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Herrera-Luis E, Martin-Almeida M, Pino-Yanes M. Asthma-Genomic Advances Toward Risk Prediction. Clin Chest Med 2024; 45:599-610. [PMID: 39069324 PMCID: PMC11284279 DOI: 10.1016/j.ccm.2024.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Asthma is a common complex airway disease whose prediction of disease risk and most severe outcomes is crucial in clinical practice for adequate clinical management. This review discusses the latest findings in asthma genomics and current obstacles faced in moving forward to translational medicine. While genome-wide association studies have provided valuable insights into the genetic basis of asthma, there are challenges that must be addressed to improve disease prediction, such as the need for diverse representation, the functional characterization of genetic variants identified, variant selection for genetic testing, and refining prediction models using polygenic risk scores.
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Affiliation(s)
- Esther Herrera-Luis
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe Street, Baltimore, MD 21205, USA.
| | - Mario Martin-Almeida
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), Avenida Astrofísico Francisco Sánchez, s/n. Facultad de Ciencias, San Cristóbal de La Laguna, S/C de Tenerife La Laguna 38200, Tenerife, Spain
| | - Maria Pino-Yanes
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), Avenida Astrofísico Francisco Sánchez, s/n. Facultad de Ciencias, San Cristóbal de La Laguna, S/C de Tenerife La Laguna 38200, Tenerife, Spain; CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid 28029, Spain; Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna (ULL), San Cristóbal de La Laguna 38200, Tenerife, Spain
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2
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Stikker B, Trap L, Sedaghati-Khayat B, de Bruijn MJW, van Ijcken WFJ, de Roos E, Ikram A, Hendriks RW, Brusselle G, van Rooij J, Stadhouders R. Epigenomic partitioning of a polygenic risk score for asthma reveals distinct genetically driven disease pathways. Eur Respir J 2024; 64:2302059. [PMID: 38901884 PMCID: PMC11358516 DOI: 10.1183/13993003.02059-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 05/28/2024] [Indexed: 06/22/2024]
Abstract
BACKGROUND Individual differences in susceptibility to developing asthma, a heterogeneous chronic inflammatory lung disease, are poorly understood. Whether genetics can predict asthma risk and how genetic variants modulate the complex pathophysiology of asthma are still debated. AIM To build polygenic risk scores for asthma risk prediction and epigenomically link predictive genetic variants to pathophysiological mechanisms. METHODS Restricted polygenic risk scores were constructed using single nucleotide variants derived from genome-wide association studies and validated using data generated in the Rotterdam Study, a Dutch prospective cohort of 14 926 individuals. Outcomes used were asthma, childhood-onset asthma, adulthood-onset asthma, eosinophilic asthma and asthma exacerbations. Genome-wide chromatin analysis data from 19 disease-relevant cell types were used for epigenomic polygenic risk score partitioning. RESULTS The polygenic risk scores obtained predicted asthma and related outcomes, with the strongest associations observed for childhood-onset asthma (2.55 odds ratios per polygenic risk score standard deviation, area under the curve of 0.760). Polygenic risk scores allowed for the classification of individuals into high-risk and low-risk groups. Polygenic risk score partitioning using epigenomic profiles identified five clusters of variants within putative gene regulatory regions linked to specific asthma-relevant cells, genes and biological pathways. CONCLUSIONS Polygenic risk scores were associated with asthma(-related traits) in a Dutch prospective cohort, with substantially higher predictive power observed for childhood-onset than adult-onset asthma. Importantly, polygenic risk score variants could be epigenomically partitioned into clusters of regulatory variants with different pathophysiological association patterns and effect estimates, which likely represent distinct genetically driven disease pathways. Our findings have potential implications for personalised risk mitigation and treatment strategies.
