetically determined CRP was associated with hundreds of human phenotypes to guide anti-inflammatory interventions. Methods We used individual data from the UK Biobank to perform a phenome-wide two-stage least squares (2SLS) Mendelian randomization (MR) analysis for CRP with 879 diseases. Summary-level data from the FinnGen consortium were utilized to perform phenome-wide two-sample MR analysis on 821 phenotypes. Systematic two-sample MR methods included MR-IVW, MR-WME, MR-Mod, and MR-PRESSO as sensitivity analyses combined with multivariable MR to identify robust associations. Genetic correlation analysis was applied to identify shared genetic risks. Results We found genetically determined CRP was robustly associated with 15 diseases in the UK Biobank and 11 diseases in the FinnGen population (P < 0.05 for all MR analyses). CRP was positively associated with tongue cancer, bronchitis, hydronephrosis, and acute pancreatitis and negatively associated with colorectal cancer, colon cancer, cerebral ischemia, electrolyte imbalance, Parkinson's disease, epilepsy, anemia of chronic disease, encephalitis, psychophysical visual disturbances, and aseptic necrosis of bone in the UK Biobank. There were positive associations with impetigo, vascular dementia, bipolar disorders, hypercholesterolemia, vertigo, and neurological diseases, and negative correlations with degenerative macular diseases, metatarsalgia, interstitial lung disease, and idiopathic pulmonary fibrosis, and others. in the FinnGen population. The electrolyte imbalance and anemia of chronic disease in UK Biobank and hypercholesterolemia and neurological diseases in FinnGen pass the FDR corrections. Neurological diseases and bipolar disorders also presented positive genetic correlations with CRP. We found no overlapping causal associations between the populations. Previous causal evidence also failed to support these associations (except for bipolar disorders). Conclusions Genetically determined CRP was robustly associated with several diseases in the UK Biobank and the FinnGen population, but could not be replicated, suggesting heterogeneous and non-repeatable effects of CRP across populations. This implies that interventions at CRP are unlikely to result in decreased risk for most human diseases in the general population but may benefit specific high-risk populations. The limited causal evidence and potential double-sided effects remind us to be cautious about CRP interventions.
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Affiliation(s)
- Shucheng Si
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Institute for Medical Dataology, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Jiqing Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Institute for Medical Dataology, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Marlvin Anemey Tewara
- Center for Health Promotion and Research (Former Tuberculosis Reference Laboratory), Bamenda, Cameroon
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Institute for Medical Dataology, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
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203
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Lin SH, Brown DW, Rose B, Day F, Lee OW, Khan SM, Hislop J, Chanock SJ, Perry JRB, Machiela MJ. Incident disease associations with mosaic chromosomal alterations on autosomes, X and Y chromosomes: insights from a phenome-wide association study in the UK Biobank. Cell Biosci 2021; 11:143. [PMID: 34301302 PMCID: PMC8299574 DOI: 10.1186/s13578-021-00651-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 07/06/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Mosaic chromosomal alterations (mCAs) are large chromosomal gains, losses and copy-neutral losses of heterozygosity (LOH) in peripheral leukocytes. While many individuals with detectable mCAs have no notable adverse outcomes, mCA-associated gene dosage alterations as well as clonal expansion of mutated leukocyte clones could increase susceptibility to disease. RESULTS We performed a phenome-wide association study (PheWAS) using existing data from 482,396 UK Biobank (UKBB) participants to investigate potential associations between mCAs and incident disease. Of the 1290 ICD codes we examined, our adjusted analysis identified a total of 50 incident disease outcomes associated with mCAs at PheWAS significance levels. We observed striking differences in the diseases associated with each type of alteration, with autosomal mCAs most associated with increased hematologic malignancies, incident infections and possibly cancer therapy-related conditions. Alterations of chromosome X were associated with increased lymphoid leukemia risk and, mCAs of chromosome Y were linked to potential reduced metabolic disease risk. CONCLUSIONS Our findings demonstrate that a wide range of diseases are potential sequelae of mCAs and highlight the critical importance of careful covariate adjustment in mCA disease association studies.
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Affiliation(s)
- Shu-Hong Lin
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD, 20892, USA
| | - Derek W Brown
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD, 20892, USA
| | - Brandon Rose
- University of Miami Miller School of Medicine, Miami, FL, 33136, USA
| | - Felix Day
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Olivia W Lee
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD, 20892, USA
| | - Sairah M Khan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD, 20892, USA
| | - Jada Hislop
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD, 20892, USA
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD, 20892, USA
| | - John R B Perry
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Mitchell J Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD, 20892, USA.
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204
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Abstract
Electronic health records (EHRs) are a rich source of data for researchers, but extracting meaningful information out of this highly complex data source is challenging. Phecodes represent one strategy for defining phenotypes for research using EHR data. They are a high-throughput phenotyping tool based on ICD (International Classification of Diseases) codes that can be used to rapidly define the case/control status of thousands of clinically meaningful diseases and conditions. Phecodes were originally developed to conduct phenome-wide association studies to scan for phenotypic associations with common genetic variants. Since then, phecodes have been used to support a wide range of EHR-based phenotyping methods, including the phenotype risk score. This review aims to comprehensively describe the development, validation, and applications of phecodes and suggest some future directions for phecodes and high-throughput phenotyping.
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Affiliation(s)
- Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA;
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205
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Wang L, Zhang X, Meng X, Koskeridis F, Georgiou A, Yu L, Campbell H, Theodoratou E, Li X. Methodology in phenome-wide association studies: a systematic review. J Med Genet 2021; 58:720-728. [PMID: 34272311 DOI: 10.1136/jmedgenet-2021-107696] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 05/27/2021] [Indexed: 11/04/2022]
Abstract
Phenome-wide association study (PheWAS) has been increasingly used to identify novel genetic associations across a wide spectrum of phenotypes. This systematic review aims to summarise the PheWAS methodology, discuss the advantages and challenges of PheWAS, and provide potential implications for future PheWAS studies. Medical Literature Analysis and Retrieval System Online (MEDLINE) and Excerpta Medica Database (EMBASE) databases were searched to identify all published PheWAS studies up until 24 April 2021. The PheWAS methodology incorporating how to perform PheWAS analysis and which software/tool could be used, were summarised based on the extracted information. A total of 1035 studies were identified and 195 eligible articles were finally included. Among them, 137 (77.0%) contained 10 000 or more study participants, 164 (92.1%) defined the phenome based on electronic medical records data, 140 (78.7%) used genetic variants as predictors, and 73 (41.0%) conducted replication analysis to validate PheWAS findings and almost all of them (94.5%) received consistent results. The methodology applied in these PheWAS studies was dissected into several critical steps, including quality control of the phenome, selecting predictors, phenotyping, statistical analysis, interpretation and visualisation of PheWAS results, and the workflow for performing a PheWAS was established with detailed instructions on each step. This study provides a comprehensive overview of PheWAS methodology to help practitioners achieve a better understanding of the PheWAS design, to detect understudied or overstudied outcomes, and to direct their research by applying the most appropriate software and online tools for their study data structure.
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Affiliation(s)
- Lijuan Wang
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xiaomeng Zhang
- Centre for Global Health, The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK
| | - Xiangrui Meng
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Fotios Koskeridis
- Department of Hygiene and Epidemiology, University of Ioannina, Ioannina, Epirus, Greece
| | - Andrea Georgiou
- Department of Hygiene and Epidemiology, University of Ioannina, Ioannina, Epirus, Greece
| | - Lili Yu
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Harry Campbell
- Centre for Global Health, The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK
| | - Evropi Theodoratou
- Centre for Global Health, The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK.,Cancer Research UK Edinburgh Centre, The University of Edinburgh MRC Institute of Genetics and Molecular Medicine, Edinburgh, UK
| | - Xue Li
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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206
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Kawai VK, Shi M, Liu G, Feng Q, Wei W, Chung CP, Walunas TL, Gordon AS, Linneman JG, Hebbring SJ, Harley JB, Cox NJ, Roden DM, Stein CM, Mosley JD. Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis. Lupus 2021; 30:1264-1272. [PMID: 33977795 PMCID: PMC8205989 DOI: 10.1177/09612033211014952] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Abstract
OBJECTIVES To test the hypothesis that genetic predisposition to systemic lupus erythematosus (SLE) increases the risk of cardiometabolic disorders. METHODS Using 41 single nucleotide polymorphisms (SNPs) associated with SLE, we calculated a weighted genetic risk score (wGRS) for SLE. In a large biobank we tested the association between this wGRS and 9 cardiometabolic phenotypes previously associated with SLE: atrial fibrillation, ischemic stroke, coronary artery disease, type 1 and type 2 diabetes, obesity, chronic kidney disease, hypertension, and hypercholesterolemia. Additionally, we performed a phenome-wide association analysis (pheWAS) to discover novel clinical associations with a genetic predisposition to SLE. Findings were replicated in the Electronic Medical Records and Genomics (eMERGE) Network. To further define the association between SLE-related risk alleles and the selected cardiometabolic phenotypes, we performed an inverse variance weighted regression (IVWR) meta-analysis. RESULTS The wGRS for SLE was calculated in 74,759 individuals of European ancestry. Among the pre-selected phenotypes, the wGRS was significantly associated with type 1 diabetes (OR [95%CI] =1.11 [1.06, 1.17], P-value = 1.05x10-5). In the PheWAS, the wGRS was associated with several autoimmune phenotypes, kidney disorders, and skin neoplasm; but only the associations with autoimmune phenotypes were replicated. In the IVWR meta-analysis, SLE-related risk alleles were nominally associated with type 1 diabetes (P = 0.048) but the associations were heterogeneous and did not meet the adjusted significance threshold. CONCLUSION A weighted GRS for SLE was associated with an increased risk of several autoimmune-related phenotypes including type I diabetes but not with cardiometabolic disorders.
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Affiliation(s)
- Vivian K. Kawai
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Mingjian Shi
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Ge Liu
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - QiPing Feng
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - WeiQi Wei
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Cecilia P. Chung
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Rheumatology, Department of Medicine Vanderbilt University Medical Center, Nashville, TN, USA
- Tennessee Valley Healthcare System - Nashville Campus
| | - Theresa L. Walunas
- Center for Health Information Partnerships, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Adam S. Gordon
- Center for Genetic Medicine, Northwestern University, Chicago, IL
| | - James G. Linneman
- Office of Research, Computing, and Analytics, Marshfield Clinic Research Institute, Marshfield, WI
| | - Scott J. Hebbring
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, Wisconsin USA
| | - John B. Harley
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Cincinnati VA Medical Center, Cincinnati, OH, USA
| | - Nancy J. Cox
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Dan M. Roden
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - C. Michael Stein
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jonathan D. Mosley
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
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207
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Liu Y, Basty N, Whitcher B, Bell JD, Sorokin EP, van Bruggen N, Thomas EL, Cule M. Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning. eLife 2021; 10:e65554. [PMID: 34128465 PMCID: PMC8205492 DOI: 10.7554/elife.65554] [Citation(s) in RCA: 114] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 05/09/2021] [Indexed: 12/24/2022] Open
Abstract
Cardiometabolic diseases are an increasing global health burden. While socioeconomic, environmental, behavioural, and genetic risk factors have been identified, a better understanding of the underlying mechanisms is required to develop more effective interventions. Magnetic resonance imaging (MRI) has been used to assess organ health, but biobank-scale studies are still in their infancy. Using over 38,000 abdominal MRI scans in the UK Biobank, we used deep learning to quantify volume, fat, and iron in seven organs and tissues, and demonstrate that imaging-derived phenotypes reflect health status. We show that these traits have a substantial heritable component (8-44%) and identify 93 independent genome-wide significant associations, including four associations with liver traits that have not previously been reported. Our work demonstrates the tractability of deep learning to systematically quantify health parameters from high-throughput MRI across a range of organs and tissues, and use the largest-ever study of its kind to generate new insights into the genetic architecture of these traits.
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Affiliation(s)
- Yi Liu
- Calico Life Sciences LLCSouth San FranciscoUnited States
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of WestminsterLondonUnited Kingdom
| | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of WestminsterLondonUnited Kingdom
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of WestminsterLondonUnited Kingdom
| | | | | | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of WestminsterLondonUnited Kingdom
| | - Madeleine Cule
- Calico Life Sciences LLCSouth San FranciscoUnited States
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208
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Zhao L, Batta I, Matloff W, O'Driscoll C, Hobel S, Toga AW. Neuroimaging PheWAS (Phenome-Wide Association Study): A Free Cloud-Computing Platform for Big-Data, Brain-Wide Imaging Association Studies. Neuroinformatics 2021; 19:285-303. [PMID: 32822005 DOI: 10.1007/s12021-020-09486-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Large-scale, case-control genome-wide association studies (GWASs) have revealed genetic variations associated with diverse neurological and psychiatric disorders. Recent advances in neuroimaging and genomic databases of large healthy and diseased cohorts have empowered studies to characterize effects of the discovered genetic factors on brain structure and function, implicating neural pathways and genetic mechanisms in the underlying biology. However, the unprecedented scale and complexity of the imaging and genomic data requires new advanced biomedical data science tools to manage, process and analyze the data. In this work, we introduce Neuroimaging PheWAS (phenome-wide association study): a web-based system for searching over a wide variety of brain-wide imaging phenotypes to discover true system-level gene-brain relationships using a unified genotype-to-phenotype strategy. This design features a user-friendly graphical user interface (GUI) for anonymous data uploading, study definition and management, and interactive result visualizations as well as a cloud-based computational infrastructure and multiple state-of-art methods for statistical association analysis and multiple comparison correction. We demonstrated the potential of Neuroimaging PheWAS with a case study analyzing the influences of the apolipoprotein E (APOE) gene on various brain morphological properties across the brain in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Benchmark tests were performed to evaluate the system's performance using data from UK Biobank. The Neuroimaging PheWAS system is freely available. It simplifies the execution of PheWAS on neuroimaging data and provides an opportunity for imaging genetics studies to elucidate routes at play for specific genetic variants on diseases in the context of detailed imaging phenotypic data.
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Affiliation(s)
- Lu Zhao
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Ishaan Batta
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - William Matloff
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Caroline O'Driscoll
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Samuel Hobel
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA.
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209
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Hall MA, Wallace J, Lucas AM, Bradford Y, Verma SS, Müller-Myhsok B, Passero K, Zhou J, McGuigan J, Jiang B, Pendergrass SA, Zhang Y, Peissig P, Brilliant M, Sleiman P, Hakonarson H, Harley JB, Kiryluk K, Van Steen K, Moore JH, Ritchie MD. Novel EDGE encoding method enhances ability to identify genetic interactions. PLoS Genet 2021; 17:e1009534. [PMID: 34086673 PMCID: PMC8208534 DOI: 10.1371/journal.pgen.1009534] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 06/16/2021] [Accepted: 04/06/2021] [Indexed: 11/26/2022] Open
Abstract
Assumptions are made about the genetic model of single nucleotide polymorphisms (SNPs) when choosing a traditional genetic encoding: additive, dominant, and recessive. Furthermore, SNPs across the genome are unlikely to demonstrate identical genetic models. However, running SNP-SNP interaction analyses with every combination of encodings raises the multiple testing burden. Here, we present a novel and flexible encoding for genetic interactions, the elastic data-driven genetic encoding (EDGE), in which SNPs are assigned a heterozygous value based on the genetic model they demonstrate in a dataset prior to interaction testing. We assessed the power of EDGE to detect genetic interactions using 29 combinations of simulated genetic models and found it outperformed the traditional encoding methods across 10%, 30%, and 50% minor allele frequencies (MAFs). Further, EDGE maintained a low false-positive rate, while additive and dominant encodings demonstrated inflation. We evaluated EDGE and the traditional encodings with genetic data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes: age-related macular degeneration (AMD), age-related cataract, glaucoma, type 2 diabetes (T2D), and resistant hypertension. A multi-encoding genome-wide association study (GWAS) for each phenotype was performed using the traditional encodings, and the top results of the multi-encoding GWAS were considered for SNP-SNP interaction using the traditional encodings and EDGE. EDGE identified a novel SNP-SNP interaction for age-related cataract that no other method identified: rs7787286 (MAF: 0.041; intergenic region of chromosome 7)–rs4695885 (MAF: 0.34; intergenic region of chromosome 4) with a Bonferroni LRT p of 0.018. A SNP-SNP interaction was found in data from the UK Biobank within 25 kb of these SNPs using the recessive encoding: rs60374751 (MAF: 0.030) and rs6843594 (MAF: 0.34) (Bonferroni LRT p: 0.026). We recommend using EDGE to flexibly detect interactions between SNPs exhibiting diverse action. Although traditional genetic encodings are widely implemented in genetics research, including in genome-wide association studies (GWAS) and epistasis, each method makes assumptions that may not reflect the underlying etiology. Here, we introduce a novel encoding method that estimates and assigns an individualized data-driven encoding for each single nucleotide polymorphism (SNP): the elastic data-driven genetic encoding (EDGE). With simulations, we demonstrate that this novel method is more accurate and robust than traditional encoding methods in estimating heterozygous genotype values, reducing the type I error, and detecting SNP-SNP interactions. We further applied the traditional encodings and EDGE to biomedical data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes, and EDGE identified a novel interaction for age-related cataract not detected by traditional methods, which replicated in data from the UK Biobank. EDGE provides an alternative approach to understanding and modeling diverse SNP models and is recommended for studying complex genetics in common human phenotypes.
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Affiliation(s)
- Molly A. Hall
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Penn State Cancer Institute, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- * E-mail:
| | - John Wallace
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Anastasia M. Lucas
- Department of Genetics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Yuki Bradford
- Department of Genetics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Shefali S. Verma
- Department of Genetics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Bertram Müller-Myhsok
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Kristin Passero
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Jiayan Zhou
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - John McGuigan
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Beibei Jiang
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
| | | | - Yanfei Zhang
- Genomic Medicine Institute, Geisinger Health System, Danville, Pennsylvania, United States of America
| | - Peggy Peissig
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States of America
| | - Murray Brilliant
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States of America
| | - Patrick Sleiman
- Department of Pediatrics, Center for Applied Genomics, Children’s Hospital of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Hakon Hakonarson
- Department of Pediatrics, Center for Applied Genomics, Children’s Hospital of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - John B. Harley
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
- United States Department of Veterans Affairs Medical Center, Cincinnati, Ohio, United States of America
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York, United States of America
| | - Kristel Van Steen
- WELBIO, GIGA-R Medical Genomics-BIO3, University of Liège, Liège, Belgium
- Department of Human Genetics, University of Leuven, Leuven, Belgium
| | - Jason H. Moore
- Department of Genetics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Marylyn D. Ritchie
- Department of Genetics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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210
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Linden T, De Jong J, Lu C, Kiri V, Haeffs K, Fröhlich H. An Explainable Multimodal Neural Network Architecture for Predicting Epilepsy Comorbidities Based on Administrative Claims Data. Front Artif Intell 2021; 4:610197. [PMID: 34095818 PMCID: PMC8176093 DOI: 10.3389/frai.2021.610197] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 04/21/2021] [Indexed: 01/16/2023] Open
Abstract
Epilepsy is a complex brain disorder characterized by repetitive seizure events. Epilepsy patients often suffer from various and severe physical and psychological comorbidities (e.g., anxiety, migraine, and stroke). While general comorbidity prevalences and incidences can be estimated from epidemiological data, such an approach does not take into account that actual patient-specific risks can depend on various individual factors, including medication. This motivates to develop a machine learning approach for predicting risks of future comorbidities for individual epilepsy patients. In this work, we use inpatient and outpatient administrative health claims data of around 19,500 U.S. epilepsy patients. We suggest a dedicated multimodal neural network architecture (Deep personalized LOngitudinal convolutional RIsk model-DeepLORI) to predict the time-dependent risk of six common comorbidities of epilepsy patients. We demonstrate superior performance of DeepLORI in a comparison with several existing methods. Moreover, we show that DeepLORI-based predictions can be interpreted on the level of individual patients. Using a game theoretic approach, we identify relevant features in DeepLORI models and demonstrate that model predictions are explainable in light of existing knowledge about the disease. Finally, we validate the model on independent data from around 97,000 patients, showing good generalization and stable prediction performance over time.