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Affiliation(s)
- Bernard Stikker
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Lianne Trap
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- L. Trap and B. Sedaghati-Khayat made an equal contribution to this study
| | - Bahar Sedaghati-Khayat
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- L. Trap and B. Sedaghati-Khayat made an equal contribution to this study
| | - Marjolein J W de Bruijn
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Wilfred F J van Ijcken
- Center for Biomics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Emmely de Roos
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
| | - Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Rudi W Hendriks
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Guy Brusselle
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jeroen van Rooij
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- J. van Rooij and R. Stadhouders contributed equally to this article as lead authors and supervised the work
| | - Ralph Stadhouders
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- J. van Rooij and R. Stadhouders contributed equally to this article as lead authors and supervised the work
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3
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Sayers I, John C, Chen J, Hall IP. Genetics of chronic respiratory disease. Nat Rev Genet 2024; 25:534-547. [PMID: 38448562 DOI: 10.1038/s41576-024-00695-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/19/2024] [Indexed: 03/08/2024]
Abstract
Chronic respiratory diseases, such as chronic obstructive pulmonary disease (COPD), asthma and interstitial lung diseases are frequently occurring disorders with a polygenic basis that account for a large global burden of morbidity and mortality. Recent large-scale genetic epidemiology studies have identified associations between genetic variation and individual respiratory diseases and linked specific genetic variants to quantitative traits related to lung function. These associations have improved our understanding of the genetic basis and mechanisms underlying common lung diseases. Moreover, examining the overlap between genetic associations of different respiratory conditions, along with evidence for gene-environment interactions, has yielded additional biological insights into affected molecular pathways. This genetic information could inform the assessment of respiratory disease risk and contribute to stratified treatment approaches.
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Affiliation(s)
- Ian Sayers
- NIHR Nottingham Biomedical Research Centre, University of Nottingham, University Park, Nottingham, UK
- Biodiscovery Institute, School of Medicine, University of Nottingham, University Park, Nottingham, UK
| | - Catherine John
- University of Leicester, Leicester, UK
- University Hospitals of Leicester, Leicester, UK
| | - Jing Chen
- University of Leicester, Leicester, UK
| | - Ian P Hall
- NIHR Nottingham Biomedical Research Centre, University of Nottingham, University Park, Nottingham, UK.
- Biodiscovery Institute, School of Medicine, University of Nottingham, University Park, Nottingham, UK.
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4
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Ramsay M, Crampin AC, Bawah AA, Gitau E, Herbst K. The Value Proposition of Coordinated Population Cohorts Across Africa. Annu Rev Biomed Data Sci 2024; 7:277-294. [PMID: 39178423 DOI: 10.1146/annurev-biodatasci-020722-015026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2024]
Abstract
Building longitudinal population cohorts in Africa for coordinated research and surveillance can influence the setting of national health priorities, lead to the introduction of appropriate interventions, and provide evidence for targeted treatment, leading to better health across the continent. However, compared to cohorts from the global north, longitudinal continental African population cohorts remain scarce, are relatively small in size, and lack data complexity. As infections and noncommunicable diseases disproportionately affect Africa's approximately 1.4 billion inhabitants, African cohorts present a unique opportunity for research and surveillance. High genetic diversity in African populations and multiomic research studies, together with detailed phenotyping and clinical profiling, will be a treasure trove for discovery. The outcomes, including novel drug targets, biological pathways for disease, and gene-environment interactions, will boost precision medicine approaches, not only in Africa but across the globe.
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Affiliation(s)
- Michèle Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa;
| | - Amelia C Crampin
- Malawi Epidemiology and Intervention Research Unit, Lilongwe, Malawi
| | - Ayaga A Bawah
- Regional Institute for Population Studies, University of Ghana, Accra, Ghana
| | - Evelyn Gitau
- African Population and Health Research Center, Nairobi, Kenya
| | - Kobus Herbst
- Africa Health Research Institute, Durban, South Africa
- South African Population Research Infrastructure Network, Department of Science and Innovation and South African Medical Research Council, Durban, South Africa
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5
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Sun C, Cheng X, Xu J, Chen H, Tao J, Dong Y, Wei S, Chen R, Meng X, Ma Y, Tian H, Guo X, Bi S, Zhang C, Kang J, Zhang M, Lv H, Shang Z, Lv W, Zhang R, Jiang Y. A review of disease risk prediction methods and applications in the omics era. Proteomics 2024:e2300359. [PMID: 38522029 DOI: 10.1002/pmic.202300359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 03/25/2024]
Abstract
Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.
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Affiliation(s)
- Chen Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Xiangshu Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Jing Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Haiyan Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Junxian Tao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Yu Dong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Siyu Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Rui Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xin Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yingnan Ma
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Hongsheng Tian
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xuying Guo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuo Bi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chen Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jingxuan Kang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Mingming Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongchao Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhenwei Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wenhua Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ruijie Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
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6
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Wang L, Zhou L, Zheng P, Mao Z, Liu H. Mild asthma is not mild: risk factors and predictive biomarkers for severe acute exacerbations and progression in mild asthma. Expert Rev Respir Med 2023; 17:1261-1271. [PMID: 38315090 DOI: 10.1080/17476348.2024.2314535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 02/01/2024] [Indexed: 02/07/2024]
Abstract
INTRODUCTION Asthma is a common chronic respiratory disease characterized by chronic airway inflammation, airway hyperresponsiveness, reversible airflow limitation, and airway remodeling. Mild asthma is the most common type of asthma, but it is the most neglected. Sometimes mild asthma can lead to acute severe exacerbations or even death. AREAS COVERED This article reviews the epidemiology, risk factors, and possible predictors of acute severe exacerbations and disease progression in mild asthma to improve the understanding of mild asthma and its severe acute exacerbations and progression. EXPERT OPINION There is a necessity to improve asthma patient categorization and redefine mild asthma's concept to heighten patient and physician attention. Identifying mild asthma patients that are highly vulnerable to severe acute exacerbations and researching the mechanisms are future prioritizations.
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Affiliation(s)
- Lingling Wang
- Department of Respiratory and Critical Care Medicine, Key Laboratory of Pulmonary Diseases of Health Ministry, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ling Zhou
- Department of Respiratory and Critical Care Medicine, Key Laboratory of Pulmonary Diseases of Health Ministry, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Pengdou Zheng
- Department of Respiratory and Critical Care Medicine, Key Laboratory of Pulmonary Diseases of Health Ministry, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhenyu Mao
- Department of Respiratory and Critical Care Medicine, Key Laboratory of Pulmonary Diseases of Health Ministry, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Huiguo Liu
- Department of Respiratory and Critical Care Medicine, Key Laboratory of Pulmonary Diseases of Health Ministry, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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7
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Dapas M, Lee YL, Wentworth-Sheilds W, Im HK, Ober C, Schoettler N. Revealing polygenic pleiotropy using genetic risk scores for asthma. HGG ADVANCES 2023; 4:100233. [PMID: 37663543 PMCID: PMC10474095 DOI: 10.1016/j.xhgg.2023.100233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 08/11/2023] [Indexed: 09/05/2023] Open
Abstract
In this study we examined how genetic risk for asthma associates with different features of the disease and with other medical conditions and traits. Using summary statistics from two multi-ancestry genome-wide association studies of asthma, we modeled polygenic risk scores (PRSs) and validated their predictive performance in the UK Biobank. We then performed phenome-wide association studies of the asthma PRSs with 371 heritable traits in the UK Biobank. We identified 228 total significant associations across a variety of organ systems, including associations that varied by PRS model, sex, age of asthma onset, ancestry, and human leukocyte antigen region alleles. Our results highlight pervasive pleiotropy between asthma and numerous other traits and conditions and elucidate pathways that contribute to asthma and its comorbidities.