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Affiliation(s)
- Thomas Linden
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany
- UCB Biosciences GmbH, Monheim, Germany
| | | | - Chao Lu
- UCB Ltd., Raleigh, NC, United States
| | | | | | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany
- UCB Biosciences GmbH, Monheim, Germany
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211
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Martucci VL, Richmond B, Davis LK, Blackwell TS, Cox NJ, Samuels D, Velez Edwards D, Aldrich MC. Fate or coincidence: do COPD and major depression share genetic risk factors? Hum Mol Genet 2021; 30:619-628. [PMID: 33704461 PMCID: PMC8120137 DOI: 10.1093/hmg/ddab068] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 02/24/2021] [Accepted: 02/27/2021] [Indexed: 01/12/2023] Open
Abstract
Major depressive disorder (MDD) is a common comorbidity in chronic obstructive pulmonary disease (COPD), affecting up to 57% of patients with COPD. Although the comorbidity of COPD and MDD is well established, the causal relationship between these two diseases is unclear. A large-scale electronic health record clinical biobank and genome-wide association study summary statistics for MDD and lung function traits were used to investigate potential shared underlying genetic susceptibility between COPD and MDD. Linkage disequilibrium score regression was used to estimate genetic correlation between phenotypes. Polygenic risk scores (PRS) for MDD and lung function traits were developed and used to perform a phenome-wide association study (PheWAS). Multi-trait-based conditional and joint analysis identified single-nucleotide polymorphisms (SNPs) influencing both lung function and MDD. We found genetic correlations between MDD and all lung function traits were small and not statistically significant. A PRS-MDD was significantly associated with an increased risk of COPD in a PheWAS [odds ratio (OR) = 1.12, 95% confidence interval (CI): 1.09-1.16] when adjusting for age, sex and genetic ancestry, but this relationship became attenuated when controlling for smoking history (OR = 1.08, 95% CI: 1.04-1.13). No significant associations were found between the lung function PRS and MDD. Multi-trait-based conditional and joint analysis identified three SNPs that may contribute to both traits, two of which were previously associated with mood disorders and COPD. Our findings suggest that the observed relationship between COPD and MDD may not be driven by a strong shared genetic architecture.
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Affiliation(s)
- Victoria L Martucci
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Bradley Richmond
- Department of Veterans Affairs Medical Center, Nashville, TN 37212, USA
- Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Lea K Davis
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Timothy S Blackwell
- Department of Veterans Affairs Medical Center, Nashville, TN 37212, USA
- Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Nancy J Cox
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - David Samuels
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Digna Velez Edwards
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Melinda C Aldrich
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
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212
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Bezamat M, Modesto A, Vieira AR. Phenome-Wide Association Study With Focus on Oral Health Disparities and Individuals Who Did Not Have Cancer. FRONTIERS IN DENTAL MEDICINE 2021. [DOI: 10.3389/fdmed.2021.641246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The goal of this study was to test if oral health outcomes are associated with the same genetic markers in Black and White individuals who did not have cancer. From a total of 6,100 subjects from the Dental Registry and DNA Repository project, 1,042 individuals who self-identified as White and 266 as Black without a history of cancer were included in this analysis. Genotyping data from IRE1—rs196929, RHEB—rs2374261 and rs1109089, AXIN2—rs2240308 and rs11867417, and RPTOR—rs4396582, present in cell regulatory pathways, were analyzed. We ran separate analyses in self-reported Black and White groups to reduce possible confounding effects of population stratification. Internal diagnostic codes from our dental registry were converted into Phecodes in order to run the analysis using the PheWAS package, installed in R Studio software. Periodontitis was associated with RHEB in both Black and White patients, with the minor allele increasing the likelihood of developing periodontitis in the White group and yielding a protective effect in the Black individuals. The presence of ulcers and gingivitis were associated with RPTOR and AXIN2, respectively, in the White group, but an association was not detected for the Black group. On the other hand, phenotypes such as dental fracture, diseases of the tongue, attrition, erosion, abrasion, fordyce granules, and torus and exostosis were uniquely associated with the Black group. Periodontitis associated with RHEB in both Black and White patients, and associations found in Black individuals may be the result of social disparities that lead to higher levels of stress, and these observed differences require further study.
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213
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Shang N, Khan A, Polubriaginof F, Zanoni F, Mehl K, Fasel D, Drawz PE, Carrol RJ, Denny JC, Hathcock MA, Arruda-Olson AM, Peissig PL, Dart RA, Brilliant MH, Larson EB, Carrell DS, Pendergrass S, Verma SS, Ritchie MD, Benoit B, Gainer VS, Karlson EW, Gordon AS, Jarvik GP, Stanaway IB, Crosslin DR, Mohan S, Ionita-Laza I, Tatonetti NP, Gharavi AG, Hripcsak G, Weng C, Kiryluk K. Medical records-based chronic kidney disease phenotype for clinical care and "big data" observational and genetic studies. NPJ Digit Med 2021; 4:70. [PMID: 33850243 PMCID: PMC8044136 DOI: 10.1038/s41746-021-00428-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 02/25/2021] [Indexed: 12/19/2022] Open
Abstract
Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational and genetic studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods to automatically place patients on the staging grid of albuminuria by glomerular filtration rate ("A-by-G" grid). We manually validated the algorithm by 451 chart reviews across three medical systems, demonstrating overall positive predictive value of 95% for CKD cases and 97% for healthy controls. Independent case-control validation using 2350 patient records demonstrated diagnostic specificity of 97% and sensitivity of 87%. Application of the phenotype to 1.3 million patients demonstrated that over 80% of CKD cases are undetected using ICD codes alone. We also demonstrated several large-scale applications of the phenotype, including identifying stage-specific kidney disease comorbidities, in silico estimation of kidney trait heritability in thousands of pedigrees reconstructed from medical records, and biobank-based multicenter genome-wide and phenome-wide association studies.
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Affiliation(s)
- Ning Shang
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Atlas Khan
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Fernanda Polubriaginof
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Francesca Zanoni
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Karla Mehl
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - David Fasel
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Paul E Drawz
- Department of Medicine, University of Minnesota, Minnesota, MN, USA
| | - Robert J Carrol
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
- Departments of Medicine, Vanderbilt University, Nashville, TN, USA
| | | | | | | | - Richard A Dart
- Marshfield Clinic Research Institute, Marshfield, WI, USA
| | | | - Eric B Larson
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - David S Carrell
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | | | | | | | | | | | | | - Adam S Gordon
- Center for Genetic Medicine, Northwestern University, Chicago, IL, USA
| | - Gail P Jarvik
- Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Ian B Stanaway
- Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - David R Crosslin
- Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Sumit Mohan
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Iuliana Ionita-Laza
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Nicholas P Tatonetti
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Ali G Gharavi
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA.
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214
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Verbitsky M, Krithivasan P, Batourina E, Khan A, Graham SE, Marasà M, Kim H, Lim TY, Weng PL, Sánchez-Rodríguez E, Mitrotti A, Ahram DF, Zanoni F, Fasel DA, Westland R, Sampson MG, Zhang JY, Bodria M, Kil BH, Shril S, Gesualdo L, Torri F, Scolari F, Izzi C, van Wijk JA, Saraga M, Santoro D, Conti G, Barton DE, Dobson MG, Puri P, Furth SL, Warady BA, Pisani I, Fiaccadori E, Allegri L, Degl'Innocenti ML, Piaggio G, Alam S, Gigante M, Zaza G, Esposito P, Lin F, Simões-e-Silva AC, Brodkiewicz A, Drozdz D, Zachwieja K, Miklaszewska M, Szczepanska M, Adamczyk P, Tkaczyk M, Tomczyk D, Sikora P, Mizerska-Wasiak M, Krzemien G, Szmigielska A, Zaniew M, Lozanovski VJ, Gucev Z, Ionita-Laza I, Stanaway IB, Crosslin DR, Wong CS, Hildebrandt F, Barasch J, Kenny EE, Loos RJ, Levy B, Ghiggeri GM, Hakonarson H, Latos-Bieleńska A, Materna-Kiryluk A, Darlow JM, Tasic V, Willer C, Kiryluk K, Sanna-Cherchi S, Mendelsohn CL, Gharavi AG. Copy Number Variant Analysis and Genome-wide Association Study Identify Loci with Large Effect for Vesicoureteral Reflux. J Am Soc Nephrol 2021; 32:805-820. [PMID: 33597122 PMCID: PMC8017540 DOI: 10.1681/asn.2020050681] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 12/04/2020] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Vesicoureteral reflux (VUR) is a common, familial genitourinary disorder, and a major cause of pediatric urinary tract infection (UTI) and kidney failure. The genetic basis of VUR is not well understood. METHODS A diagnostic analysis sought rare, pathogenic copy number variant (CNV) disorders among 1737 patients with VUR. A GWAS was performed in 1395 patients and 5366 controls, of European ancestry. RESULTS Altogether, 3% of VUR patients harbored an undiagnosed rare CNV disorder, such as the 1q21.1, 16p11.2, 22q11.21, and triple X syndromes ((OR, 3.12; 95% CI, 2.10 to 4.54; P=6.35×10-8) The GWAS identified three study-wide significant and five suggestive loci with large effects (ORs, 1.41-6.9), containing canonical developmental genes expressed in the developing urinary tract (WDPCP, OTX1, BMP5, VANGL1, and WNT5A). In particular, 3.3% of VUR patients were homozygous for an intronic variant in WDPCP (rs13013890; OR, 3.65; 95% CI, 2.39 to 5.56; P=1.86×10-9). This locus was associated with multiple genitourinary phenotypes in the UK Biobank and eMERGE studies. Analysis of Wnt5a mutant mice confirmed the role of Wnt5a signaling in bladder and ureteric morphogenesis. CONCLUSIONS These data demonstrate the genetic heterogeneity of VUR. Altogether, 6% of patients with VUR harbored a rare CNV or a common variant genotype conferring an OR >3. Identification of these genetic risk factors has multiple implications for clinical care and for analysis of outcomes in VUR.
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Affiliation(s)
- Miguel Verbitsky
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | - Priya Krithivasan
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | | | - Atlas Khan
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | - Sarah E. Graham
- Department of Internal Medicine, Cardiology, University of Michigan, Ann Arbor, Michigan
| | - Maddalena Marasà
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | - Hyunwoo Kim
- Department of Urology, Columbia University, New York, New York
| | - Tze Y. Lim
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | - Patricia L. Weng
- Department of Pediatric Nephrology, University of California, Los Angeles Medical Center and University of California, Los Angeles Medical Center-Santa Monica, Los Angeles, California
| | | | - Adele Mitrotti
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
- Section of Nephrology, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Dina F. Ahram
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | - Francesca Zanoni
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | - David A. Fasel
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | - Rik Westland
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
- Department of Pediatric Nephrology, Vrije Universiteit University Medical Center, Amsterdam, The Netherlands
| | - Matthew G. Sampson
- Division of Nephrology, Boston Children’s Hospital, Boston, Massachusetts
| | - Jun Y. Zhang
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | - Monica Bodria
- Division of Nephrology, Dialysis, Transplantation, and Laboratory on Pathophysiology of Uremia, Istituto G. Gaslini, Genoa, Italy
| | - Byum Hee Kil
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | - Shirlee Shril
- Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Loreto Gesualdo
- Section of Nephrology, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Fabio Torri
- Department of Pediatric Surgery, Spedali Civili Children’s Hospital of Brescia, Brescia, Italy
| | - Francesco Scolari
- Chair and Division of Nephrology, University and Spedali Civili Hospital, Brescia, Italy
| | - Claudia Izzi
- Division of Nephrology and Department of Obstetrics and Gynecology, ASST Spedali Civili of Brescia, Brescia, Italy
| | - Joanna A.E. van Wijk
- Department of Pediatric Nephrology, Vrije Universiteit University Medical Center, Amsterdam, The Netherlands
| | - Marijan Saraga
- Department of Pediatrics, University Hospital of Split, Split, Croatia
- School of Medicine, University of Split, Split, Croatia
| | - Domenico Santoro
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Giovanni Conti
- Department of Pediatric Nephrology, Azienda Ospedaliera Universitaria “G. Martino,” Messina, Italy
| | - David E. Barton
- University College Dublin School of Medicine, Our Lady’s Children’s Hospital Crumlin, Dublin, Ireland
- Department of Clinical Genetics, Our Lady’s Children’s Hospital Crumlin, Dublin, Ireland
| | - Mark G. Dobson
- Department of Clinical Genetics, Our Lady’s Children’s Hospital Crumlin, Dublin, Ireland
- National Children’s Research Centre, Our Lady’s Children’s Hospital Crumlin, Dublin, Ireland
| | - Prem Puri
- National Children’s Research Centre, Our Lady’s Children’s Hospital Crumlin, Dublin, Ireland
- Department of Pediatric Surgery, Beacon Hospital, University College Dublin, Dublin, Ireland
| | - Susan L. Furth
- Division of Nephrology, Departments of Pediatrics and Epidemiology, Perelman School of Medicine at the University of Pennsylvania, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Bradley A. Warady
- Division of Nephrology, Department of Pediatrics, University of Missouri-Kansas City School of Medicine, Children’s Mercy Kansas City, Kansas City, Missouri
| | - Isabella Pisani
- Nephrology Unit, Parma University Hospital and Department of Medicine and Surgery, Parma University Medical School, Parma, Italy
| | - Enrico Fiaccadori
- Nephrology Unit, Parma University Hospital and Department of Medicine and Surgery, Parma University Medical School, Parma, Italy
| | - Landino Allegri
- Nephrology Unit, Parma University Hospital and Department of Medicine and Surgery, Parma University Medical School, Parma, Italy
| | - Maria Ludovica Degl'Innocenti
- Division of Nephrology, Dialysis, Transplantation, and Laboratory on Pathophysiology of Uremia, Istituto G. Gaslini, Genoa, Italy
| | - Giorgio Piaggio
- Division of Nephrology, Dialysis, Transplantation, and Laboratory on Pathophysiology of Uremia, Istituto G. Gaslini, Genoa, Italy
| | - Shumyle Alam
- Department of Pediatric Urology, Columbia University College of Physicians and Surgeons, New York, New York
| | - Maddalena Gigante
- Section of Nephrology, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Gianluigi Zaza
- Renal and Dialysis Unit, Department of Medicine, School of Medicine, University of Verona, Verona, Italy
| | - Pasquale Esposito
- Department of Internal Medicine, Nephrology, Dialysis and Transplantation Clinics, Genoa University and IRCCS Policlinico San Martino, Genova, Italy
| | - Fangming Lin
- Division of Pediatric Nephrology, Department of Pediatrics, Columbia University, New York, New York
| | - Ana Cristina Simões-e-Silva
- Department of Pediatrics, Unit of Pediatric Nephrology, Interdisciplinary Laboratory of Medical Investigation, Faculty of Medicine, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Andrzej Brodkiewicz
- Department of Pediatrics, Child Nephrology, Dialysotheraphy and Management of Acute Poisoning, Pomeranian Medical University, Szczecin, Poland
| | - Dorota Drozdz
- Department of Pediatric Nephrology and Hypertension, Jagiellonian University Medical College, Krakow, Poland
| | - Katarzyna Zachwieja
- Department of Pediatric Nephrology and Hypertension, Jagiellonian University Medical College, Krakow, Poland
| | - Monika Miklaszewska
- Department of Pediatric Nephrology and Hypertension, Jagiellonian University Medical College, Krakow, Poland
| | - Maria Szczepanska
- Department of Pediatrics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Katowice, Poland
| | - Piotr Adamczyk
- Department of Pediatrics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Katowice, Poland
| | - Marcin Tkaczyk
- Department of Pediatrics, Immunology and Nephrology, Polish Mother’s Memorial Hospital Research Institute, Lodz, Poland
| | - Daria Tomczyk
- Department of Pediatrics, Immunology and Nephrology, Polish Mother’s Memorial Hospital Research Institute, Lodz, Poland
| | - Przemyslaw Sikora
- Department of Pediatric Nephrology, Medical University of Lublin, Lublin, Poland
| | | | - Grazyna Krzemien
- Department of Pediatrics and Nephrology, Medical University of Warsaw, Poland
| | | | - Marcin Zaniew
- Department of Pediatrics, University of Zielona Góra, Zielona Góra, Poland
| | - Vladimir J. Lozanovski
- University Clinic for General, Visceral and Transplantation Surgery, University of Heidelberg, Heidelberg, Germany
- University Children’s Hospital, Medical Faculty of Skopje, Skopje, Macedonia
| | - Zoran Gucev
- University Children’s Hospital, Medical Faculty of Skopje, Skopje, Macedonia
| | | | - Ian B. Stanaway
- Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, Washington
| | - David R. Crosslin
- Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, Washington
| | - Craig S. Wong
- Division of Pediatric Nephrology, University of New Mexico Children’s Hospital, Albuquerque, New Mexico
| | - Friedhelm Hildebrandt
- Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jonathan Barasch
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
- Department of Urology, Columbia University, New York, New York
| | - Eimear E. Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ruth J.F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York
| | - Brynn Levy
- Department of Pathology and Cell Biology, Columbia University, New York, New York
| | - Gian Marco Ghiggeri
- Division of Nephrology, Dialysis, Transplantation, and Laboratory on Pathophysiology of Uremia, Istituto G. Gaslini, Genoa, Italy
| | - Hakon Hakonarson
- Center for Applied Genomics, The Children’s Hospital of Philadelphia and Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Anna Latos-Bieleńska
- Department of Medical Genetics, Poznan University of Medical Sciences, and NZOZ Center for Medical Genetics GENESIS, Poznan, Poland
| | - Anna Materna-Kiryluk
- Department of Medical Genetics, Poznan University of Medical Sciences, and NZOZ Center for Medical Genetics GENESIS, Poznan, Poland
| | - John M. Darlow
- Department of Clinical Genetics, Our Lady’s Children’s Hospital Crumlin, Dublin, Ireland
- National Children’s Research Centre, Our Lady’s Children’s Hospital Crumlin, Dublin, Ireland
| | - Velibor Tasic
- University Children’s Hospital, Medical Faculty of Skopje, Skopje, Macedonia
| | - Cristen Willer
- Department of Internal Medicine, Cardiology, University of Michigan, Ann Arbor, Michigan
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan
- Department of Computational Medicine and Bioinformatics, Ann Arbor, Michigan
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | - Simone Sanna-Cherchi
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | | | - Ali G. Gharavi
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
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215
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Plym A, Penney KL, Kalia S, Kraft P, Conti DV, Haiman C, Mucci LA, Kibel AS. Evaluation of a Multiethnic Polygenic Risk Score Model for Prostate Cancer. J Natl Cancer Inst 2021; 114:771-774. [PMID: 33792693 PMCID: PMC9086757 DOI: 10.1093/jnci/djab058] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 02/17/2021] [Accepted: 03/29/2021] [Indexed: 11/14/2022] Open
Abstract
Polygenic risk scores (PRSs) of common genetic variants have shown promise in prostate cancer risk stratification, but their validity across populations has yet to be confirmed. We evaluated a multiethnic PRS model based on 269 germline genetic risk variants (261 were available for analysis) using an independent population of 13 628 US men. The PRS was strongly associated with prostate cancer but not with any other disease. Comparing men in the top PRS decile with those at average risk (40%-60%), the odds ratio of prostate cancer was 3.89 (95% confidence interval = 3.24 to 4.68) for men of European ancestry and 3.81 (95% confidence interval = 1.48 to 10.19) for men of African ancestry. By age 85 years, the cumulative incidence of prostate cancer for European American men was 7.1% in the bottom decile and 54.1% in the top decile. This suggests that the PRS can be used to identify a substantial proportion of men at high risk for prostate cancer.
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Affiliation(s)
- Anna Plym
- Urology Division, Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.,Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kathryn L Penney
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Sarah Kalia
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Peter Kraft
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.,Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - David V Conti
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California
| | - Christopher Haiman
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California
| | - Lorelei A Mucci
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Adam S Kibel
- Urology Division, Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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216
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Sanchez-Roige S, Cox NJ, Johnson EO, Hancock DB, Davis LK. Alcohol and cigarette smoking consumption as genetic proxies for alcohol misuse and nicotine dependence. Drug Alcohol Depend 2021; 221:108612. [PMID: 33631543 PMCID: PMC8026738 DOI: 10.1016/j.drugalcdep.2021.108612] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 12/24/2020] [Accepted: 01/21/2021] [Indexed: 11/28/2022]
Abstract
PURPOSE To investigate the role of consumption phenotypes as genetic proxies for alcohol misuse and nicotine dependence. METHODS We leveraged GWAS data from well-powered studies of consumption, alcohol misuse, and nicotine dependence phenotypes measured in individuals of European ancestry from the UK Biobank (UKB) and other population-based cohorts (largest total N = 263,954), and performed genetic correlations within a medical-center cohort, BioVU (N = 66,915). For alcohol, we used quantitative measures of consumption and misuse via AUDIT from UKB. For smoking, we used cigarettes per day from UKB and non-UKB cohorts comprising the GSCAN consortium, and nicotine dependence via ICD codes from UKB and Fagerström Test for Nicotine Dependence from non-UKB cohorts. RESULTS In a large phenome-wide association study, we show that smoking consumption and dependence phenotypes show similar strongly negatively associations with a plethora of diseases, whereas alcohol consumption shows patterns of genetic association that diverge from those of alcohol misuse. CONCLUSIONS Our study suggests that cigarette smoking consumption, which can be easily measured in the general population, may be good a genetic proxy for nicotine dependence, whereas alcohol consumption is not a direct genetic proxy of alcohol misuse.