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Affiliation(s)
- Matthew Dapas
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Yu Lin Lee
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Biological Sciences Collegiate Division, University of Chicago, Chicago, IL, USA
| | | | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Nathan Schoettler
- Section of Pulmonary and Critical Care Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
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8
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Moll M, Peljto AL, Kim JS, Xu H, Debban CL, Chen X, Menon A, Putman RK, Ghosh AJ, Saferali A, Nishino M, Hatabu H, Hobbs BD, Hecker J, McDermott G, Sparks JA, Wain LV, Allen RJ, Tobin MD, Raby BA, Chun S, Silverman EK, Zamora AC, Ortega VE, Garcia CK, Barr RG, Bleecker ER, Meyers DA, Kaner RJ, Rich SS, Manichaikul A, Rotter JI, Dupuis J, O’Connor GT, Fingerlin TE, Hunninghake GM, Schwartz DA, Cho MH. A Polygenic Risk Score for Idiopathic Pulmonary Fibrosis and Interstitial Lung Abnormalities. Am J Respir Crit Care Med 2023; 208:791-801. [PMID: 37523715 PMCID: PMC10563194 DOI: 10.1164/rccm.202212-2257oc] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 07/31/2023] [Indexed: 08/02/2023] Open
Abstract
Rationale: In addition to rare genetic variants and the MUC5B locus, common genetic variants contribute to idiopathic pulmonary fibrosis (IPF) risk. The predictive power of common variants outside the MUC5B locus for IPF and interstitial lung abnormalities (ILAs) is unknown. Objectives: We tested the predictive value of IPF polygenic risk scores (PRSs) with and without the MUC5B region on IPF, ILA, and ILA progression. Methods: We developed PRSs that included (PRS-M5B) and excluded (PRS-NO-M5B) the MUC5B region (500-kb window around rs35705950-T) using an IPF genome-wide association study. We assessed PRS associations with area under the receiver operating characteristic curve (AUC) metrics for IPF, ILA, and ILA progression. Measurements and Main Results: We included 14,650 participants (1,970 IPF; 1,068 ILA) from six multi-ancestry population-based and case-control cohorts. In cases excluded from genome-wide association study, the PRS-M5B (odds ratio [OR] per SD of the score, 3.1; P = 7.1 × 10-95) and PRS-NO-M5B (OR per SD, 2.8; P = 2.5 × 10-87) were associated with IPF. Participants in the top PRS-NO-M5B quintile had ∼sevenfold odds for IPF compared with those in the first quintile. A clinical model predicted IPF (AUC, 0.61); rs35705950-T and PRS-NO-M5B demonstrated higher AUCs (0.73 and 0.7, respectively), and adding both genetic predictors to a clinical model yielded the highest performance (AUC, 0.81). The PRS-NO-M5B was associated with ILA (OR, 1.25) and ILA progression (OR, 1.16) in European ancestry participants. Conclusions: A common genetic variant risk score complements the MUC5B variant to identify individuals at high risk of interstitial lung abnormalities and pulmonary fibrosis.