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Affiliation(s)
- Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA; Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
| | - Nancy J Cox
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Eric O Johnson
- GenOmics, Bioinformatics, and Translational Research Center, RTI International, Research Triangle Park, NC 27709, USA; Fellow Program, RTI International, Research Triangle Park, NC 27709, USA
| | - Dana B Hancock
- GenOmics, Bioinformatics, and Translational Research Center, RTI International, Research Triangle Park, NC 27709, USA
| | - Lea K Davis
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
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217
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Disrupting upstream translation in mRNAs is associated with human disease. Nat Commun 2021; 12:1515. [PMID: 33750777 PMCID: PMC7943595 DOI: 10.1038/s41467-021-21812-1] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 02/12/2021] [Indexed: 12/04/2022] Open
Abstract
Ribosome-profiling has uncovered pervasive translation in non-canonical open reading frames, however the biological significance of this phenomenon remains unclear. Using genetic variation from 71,702 human genomes, we assess patterns of selection in translated upstream open reading frames (uORFs) in 5’UTRs. We show that uORF variants introducing new stop codons, or strengthening existing stop codons, are under strong negative selection comparable to protein-coding missense variants. Using these variants, we map and validate gene-disease associations in two independent biobanks containing exome sequencing from 10,900 and 32,268 individuals, respectively, and elucidate their impact on protein expression in human cells. Our results suggest translation disrupting mechanisms relating uORF variation to reduced protein expression, and demonstrate that translation at uORFs is genetically constrained in 50% of human genes. The significance of translated upstream open reading frames is not well known. Here, the authors investigate genetic variants in these regions, finding that they are under high evolutionary constraint and may contribute to disease.
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218
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Read RW, Schlauch KA, Lombardi VC, Cirulli ET, Washington NL, Lu JT, Grzymski JJ. Genome-Wide Identification of Rare and Common Variants Driving Triglyceride Levels in a Nevada Population. Front Genet 2021; 12:639418. [PMID: 33763119 PMCID: PMC7982958 DOI: 10.3389/fgene.2021.639418] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 02/12/2021] [Indexed: 01/08/2023] Open
Abstract
Clinical conditions correlated with elevated triglyceride levels are well-known: coronary heart disease, hypertension, and diabetes. Underlying genetic and phenotypic mechanisms are not fully understood, partially due to lack of coordinated genotypic-phenotypic data. Here we use a subset of the Healthy Nevada Project, a population of 9,183 sequenced participants with longitudinal electronic health records to examine consequences of altered triglyceride levels. Specifically, Healthy Nevada Project participants sequenced by the Helix Exome+ platform were cross-referenced to their electronic medical records to identify: (1) rare and common single-variant genome-wide associations; (2) gene-based associations using a Sequence Kernel Association Test; (3) phenome-wide associations with triglyceride levels; and (4) pleiotropic variants linked to triglyceride levels. The study identified 549 significant single-variant associations (p < 8.75 × 10-9), many in chromosome 11's triglyceride hotspot: ZPR1, BUD13, APOC3, APOA5. A well-known protective loss-of-function variant in APOC3 (R19X) was associated with a 51% decrease in triglyceride levels in the cohort. Sixteen gene-based triglyceride associations were identified; six of these genes surprisingly did not include a single variant with significant associations. Results at the variant and gene level were validated with the UK Biobank. The combination of a single-variant genome-wide association, a gene-based association method, and phenome wide-association studies identified rare and common variants, genes, and phenotypes associated with elevated triglyceride levels, some of which may have been overlooked with standard approaches.
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Affiliation(s)
- Robert W. Read
- Center for Genomic Medicine, Desert Research Institute, Reno, NV, United States
| | - Karen A. Schlauch
- Center for Genomic Medicine, Desert Research Institute, Reno, NV, United States
| | - Vincent C. Lombardi
- Department of Microbiology and Immunology, School of Medicine, University of Nevada, Reno, Reno, NV, United States
| | | | | | - James T. Lu
- Helix Opco, LLC., San Mateo, CA, United States
| | - Joseph J. Grzymski
- Center for Genomic Medicine, Desert Research Institute, Reno, NV, United States
- Renown Health, Reno, NV, United States
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219
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A Phenome-Wide Analysis of Healthcare Costs Associated with Inflammatory Bowel Diseases. Dig Dis Sci 2021; 66:760-767. [PMID: 32436120 DOI: 10.1007/s10620-020-06329-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 05/08/2020] [Indexed: 02/07/2023]
Abstract
INTRODUCTION Crohn's disease (CD) and ulcerative colitis (UC) are associated with considerable direct healthcare costs. There have been few comprehensive analyses of all IBD- and non-IBD comorbidities that determine direct costs in this population. METHODS We used data from a validated cohort of patients with inflammatory bowel disease (IBD). Total healthcare costs were estimated as a sum of costs associated with IBD-related hospitalizations and surgery, imaging (CT or MR scans), outpatient visits, endoscopic evaluation, and emergency room (ER) care. All ICD-9 codes were extracted for each patient and clustered into 1804 distinct phecode clusters representing individual phenotypes. A phenome-wide association analysis (PheWAS) was performed using logistic regression to identify predictors of being in the top decile of costs. RESULTS Our cohort is comprised of 10,721 patients with IBD among whom 50% had CD. The median age was 46 years. The median total cost per patient is $11,203 (IQR $2396-30,563). The strongest association with total healthcare costs was intestinal obstruction without mention of hernia (p = 5.93 × 10-156) and other intestinal obstruction (p = 9.24 × 10-131). In addition, strong associations were observed for symptoms consistent with severity of IBD including the presence of fluid-electrolyte imbalance (p = 1.90 × 10-130), hypovolemia (p = 1.65 × 10-114), abdominal pain (p = 7.29 × 10-60), and anemia (p = 1.90-10-83). Cardiopulmonary diseases and psychological comorbidity also demonstrated significant associations with total costs with the latter being more strongly associated with ER visit-related costs. CONCLUSIONS Surrogate markers suggesting possible irreversible bowel damage and active disease demonstrate the greatest influence on IBD-related healthcare costs.
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220
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Ahuja Y, Kim N, Liang L, Cai T, Dahal K, Seyok T, Lin C, Finan S, Liao K, Savovoa G, Chitnis T, Cai T, Xia Z. Leveraging electronic health records data to predict multiple sclerosis disease activity. Ann Clin Transl Neurol 2021; 8:800-810. [PMID: 33626237 PMCID: PMC8045951 DOI: 10.1002/acn3.51324] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 12/26/2020] [Accepted: 02/01/2021] [Indexed: 12/26/2022] Open
Abstract
Objective No relapse risk prediction tool is currently available to guide treatment selection for multiple sclerosis (MS). Leveraging electronic health record (EHR) data readily available at the point of care, we developed a clinical tool for predicting MS relapse risk. Methods Using data from a clinic‐based research registry and linked EHR system between 2006 and 2016, we developed models predicting relapse events from the registry in a training set (n = 1435) and tested the model performance in an independent validation set of MS patients (n = 186). This iterative process identified prior 1‐year relapse history as a key predictor of future relapse but ascertaining relapse history through the labor‐intensive chart review is impractical. We pursued two‐stage algorithm development: (1) L1‐regularized logistic regression (LASSO) to phenotype past 1‐year relapse status from contemporaneous EHR data, (2) LASSO to predict future 1‐year relapse risk using imputed prior 1‐year relapse status and other algorithm‐selected features. Results The final model, comprising age, disease duration, and imputed prior 1‐year relapse history, achieved a predictive AUC and F score of 0.707 and 0.307, respectively. The performance was significantly better than the baseline model (age, sex, race/ethnicity, and disease duration) and noninferior to a model containing actual prior 1‐year relapse history. The predicted risk probability declined with disease duration and age. Conclusion Our novel machine‐learning algorithm predicts 1‐year MS relapse with accuracy comparable to other clinical prediction tools and has applicability at the point of care. This EHR‐based two‐stage approach of outcome prediction may have application to neurological disease beyond MS.
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Affiliation(s)
- Yuri Ahuja
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Nicole Kim
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Liang Liang
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Tianrun Cai
- Division of Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Kumar Dahal
- Division of Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Thany Seyok
- Division of Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Chen Lin
- Clinical Natural Language Processing Program, Boston Children's Hospital, Boston, MA, USA
| | - Sean Finan
- Clinical Natural Language Processing Program, Boston Children's Hospital, Boston, MA, USA
| | - Katherine Liao
- Division of Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Guergana Savovoa
- Clinical Natural Language Processing Program, Boston Children's Hospital, Boston, MA, USA
| | - Tanuja Chitnis
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Tianxi Cai
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Zongqi Xia
- Department of Neurology and Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
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Salvatore M, Beesley LJ, Fritsche LG, Hanauer D, Shi X, Mondul AM, Pearce CL, Mukherjee B. Phenotype risk scores (PheRS) for pancreatic cancer using time-stamped electronic health record data: Discovery and validation in two large biobanks. J Biomed Inform 2021; 113:103652. [PMID: 33279681 PMCID: PMC7855433 DOI: 10.1016/j.jbi.2020.103652] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 10/27/2020] [Accepted: 11/30/2020] [Indexed: 12/31/2022]
Abstract
BACKGROUND Traditional methods for disease risk prediction and assessment, such as diagnostic tests using serum, urine, blood, saliva or imaging biomarkers, have been important for identifying high-risk individuals for many diseases, leading to early detection and improved survival. For pancreatic cancer, traditional methods for screening have been largely unsuccessful in identifying high-risk individuals in advance of disease progression leading to high mortality and poor survival. Electronic health records (EHR) linked to genetic profiles provide an opportunity to integrate multiple sources of patient information for risk prediction and stratification. We leverage a constellation of temporally associated diagnoses available in the EHR to construct a summary risk score, called a phenotype risk score (PheRS), for identifying individuals at high-risk for having pancreatic cancer. The proposed PheRS approach incorporates the time with respect to disease onset into the prediction framework. We combine and contrast the PheRS with more well-known measures of inherited susceptibility, namely, the polygenic risk scores (PRS) for prediction of pancreatic cancer. METHODOLOGY We first calculated pairwise, unadjusted associations between pancreatic cancer diagnosis and all possible other diagnoses across the medical phenome. We call these pairwise associations co-occurrences. After accounting for cross-phenotype correlations, the multivariable association estimates from a subset of relatively independent diagnoses were used to create a weighted sum PheRS. We constructed time-restricted risk scores using data from 38,359 participants in the Michigan Genomics Initiative (MGI) based on the diagnoses contained in the EHR at 0, 1, 2, and 5 years prior to the target pancreatic cancer diagnosis. The PheRS was assessed for predictability in the UK Biobank (UKB). We tested the relative contribution of PheRS when added to a model containing a summary measure of inherited genetic susceptibility (PRS) plus other covariates like age, sex, smoking status, drinking status, and body mass index (BMI). RESULTS Our exploration of co-occurrence patterns identified expected associations while also revealing unexpected relationships that may warrant closer attention. Solely using the pancreatic cancer PheRS at 5 years before the target diagnoses yielded an AUC of 0.60 (95% CI = [0.58, 0.62]) in UKB. A larger predictive model including PheRS, PRS, and the covariates at the 5-year threshold achieved an AUC of 0.74 (95% CI = [0.72, 0.76]) in UKB. We note that PheRS does contribute independently in the joint model. Finally, scores at the top percentiles of the PheRS distribution demonstrated promise in terms of risk stratification. Scores in the top 2% were 10.20 (95% CI = [9.34, 12.99]) times more likely to identify cases than those in the bottom 98% in UKB at the 5-year threshold prior to pancreatic cancer diagnosis. CONCLUSIONS We developed a framework for creating a time-restricted PheRS from EHR data for pancreatic cancer using the rich information content of a medical phenome. In addition to identifying hypothesis-generating associations for future research, this PheRS demonstrates a potentially important contribution in identifying high-risk individuals, even after adjusting for PRS for pancreatic cancer and other traditional epidemiologic covariates. The methods are generalizable to other phenotypic traits.
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Affiliation(s)
- Maxwell Salvatore
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States
| | - Lauren J Beesley
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States
| | - Lars G Fritsche
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States; Rogel Cancer Center, University of Michigan Medicine, Ann Arbor, MI 48109, United States; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, United States
| | - David Hanauer
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI 48109, United States
| | - Xu Shi
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States
| | - Alison M Mondul
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States
| | - Celeste Leigh Pearce
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.
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Exome-wide evaluation of rare coding variants using electronic health records identifies new gene-phenotype associations. Nat Med 2021; 27:66-72. [PMID: 33432171 PMCID: PMC8775355 DOI: 10.1038/s41591-020-1133-8] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 10/13/2020] [Indexed: 01/18/2023]
Abstract
The clinical impact of rare loss-of-function variants has yet to be determined for most genes. Integration of DNA sequencing data with electronic health records (EHRs) could enhance our understanding of the contribution of rare genetic variation to human disease1. By leveraging 10,900 whole-exome sequences linked to EHR data in the Penn Medicine Biobank, we addressed the association of the cumulative effects of rare predicted loss-of-function variants for each individual gene on human disease on an exome-wide scale, as assessed using a set of diverse EHR phenotypes. After discovering 97 genes with exome-by-phenome-wide significant phenotype associations (P < 10-6), we replicated 26 of these in the Penn Medicine Biobank, as well as in three other medical biobanks and the population-based UK Biobank. Of these 26 genes, five had associations that have been previously reported and represented positive controls, whereas 21 had phenotype associations not previously reported, among which were genes implicated in glaucoma, aortic ectasia, diabetes mellitus, muscular dystrophy and hearing loss. These findings show the value of aggregating rare predicted loss-of-function variants into 'gene burdens' for identifying new gene-disease associations using EHR phenotypes in a medical biobank. We suggest that application of this approach to even larger numbers of individuals will provide the statistical power required to uncover unexplored relationships between rare genetic variation and disease phenotypes.
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223
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Genome-wide association study of problematic opioid prescription use in 132,113 23andMe research participants of European ancestry. Mol Psychiatry 2021; 26:6209-6217. [PMID: 34728798 PMCID: PMC8562028 DOI: 10.1038/s41380-021-01335-3] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 09/21/2021] [Accepted: 09/29/2021] [Indexed: 12/31/2022]
Abstract
The growing prevalence of opioid use disorder (OUD) constitutes an urgent health crisis. Ample evidence indicates that risk for OUD is heritable. As a surrogate (or proxy) for OUD, we explored the genetic basis of using prescription opioids 'not as prescribed'. We hypothesized that misuse of opiates might be a heritable risk factor for OUD. To test this hypothesis, we performed a genome-wide association study (GWAS) of problematic opioid use (POU) in 23andMe research participants of European ancestry (N = 132,113; 21% cases). We identified two genome-wide significant loci (rs3791033, an intronic variant of KDM4A; rs640561, an intergenic variant near LRRIQ3). POU showed positive genetic correlations with the two largest available GWAS of OUD and opioid dependence (rg = 0.64, 0.80, respectively). We also identified numerous additional genetic correlations with POU, including alcohol dependence (rg = 0.74), smoking initiation (rg = 0.63), pain relief medication intake (rg = 0.49), major depressive disorder (rg = 0.44), chronic pain (rg = 0.42), insomnia (rg = 0.39), and loneliness (rg = 0.28). Although POU was positively genetically correlated with risk-taking (rg = 0.38), conditioning POU on risk-taking did not substantially alter the magnitude or direction of these genetic correlations, suggesting that POU does not simply reflect a genetic tendency towards risky behavior. Lastly, we performed phenome- and lab-wide association analyses, which uncovered additional phenotypes that were associated with POU, including respiratory failure, insomnia, ischemic heart disease, and metabolic and blood-related biomarkers. We conclude that opioid misuse can be measured in population-based cohorts and provides a cost-effective complementary strategy for understanding the genetic basis of OUD.
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224
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Namjou B, Stanaway IB, Lingren T, Mentch FD, Benoit B, Dikilitas O, Niu X, Shang N, Shoemaker AH, Carey DJ, Mirshahi T, Singh R, Nestor JG, Hakonarson H, Denny JC, Crosslin DR, Jarvik GP, Kullo IJ, Williams MS, Harley JB. Evaluation of the MC4R gene across eMERGE network identifies many unreported obesity-associated variants. Int J Obes (Lond) 2021; 45:155-169. [PMID: 32952152 PMCID: PMC7752751 DOI: 10.1038/s41366-020-00675-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 08/07/2020] [Accepted: 09/03/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND/OBJECTIVES Melanocortin-4 receptor (MC4R) plays an essential role in food intake and energy homeostasis. More than 170 MC4R variants have been described over the past two decades, with conflicting reports regarding the prevalence and phenotypic effects of these variants in diverse cohorts. To determine the frequency of MC4R variants in large cohort of different ancestries, we evaluated the MC4R coding region for 20,537 eMERGE participants with sequencing data plus additional 77,454 independent individuals with genome-wide genotyping data at this locus. SUBJECTS/METHODS The sequencing data were obtained from the eMERGE phase III study, in which multisample variant call format calls have been generated, curated, and annotated. In addition to penetrance estimation using body mass index (BMI) as a binary outcome, GWAS and PheWAS were performed using median BMI in linear regression analyses. All results were adjusted for principal components, age, sex, and sites of genotyping. RESULTS Targeted sequencing data of MC4R revealed 125 coding variants in 1839 eMERGE participants including 30 unreported coding variants that were predicted to be functionally damaging. Highly penetrant unreported variants included (L325I, E308K, D298N, S270F, F261L, T248A, D111V, and Y80F) in which seven participants had obesity class III defined as BMI ≥ 40 kg/m2. In GWAS analysis, in addition to known risk haplotype upstream of MC4R (best variant rs6567160 (P = 5.36 × 10-25, Beta = 0.37), a novel rare haplotype was detected which was protective against obesity and encompassed the V103I variant with known gain-of-function properties (P = 6.23 × 10-08, Beta = -0.62). PheWAS analyses extended this protective effect of V103I to type 2 diabetes, diabetic nephropathy, and chronic renal failure independent of BMI. CONCLUSIONS MC4R screening in a large eMERGE cohort confirmed many previous findings, extend the MC4R pleotropic effects, and discovered additional MC4R rare alleles that probably contribute to obesity.
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Affiliation(s)
- Bahram Namjou
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center (CCHMC), Cincinnati, OH, USA.
- College of Medicine, University of Cincinnati, Cincinnati, OH, USA.
| | - Ian B Stanaway
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, WA, USA
| | - Todd Lingren
- College of Medicine, University of Cincinnati, Cincinnati, OH, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Frank D Mentch
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Barbara Benoit
- Research Information Science and Computing, Partners HealthCare, Somerville, MA, USA
| | - Ozan Dikilitas
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Xinnan Niu
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, TN, USA
| | - Ning Shang
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Ashley H Shoemaker
- Department of Pediatrics, Division of Endocrinology and Diabetes, Vanderbilt University Medical Center, Nashville, TN, USA
| | - David J Carey
- Department of Molecular and Functional Genomics, Geisinger, Danville, PA, USA
| | - Tooraj Mirshahi
- Department of Molecular and Functional Genomics, Geisinger, Danville, PA, USA
| | | | - Jordan G Nestor
- Department of Medicine, Division of Nephrology, Columbia University, New York, NY, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joshua C Denny
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, TN, USA
| | - David R Crosslin
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, WA, USA
| | - Gail P Jarvik
- Department of Medicine (Medical Genetics), University of Washington Medical Center, Seattle, WA, USA
- Department Genome Sciences, University of Washington Medical Center, Seattle, WA, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Marc S Williams
- Genomic Medicine Institute (M.S.W.), Geisinger, Danville, PA, USA
| | - John B Harley
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center (CCHMC), Cincinnati, OH, USA
- College of Medicine, University of Cincinnati, Cincinnati, OH, USA
- U.S. Department of Veterans Affairs Medical Center, Cincinnati, OH, USA
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Genetic risk for major depressive disorder and loneliness in sex-specific associations with coronary artery disease. Mol Psychiatry 2021; 26:4254-4264. [PMID: 31796895 PMCID: PMC7266730 DOI: 10.1038/s41380-019-0614-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 09/24/2019] [Accepted: 11/18/2019] [Indexed: 01/23/2023]
Abstract
Major depressive disorder (MDD) and loneliness are phenotypically and genetically correlated with coronary artery disease (CAD), but whether these associations are explained by pleiotropic genetic variants or shared comorbidities is unclear. To tease apart these scenarios, we first assessed the medical morbidity pattern associated with genetic risk factors for MDD and loneliness by conducting a phenome-wide association study in 18,385 European-ancestry individuals in the Vanderbilt University Medical Center biobank, BioVU. Polygenic scores for MDD and loneliness were developed for each person using previously published meta-GWAS summary statistics, and were tested for association with 882 clinical diagnoses ascertained via billing codes in electronic health records. We discovered strong associations with heart disease diagnoses, and next embarked on targeted analyses of CAD in 3893 cases and 4197 controls. We found odds ratios of 1.11 (95% CI, 1.04-1.18; P 8.43 × 10-4) and 1.13 (95% CI, 1.07-1.20; P 4.51 × 10-6) per 1-SD increase in the polygenic scores for MDD and loneliness, respectively. Results were similar in patients without psychiatric symptoms, and the increased risk persisted in females even after adjusting for multiple conventional risk factors and a polygenic score for CAD. In a final sensitivity analysis, we statistically adjusted for the genetic correlation between MDD and loneliness and re-computed polygenic scores. The polygenic score unique to loneliness remained associated with CAD (OR 1.09, 95% CI 1.03-1.15; P 0.002), while the polygenic score unique to MDD did not (OR 1.00, 95% CI 0.95-1.06; P 0.97). Our replication sample was the Atherosclerosis Risk in Communities (ARIC) cohort of 7197 European-ancestry participants (1598 incident CAD cases). In ARIC, polygenic scores for MDD and loneliness were associated with hazard ratios of 1.07 (95% CI, 0.99-1.14; P = 0.07) and 1.07 (1.01-1.15; P = 0.03), respectively, and we replicated findings from the BioVU sensitivity analyses. We conclude that genetic risk factors for MDD and loneliness act pleiotropically to increase CAD risk in females.
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Zhou J, Liu C, Sun Y, Huang W, Ye K. Cognitive disorders associated with hospitalization of COVID-19: Results from an observational cohort study. Brain Behav Immun 2021; 91:383-392. [PMID: 33148439 PMCID: PMC7584518 DOI: 10.1016/j.bbi.2020.10.019] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 09/30/2020] [Accepted: 10/19/2020] [Indexed: 12/26/2022] Open
Abstract
INTRODUCTION Our understanding of risk factors for COVID‑19, including pre-existing medical conditions and genetic variations, is limited. To what extent the pre-existing clinical condition and genetic background have implications for COVID-19 still needs to be explored. METHODS Our study included 389,620 participants of European descent from the UK Biobank, of whom 3,884 received the COVID-19 test and 1,091 were tested positive for COVID-19. We examined the association of COVID-19 status with an extensive list of 974 medical conditions and 30 blood biomarkers. Additionally, we tested the association of genetic variants in two key genes related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, angiotensin-converting enzyme 2 (ACE2) and transmembrane protease serine 2 (TMPRSS2), with COVID-19 or any other phenotypes. RESULTS The most significant risk factors for COVID-19 include Alzheimer's disease (OR = 2.29, 95% CI: 1.25-4.16), dementia (OR = 2.16, 95% CI: 1.36-3.42), and the overall category of delirium, dementia, amnestic and other cognitive disorders (OR = 1.90, 95% CI: 1.24-2.90). Evidence suggesting associations of genetic variants in SARS-CoV-2 infection-related genes with COVID-19 (rs7282236, OR = 1.33, 95% CI: 1.14-1.54, p = 2.31 × 10-4) and other phenotypes, such as an immune deficiency (p = 5.65 × 10-5) and prostate cancer (p = 1.1 × 10-5), was obtained. CONCLUSIONS Our unbiased and extensive search identified pre-existing Alzheimer's disease and dementia as top risk factors for hospital admission due to COVID-19, highlighting the importance of providing special protective care for patients with cognitive disorders during this pandemic. We also obtained evidence suggesting a direct association of genetic variants with COVID-19.
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Affiliation(s)
- Jingqi Zhou
- Department of Genetics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, USA; School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Chang Liu
- Department of Genetics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, USA; College of Life Sciences, Wuhan University, Wuhan, PR China
| | - Yitang Sun
- Department of Genetics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, USA
| | - Weishan Huang
- Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA, USA; Department of Microbiology and Immunology, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Kaixiong Ye
- Department of Genetics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, USA; Institute of Bioinformatics, University of Georgia, Athens, GA, USA.
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227
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Deciphering the genetic and epidemiological landscape of mitochondrial DNA abundance. Hum Genet 2020; 140:849-861. [PMID: 33385171 PMCID: PMC8099832 DOI: 10.1007/s00439-020-02249-w] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 12/15/2020] [Indexed: 12/13/2022]
Abstract
Mitochondrial (MT) dysfunction is a hallmark of aging and has been associated with most aging-related diseases as well as immunological processes. However, little is known about aging, lifestyle and genetic factors influencing mitochondrial DNA (mtDNA) abundance. In this study, mtDNA abundance was estimated from the weighted intensities of probes mapping to the MT genome in 295,150 participants from the UK Biobank. We found that the abundance of mtDNA was significantly elevated in women compared to men, was negatively correlated with advanced age, higher smoking exposure, greater body-mass index, higher frailty index as well as elevated red and white blood cell count and lower mortality. In addition, several biochemistry markers in blood-related to cholesterol metabolism, ion homeostasis and kidney function were found to be significantly associated with mtDNA abundance. By performing a genome-wide association study, we identified 50 independent regions genome-wide significantly associated with mtDNA abundance which harbour multiple genes involved in the immune system, cancer as well as mitochondrial function. Using mixed effects models, we estimated the SNP-heritability of mtDNA abundance to be around 8%. To investigate the consequence of altered mtDNA abundance, we performed a phenome-wide association study and found that mtDNA abundance is involved in risk for leukaemia, hematologic diseases as well as hypertension. Thus, estimating mtDNA abundance from genotyping arrays has the potential to provide novel insights into age- and disease-relevant processes, particularly those related to immunity and established mitochondrial functions.
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228
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Bonkowsky JL, Wilkes J, Ying J, Wei WQ. Novel and known morbidities of leukodystrophies identified using a phenome-wide association study. Neurol Clin Pract 2020; 10:406-414. [PMID: 33299668 DOI: 10.1212/cpj.0000000000000783] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 09/23/2019] [Indexed: 11/15/2022]
Abstract
Objective To determine shared comorbidities and to identify underrecognized or unexpected morbidities in children with leukodystrophies using an unbiased phenome-wide association study (PheWAS) analysis of a nationwide pediatric clinical and financial database. Methods Data were extracted from the Pediatric Health Information System database. Patients with leukodystrophy were identified with International Classification of Diseases, 10th revision, clinical modification, diagnostic codes for any of 4 specific leukodystrophies (X-linked adrenoleukodystrophy (E71.52x), Hurler disease (E76.01), Krabbe disease (E75.23), and metachromatic leukodystrophy (E75.25)) over a 3-year time period. Confirmed leukodystrophy cases (n = 553) were matched with 1659 controls. A PheWAS analysis was performed on all available ICD diagnostic codes for cases and controls. Comparisons were performed for all 4 leukodystrophies as a group and individually. Results We found 174 phecodes (grouped ICD codes) associated with leukodystrophies, including 28 codes with a rate difference (RD) > 20%. Known comorbidities of leukodystrophies including developmental delay, epilepsy, and adrenal insufficiency were identified. Unexpected associations identified included hypertension (RD 30%, OR 25), hearing loss (RD 28%, OR 15), and cardiac dysrhythmias (RD 27%, OR 9). Hurler disease had a greater number of unique disease conditions. Conclusions PheWAS analysis from a national database demonstrates shared and unique features of leukodystrophies. Developmental delay, cardiac dysrhythmias, fluid and electrolyte disturbances, and respiratory issues were common to all 4 leukodystrophy diseases. Use of a PheWAS in leukodystrophies and other pediatric neurologic diseases offers a method for targeting improved care for patients by identification of morbidities.
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Affiliation(s)
- Joshua L Bonkowsky
- Division of Pediatric Neurology (JLB), Department of Pediatrics, University of Utah School of Medicine; Brain and Spine Center (JLB), Primary Children's Hospital, Intermountain Healthcare, Salt Lake City; Intermountain Healthcare (JW), Salt Lake City; Department of Internal Medicine (JY), University of Utah School of Medicine, Salt Lake City; and Department of Biomedical Informatics (W-QW), Vanderbilt University Medical Center, Nashville, TN
| | - Jacob Wilkes
- Division of Pediatric Neurology (JLB), Department of Pediatrics, University of Utah School of Medicine; Brain and Spine Center (JLB), Primary Children's Hospital, Intermountain Healthcare, Salt Lake City; Intermountain Healthcare (JW), Salt Lake City; Department of Internal Medicine (JY), University of Utah School of Medicine, Salt Lake City; and Department of Biomedical Informatics (W-QW), Vanderbilt University Medical Center, Nashville, TN
| | - Jian Ying
- Division of Pediatric Neurology (JLB), Department of Pediatrics, University of Utah School of Medicine; Brain and Spine Center (JLB), Primary Children's Hospital, Intermountain Healthcare, Salt Lake City; Intermountain Healthcare (JW), Salt Lake City; Department of Internal Medicine (JY), University of Utah School of Medicine, Salt Lake City; and Department of Biomedical Informatics (W-QW), Vanderbilt University Medical Center, Nashville, TN
| | - Wei-Qi Wei
- Division of Pediatric Neurology (JLB), Department of Pediatrics, University of Utah School of Medicine; Brain and Spine Center (JLB), Primary Children's Hospital, Intermountain Healthcare, Salt Lake City; Intermountain Healthcare (JW), Salt Lake City; Department of Internal Medicine (JY), University of Utah School of Medicine, Salt Lake City; and Department of Biomedical Informatics (W-QW), Vanderbilt University Medical Center, Nashville, TN
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229
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Johnson EC, Demontis D, Thorgeirsson TE, Walters RK, Polimanti R, Hatoum AS, Sanchez-Roige S, Paul SE, Wendt FR, Clarke TK, Lai D, Reginsson GW, Zhou H, He J, Baranger DAA, Gudbjartsson DF, Wedow R, Adkins DE, Adkins AE, Alexander J, Bacanu SA, Bigdeli TB, Boden J, Brown SA, Bucholz KK, Bybjerg-Grauholm J, Corley RP, Degenhardt L, Dick DM, Domingue BW, Fox L, Goate AM, Gordon SD, Hack LM, Hancock DB, Hartz SM, Hickie IB, Hougaard DM, Krauter K, Lind PA, McClintick JN, McQueen MB, Meyers JL, Montgomery GW, Mors O, Mortensen PB, Nordentoft M, Pearson JF, Peterson RE, Reynolds MD, Rice JP, Runarsdottir V, Saccone NL, Sherva R, Silberg JL, Tarter RE, Tyrfingsson T, Wall TL, Webb BT, Werge T, Wetherill L, Wright MJ, Zellers S, Adams MJ, Bierut LJ, Boardman JD, Copeland WE, Farrer LA, Foroud TM, Gillespie NA, Grucza RA, Harris KM, Heath AC, Hesselbrock V, Hewitt JK, Hopfer CJ, Horwood J, Iacono WG, Johnson EO, Kendler KS, Kennedy MA, Kranzler HR, Madden PAF, Maes HH, Maher BS, Martin NG, McGue M, McIntosh AM, Medland SE, Nelson EC, Porjesz B, Riley BP, Stallings MC, Vanyukov MM, Vrieze S, Davis LK, Bogdan R, Gelernter J, Edenberg HJ, Stefansson K, et alJohnson EC, Demontis D, Thorgeirsson TE, Walters RK, Polimanti R, Hatoum AS, Sanchez-Roige S, Paul SE, Wendt FR, Clarke TK, Lai D, Reginsson GW, Zhou H, He J, Baranger DAA, Gudbjartsson DF, Wedow R, Adkins DE, Adkins AE, Alexander J, Bacanu SA, Bigdeli TB, Boden J, Brown SA, Bucholz KK, Bybjerg-Grauholm J, Corley RP, Degenhardt L, Dick DM, Domingue BW, Fox L, Goate AM, Gordon SD, Hack LM, Hancock DB, Hartz SM, Hickie IB, Hougaard DM, Krauter K, Lind PA, McClintick JN, McQueen MB, Meyers JL, Montgomery GW, Mors O, Mortensen PB, Nordentoft M, Pearson JF, Peterson RE, Reynolds MD, Rice JP, Runarsdottir V, Saccone NL, Sherva R, Silberg JL, Tarter RE, Tyrfingsson T, Wall TL, Webb BT, Werge T, Wetherill L, Wright MJ, Zellers S, Adams MJ, Bierut LJ, Boardman JD, Copeland WE, Farrer LA, Foroud TM, Gillespie NA, Grucza RA, Harris KM, Heath AC, Hesselbrock V, Hewitt JK, Hopfer CJ, Horwood J, Iacono WG, Johnson EO, Kendler KS, Kennedy MA, Kranzler HR, Madden PAF, Maes HH, Maher BS, Martin NG, McGue M, McIntosh AM, Medland SE, Nelson EC, Porjesz B, Riley BP, Stallings MC, Vanyukov MM, Vrieze S, Davis LK, Bogdan R, Gelernter J, Edenberg HJ, Stefansson K, Børglum AD, Agrawal A. A large-scale genome-wide association study meta-analysis of cannabis use disorder. Lancet Psychiatry 2020; 7:1032-1045. [PMID: 33096046 PMCID: PMC7674631 DOI: 10.1016/s2215-0366(20)30339-4] [Show More Authors] [Citation(s) in RCA: 207] [Impact Index Per Article: 41.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 07/15/2020] [Accepted: 07/16/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Variation in liability to cannabis use disorder has a strong genetic component (estimated twin and family heritability about 50-70%) and is associated with negative outcomes, including increased risk of psychopathology. The aim of the study was to conduct a large genome-wide association study (GWAS) to identify novel genetic variants associated with cannabis use disorder. METHODS To conduct this GWAS meta-analysis of cannabis use disorder and identify associations with genetic loci, we used samples from the Psychiatric Genomics Consortium Substance Use Disorders working group, iPSYCH, and deCODE (20 916 case samples, 363 116 control samples in total), contrasting cannabis use disorder cases with controls. To examine the genetic overlap between cannabis use disorder and 22 traits of interest (chosen because of previously published phenotypic correlations [eg, psychiatric disorders] or hypothesised associations [eg, chronotype] with cannabis use disorder), we used linkage disequilibrium score regression to calculate genetic correlations. FINDINGS We identified two genome-wide significant loci: a novel chromosome 7 locus (FOXP2, lead single-nucleotide polymorphism [SNP] rs7783012; odds ratio [OR] 1·11, 95% CI 1·07-1·15, p=1·84 × 10-9) and the previously identified chromosome 8 locus (near CHRNA2 and EPHX2, lead SNP rs4732724; OR 0·89, 95% CI 0·86-0·93, p=6·46 × 10-9). Cannabis use disorder and cannabis use were genetically correlated (rg 0·50, p=1·50 × 10-21), but they showed significantly different genetic correlations with 12 of the 22 traits we tested, suggesting at least partially different genetic underpinnings of cannabis use and cannabis use disorder. Cannabis use disorder was positively genetically correlated with other psychopathology, including ADHD, major depression, and schizophrenia. INTERPRETATION These findings support the theory that cannabis use disorder has shared genetic liability with other psychopathology, and there is a distinction between genetic liability to cannabis use and cannabis use disorder. FUNDING National Institute of Mental Health; National Institute on Alcohol Abuse and Alcoholism; National Institute on Drug Abuse; Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing; The European Commission, Horizon 2020; National Institute of Child Health and Human Development; Health Research Council of New Zealand; National Institute on Aging; Wellcome Trust Case Control Consortium; UK Research and Innovation Medical Research Council (UKRI MRC); The Brain & Behavior Research Foundation; National Institute on Deafness and Other Communication Disorders; Substance Abuse and Mental Health Services Administration (SAMHSA); National Institute of Biomedical Imaging and Bioengineering; National Health and Medical Research Council (NHMRC) Australia; Tobacco-Related Disease Research Program of the University of California; Families for Borderline Personality Disorder Research (Beth and Rob Elliott) 2018 NARSAD Young Investigator Grant; The National Child Health Research Foundation (Cure Kids); The Canterbury Medical Research Foundation; The New Zealand Lottery Grants Board; The University of Otago; The Carney Centre for Pharmacogenomics; The James Hume Bequest Fund; National Institutes of Health: Genes, Environment and Health Initiative; National Institutes of Health; National Cancer Institute; The William T Grant Foundation; Australian Research Council; The Virginia Tobacco Settlement Foundation; The VISN 1 and VISN 4 Mental Illness Research, Education, and Clinical Centers of the US Department of Veterans Affairs; The 5th Framework Programme (FP-5) GenomEUtwin Project; The Lundbeck Foundation; NIH-funded Shared Instrumentation Grant S10RR025141; Clinical Translational Sciences Award grants; National Institute of Neurological Disorders and Stroke; National Heart, Lung, and Blood Institute; National Institute of General Medical Sciences.