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Affiliation(s)
- Matthew Moll
- Division of Pulmonary and Critical Care Medicine, and
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Anna L. Peljto
- Department of Medicine and
- Department of Immunology, Division of Pulmonary Medicine, University of Colorado, Aurora, Colorado
| | - John S. Kim
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Virginia, Charlottesville, Virginia
| | - Hanfei Xu
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Catherine L. Debban
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Xianfeng Chen
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Phoenix, Arizona
| | - Aravind Menon
- Division of Pulmonary and Critical Care Medicine, and
| | | | - Auyon J. Ghosh
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, State University of New York Upstate Medical Center, Syracuse, New York
| | - Aabida Saferali
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Mizuki Nishino
- Center for Pulmonary Functional Imaging, Department of Radiology
| | - Hiroto Hatabu
- Center for Pulmonary Functional Imaging, Department of Radiology
| | - Brian D. Hobbs
- Division of Pulmonary and Critical Care Medicine, and
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Julian Hecker
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Gregory McDermott
- Division of Rheumatology, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Jeffrey A. Sparks
- Division of Rheumatology, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Louise V. Wain
- Department of Health Sciences, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research, Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Richard J. Allen
- Department of Health Sciences, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research, Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Martin D. Tobin
- Department of Health Sciences, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research, Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Benjamin A. Raby
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Pediatrics
- Division of Pulmonary Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Sung Chun
- Division of Pulmonary Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Edwin K. Silverman
- Division of Pulmonary and Critical Care Medicine, and
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Ana C. Zamora
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Phoenix, Arizona
| | - Victor E. Ortega
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Phoenix, Arizona
| | - Christine K. Garcia
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | - R. Graham Barr
- Department of Medicine and
- Division of General Medicine, Department of Epidemiology, Columbia University Medical Center, New York, New York
| | - Eugene R. Bleecker
- Division of Genetics, Genomics, and Precision Medicine, Department of Medicine, University of Arizona, Tucson, Arizona
| | - Deborah A. Meyers
- Division of Genetics, Genomics, and Precision Medicine, Department of Medicine, University of Arizona, Tucson, Arizona
| | - Robert J. Kaner
- Division of Pulmonary Medicine, Weill Cornell School of Medicine, New York, New York
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-University of California, Los Angeles Medical Center, Torrance, California
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill University Faculty of Medicine and Health Sciences, Montreal, Quebec, Canada
| | - George T. O’Connor
- Department of Medicine, Pulmonary Center, Boston University School of Medicine, Boston, Massachusetts; and
| | - Tasha E. Fingerlin
- The National Jewish Health Cohen Family Asthma Institute, Division of Allergy and Immunology, National Jewish Health, Denver, Colorado
| | | | - David A. Schwartz
- Department of Medicine and
- Department of Immunology, Division of Pulmonary Medicine, University of Colorado, Aurora, Colorado
| | - Michael H. Cho
- Division of Pulmonary and Critical Care Medicine, and
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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9
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Stikker BS, Hendriks RW, Stadhouders R. Decoding the genetic and epigenetic basis of asthma. Allergy 2023; 78:940-956. [PMID: 36727912 DOI: 10.1111/all.15666] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 01/17/2023] [Accepted: 01/30/2023] [Indexed: 02/03/2023]
Abstract
Asthma is a complex and heterogeneous chronic inflammatory disease of the airways. Alongside environmental factors, asthma susceptibility is strongly influenced by genetics. Given its high prevalence and our incomplete understanding of the mechanisms underlying disease susceptibility, asthma is frequently studied in genome-wide association studies (GWAS), which have identified thousands of genetic variants associated with asthma development. Virtually all these genetic variants reside in non-coding genomic regions, which has obscured the functional impact of asthma-associated variants and their translation into disease-relevant mechanisms. Recent advances in genomics technology and epigenetics now offer methods to link genetic variants to gene regulatory elements embedded within non-coding regions, which have started to unravel the molecular mechanisms underlying the complex (epi)genetics of asthma. Here, we provide an integrated overview of (epi)genetic variants associated with asthma, focusing on efforts to link these disease associations to biological insight into asthma pathophysiology using state-of-the-art genomics methodology. Finally, we provide a perspective as to how decoding the genetic and epigenetic basis of asthma has the potential to transform clinical management of asthma and to predict the risk of asthma development.
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Affiliation(s)
- Bernard S Stikker
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Rudi W Hendriks
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Ralph Stadhouders
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.,Department of Cell Biology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
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10
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Tsuo K, Zhou W, Wang Y, Kanai M, Namba S, Gupta R, Majara L, Nkambule LL, Morisaki T, Okada Y, Neale BM, Daly MJ, Martin AR. Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity. CELL GENOMICS 2022; 2:100212. [PMID: 36778051 PMCID: PMC9903683 DOI: 10.1016/j.xgen.2022.100212] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 09/01/2022] [Accepted: 10/12/2022] [Indexed: 11/09/2022]
Abstract
Asthma is a complex disease that varies widely in prevalence across populations. The extent to which genetic variation contributes to these disparities is unclear, as the genetics underlying asthma have been investigated primarily in populations of European descent. As part of the Global Biobank Meta-analysis Initiative, we conducted a large-scale genome-wide association study of asthma (153,763 cases and 1,647,022 controls) via meta-analysis across 22 biobanks spanning multiple ancestries. We discovered 179 asthma-associated loci, 49 of which were not previously reported. Despite the wide range in asthma prevalence among biobanks, we found largely consistent genetic effects across biobanks and ancestries. The meta-analysis also improved polygenic risk prediction in non-European populations compared with previous studies. Additionally, we found considerable genetic overlap between age-of-onset subtypes and between asthma and comorbid diseases. Our work underscores the multi-factorial nature of asthma development and offers insight into its shared genetic architecture.
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Affiliation(s)
- Kristin Tsuo
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Wei Zhou
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Rahul Gupta
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Lerato Majara
- Department of Psychiatry and Mental Health, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Lethukuthula L. Nkambule
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Takayuki Morisaki
- Division of Molecular Pathology, The Institute of Medical Science, The University of Tokyo, Minatu-ku, Tokyo, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita 565-0871, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita 565-0871, Japan
| | - Benjamin M. Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Global Biobank Meta-analysis Initiative
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry and Mental Health, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Division of Molecular Pathology, The Institute of Medical Science, The University of Tokyo, Minatu-ku, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita 565-0871, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita 565-0871, Japan
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Mark J. Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Alicia R. Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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11
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Huang YJ, Chu YC, Chen CW, Yang HC, Huang HL, Hwang JS, Chen CH, Chan TC. Relationship among genetic variants, obesity traits and asthma in the Taiwan Biobank. BMJ Open Respir Res 2022; 9:9/1/e001355. [PMID: 36600406 PMCID: PMC9730389 DOI: 10.1136/bmjresp-2022-001355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND AND OBJECTIVE Obesity and asthma impose a heavy health and economic burden on millions of people around the world. The complex interaction between genetic traits and phenotypes caused the mechanism between obesity and asthma is still vague. This study investigates the relationship among obesity-related polygenic risk score (PRS), obesity phenotypes and the risk of having asthma. METHODS This is a matched case-control study, with 4 controls (8288 non-asthmatic) for each case (2072 asthmatic). Data were obtained from the 2008-2015 Taiwan Biobank Database and linked to the 2000-2016 National Health Insurance Research Database. All participants were ≥30 years old with no history of cancer and had a complete questionnaire, as well as physical examination, genome-wide single nucleotide polymorphisms and clinical diagnosis data. Environmental exposure, PM2.5, was also considered. Multivariate adjusted ORs and 95% CIs were calculated using conditional logistic regression stratified by age and sex. Mediation analysis was also assessed, using a generalised linear model. RESULTS We found that the obese phenotype was associated with significantly increased odds of asthma by approximately 26%. Four obesity-related PRS, including body mass index (OR=1.07 (1.01-1.13)), waist circumference (OR=1.10 (1.04-1.17)), central obesity as defined by waist-to-height ratio (OR=1.09 (1.03-1.15)) and general-central obesity (OR=1.06 (1.00-1.12)), were associated with increased odds of asthma. Additional independent risk factors for asthma included lower educational level, family history of asthma, certain chronic diseases and increased PM2.5 exposure. Obesity-related PRS is an indirect risk factor for asthma, the link being fully mediated by the trait of obesity. CONCLUSIONS Obese phenotypes and obesity-related PRS are independent risk factors for having asthma in adults in the Taiwan Biobank. Overall, genetic risk for obesity increases the risk of asthma by affecting the obese phenotype.