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Affiliation(s)
- Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA.
| | - Ditte Demontis
- Department of Biomedicine-Human Genetics and Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark; The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark; Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | | | - Raymond K Walters
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Renato Polimanti
- Division of Human Genetics, Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA; Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Alexander S Hatoum
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sarah E Paul
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, St. Louis, MO, USA
| | - Frank R Wendt
- Division of Human Genetics, Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA; Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Toni-Kim Clarke
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Hang Zhou
- Division of Human Genetics, Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA; Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - June He
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - David A A Baranger
- Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Daniel F Gudbjartsson
- Statistics Department, Reykjavik, Iceland; School of Engineering and Natural Sciences, Iceland University, Reykjavik, Iceland
| | - Robbee Wedow
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel E Adkins
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA; Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA; College Behavioral and Emotional Health Institute, Virginia Commonwealth University, Richmond, VA, USA
| | - Amy E Adkins
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA; Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA; College Behavioral and Emotional Health Institute, Virginia Commonwealth University, Richmond, VA, USA
| | - Jeffry Alexander
- Virginia Commonwealth University Alcohol Research Center, Virginia Commonwealth University, Richmond, VA, USA
| | - Silviu-Alin Bacanu
- Virginia Commonwealth University Alcohol Research Center, Virginia Commonwealth University, Richmond, VA, USA
| | - Tim B Bigdeli
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Joseph Boden
- Department of Psychological Medicine, University of Otago, Christchurch, New Zealand
| | - Sandra A Brown
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Department of Psychology and Office of Research Affairs, University of California San Diego, La Jolla, CA, USA
| | - Kathleen K Bucholz
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Jonas Bybjerg-Grauholm
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark; Department for Congenital Disorders, Center for Neonatal Screening, Statens Serum Institut, Copenhagen, Denmark
| | - Robin P Corley
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Louisa Degenhardt
- National Drug and Alcohol Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Danielle M Dick
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA; Department of Human & Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Benjamin W Domingue
- Stanford University Graduate School of Education, Stanford University, Stanford, CA, USA
| | - Louis Fox
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Alison M Goate
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Scott D Gordon
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Laura M Hack
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Dana B Hancock
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, Durham, NC, USA
| | - Sarah M Hartz
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Ian B Hickie
- Brain and Mind Centre, University of Sydney, Sydney, Australia
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark; Department for Congenital Disorders, Center for Neonatal Screening, Statens Serum Institut, Copenhagen, Denmark
| | - Kenneth Krauter
- Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA; University of Colorado Boulder, Boulder, CO, USA
| | - Penelope A Lind
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jeanette N McClintick
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Matthew B McQueen
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA
| | - Jacquelyn L Meyers
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA; Henri Begleiter Neurodynamics Laboratory, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Grant W Montgomery
- Institute for Molecular Bioscience, University of Queensland, QLD, Australia
| | - Ole Mors
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark; Psychosis Research Unit, Aarhus University Hospital, Aarhus, Denmark
| | - Preben B Mortensen
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark; Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark; The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
| | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark; Mental Health Services in the Capital Region of Denmark, Mental Health Center Copenhagen, University of Copenhagen, Copenhagen, Denmark
| | - John F Pearson
- Biostatistics and Computational Biology Unit, University of Otago, Christchurch, New Zealand; Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | - Roseann E Peterson
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | | | - John P Rice
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | | | - Nancy L Saccone
- Department of Genetics, Washington University School of Medicine, St Louis, MO, USA; Division of Biostatistics, Washington University School of Medicine, St Louis, MO, USA
| | - Richard Sherva
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Judy L Silberg
- Department of Human & Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA; Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Ralph E Tarter
- School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Tamara L Wall
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Bradley T Webb
- Virginia Commonwealth University Alcohol Research Center, Virginia Commonwealth University, Richmond, VA, USA
| | - Thomas Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark; Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Copenhagen, Denmark; Department of Clinical Medicine, and Center for GeoGenetics, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Leah Wetherill
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, QLD, Australia
| | - Stephanie Zellers
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Mark J Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Laura J Bierut
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Jason D Boardman
- Institute of Behavioral Science and Department of Sociology, University of Colorado Boulder, Boulder, CO, USA
| | - William E Copeland
- Department of Psychiatry, University of Vermont Medical Center, Burlington, VT, USA
| | - Lindsay A Farrer
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Tatiana M Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Nathan A Gillespie
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Richard A Grucza
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Kathleen Mullan Harris
- Department of Sociology, and The Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Andrew C Heath
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Victor Hesselbrock
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - John K Hewitt
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Christian J Hopfer
- Department of Psychiatry, University of Colorado Denver, Aurora, CO, USA
| | - John Horwood
- Department of Psychological Medicine, University of Otago, Christchurch, New Zealand
| | - William G Iacono
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Eric O Johnson
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, Durham, NC, USA
| | - Kenneth S Kendler
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Martin A Kennedy
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | - Henry R Kranzler
- Center for Studies of Addiction, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; VISN 4 MIRECC, Crescenz VAMC, Philadelphia, PA, USA
| | - Pamela A F Madden
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Hermine H Maes
- Department of Human & Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA; Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Brion S Maher
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Matthew McGue
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | | | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Elliot C Nelson
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Bernice Porjesz
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA; Henri Begleiter Neurodynamics Laboratory, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Brien P Riley
- Virginia Commonwealth University Alcohol Research Center, Virginia Commonwealth University, Richmond, VA, USA
| | - Michael C Stallings
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | | | - Scott Vrieze
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Lea K Davis
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Psychiatry and Behavioral Sciences, and Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ryan Bogdan
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, St. Louis, MO, USA
| | - Joel Gelernter
- Division of Human Genetics, Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA; Department of Genetics, and Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA; Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Howard J Edenberg
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kari Stefansson
- deCODE Genetics/Amgen, Reykjavik, Iceland; Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Anders D Børglum
- Department of Biomedicine-Human Genetics and Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark; The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark; Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
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230
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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: 5.4] [Reference Citation Analysis] [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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231
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Small AM, Huffman JE, Klarin D, Lynch JA, Assimes T, DuVall S, Sun YV, Shere L, Natarajan P, Gaziano M, Rader DJ, Wilson PWF, Tsao PS, Chang KM, Cho K, O’Donnell CJ, Casas JP, Damrauer SM, on behalf of the VA Million Veteran Program. PCSK9 loss of function is protective against extra-coronary atherosclerotic cardiovascular disease in a large multi-ethnic cohort. PLoS One 2020; 15:e0239752. [PMID: 33166319 PMCID: PMC7652310 DOI: 10.1371/journal.pone.0239752] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 09/14/2020] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND Therapeutic inhibition of PCSK9 protects against coronary artery disease (CAD) and ischemic stroke (IS). The impact on other diseases remains less well characterized. METHODS We created a genetic risk score (GRS) for PCSK9 using four single nucleotide polymorphisms (SNPs) at or near the PCSK9 locus known to impact lower LDL-Cholesterol (LDL-C): rs11583680, rs11591147, rs2479409, and rs11206510. We then used our GRS to calculate weighted odds ratios reflecting the impact of a genetically determined 10 mg/dL decrease in LDL-C on several pre-specified phenotypes including CAD, IS, peripheral artery disease (PAD), abdominal aortic aneurysm (AAA), type 2 diabetes, dementia, chronic obstructive pulmonary disease, and cancer. Finally, we used our weighted GRS to perform a phenome-wide association study. RESULTS Genetic and electronic health record data that passed quality control was available in 312,097 individuals, (227,490 White participants, 58,907 Black participants, and 25,700 Hispanic participants). PCSK9 mediated reduction in LDL-C was associated with a reduced risk of CAD and AAA in trans-ethnic meta-analysis (CAD OR 0.83 [95% CI 0.80-0.87], p = 6.0 x 10-21; AAA OR 0.76 [95% CI 0.68-0.86], p = 2.9 x 10-06). Significant protective effects were noted for PAD in White individuals (OR 0.83 [95% CI 0.71-0.97], p = 2.3 x 10-04) but not in other genetic ancestries. Genetically reduced PCSK9 function associated with a reduced risk of dementia in trans-ethnic meta-analysis (OR 0.86 [95% CI 0.78-0.93], p = 5.0 x 10-04). CONCLUSIONS Genetically reduced PCSK9 function results in a reduction in risk of several important extra-coronary atherosclerotic phenotypes in addition to known effects on CAD and IS, including PAD and AAA. We also highlight a novel reduction in risk of dementia, supporting a well-recognized vascular component to cognitive impairment and an opportunity for therapeutic repositioning.
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Affiliation(s)
- Aeron M. Small
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, United States of America
- Department of Medicine, Yale University School of Medicine, New Haven, CT, United States of America
| | - Jennifer E. Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, United States of America
| | - Derek Klarin
- Boston VA Healthcare System, Boston, MA, United States of America
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, United States of America
- Department of Surgery, Massachusetts General Hospital, Boston, MA, United States of America
| | - Julie A. Lynch
- Department of Veterans Affairs, Salt Lake City Health Care System, Salt Lake City, UT, United States of America
- University of Massachusetts College of Nursing & Health Sciences, Boston, MA, United States of America
- Center for Healthcare Organization and Implementation Research, Edith Nourse Rogers Memorial VA Hospital, Bedford, MA, United States of America
| | - Themistocles Assimes
- Department of Medicine, VA Palo Alto Health Care System, Palo Alto, CA, United States of America
- Stanford University School of Medicine, Stanford, CA, United States of America
| | - Scott DuVall
- Department of Veterans Affairs, Salt Lake City Health Care System, Salt Lake City, UT, United States of America
- Veterans Affairs Informatics and Computing Infrastructure
| | - Yan V. Sun
- Atlanta VA Health Care System, Decatur, GA, United States of America
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, United States of America
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, Unites States of America
| | - Labiba Shere
- Palo Alto Epidemiology Research and Information Center for Genomics, VA Palo Alto Health Care System
| | - Pradeep Natarajan
- Boston VA Healthcare System, Boston, MA, United States of America
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, United States of America
| | - Michael Gaziano
- Boston VA Healthcare System, Boston, MA, United States of America
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Daniel J. Rader
- Department of Medicine, Perlman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Peter W. F. Wilson
- Atlanta VA Health Care System, Decatur, GA, United States of America
- Emory Clinical Cardiovascular Research Institute, Atlanta, GA, United States of America
| | - Philip S. Tsao
- Department of Medicine, VA Palo Alto Health Care System, Palo Alto, CA, United States of America
- Stanford University School of Medicine, Stanford, CA, United States of America
| | - Kyong-Mi Chang
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, United States of America
- Department of Medicine, Perlman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Kelly Cho
- Boston VA Healthcare System, Boston, MA, United States of America
| | - Christopher J. O’Donnell
- Boston VA Healthcare System, Boston, MA, United States of America
- Cardiovascular Medicine Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Juan P. Casas
- Boston VA Healthcare System, Boston, MA, United States of America
| | - Scott M. Damrauer
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, United States of America
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
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232
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Fritsche LG, Patil S, Beesley LJ, VandeHaar P, Salvatore M, Ma Y, Peng RB, Taliun D, Zhou X, Mukherjee B. Cancer PRSweb: An Online Repository with Polygenic Risk Scores for Major Cancer Traits and Their Evaluation in Two Independent Biobanks. Am J Hum Genet 2020; 107:815-836. [PMID: 32991828 PMCID: PMC7675001 DOI: 10.1016/j.ajhg.2020.08.025] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Accepted: 08/28/2020] [Indexed: 02/06/2023] Open
Abstract
To facilitate scientific collaboration on polygenic risk scores (PRSs) research, we created an extensive PRS online repository for 35 common cancer traits integrating freely available genome-wide association studies (GWASs) summary statistics from three sources: published GWASs, the NHGRI-EBI GWAS Catalog, and UK Biobank-based GWASs. Our framework condenses these summary statistics into PRSs using various approaches such as linkage disequilibrium pruning/p value thresholding (fixed or data-adaptively optimized thresholds) and penalized, genome-wide effect size weighting. We evaluated the PRSs in two biobanks: the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort at Michigan Medicine, and the population-based UK Biobank (UKB). For each PRS construct, we provide measures on predictive performance and discrimination. Besides PRS evaluation, the Cancer-PRSweb platform features construct downloads and phenome-wide PRS association study results (PRS-PheWAS) for predictive PRSs. We expect this integrated platform to accelerate PRS-related cancer research.
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Affiliation(s)
- Lars G Fritsche
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; University of Michigan Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Snehal Patil
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Lauren J Beesley
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Peter VandeHaar
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Maxwell Salvatore
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Ying Ma
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Robert B Peng
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Department of Statistics, Northwestern University, Evanston, IL 60208, USA
| | - Daniel Taliun
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA; Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; University of Michigan Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA.
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233
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Werfel TA, Hicks DJ, Rahman B, Bendeman WE, Duvernay MT, Maeng JG, Hamm H, Lavieri RR, Joly MM, Pulley JM, Elion DL, Brantley-Sieders DM, Cook RS. Repurposing of a Thromboxane Receptor Inhibitor Based on a Novel Role in Metastasis Identified by Phenome-Wide Association Study. Mol Cancer Ther 2020; 19:2454-2464. [PMID: 33033174 DOI: 10.1158/1535-7163.mct-19-1106] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 03/03/2020] [Accepted: 09/15/2020] [Indexed: 11/16/2022]
Abstract
Although new drug discoveries are revolutionizing cancer treatments, repurposing existing drugs would accelerate the timeline and lower the cost for bringing treatments to cancer patients. Our goal was to repurpose CPI211, a potent and selective antagonist of the thromboxane A2-prostanoid receptor (TPr), a G-protein-coupled receptor that regulates coagulation, blood pressure, and cardiovascular homeostasis. To identify potential new clinical indications for CPI211, we performed a phenome-wide association study (PheWAS) of the gene encoding TPr, TBXA2R, using robust deidentified health records and matched genomic data from more than 29,000 patients. Specifically, PheWAS was used to identify clinical manifestations correlating with a TBXA2R single-nucleotide polymorphism (rs200445019), which generates a T399A substitution within TPr that enhances TPr signaling. Previous studies have correlated 200445019 with chronic venous hypertension, which was recapitulated by this PheWAS analysis. Unexpectedly, PheWAS uncovered an rs200445019 correlation with cancer metastasis across several cancer types. When tested in several mouse models of metastasis, TPr inhibition using CPI211 potently blocked spontaneous metastasis from primary tumors, without affecting tumor cell proliferation, motility, or tumor growth. Further, metastasis following intravenous tumor cell delivery was blocked in mice treated with CPI211. Interestingly, TPr signaling in vascular endothelial cells induced VE-cadherin internalization, diminished endothelial barrier function, and enhanced transendothelial migration by tumor cells, phenotypes that were decreased by CPI211. These studies provide evidence that TPr signaling promotes cancer metastasis, supporting the study of TPr inhibitors as antimetastatic agents and highlighting the use of PheWAS as an approach to accelerate drug repurposing.
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Affiliation(s)
- Thomas A Werfel
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee.,Department of Chemical Engineering, University of Mississippi, Oxford, Mississippi
| | - Donna J Hicks
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Bushra Rahman
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Wendy E Bendeman
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Matthew T Duvernay
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jae G Maeng
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Heidi Hamm
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Robert R Lavieri
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Meghan M Joly
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jill M Pulley
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee
| | - David L Elion
- Program in Cancer Biology, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Dana M Brantley-Sieders
- Breast Cancer Research Program, Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee.,Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Rebecca S Cook
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee. .,Program in Cancer Biology, Vanderbilt University School of Medicine, Nashville, Tennessee.,Breast Cancer Research Program, Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee.,Department of Biomedical Engineering, Vanderbilt University School of Engineering, Nashville, Tennessee
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234
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Levin MG, Judy R, Gill D, Vujkovic M, Verma SS, Bradford Y, Regeneron Genetics Center, Ritchie MD, Hyman MC, Nazarian S, Rader DJ, Voight BF, Damrauer SM. Genetics of height and risk of atrial fibrillation: A Mendelian randomization study. PLoS Med 2020; 17:e1003288. [PMID: 33031386 PMCID: PMC7544133 DOI: 10.1371/journal.pmed.1003288] [Citation(s) in RCA: 116] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Accepted: 09/03/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Observational studies have identified height as a strong risk factor for atrial fibrillation, but this finding may be limited by residual confounding. We aimed to examine genetic variation in height within the Mendelian randomization (MR) framework to determine whether height has a causal effect on risk of atrial fibrillation. METHODS AND FINDINGS In summary-level analyses, MR was performed using summary statistics from genome-wide association studies of height (GIANT/UK Biobank; 693,529 individuals) and atrial fibrillation (AFGen; 65,446 cases and 522,744 controls), finding that each 1-SD increase in genetically predicted height increased the odds of atrial fibrillation (odds ratio [OR] 1.34; 95% CI 1.29 to 1.40; p = 5 × 10-42). This result remained consistent in sensitivity analyses with MR methods that make different assumptions about the presence of pleiotropy, and when accounting for the effects of traditional cardiovascular risk factors on atrial fibrillation. Individual-level phenome-wide association studies of height and a height genetic risk score were performed among 6,567 European-ancestry participants of the Penn Medicine Biobank (median age at enrollment 63 years, interquartile range 55-72; 38% female; recruitment 2008-2015), confirming prior observational associations between height and atrial fibrillation. Individual-level MR confirmed that each 1-SD increase in height increased the odds of atrial fibrillation, including adjustment for clinical and echocardiographic confounders (OR 1.89; 95% CI 1.50 to 2.40; p = 0.007). The main limitations of this study include potential bias from pleiotropic effects of genetic variants, and lack of generalizability of individual-level findings to non-European populations. CONCLUSIONS In this study, we observed evidence that height is likely a positive causal risk factor for atrial fibrillation. Further study is needed to determine whether risk prediction tools including height or anthropometric risk factors can be used to improve screening and primary prevention of atrial fibrillation, and whether biological pathways involved in height may offer new targets for treatment of atrial fibrillation.
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Affiliation(s)
- Michael G. Levin
- Division of Cardiovascular Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, United States of America
| | - Renae Judy
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Centre for Pharmacology & Therapeutics, Department of Medicine, Imperial College London, London, United Kingdom
- Novo Nordisk Research Centre Oxford, Oxford, United Kingdom
- Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George’s, University of London, London, United Kingdom
- Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George’s University Hospitals NHS Foundation Trust, London, United Kingdom
| | - Marijana Vujkovic
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, United States of America
| | - Shefali S. Verma
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Yuki Bradford
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | | | - Marylyn D. Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Matthew C. Hyman
- Division of Cardiovascular Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Saman Nazarian
- Division of Cardiovascular Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Daniel J. Rader
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Benjamin F. Voight
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Scott M. Damrauer
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, United States of America
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
- * E-mail:
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235
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Klarin D, Verma SS, Judy R, Dikilitas O, Wolford BN, Paranjpe I, Levin MG, Pan C, Tcheandjieu C, Spin JM, Lynch J, Assimes TL, Åldstedt Nyrønning L, Mattsson E, Edwards TL, Denny J, Larson E, Lee MTM, Carrell D, Zhang Y, Jarvik GP, Gharavi AG, Harley J, Mentch F, Pacheco JA, Hakonarson H, Skogholt AH, Thomas L, Gabrielsen ME, Hveem K, Nielsen JB, Zhou W, Fritsche L, Huang J, Natarajan P, Sun YV, DuVall SL, Rader DJ, Cho K, Chang KM, Wilson PWF, O'Donnell CJ, Kathiresan S, Scali ST, Berceli SA, Willer C, Jones GT, Bown MJ, Nadkarni G, Kullo IJ, Ritchie M, Damrauer SM, Tsao PS. Genetic Architecture of Abdominal Aortic Aneurysm in the Million Veteran Program. Circulation 2020; 142:1633-1646. [PMID: 32981348 PMCID: PMC7580856 DOI: 10.1161/circulationaha.120.047544] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Supplemental Digital Content is available in the text. Abdominal aortic aneurysm (AAA) is an important cause of cardiovascular mortality; however, its genetic determinants remain incompletely defined. In total, 10 previously identified risk loci explain a small fraction of AAA heritability.
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Affiliation(s)
- Derek Klarin
- Malcolm Randall VA Medical Center, Gainesville, FL (D.K., S.T.S., S.A.B.).,Division of Vascular Surgery and Endovascular Therapy, University of Florida College of Medicine, Gainesville (D.K., S.T.S., S.A.B.).,Center for Genomic Medicine (D.K., W.Z., P.N.), Massachusetts General Hospital, Harvard Medical School, Boston.,Program in Medical and Population Genetics (D.K.), Broad Institute of MIT and Harvard, Cambridge, MA
| | - Shefali Setia Verma
- Department of Genetics (S.S.V., M.R.), Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Renae Judy
- Department of Surgery (R.J., S.M.D.), Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA (R.J., M.G.L., K.-M.C., S.M.D.)
| | - Ozan Dikilitas
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN (O.D., I.J.K.)
| | - Brooke N Wolford
- Department of Computational Medicine and Bioinformatics (B.N.W., C.W.), University of Michigan Medical School, Ann Arbor
| | - Ishan Paranjpe
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY (I.P., G.N.)
| | - Michael G Levin
- Division of Cardiovascular Medicine (M.G.L.), Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Department of Medicine (M.G.L., D.J.R., K.-M.C.), Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA (R.J., M.G.L., K.-M.C., S.M.D.)
| | - Cuiping Pan
- Palo Alto Epidemiology Research and Information Center for Genomics (C.P.), CA
| | - Catherine Tcheandjieu
- VA Palo Alto Health Care System (C.T., J.M.S., T.L.A., P.S.T.), CA.,Division of Cardiovascular Medicine, Department of Medicine (C.T., J.M.S., T.L.A., P.S.T.), Stanford University School of Medicine, CA.,Department of Pediatric Cardiology (C.T.), Stanford University School of Medicine, CA
| | - Joshua M Spin
- VA Palo Alto Health Care System (C.T., J.M.S., T.L.A., P.S.T.), CA.,Division of Cardiovascular Medicine, Department of Medicine (C.T., J.M.S., T.L.A., P.S.T.), Stanford University School of Medicine, CA
| | - Julie Lynch
- Edith Nourse VA Medical Center, Bedford, MA (J.L.).,VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, UT (J.L., S.L.D.)
| | - Themistocles L Assimes
- VA Palo Alto Health Care System (C.T., J.M.S., T.L.A., P.S.T.), CA.,Division of Cardiovascular Medicine, Department of Medicine (C.T., J.M.S., T.L.A., P.S.T.), Stanford University School of Medicine, CA
| | - Linn Åldstedt Nyrønning
- Department of Vascular Surgery, St. Olavs Hospital, Trondheim, Norway (L.Å.N., E.M.).,Department of Circulation and Medical Imaging (L.Å.N., E.M.), Norwegian University of Science and Technology, Trondheim, Norway
| | - Erney Mattsson
- Department of Vascular Surgery, St. Olavs Hospital, Trondheim, Norway (L.Å.N., E.M.).,Department of Circulation and Medical Imaging (L.Å.N., E.M.), Norwegian University of Science and Technology, Trondheim, Norway
| | - Todd L Edwards
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center (T.L.E.), Vanderbilt University Medical Center, Nashville, TN.,Vanderbilt Genetics Institute (T.L.E., J.D.), Vanderbilt University Medical Center, Nashville, TN
| | - Josh Denny
- Vanderbilt Genetics Institute (T.L.E., J.D.), Vanderbilt University Medical Center, Nashville, TN.,Department of Biomedical Informatics (J.D., E.L., D.C.), Vanderbilt University Medical Center, Nashville, TN.,Kaiser Permanente Washington Health Research Institute, Seattle (J.D., E.L., D.C.)