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Affiliation(s)
- Ying-Jhen Huang
- Research Center for Humanities and Social Sciences, Academia Sinica, Taipei City, Taiwan
| | - Yi-Chi Chu
- Research Center for Humanities and Social Sciences, Academia Sinica, Taipei City, Taiwan
| | - Chia-Wei Chen
- Institute of Statistical Science, Academia Sinica, Taipei City, Taiwan
| | - Hsin-Chou Yang
- Institute of Statistical Science, Academia Sinica, Taipei City, Taiwan
| | - Hung-Ling Huang
- Department of Internal Medicine, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung City, Taiwan,Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung City, Taiwan,Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung City, Taiwan
| | - Jing-Shiang Hwang
- Institute of Statistical Science, Academia Sinica, Taipei City, Taiwan
| | - Chun-Houh Chen
- Institute of Statistical Science, Academia Sinica, Taipei City, Taiwan
| | - Ta-Chien Chan
- Research Center for Humanities and Social Sciences, Academia Sinica, Taipei City, Taiwan,Institute of Public Health, School of Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
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12
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Wheatley LM, Holloway JW, Svanes C, Sears MR, Breton C, Fedulov AV, Nilsson E, Vercelli D, Zhang H, Togias A, Arshad SH. The role of epigenetics in multi-generational transmission of asthma: An NIAID workshop report-based narrative review. Clin Exp Allergy 2022; 52:1264-1275. [PMID: 36073598 PMCID: PMC9613603 DOI: 10.1111/cea.14223] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 08/27/2022] [Accepted: 08/29/2022] [Indexed: 01/26/2023]
Abstract
There is mounting evidence that environmental exposures can result in effects on health that can be transmitted across generations, without the need for a direct exposure to the original factor, for example, the effect of grandparental smoking on grandchildren. Hence, an individual's health should be investigated with the knowledge of cross-generational influences. Epigenetic factors are molecular factors or processes that regulate genome activity and may impact cross-generational effects. Epigenetic transgenerational inheritance has been demonstrated in plants and animals, but the presence and extent of this process in humans are currently being investigated. Experimental data in animals support transmission of asthma risk across generations from a single exposure to the deleterious factor and suggest that the nature of this transmission is in part due to changes in DNA methylation, the most studied epigenetic process. The association of father's prepuberty exposure with offspring risk of asthma and lung function deficit may also be mediated by epigenetic processes. Multi-generational birth cohorts are ideal to investigate the presence and impact of transfer of disease susceptibility across generations and underlying mechanisms. However, multi-generational studies require recruitment and assessment of participants over several decades. Investigation of adult multi-generation cohorts is less resource intensive but run the risk of recall bias. Statistical analysis is challenging given varying degrees of longitudinal and hierarchical data but path analyses, structural equation modelling and multilevel modelling can be employed, and directed networks addressing longitudinal effects deserve exploration as an effort to study causal pathways.