| | - Eric Larson
- Department of Biomedical Informatics (J.D., E.L., D.C.), Vanderbilt University Medical Center, Nashville, TN.,Kaiser Permanente Washington Health Research Institute, Seattle (J.D., E.L., D.C.).,Departments of Medicine and Health Services (E.L.), University of Washington, Seattle
| | - Ming Ta Michael Lee
- Genomic Medicine Institute, Geisinger Health System, Danville, PA (M.T.M.L., Y.Z.)
| | - David Carrell
- Department of Biomedical Informatics (J.D., E.L., D.C.), Vanderbilt University Medical Center, Nashville, TN.,Kaiser Permanente Washington Health Research Institute, Seattle (J.D., E.L., D.C.)
| | - Yanfei Zhang
- Genomic Medicine Institute, Geisinger Health System, Danville, PA (M.T.M.L., Y.Z.)
| | - Gail P Jarvik
- Division of Medical Genetics, Departments of Medicine and Genome Sciences (G.P.J.), University of Washington, Seattle
| | - Ali G Gharavi
- Division of Nephrology and Center for Precision Medicine and Genomics, Columbia University, New York, NY (A.G.G.)
| | - John Harley
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children's Hospital Medical Center, OH (J.H.).,Department of Pediatrics, University of Cincinnati College of Medicine, OH (J.H.).,US Department of Veterans Affairs, Cincinnati, OH (J.H.)
| | - Frank Mentch
- Center for Applied Genomics, The Children's Hospital of Philadelphia, PA (F.M., H.H.)
| | - Jennifer A Pacheco
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (J.A.P.)
| | - Hakon Hakonarson
- Department of Pediatrics (H.H.), Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Center for Applied Genomics, The Children's Hospital of Philadelphia, PA (F.M., H.H.)
| | - Anne Heidi Skogholt
- Faculty of Medicine and Health Sciences (A.H.S., L.T., M.E.G., K.H., J.B.N.), Norwegian University of Science and Technology, Trondheim, Norway
| | - Laurent Thomas
- Faculty of Medicine and Health Sciences (A.H.S., L.T., M.E.G., K.H., J.B.N.), Norwegian University of Science and Technology, Trondheim, Norway.,Department of Clinical and Molecular Medicine (L.T.), Norwegian University of Science and Technology, Trondheim, Norway
| | - Maiken Elvestad Gabrielsen
- Faculty of Medicine and Health Sciences (A.H.S., L.T., M.E.G., K.H., J.B.N.), Norwegian University of Science and Technology, Trondheim, Norway
| | - Kristian Hveem
- Faculty of Medicine and Health Sciences (A.H.S., L.T., M.E.G., K.H., J.B.N.), Norwegian University of Science and Technology, Trondheim, Norway
| | - Jonas Bille Nielsen
- Faculty of Medicine and Health Sciences (A.H.S., L.T., M.E.G., K.H., J.B.N.), Norwegian University of Science and Technology, Trondheim, Norway.,K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Department of Epidemiology Research, Statens Serum Institute, Copenhagen, Denmark (J.B.N.)
| | - Wei Zhou
- Center for Genomic Medicine (D.K., W.Z., P.N.), Massachusetts General Hospital, Harvard Medical School, Boston.,Stanley Center for Psychiatric Research (W.Z.), Broad Institute of MIT and Harvard, Cambridge, MA.,Analytic and Translational Genetics Unit (W.Z.), Massachusetts General Hospital, Boston
| | - Lars Fritsche
- Department of Biostatistics (L.F.), University of Michigan Medical School, Ann Arbor
| | - Jie Huang
- Boston VA Healthcare System, MA (J.H., P.N., K.C., C.J.O.)
| | - Pradeep Natarajan
- Center for Genomic Medicine (D.K., W.Z., P.N.), Massachusetts General Hospital, Harvard Medical School, Boston.,Department of Medicine (P.N.), Massachusetts General Hospital, Harvard Medical School, Boston.,Cardiovascular Research Center (P.N.), Massachusetts General Hospital, Boston.,Boston VA Healthcare System, MA (J.H., P.N., K.C., C.J.O.)
| | - Yan V Sun
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA (Y.V.S.).,Atlanta VA Health Care System, Decatur, GA (Y.V.S., P.W.F.W.)
| | - Scott L DuVall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, UT (J.L., S.L.D.).,Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City (S.L.D.)
| | - Daniel J Rader
- Department of Medicine (M.G.L., D.J.R., K.-M.C.), Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Kelly Cho
- Boston VA Healthcare System, MA (J.H., P.N., K.C., C.J.O.)
| | - Kyong-Mi Chang
- Department of Medicine (M.G.L., D.J.R., K.-M.C.), Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA (R.J., M.G.L., K.-M.C., S.M.D.)
| | - Peter W F Wilson
- Atlanta VA Health Care System, Decatur, GA (Y.V.S., P.W.F.W.).,Emory Clinical Cardiovascular Research Institute, Atlanta, GA (P.W.F.W.)
| | - Christopher J O'Donnell
- Boston VA Healthcare System, MA (J.H., P.N., K.C., C.J.O.).,Cardiovascular Medicine Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (C.J.O.)
| | | | - Salvatore T Scali
- Malcolm Randall VA Medical Center, Gainesville, FL (D.K., S.T.S., S.A.B.).,Division of Vascular Surgery and Endovascular Therapy, University of Florida College of Medicine, Gainesville (D.K., S.T.S., S.A.B.)
| | - Scott A Berceli
- Malcolm Randall VA Medical Center, Gainesville, FL (D.K., S.T.S., S.A.B.).,Division of Vascular Surgery and Endovascular Therapy, University of Florida College of Medicine, Gainesville (D.K., S.T.S., S.A.B.)
| | - Cristen Willer
- Department of Computational Medicine and Bioinformatics (B.N.W., C.W.), University of Michigan Medical School, Ann Arbor.,Department of Internal Medicine, Division of Cardiology (C.W.), University of Michigan Medical School, Ann Arbor.,Department of Human Genetics (C.W.), University of Michigan Medical School, Ann Arbor
| | - Gregory T Jones
- Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, New Zealand (G.T.J.)
| | - Matthew J Bown
- Department of Cardiovascular Sciences and NIHR Leicester Biomedical Research Centre, University of Leicester, United Kingdom (M.J.B.)
| | - Girish Nadkarni
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY (I.P., G.N.)
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN (O.D., I.J.K.)
| | - Marylyn Ritchie
- Department of Genetics (S.S.V., M.R.), Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Scott M Damrauer
- Department of Surgery (R.J., S.M.D.), Perelman School of Medicine, University of Pennsylvania, Philadelphia.,Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA (R.J., M.G.L., K.-M.C., S.M.D.)
| | - Philip S Tsao
- VA Palo Alto Health Care System (C.T., J.M.S., T.L.A., P.S.T.), CA.,Division of Cardiovascular Medicine, Department of Medicine (C.T., J.M.S., T.L.A., P.S.T.), Stanford University School of Medicine, CA
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Chen MH, Raffield LM, Mousas A, Sakaue S, Huffman JE, Moscati A, Trivedi B, Jiang T, Akbari P, Vuckovic D, Bao EL, Zhong X, Manansala R, Laplante V, Chen M, Lo KS, Qian H, Lareau CA, Beaudoin M, Hunt KA, Akiyama M, Bartz TM, Ben-Shlomo Y, Beswick A, Bork-Jensen J, Bottinger EP, Brody JA, van Rooij FJA, Chitrala K, Cho K, Choquet H, Correa A, Danesh J, Di Angelantonio E, Dimou N, Ding J, Elliott P, Esko T, Evans MK, Floyd JS, Broer L, Grarup N, Guo MH, Greinacher A, Haessler J, Hansen T, Howson JMM, Huang QQ, Huang W, Jorgenson E, Kacprowski T, Kähönen M, Kamatani Y, Kanai M, Karthikeyan S, Koskeridis F, Lange LA, Lehtimäki T, Lerch MM, Linneberg A, Liu Y, Lyytikäinen LP, Manichaikul A, Martin HC, Matsuda K, Mohlke KL, Mononen N, Murakami Y, Nadkarni GN, Nauck M, Nikus K, Ouwehand WH, Pankratz N, Pedersen O, Preuss M, Psaty BM, Raitakari OT, Roberts DJ, Rich SS, Rodriguez BAT, Rosen JD, Rotter JI, Schubert P, Spracklen CN, Surendran P, Tang H, Tardif JC, Trembath RC, Ghanbari M, Völker U, Völzke H, Watkins NA, Zonderman AB, Wilson PWF, Li Y, Butterworth AS, Gauchat JF, Chiang CWK, Li B, Loos RJF, et alChen MH, Raffield LM, Mousas A, Sakaue S, Huffman JE, Moscati A, Trivedi B, Jiang T, Akbari P, Vuckovic D, Bao EL, Zhong X, Manansala R, Laplante V, Chen M, Lo KS, Qian H, Lareau CA, Beaudoin M, Hunt KA, Akiyama M, Bartz TM, Ben-Shlomo Y, Beswick A, Bork-Jensen J, Bottinger EP, Brody JA, van Rooij FJA, Chitrala K, Cho K, Choquet H, Correa A, Danesh J, Di Angelantonio E, Dimou N, Ding J, Elliott P, Esko T, Evans MK, Floyd JS, Broer L, Grarup N, Guo MH, Greinacher A, Haessler J, Hansen T, Howson JMM, Huang QQ, Huang W, Jorgenson E, Kacprowski T, Kähönen M, Kamatani Y, Kanai M, Karthikeyan S, Koskeridis F, Lange LA, Lehtimäki T, Lerch MM, Linneberg A, Liu Y, Lyytikäinen LP, Manichaikul A, Martin HC, Matsuda K, Mohlke KL, Mononen N, Murakami Y, Nadkarni GN, Nauck M, Nikus K, Ouwehand WH, Pankratz N, Pedersen O, Preuss M, Psaty BM, Raitakari OT, Roberts DJ, Rich SS, Rodriguez BAT, Rosen JD, Rotter JI, Schubert P, Spracklen CN, Surendran P, Tang H, Tardif JC, Trembath RC, Ghanbari M, Völker U, Völzke H, Watkins NA, Zonderman AB, Wilson PWF, Li Y, Butterworth AS, Gauchat JF, Chiang CWK, Li B, Loos RJF, Astle WJ, Evangelou E, van Heel DA, Sankaran VG, Okada Y, Soranzo N, Johnson AD, Reiner AP, Auer PL, Lettre G. Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations. Cell 2020; 182:1198-1213.e14. [PMID: 32888493 PMCID: PMC7480402 DOI: 10.1016/j.cell.2020.06.045] [Show More Authors] [Citation(s) in RCA: 413] [Impact Index Per Article: 82.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 04/16/2020] [Accepted: 06/29/2020] [Indexed: 12/14/2022]
Abstract
Most loci identified by GWASs have been found in populations of European ancestry (EUR). In trans-ethnic meta-analyses for 15 hematological traits in 746,667 participants, including 184,535 non-EUR individuals, we identified 5,552 trait-variant associations at p < 5 × 10-9, including 71 novel associations not found in EUR populations. We also identified 28 additional novel variants in ancestry-specific, non-EUR meta-analyses, including an IL7 missense variant in South Asians associated with lymphocyte count in vivo and IL-7 secretion levels in vitro. Fine-mapping prioritized variants annotated as functional and generated 95% credible sets that were 30% smaller when using the trans-ethnic as opposed to the EUR-only results. We explored the clinical significance and predictive value of trans-ethnic variants in multiple populations and compared genetic architecture and the effect of natural selection on these blood phenotypes between populations. Altogether, our results for hematological traits highlight the value of a more global representation of populations in genetic studies.
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Affiliation(s)
- Ming-Huei Chen
- The Framingham Heart Study, National Heart, Lung and Blood Institute, Framingham, MA 01702, USA; Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham, MA 01702, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Abdou Mousas
- Montreal Heart Institute, Montreal, QC H1T 1C8, Canada
| | - Saori Sakaue
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan; Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Arden Moscati
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Bhavi Trivedi
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK
| | - Tao Jiang
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Parsa Akbari
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK; Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK; The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics, University of Cambridge, Cambridge CB2 0QQ, UK
| | | | - Erik L Bao
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02446, USA
| | - Xue Zhong
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA
| | - Regina Manansala
- Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA
| | - Véronique Laplante
- Département de Pharmacologie et Physiologie, Faculty of Medicine, Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Minhui Chen
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Ken Sin Lo
- Montreal Heart Institute, Montreal, QC H1T 1C8, Canada
| | - Huijun Qian
- Department of Statistics and Operation Research, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Caleb A Lareau
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02446, USA
| | | | - Karen A Hunt
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK
| | - Masato Akiyama
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan; Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8581, Japan
| | - Traci M Bartz
- Department of Biostatistics, University of Washington, Seattle, WA 98101, USA
| | - Yoav Ben-Shlomo
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
| | - Andrew Beswick
- Translational Health Sciences, Musculoskeletal Research Unit, Bristol Medical School, University of Bristol, Bristol BS10 5NB, UK
| | - Jette Bork-Jensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Erwin P Bottinger
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Hasso-Plattner-Institut, Universität Potsdam, Potsdam 14469, Germany
| | - Jennifer A Brody
- Department of Medicine, University of Washington, Seattle, WA 98101, USA
| | - Frank J A van Rooij
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam 3015, the Netherlands
| | - Kumaraswamynaidu Chitrala
- Laboratory of Epidemiology and Population Science, National Institute on Aging/NIH, Baltimore, MD 21224, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA; Department of Medicine, Division on Aging, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Hélène Choquet
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - John Danesh
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK; The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics, University of Cambridge, Cambridge CB2 0QQ, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK; National Institute for Health Research Cambridge Biomedical Research Centre, Cambridge University Hospitals, Cambridge CB2 0QQ, UK; British Heart Foundation Centre of Research Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Emanuele Di Angelantonio
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics, University of Cambridge, Cambridge CB2 0QQ, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK; National Institute for Health Research Cambridge Biomedical Research Centre, Cambridge University Hospitals, Cambridge CB2 0QQ, UK; British Heart Foundation Centre of Research Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Niki Dimou
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon 69008, France; Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina 45110, Greece
| | - Jingzhong Ding
- Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK; Imperial Biomedical Research Centre, Imperial College London and Imperial College NHS Healthcare Trust, London SW7 2AZ, UK; Medical Research Council-Public Health England Centre for Environment, Imperial College London, London SW7 2AZ, UK; UK Dementia Research Institute, Imperial College London, London SW7 2AZ, UK; Health Data Research UK - London, London SW7 2AZ, UK
| | - Tõnu Esko
- Broad Institute of Harvard and MIT, Cambridge, MA 02446, USA
| | - Michele K Evans
- Laboratory of Epidemiology and Population Science, National Institute on Aging/NIH, Baltimore, MD 21224, USA
| | - James S Floyd
- Department of Medicine, University of Washington, Seattle, WA 98101, USA; Department of Epidemiology, University of Washington, Seattle, WA 98101, USA
| | - Linda Broer
- Department of Internal Medicine, Erasmus University Medical Center Rotterdam, Rotterdam 3015, the Netherlands
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Michael H Guo
- Broad Institute of Harvard and MIT, Cambridge, MA 02446, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Andreas Greinacher
- Institute for Immunology and Transfusion Medicine, University Medicine Greifswald, Greifswald 17475, Germany
| | - Jeff Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Joanna M M Howson
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Novo Nordisk Research Centre Oxford, Innovation Building, Old Road Campus, Oxford OX3 7FZ, UK
| | - Qin Qin Huang
- Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK
| | - Wei Huang
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center and Shanghai Industrial Technology Institute (SITI), Shanghai 201203, China
| | - Eric Jorgenson
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Tim Kacprowski
- Interfaculty Institute of Genetics and Functional Genomics, University Medicine Greifswald, Greifswald 17475, Germany; Chair of Experimental Bioinformatics, Research Group Computational Systems Medicine, Technical University of Munich, Freising-Weihenstephan 85354, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald 17475, Germany
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, Tampere 33521, Finland; Department of Clinical Physiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33014, Finland
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan; Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo 108-8639, Japan
| | - Masahiro Kanai
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Savita Karthikeyan
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Fotis Koskeridis
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina 45110, Greece
| | - Leslie A Lange
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland; Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33014, Finland
| | - Markus M Lerch
- Department of Internal Medicine, University Medicine Greifswald, Greifswald 17475, Germany
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg 2000, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Yongmei Liu
- Duke Molecular Physiology Institute, Department of Medicine, Division of Cardiology, Duke University Medical Center, Durham, NC 27701, USA
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland; Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33014, Finland
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22903, USA
| | - Hilary C Martin
- Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK
| | - Koichi Matsuda
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo 108-8639, Japan
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Nina Mononen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland; Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33014, Finland
| | - Yoshinori Murakami
- Division of Molecular Pathology, The Institute of Medical Sciences, The University of Tokyo, Tokyo 108-8639, Japan
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Matthias Nauck
- German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald 17475, Germany; Institue of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald 17475, Germany
| | - Kjell Nikus
- Department of Cardiology, Heart Center, Tampere University Hospital, Tampere 33521, Finland; Department of Cardiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33014, Finland
| | - Willem H Ouwehand
- Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK; British Heart Foundation Centre of Research Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK; Department of Hematology, University of Cambridge, Cambridge CB2 0PT, UK; National Health Service (NHS) Blood and Transplant, Cambridge Biomedical Campus, Cambridge CB2 0PT, UK
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Michael Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Bruce M Psaty
- Department of Medicine, University of Washington, Seattle, WA 98101, USA; Department of Epidemiology, University of Washington, Seattle, WA 98101, USA; Department of Health Services, University of Washington, Seattle, WA 98101, USA; Kaiser Permanente Washington Health Research Institute, Seattle, WA 98101, USA
| | - Olli T Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku 20521, Finland; Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku 20521, Finland; Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku 20521, Finland
| | - David J Roberts
- The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics, University of Cambridge, Cambridge CB2 0QQ, UK; Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK; Department of Haematology, Churchill Hospital, Oxford OX3 7LE, UK; NHS Blood and Transplant - Oxford Centre, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22903, USA
| | - Benjamin A T Rodriguez
- The Framingham Heart Study, National Heart, Lung and Blood Institute, Framingham, MA 01702, USA; Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham, MA 01702, USA
| | - Jonathan D Rosen
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Cassandra N Spracklen
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01002, USA
| | - Praveen Surendran
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; Rutherford Fund Fellow, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Hua Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Jean-Claude Tardif
- Montreal Heart Institute, Montreal, QC H1T 1C8, Canada; Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Richard C Trembath
- School of Basic and Medical Biosciences, Faculty of Life Sciences and Medicine, King's College London, London SE1 1UL, UK
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam 3015, the Netherlands; Department of Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad 9177948564, Iran
| | - Uwe Völker
- Interfaculty Institute of Genetics and Functional Genomics, University Medicine Greifswald, Greifswald 17475, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald 17475, Germany
| | - Henry Völzke
- German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald 17475, Germany; Institute for Community Medicine, University Medicine Greifswald, Greifswald 17475, Germany
| | - Nicholas A Watkins
- National Health Service (NHS) Blood and Transplant, Cambridge Biomedical Campus, Cambridge CB2 0PT, UK
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Science, National Institute on Aging/NIH, Baltimore, MD 21224, USA
| | | | - Yun Li
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Adam S Butterworth
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics, University of Cambridge, Cambridge CB2 0QQ, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge CB10 1SA, UK
| | - Jean-François Gauchat
- Département de Pharmacologie et Physiologie, Faculty of Medicine, Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Charleston W K Chiang
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA; Quantitative and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Bingshan Li
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - William J Astle
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK; The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics, University of Cambridge, Cambridge CB2 0QQ, UK; NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Strangeways Laboratory, Cambridge CB1 8RN, UK
| | - Evangelos Evangelou
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina 45110, Greece; Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK
| | - David A van Heel
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK
| | - Vijay G Sankaran
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02446, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan; Laboratory of Statistical Immunology, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan
| | - Nicole Soranzo
- Human Genetics, Wellcome Sanger Institute, Hinxton CB10 1SA, UK; Department of Hematology, University of Cambridge, Cambridge CB2 0PT, UK
| | - Andrew D Johnson
- The Framingham Heart Study, National Heart, Lung and Blood Institute, Framingham, MA 01702, USA; Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham, MA 01702, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA 98109, USA
| | - Paul L Auer
- Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA.
| | - Guillaume Lettre
- Montreal Heart Institute, Montreal, QC H1T 1C8, Canada; Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, QC H3T 1J4, Canada.
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Gagliano Taliun SA, VandeHaar P, Boughton AP, Welch RP, Taliun D, Schmidt EM, Zhou W, Nielsen JB, Willer CJ, Lee S, Fritsche LG, Boehnke M, Abecasis GR. Exploring and visualizing large-scale genetic associations by using PheWeb. Nat Genet 2020; 52:550-552. [PMID: 32504056 DOI: 10.1038/s41588-020-0622-5] [Citation(s) in RCA: 144] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Sarah A Gagliano Taliun
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.,Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Peter VandeHaar
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.,Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Andrew P Boughton
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.,Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Ryan P Welch
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.,Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Daniel Taliun
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.,Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Ellen M Schmidt
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.,Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Wei Zhou
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Jonas B Nielsen
- Department of Internal Medicine, Division of Cardiology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Cristen J Willer
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.,Department of Internal Medicine, Division of Cardiology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Seunggeun Lee
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.,Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Lars G Fritsche
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.,Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Michael Boehnke
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.,Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Gonçalo R Abecasis
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA. .,Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA. .,Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY, USA.
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238
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Zhang T, Goodman M, Zhu F, Healy B, Carruthers R, Chitnis T, Weiner H, Cai T, De Jager P, Tremlett H, Xia Z. Phenome-wide examination of comorbidity burden and multiple sclerosis disease severity. NEUROLOGY-NEUROIMMUNOLOGY & NEUROINFLAMMATION 2020; 7:7/6/e864. [PMID: 32817202 PMCID: PMC7673286 DOI: 10.1212/nxi.0000000000000864] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 06/17/2020] [Indexed: 12/20/2022]
Abstract
Objective We assessed the comorbidity burden associated with multiple sclerosis (MS) severity by performing a phenome-wide association study (PheWAS). Methods We conducted a PheWAS in 2 independent cohorts: a discovery (Boston, United States; 1993–2014) and extension cohort (British Columbia, Canada; 1991–2008). We included adults with MS, ≥1 Expanded Disability Status Scale (EDSS) score, and ≥1 International Classification of Diseases (ICD) code other than MS. We calculated the Multiple Sclerosis Severity Score (MSSS) using the EDSS. We mapped ICD codes into PheCodes (phenotypes), using a published system with each PheCode representing a unique medical condition. Association between the MSSS and the presence of each condition was assessed using logistic regression adjusted for covariates. Results The discovery and extension cohorts included 3,439 and 4,876 participants, respectively. After Bonferroni correction and covariate adjustments, a higher MSSS was associated with 37 coexisting conditions in the discovery cohort. Subsequently, 16 conditions, including genitourinary, infectious, metabolic, epilepsy, and movement disorders, met the reporting criteria, reaching the Bonferroni threshold of significance with the same direction of effect in the discovery and extension cohort. Notably, benign neoplasm of the skin was inversely associated with the MSSS. Conclusion The phenome-wide approach enabled a systematic interrogation of the comorbidity burden and highlighted clinically relevant medical conditions associated with MS severity (beyond MS-specific consequences) and defines a roadmap for comprehensive investigation of comorbidities in chronic neurologic diseases. Further prospective investigation of the bidirectional relationship between disability and comorbidities could inform the individualized patient management.
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Affiliation(s)
- Tingting Zhang
- From the Department of Health Services (T.Z.), Policy and Practice, Brown University, Providence, RI; Department of Biostatistics (M.G., T. Cai), Harvard T. H. Chan School of Public Health, Boston, MA; Department of Medicine (Neurology) (F.Z., R.C., H.T.), and the Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada; Department of Neurology (B.H., T. Chitnis, H.W., P.D.J., Z.X.), Brigham and Women's Hospital, Boston, MA; Department of Neurology (P.D.J.), Columbia University, New York, NY; and Department of Neurology (Z.X.), University of Pittsburgh, PA
| | - Matthew Goodman
- From the Department of Health Services (T.Z.), Policy and Practice, Brown University, Providence, RI; Department of Biostatistics (M.G., T. Cai), Harvard T. H. Chan School of Public Health, Boston, MA; Department of Medicine (Neurology) (F.Z., R.C., H.T.), and the Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada; Department of Neurology (B.H., T. Chitnis, H.W., P.D.J., Z.X.), Brigham and Women's Hospital, Boston, MA; Department of Neurology (P.D.J.), Columbia University, New York, NY; and Department of Neurology (Z.X.), University of Pittsburgh, PA
| | - Feng Zhu
- From the Department of Health Services (T.Z.), Policy and Practice, Brown University, Providence, RI; Department of Biostatistics (M.G., T. Cai), Harvard T. H. Chan School of Public Health, Boston, MA; Department of Medicine (Neurology) (F.Z., R.C., H.T.), and the Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada; Department of Neurology (B.H., T. Chitnis, H.W., P.D.J., Z.X.), Brigham and Women's Hospital, Boston, MA; Department of Neurology (P.D.J.), Columbia University, New York, NY; and Department of Neurology (Z.X.), University of Pittsburgh, PA
| | - Brian Healy
- From the Department of Health Services (T.Z.), Policy and Practice, Brown University, Providence, RI; Department of Biostatistics (M.G., T. Cai), Harvard T. H. Chan School of Public Health, Boston, MA; Department of Medicine (Neurology) (F.Z., R.C., H.T.), and the Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada; Department of Neurology (B.H., T. Chitnis, H.W., P.D.J., Z.X.), Brigham and Women's Hospital, Boston, MA; Department of Neurology (P.D.J.), Columbia University, New York, NY; and Department of Neurology (Z.X.), University of Pittsburgh, PA
| | - Robert Carruthers
- From the Department of Health Services (T.Z.), Policy and Practice, Brown University, Providence, RI; Department of Biostatistics (M.G., T. Cai), Harvard T. H. Chan School of Public Health, Boston, MA; Department of Medicine (Neurology) (F.Z., R.C., H.T.), and the Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada; Department of Neurology (B.H., T. Chitnis, H.W., P.D.J., Z.X.), Brigham and Women's Hospital, Boston, MA; Department of Neurology (P.D.J.), Columbia University, New York, NY; and Department of Neurology (Z.X.), University of Pittsburgh, PA
| | - Tanuja Chitnis
- From the Department of Health Services (T.Z.), Policy and Practice, Brown University, Providence, RI; Department of Biostatistics (M.G., T. Cai), Harvard T. H. Chan School of Public Health, Boston, MA; Department of Medicine (Neurology) (F.Z., R.C., H.T.), and the Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada; Department of Neurology (B.H., T. Chitnis, H.W., P.D.J., Z.X.), Brigham and Women's Hospital, Boston, MA; Department of Neurology (P.D.J.), Columbia University, New York, NY; and Department of Neurology (Z.X.), University of Pittsburgh, PA
| | - Howard Weiner
- From the Department of Health Services (T.Z.), Policy and Practice, Brown University, Providence, RI; Department of Biostatistics (M.G., T. Cai), Harvard T. H. Chan School of Public Health, Boston, MA; Department of Medicine (Neurology) (F.Z., R.C., H.T.), and the Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada; Department of Neurology (B.H., T. Chitnis, H.W., P.D.J., Z.X.), Brigham and Women's Hospital, Boston, MA; Department of Neurology (P.D.J.), Columbia University, New York, NY; and Department of Neurology (Z.X.), University of Pittsburgh, PA
| | - Tianxi Cai
- From the Department of Health Services (T.Z.), Policy and Practice, Brown University, Providence, RI; Department of Biostatistics (M.G., T. Cai), Harvard T. H. Chan School of Public Health, Boston, MA; Department of Medicine (Neurology) (F.Z., R.C., H.T.), and the Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada; Department of Neurology (B.H., T. Chitnis, H.W., P.D.J., Z.X.), Brigham and Women's Hospital, Boston, MA; Department of Neurology (P.D.J.), Columbia University, New York, NY; and Department of Neurology (Z.X.), University of Pittsburgh, PA
| | - Philip De Jager
- From the Department of Health Services (T.Z.), Policy and Practice, Brown University, Providence, RI; Department of Biostatistics (M.G., T. Cai), Harvard T. H. Chan School of Public Health, Boston, MA; Department of Medicine (Neurology) (F.Z., R.C., H.T.), and the Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada; Department of Neurology (B.H., T. Chitnis, H.W., P.D.J., Z.X.), Brigham and Women's Hospital, Boston, MA; Department of Neurology (P.D.J.), Columbia University, New York, NY; and Department of Neurology (Z.X.), University of Pittsburgh, PA
| | - Helen Tremlett
- From the Department of Health Services (T.Z.), Policy and Practice, Brown University, Providence, RI; Department of Biostatistics (M.G., T. Cai), Harvard T. H. Chan School of Public Health, Boston, MA; Department of Medicine (Neurology) (F.Z., R.C., H.T.), and the Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada; Department of Neurology (B.H., T. Chitnis, H.W., P.D.J., Z.X.), Brigham and Women's Hospital, Boston, MA; Department of Neurology (P.D.J.), Columbia University, New York, NY; and Department of Neurology (Z.X.), University of Pittsburgh, PA
| | - Zongqi Xia
- From the Department of Health Services (T.Z.), Policy and Practice, Brown University, Providence, RI; Department of Biostatistics (M.G., T. Cai), Harvard T. H. Chan School of Public Health, Boston, MA; Department of Medicine (Neurology) (F.Z., R.C., H.T.), and the Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada; Department of Neurology (B.H., T. Chitnis, H.W., P.D.J., Z.X.), Brigham and Women's Hospital, Boston, MA; Department of Neurology (P.D.J.), Columbia University, New York, NY; and Department of Neurology (Z.X.), University of Pittsburgh, PA.
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239
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Apolipoprotein E (APOE) genotype-associated disease risks: a phenome-wide, registry-based, case-control study utilising the UK Biobank. EBioMedicine 2020; 59:102954. [PMID: 32818802 PMCID: PMC7452404 DOI: 10.1016/j.ebiom.2020.102954] [Citation(s) in RCA: 153] [Impact Index Per Article: 30.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 07/27/2020] [Accepted: 08/04/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND The three main alleles of the APOE gene (ε4, ε3 and ε2) carry differential risks for conditions including Alzheimer's disease (AD) and cardiovascular disease. Due to their clinical significance, we explored disease associations of the APOE genotypes using a hypothesis-free, data-driven, phenome-wide association study (PheWAS) approach. METHODS We used data from the UK Biobank to screen for associations between APOE genotypes and over 950 disease outcomes using genotype ε3ε3 as a reference. Data was restricted to 337,484 white British participants (aged 37-73 years). FINDINGS After correction for multiple testing, PheWAS analyses identified associations with 37 outcomes, representing 18 distinct diseases. As expected, ε3ε4 and ε4ε4 genotypes associated with increased odds of AD (p ≤ 7.6 × 10-46), hypercholesterolaemia (p ≤ 7.1 × 10-17) and ischaemic heart disease (p ≤ 2.3 × 10-4), while ε2ε3 provided protection for the latter two conditions (p ≤ 3.7 × 10-10) compared to ε3ε3. In contrast, ε4-associated disease protection was seen against obesity, chronic airway obstruction, type 2 diabetes, gallbladder disease, and liver disease (all p ≤ 5.2 × 10-4) while ε2ε2 homozygosity increased risks of peripheral vascular disease, thromboembolism, arterial aneurysm, peptic ulcer, cervical disorders, and hallux valgus (all p ≤ 6.1 × 10-4). Sensitivity analyses using brain neuroimaging, blood biochemistry, anthropometric, and spirometric biomarkers supported the PheWAS findings on APOE associations with respective disease outcomes. INTERPRETATION PheWAS confirms strong associations between APOE and AD, hypercholesterolaemia, and ischaemic heart disease, and suggests potential ε4-associated disease protection and harmful effects of the ε2ε2 genotype, for several conditions. FUNDING National Health and Medical Research Council of Australia.
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240
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Kawai VK, Shi M, Feng Q, Chung CP, Liu G, Cox NJ, Jarvik GP, Lee MTM, Hebbring SJ, Harley JB, Kaufman KM, Namjou B, Larson E, Gordon AS, Roden DM, Stein CM, Mosley JD. Pleiotropy in the Genetic Predisposition to Rheumatoid Arthritis: A Phenome-Wide Association Study and Inverse Variance-Weighted Meta-Analysis. Arthritis Rheumatol 2020; 72:1483-1492. [PMID: 32307929 DOI: 10.1002/art.41291] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 04/14/2020] [Indexed: 12/17/2022]
Abstract
OBJECTIVE This study was undertaken to investigate the hypothesis that a genetic predisposition toward rheumatoid arthritis (RA) increases the risk of 10 cardiometabolic and autoimmune disorders previously associated with RA in epidemiologic studies, and to define new genetic pleiotropy present in RA. METHODS Two approaches were used to test our hypothesis. First, we constructed a weighted genetic risk score (wGRS) and then examined its association with 10 prespecified disorders. Additionally, a phenome-wide association study (PheWAS) was carried out to identify potential new associations. Second, inverse variance-weighted regression (IVWR) meta-analysis was used to characterize the association between genetic susceptibility to RA and the prespecified disorders, with the results expressed as odds ratios (ORs) and 95% confidence intervals (95% CIs). RESULTS The wGRS for RA was significantly associated with type 1 diabetes mellitus (DM) (OR 1.10 [95% CI 1.04-1.16]; P = 9.82 × 10-4 ) and multiple sclerosis (OR 0.82 [95% CI 0.77-0.88]; P = 1.73 × 10-8 ), but not with other cardiometabolic phenotypes. In the PheWAS, wGRS was also associated with an increased risk of several autoimmune phenotypes including RA, thyroiditis, and systemic sclerosis, and with a decreased risk of demyelinating disorders. In the IVWR meta-analyses, RA was significantly associated with an increased risk of type 1 DM (P = 1.15 × 10-14 ), with evidence of horizontal pleiotropy (Mendelian Randomization-Egger intercept estimate P = 0.001) likely driven by rs2476601, a PTPN22 variant. The association between type 1 DM and RA remained significant (P = 9.53 × 10-9 ) after excluding rs2476601, with no evidence of horizontal pleiotropy (intercept estimate P = 0.939). RA was also significantly associated with type 2 DM and C-reactive protein levels. These associations were driven by variation in the major histocompatibility complex region. CONCLUSION This study presents evidence of pleiotropy between the genetic predisposition to RA and associated phenotypes found in other autoimmune and cardiometabolic disorders, including type 1 DM.
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Affiliation(s)
- Vivian K Kawai
- Vanderbilt University Medical Center, Nashville, Tennessee
| | - Mingjian Shi
- Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Qiping Feng
- Vanderbilt University Medical Center, Nashville, Tennessee
| | - Cecilia P Chung
- Vanderbilt University Medical Center, Tennessee Valley Healthcare System Nashville Campus, and Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Ge Liu
- Vanderbilt University Medical Center, Nashville, Tennessee
| | - Nancy J Cox
- Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee
| | | | | | | | - John B Harley
- Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, and Cincinnati VA Medical Center, Cincinnati, Ohio
| | - Kenneth M Kaufman
- Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, and Cincinnati VA Medical Center, Cincinnati, Ohio
| | - Bahram Namjou
- Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Eric Larson
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Adam S Gordon
- Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Dan M Roden
- Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee
| | | | - Jonathan D Mosley
- Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee
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241
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Mulugeta A, Zhou A, King C, Hyppönen E. Association between major depressive disorder and multiple disease outcomes: a phenome-wide Mendelian randomisation study in the UK Biobank. Mol Psychiatry 2020; 25:1469-1476. [PMID: 31427754 DOI: 10.1038/s41380-019-0486-1] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 05/26/2019] [Accepted: 06/10/2019] [Indexed: 11/09/2022]
Abstract
Depression affects all aspects of an individual's life but evidence relating to the causal effects on health is limited. We used information from 337,536 UK Biobank participants and performed hypothesis-free phenome-wide association analyses between major depressive disorder (MDD) genetic risk score (GRS) and 925 disease outcomes. GRS-disease outcome associations passing the multiple-testing corrected significance threshold (P < 1.9 × 10-3) were followed by Mendelian randomisation (MR) analyses to test for causality. MDD GRS was associated with 22 distinct diseases in the phenome-wide discovery stage, with the strongest signal observed for MDD diagnosis and related co-morbidities including anxiety and sleep disorders. In inverse-variance weighted MR analyses, MDD was associated with several inflammatory and haemorrhagic gastrointestinal diseases, including oesophagitis (OR 1.32, 95% CI 1.18-1.48), non-infectious gastroenteritis (OR 1.25, 95% CI 1.06-1.48), gastrointestinal haemorrhage (OR 1.26, 95% CI 1.11-1.43) and intestinal E.coli infections (OR 3.24, 95% CI 1.74-6.02). Signals were also observed for symptoms/disorders of the urinary system (OR 1.36, 95% CI 1.19-1.56), asthma (OR 1.23, 95% CI 1.06-1.44), and painful respiration (OR 1.28, 95% CI 1.14-1.44). MDD was associated with disorders of lipid metabolism (OR 1.22, 95% CI 1.12-1.34) and ischaemic heart disease (OR 1.30, 95% CI 1.15-1.47). Sensitivity analyses excluding pleiotropic variants provided consistent associations. Our study indicates a causal link between MDD and a broad range of diseases, suggesting a notable burden of co-morbidity. Early detection and management of MDD is important, and treatment strategies should be selected to also minimise the risk of related co-morbidities.
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Affiliation(s)
- Anwar Mulugeta
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, Adelaide, Australia.,Department of Pharmacology, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Ang Zhou
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, Adelaide, Australia
| | - Catherine King
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, Adelaide, Australia.,School of Pharmacy and Medical Sciences, University of South Australia, North Terrace, Adelaide, SA, Australia
| | - Elina Hyppönen
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, Adelaide, Australia. .,Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, London, UK. .,South Australian Health and Medical Research Institute, Adelaide, Australia.
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Zhou H, Sealock JM, Sanchez-Roige S, Clarke TK, Levey DF, Cheng Z, Li B, Polimanti R, Kember RL, Smith RV, Thygesen JH, Morgan MY, Atkinson SR, Thursz MR, Nyegaard M, Mattheisen M, Børglum AD, Johnson EC, Justice AC, Palmer AA, McQuillin A, Davis LK, Edenberg HJ, Agrawal A, Kranzler HR, Gelernter J. Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits. Nat Neurosci 2020; 23:809-818. [PMID: 32451486 PMCID: PMC7485556 DOI: 10.1038/s41593-020-0643-5] [Citation(s) in RCA: 233] [Impact Index Per Article: 46.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Accepted: 04/16/2020] [Indexed: 12/24/2022]
Abstract
Problematic alcohol use (PAU) is a leading cause of death and disability worldwide. Although genome-wide association studies have identified PAU risk genes, the genetic architecture of this trait is not fully understood. We conducted a proxy-phenotype meta-analysis of PAU, combining alcohol use disorder and problematic drinking, in 435,563 European-ancestry individuals. We identified 29 independent risk variants, 19 of them novel. PAU was genetically correlated with 138 phenotypes, including substance use and psychiatric traits. Phenome-wide polygenic risk score analysis in an independent biobank sample (BioVU, n = 67,589) confirmed the genetic correlations between PAU and substance use and psychiatric disorders. Genetic heritability of PAU was enriched in brain and in conserved and regulatory genomic regions. Mendelian randomization suggested causal effects on liability to PAU of substance use, psychiatric status, risk-taking behavior and cognitive performance. In summary, this large PAU meta-analysis identified novel risk loci and revealed genetic relationships with numerous other traits.
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Affiliation(s)
- Hang Zhou
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Julia M Sealock
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Medical Genetics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Toni-Kim Clarke
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Daniel F Levey
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Zhongshan Cheng
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Boyang Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Rachel L Kember
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | | | | | - Marsha Y Morgan
- UCL Institute for Liver & Digestive Health, Division of Medicine, Royal Free Campus, University College London, London, UK
| | - Stephen R Atkinson
- Department of Metabolism Digestion & Reproduction, Imperial College London, London, UK
| | - Mark R Thursz
- Department of Metabolism Digestion & Reproduction, Imperial College London, London, UK
| | - Mette Nyegaard
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Manuel Mattheisen
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Department of Psychiatry, Psychosomatics and Psychotherapy, University of Würzburg, Würzburg, Germany
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Anders D Børglum
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Amy C Justice
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Interdisciplinary Research on AIDS, Yale School of Public Health, New Haven, CT, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | | | - Lea K Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Medical Genetics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Henry R Kranzler
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA.
- Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT, USA.
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243
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Joo YY, Actkins K, Pacheco JA, Basile AO, Carroll R, Crosslin DR, Day F, Denny JC, Velez Edwards DR, Hakonarson H, Harley JB, Hebbring SJ, Ho K, Jarvik GP, Jones M, Karaderi T, Mentch FD, Meun C, Namjou B, Pendergrass S, Ritchie MD, Stanaway IB, Urbanek M, Walunas TL, Smith M, Chisholm RL, Kho AN, Davis L, Hayes MG. A Polygenic and Phenotypic Risk Prediction for Polycystic Ovary Syndrome Evaluated by Phenome-Wide Association Studies. J Clin Endocrinol Metab 2020; 105:dgz326. [PMID: 31917831 PMCID: PMC7453038 DOI: 10.1210/clinem/dgz326] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 01/07/2020] [Indexed: 11/19/2022]
Abstract
CONTEXT As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated to be unidentified in clinical practice. OBJECTIVE Utilizing polygenic risk prediction, we aim to identify the phenome-wide comorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventive treatment. DESIGN, PATIENTS, AND METHODS Leveraging the electronic health records (EHRs) of 124 852 individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores (PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). We evaluated its predictive capability across different ancestries and perform a PRS-based phenome-wide association study (PheWAS) to assess the phenomic expression of the heightened risk of PCOS. RESULTS The integrated polygenic prediction improved the average performance (pseudo-R2) for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null model across European, African, and multi-ancestry participants respectively. The subsequent PRS-powered PheWAS identified a high level of shared biology between PCOS and a range of metabolic and endocrine outcomes, especially with obesity and diabetes: "morbid obesity", "type 2 diabetes", "hypercholesterolemia", "disorders of lipid metabolism", "hypertension", and "sleep apnea" reaching phenome-wide significance. CONCLUSIONS Our study has expanded the methodological utility of PRS in patient stratification and risk prediction, especially in a multifactorial condition like PCOS, across different genetic origins. By utilizing the individual genome-phenome data available from the EHR, our approach also demonstrates that polygenic prediction by PRS can provide valuable opportunities to discover the pleiotropic phenomic network associated with PCOS pathogenesis.
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Affiliation(s)
- Yoonjung Yoonie Joo
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Ky’Era Actkins
- Department of Microbiology, Immunology, and Physiology, Meharry Medical College, Nashville, Tennessee
| | - Jennifer A Pacheco
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Anna O Basile
- Department of Biomedical Informatics, Columbia University New York, New York
| | - Robert Carroll
- Departments of Biomedical Informatics and Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - David R Crosslin
- Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, Wahington
| | - Felix Day
- MRC Epidemiology Unit, Cambridge Biomedical Campus, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Joshua C Denny
- Departments of Biomedical Informatics and Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Digna R Velez Edwards
- Departments of Biomedical Informatics and Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hakon Hakonarson
- Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - John B Harley
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
- Department of Pediatrics, University of Cincinnati College of Medicine; US Department of Veterans Affairs, Cincinnati, Ohio
| | - Scott J Hebbring
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, Wisconsin, USA
| | - Kevin Ho
- Biomedical and Translational Informatics, Geisinger, Danville, Pennsylvania
| | - Gail P Jarvik
- Division of Medical Genetics, Department of Medicine (Medical Genetics) and Genome Sciences, University of Washington Medical School, Seattle, Wahington
| | - Michelle Jones
- Center for Bioinformatics & Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Tugce Karaderi
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Frank D Mentch
- Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Cindy Meun
- Department of Obstetrics and Gynecology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Bahram Namjou
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
| | - Sarah Pendergrass
- Biomedical and Translational Informatics, Geisinger, Danville, Pennsylvania
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ian B Stanaway
- Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, Wahington
| | - Margrit Urbanek
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Theresa L Walunas
- Division of General Internal Medicine and Geriatrics, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Maureen Smith
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Rex L Chisholm
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Abel N Kho
- Division of General Internal Medicine and Geriatrics, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Lea Davis
- Departments of Biomedical Informatics and Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - M Geoffrey Hayes
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Department of Anthropology, Northwestern University, Evanston, Illinois
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244
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Mosley JD, Levinson RT, Farber-Eger E, Edwards TL, Hellwege JN, Hung AM, Giri A, Shuey MM, Shaffer CM, Shi M, Brittain EL, Chung WK, Kullo IJ, Arruda-Olson AM, Jarvik GP, Larson EB, Crosslin DR, Williams MS, Borthwick KM, Hakonarson H, Denny JC, Wang TJ, Stein CM, Roden DM, Wells QS. The polygenic architecture of left ventricular mass mirrors the clinical epidemiology. Sci Rep 2020; 10:7561. [PMID: 32372017 PMCID: PMC7200691 DOI: 10.1038/s41598-020-64525-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Accepted: 04/16/2020] [Indexed: 02/07/2023] Open
Abstract
Left ventricular (LV) mass is a prognostic biomarker for incident heart disease and all-cause mortality. Large-scale genome-wide association studies have identified few SNPs associated with LV mass. We hypothesized that a polygenic discovery approach using LV mass measurements made in a clinical population would identify risk factors and diseases associated with adverse LV remodeling. We developed a polygenic single nucleotide polymorphism-based predictor of LV mass in 7,601 individuals with LV mass measurements made during routine clinical care. We tested for associations between this predictor and 894 clinical diagnoses measured in 58,838 unrelated genotyped individuals. There were 29 clinical phenotypes associated with the LV mass genetic predictor at FDR q < 0.05. Genetically predicted higher LV mass was associated with modifiable cardiac risk factors, diagnoses related to organ dysfunction and conditions associated with abnormal cardiac structure including heart failure and atrial fibrillation. Secondary analyses using polygenic predictors confirmed a significant association between higher LV mass and body mass index and, in men, associations with coronary atherosclerosis and systolic blood pressure. In summary, these analyses show that LV mass-associated genetic variability associates with diagnoses of cardiac diseases and with modifiable risk factors which contribute to these diseases.
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Affiliation(s)
- Jonathan D Mosley
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Rebecca T Levinson
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eric Farber-Eger
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Todd L Edwards
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jacklyn N Hellwege
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Tennessee Valley Healthcare System (626), Vanderbilt University, Nashville, TN, USA
| | - Adriana M Hung
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Tennessee Valley Healthcare System (626), Vanderbilt University, Nashville, TN, USA
| | - Ayush Giri
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN, USA
- Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Megan M Shuey
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Christian M Shaffer
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Mingjian Shi
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Evan L Brittain
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wendy K Chung
- Office of Research & Development, Department of Veterans Affairs, Washington DC, DC, USA
- Departments of Pediatrics and Medicine, Columbia University Medical Center, New York, NY, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | | | - Gail P Jarvik
- Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington, Seattle, WA, USA
| | - Eric B Larson
- Kaiser Permanente Washington Health Research Institute and Department of Medicine, University of Washington, Seattle, WA, USA
| | - David R Crosslin
- Departments of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | | | - Ken M Borthwick
- Biomedical and Translational Informatics, Geisinger, Danville, PA, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, Division of Human Genetics, Department of Pediatrics, The Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Joshua C Denny
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Thomas J Wang
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Charles M Stein
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dan M Roden
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Quinn S Wells
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA
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245
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Zhong X, Yin Z, Jia G, Zhou D, Wei Q, Faucon A, Evans P, Gamazon ER, Li B, Tao R, Rzhetsky A, Bastarache L, Cox NJ. Electronic health record phenotypes associated with genetically regulated expression of CFTR and application to cystic fibrosis. Genet Med 2020; 22:1191-1200. [PMID: 32296164 DOI: 10.1038/s41436-020-0786-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 03/17/2020] [Indexed: 11/09/2022] Open
Abstract
PURPOSE The increasing use of electronic health records (EHRs) and biobanks offers unique opportunities to study Mendelian diseases. We described a novel approach to summarize clinical manifestations from patient EHRs into phenotypic evidence for cystic fibrosis (CF) with potential to alert unrecognized patients of the disease. METHODS We estimated genetically predicted expression (GReX) of cystic fibrosis transmembrane conductance regulator (CFTR) and tested for association with clinical diagnoses in the Vanderbilt University biobank (N = 9142 persons of European descent with 71 cases of CF). The top associated EHR phenotypes were assessed in combination as a phenotype risk score (PheRS) for discriminating CF case status in an additional 2.8 million patients from Vanderbilt University Medical Center (VUMC) and 125,305 adult patients including 25,314 CF cases from MarketScan, an independent external cohort. RESULTS GReX of CFTR was associated with EHR phenotypes consistent with CF. PheRS constructed using the EHR phenotypes and weights discovered by the genetic associations improved discriminative power for CF over the initially proposed PheRS in both VUMC and MarketScan. CONCLUSION Our study demonstrates the power of EHRs for clinical description of CF and the benefits of using a genetics-informed weighing scheme in construction of a phenotype risk score. This research may find broad applications for phenomic studies of Mendelian disease genes.
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Affiliation(s)
- Xue Zhong
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. .,Vanderbilt Genetics Institute, Nashville, TN, USA.
| | - Zhijun Yin
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Gengjie Jia
- Department of Medicine, Institute of Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
| | - Dan Zhou
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.,Vanderbilt Genetics Institute, Nashville, TN, USA
| | - Qiang Wei
- Vanderbilt Genetics Institute, Nashville, TN, USA.,Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
| | - Annika Faucon
- Human Genetics Graduate Program, Vanderbilt University, Nashville, TN, USA
| | - Patrick Evans
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.,Vanderbilt Genetics Institute, Nashville, TN, USA
| | - Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.,Vanderbilt Genetics Institute, Nashville, TN, USA.,'Life Member' of Clare Hall, University of Cambridge, Cambridge, United Kingdom.,MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Bingshan Li
- Vanderbilt Genetics Institute, Nashville, TN, USA.,Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
| | - Ran Tao
- Vanderbilt Genetics Institute, Nashville, TN, USA.,Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Andrey Rzhetsky
- Department of Medicine, Institute of Genomics and Systems Biology, University of Chicago, Chicago, IL, USA.,Committee on Genomics, Genetics and Systems Biology, University of Chicago, Chicago, IL, USA.,Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nancy J Cox
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. .,Vanderbilt Genetics Institute, Nashville, TN, USA.
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246
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George G, Gan S, Huang Y, Appleby P, Nar AS, Venkatesan R, Mohan V, Palmer CNA, Doney ASF. PheGWAS: a new dimension to visualize GWAS across multiple phenotypes. Bioinformatics 2020; 36:2500-2505. [PMID: 31860083 PMCID: PMC7178436 DOI: 10.1093/bioinformatics/btz944] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 11/23/2019] [Accepted: 12/18/2019] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION PheGWAS was developed to enhance exploration of phenome-wide pleiotropy at the genome-wide level through the efficient generation of a dynamic visualization combining Manhattan plots from GWAS with PheWAS to create a 3D 'landscape'. Pleiotropy in sub-surface GWAS significance strata can be explored in a sectional view plotted within user defined levels. Further complexity reduction is achieved by confining to a single chromosomal section. Comprehensive genomic and phenomic coordinates can be displayed. RESULTS PheGWAS is demonstrated using summary data from Global Lipids Genetics Consortium GWAS across multiple lipid traits. For single and multiple traits PheGWAS highlighted all 88 and 69 loci, respectively. Further, the genes and SNPs reported in Global Lipids Genetics Consortium were identified using additional functions implemented within PheGWAS. Not only is PheGWAS capable of identifying independent signals but also provides insights to local genetic correlation (verified using HESS) and in identifying the potential regions that share causal variants across phenotypes (verified using colocalization tests). AVAILABILITY AND IMPLEMENTATION The PheGWAS software and code are freely available at (https://github.com/georgeg0/PheGWAS). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Gittu George
- NIHR Global Health Research Unit on Global Diabetes Outcomes Research, Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Sushrima Gan
- NIHR Global Health Research Unit on Global Diabetes Outcomes Research, Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Yu Huang
- NIHR Global Health Research Unit on Global Diabetes Outcomes Research, Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Philip Appleby
- NIHR Global Health Research Unit on Global Diabetes Outcomes Research, Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - A S Nar
- NIHR Global Health Research Unit on Global Diabetes Outcomes Research, Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Radha Venkatesan
- NIHR Global Health Research Unit on Global Diabetes Outcomes Research, Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Viswanathan Mohan
- NIHR Global Health Research Unit on Global Diabetes Outcomes Research, Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Colin N A Palmer
- NIHR Global Health Research Unit on Global Diabetes Outcomes Research, Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Alex S F Doney
- NIHR Global Health Research Unit on Global Diabetes Outcomes Research, Division of Population Health and Genomics, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
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247
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Beesley LJ, Fritsche LG, Mukherjee B. An analytic framework for exploring sampling and observation process biases in genome and phenome-wide association studies using electronic health records. Stat Med 2020; 39:1965-1979. [PMID: 32198773 DOI: 10.1002/sim.8524] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 02/14/2020] [Accepted: 02/14/2020] [Indexed: 12/17/2022]
Abstract
Large-scale association analyses based on observational health care databases such as electronic health records have been a topic of increasing interest in the scientific community. However, challenges due to nonprobability sampling and phenotype misclassification associated with the use of these data sources are often ignored in standard analyses. The extent of the bias introduced by ignoring these factors is not well-characterized. In this paper, we develop an analytic framework for characterizing the bias expected in disease-gene association studies based on electronic health records when disease status misclassification and the sampling mechanism are ignored. Through a sensitivity analysis approach, this framework can be used to obtain plausible values for parameters of interest given summary results from standard analysis. We develop an online tool for performing this sensitivity analysis. Simulations demonstrate promising properties of the proposed method. We apply our approach to study bias in disease-gene association studies using electronic health record data from the Michigan Genomics Initiative, a longitudinal biorepository effort within The University Michigan health system.
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Affiliation(s)
- Lauren J Beesley
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Lars G Fritsche
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
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248
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Phenome-Wide Scan Finds Potential Orofacial Risk Markers for Cancer. Sci Rep 2020; 10:4869. [PMID: 32184411 PMCID: PMC7078198 DOI: 10.1038/s41598-020-61654-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 02/21/2020] [Indexed: 11/15/2022] Open
Abstract
Cancer is a disease caused by a process that drives the transformation of normal cells into malignant cells. The late diagnosis of cancer has a negative impact on the health care system due to high treatment cost and decreased chances of favorable prognosis. Here, we aimed to identify orofacial conditions that can serve as potential risk markers for cancers by performing a phenome-wide scan (PheWAS). From a pool of 6,100 individuals, both genetic and epidemiological data of 1,671 individuals were selected: 350 because they were previously diagnosed with cancer and 1,321 to match to those individuals that had cancer, based on age, sex, and ethnicity serving as a comparison group. Results of this study showed that when analyzing the individuals affected by cancer separately, tooth loss/edentulism is associated with SNPs in AXIN2 (rs11867417 p = 0.02 and rs2240308 p = 0.02), and leukoplakia of oral mucosa is associated with both AXIN2 (rs2240308 p = 0.03) and RHEB (rs2374261 p = 0.03). These phenotypes did not show the same trends in patients that were not diagnosed with cancer, allowing for the conclusion that these phenotypes are unique to cases with higher cancer risk.
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249
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Beesley LJ, Salvatore M, Fritsche LG, Pandit A, Rao A, Brummett C, Willer CJ, Lisabeth LD, Mukherjee B. The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities. Stat Med 2020; 39:773-800. [PMID: 31859414 PMCID: PMC7983809 DOI: 10.1002/sim.8445] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 09/10/2019] [Accepted: 11/16/2019] [Indexed: 01/03/2023]
Abstract
Biobanks linked to electronic health records provide rich resources for health-related research. With improvements in administrative and informatics infrastructure, the availability and utility of data from biobanks have dramatically increased. In this paper, we first aim to characterize the current landscape of available biobanks and to describe specific biobanks, including their place of origin, size, and data types. The development and accessibility of large-scale biorepositories provide the opportunity to accelerate agnostic searches, expedite discoveries, and conduct hypothesis-generating studies of disease-treatment, disease-exposure, and disease-gene associations. Rather than designing and implementing a single study focused on a few targeted hypotheses, researchers can potentially use biobanks' existing resources to answer an expanded selection of exploratory questions as quickly as they can analyze them. However, there are many obvious and subtle challenges with the design and analysis of biobank-based studies. Our second aim is to discuss statistical issues related to biobank research such as study design, sampling strategy, phenotype identification, and missing data. We focus our discussion on biobanks that are linked to electronic health records. Some of the analytic issues are illustrated using data from the Michigan Genomics Initiative and UK Biobank, two biobanks with two different recruitment mechanisms. We summarize the current body of literature for addressing these challenges and discuss some standing open problems. This work complements and extends recent reviews about biobank-based research and serves as a resource catalog with analytical and practical guidance for statisticians, epidemiologists, and other medical researchers pursuing research using biobanks.
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Affiliation(s)
| | | | | | - Anita Pandit
- University of Michigan, Department of Biostatistics
| | - Arvind Rao
- University of Michigan, Department of Computational Medicine and Bioinformatics
| | - Chad Brummett
- University of Michigan, Department of Anesthesiology
| | - Cristen J. Willer
- University of Michigan, Department of Computational Medicine and Bioinformatics
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Bajaj A, Ihegword A, Qiu C, Small AM, Wei WQ, Bastarache L, Feng Q, Kember RL, Risman M, Bloom RD, Birtwell DL, Williams H, Shaffer CM, Chen J, Center RG, Denny JC, Rader DJ, Stein CM, Damrauer SM, Susztak K. Phenome-wide association analysis suggests the APOL1 linked disease spectrum primarily drives kidney-specific pathways. Kidney Int 2020; 97:1032-1041. [PMID: 32247630 DOI: 10.1016/j.kint.2020.01.027] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 01/13/2020] [Accepted: 01/17/2020] [Indexed: 01/13/2023]
Abstract
The relationship between commonly occurring genetic variants (G1 and G2) in the APOL1 gene in African Americans and different disease traits, such as kidney disease, cardiovascular disease, and pre-eclampsia, remains the subject of controversy. Here we took a genotype-first approach, a phenome-wide association study, to define the spectrum of phenotypes associated with APOL1 high-risk variants in 1,837 African American participants of Penn Medicine Biobank and 4,742 African American participants of Vanderbilt BioVU. In the Penn Medicine Biobank, outpatient creatinine measurement-based estimated glomerular filtration rate and multivariable regression models were used to evaluate the association between high-risk APOL1 status and renal outcomes. In meta-analysis of both cohorts, the strongest phenome-wide association study associations were for the high-risk APOL1 variants and diagnoses codes were highly significant for "kidney dialysis" (odds ratio 3.75) and "end stage kidney disease" (odds ratio 3.42). A number of phenotypes were associated with APOL1 high-risk genotypes in an analysis adjusted only for demographic variables. However, no associations were detected with non-renal phenotypes after controlling for chronic/end stage kidney disease status. Using calculated estimated glomerular filtration rate -based phenotype analysis in the Penn Medicine Biobank, APOL1 high-risk status was associated with prevalent chronic/end stage kidney disease /kidney transplant (odds ratio 2.27, 95% confidence interval 1.67-3.08). In high-risk participants, the estimated glomerular filtration rate was 15.4 mL/min/1.73m2; significantly lower than in low-risk participants. Thus, although APOL1 high-risk variants are associated with a range of phenotypes, the risks for other associated phenotypes appear much lower and in our dataset are driven by a primary effect on renal disease.
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Affiliation(s)
- Archna Bajaj
- Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Andrea Ihegword
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Chengxiang Qiu
- Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; Renal Electrolyte and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Aeron M Small
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; Department of Medicine, Yale-New Haven Hospital, New Haven, Connecticut, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - QiPing Feng
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Rachel L Kember
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania, USA; VISN 4 Mental Illness Research, Education, and Clinical Center, Corporal Michael Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA
| | - Marjorie Risman
- Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Roy D Bloom
- Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; Renal Electrolyte and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David L Birtwell
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Heather Williams
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Christian M Shaffer
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jinbo Chen
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Joshua C Denny
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Daniel J Rader
- Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - C Michael Stein
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Department of Pharmacology, Vanderbilt University, Nashville, Tennessee, USA
| | - Scott M Damrauer
- Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA; Department of Surgery, Corporal Michael Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA.
| | - Katalin Susztak
- Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; Renal Electrolyte and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
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