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Affiliation(s)
- Lisa M. Wheatley
- National Institute of Allergy and Infectious DiseaseNational Institutes of HealthBethesdaMarylandUSA
| | - John W. Holloway
- Faculty of Medicine, Human Development and HealthUniversity of SouthamptonSouthamptonUK
| | - Cecilie Svanes
- Department of Global Public Health and Primary CareUniversity of BergenBergenNorway
| | | | - Carrie Breton
- University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Alexey V. Fedulov
- Warren Alpert Medical School of Brown University, Rhode Island HospitalProvidenceRhode IslandUSA
| | - Eric Nilsson
- Washington State University PullmanPullmanWashingtonUSA
| | | | - Hongmei Zhang
- Division of Epidemiology, Biostatistics and Environmental Health, School of Public HealthUniversity of MemphisMemphisTennesseeUSA
| | - Alkis Togias
- National Institute of Allergy and Infectious DiseaseNational Institutes of HealthBethesdaMarylandUSA
| | - Syed Hasan Arshad
- Clinical and Experimental Sciences, Faculty of MedicineUniversity of SouthamptonSouthamptonUK
- The David Hide Asthma and Allergy CentreSt Mary's HospitalNewportUK
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13
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Namjou B, Lape M, Malolepsza E, DeVore SB, Weirauch MT, Dikilitas O, Jarvik GP, Kiryluk K, Kullo IJ, Liu C, Luo Y, Satterfield BA, Smoller JW, Walunas TL, Connolly J, Sleiman P, Mersha TB, Mentch FD, Hakonarson H, Prows CA, Biagini JM, Khurana Hershey GK, Martin LJ, Kottyan L. Multiancestral polygenic risk score for pediatric asthma. J Allergy Clin Immunol 2022; 150:1086-1096. [PMID: 35595084 PMCID: PMC9643615 DOI: 10.1016/j.jaci.2022.03.035] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 03/07/2022] [Accepted: 03/29/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Asthma is the most common chronic condition in children and the third leading cause of hospitalization in pediatrics. The genome-wide association study catalog reports 140 studies with genome-wide significance. A polygenic risk score (PRS) with predictive value across ancestries has not been evaluated for this important trait. OBJECTIVES This study aimed to train and validate a PRS relying on genetic determinants for asthma to provide predictions for disease occurrence in pediatric cohorts of diverse ancestries. METHODS This study applied a Bayesian regression framework method using the Trans-National Asthma Genetic Consortium genome-wide association study summary statistics to derive a multiancestral PRS score, used one Electronic Medical Records and Genomics (eMERGE) cohort as a training set, used a second independent eMERGE cohort to validate the score, and used the UK Biobank data to replicate the findings. A phenome-wide association study was performed using the PRS to identify shared genetic etiology with other phenotypes. RESULTS The multiancestral asthma PRS was associated with asthma in the 2 pediatric validation datasets. Overall, the multiancestral asthma PRS has an area under the curve (AUC) of 0.70 (95% CI, 0.69-0.72) in the pediatric validation 1 and AUC of 0.66 (0.65-0.66) in the pediatric validation 2 datasets. We found significant discrimination across pediatric subcohorts of European (AUC, 95% CI, 0.60 and 0.66), African (AUC, 95% CI, 0.61 and 0.66), admixed American (AUC, 0.64 and 0.70), Southeast Asian (AUC, 0.65), and East Asian (AUC, 0.73) ancestry. Pediatric participants with the top 5% PRS had 2.80 to 5.82 increased odds of asthma compared to the bottom 5% across the training, validation 1, and validation 2 cohorts when adjusted for ancestry. Phenome-wide association study analysis confirmed the strong association of the identified PRS with asthma (odds ratio, 2.71, PFDR = 3.71 × 10-65) and related phenotypes. CONCLUSIONS A multiancestral PRS for asthma based on Bayesian posterior genomic effect sizes identifies increased odds of pediatric asthma.
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Affiliation(s)
- Bahram Namjou
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, Ohio 45229
| | - Michael Lape
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, Ohio 45229
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229
| | - Edyta Malolepsza
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts 02142
| | - Stanley B. DeVore
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, Ohio 45229
- Division of Asthma Research, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229
| | - Matthew T. Weirauch
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, Ohio 45229
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229
- Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229
| | - Ozan Dikilitas
- Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota 55905
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota 55905
| | - Gail P. Jarvik
- Departments of Medicine (Division of Medical Genetics) and Genome Sciences, University of Washington Medical Center, Seattle, Washington 98195
| | - Krzysztof Kiryluk
- Department of Medicine, Division of Nephrology, College of Physicians and Surgeons, Columbia University, New York, New York 10032
| | - Iftikhar J. Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota 55905
| | - Cong Liu
- Department of Biomedical Informatics, Columbia University, New York, New York 10032
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611
| | | | - Jordan W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts 02115
| | - Theresa L. Walunas
- Division of General Internal Medicine and Geriatrics, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611
| | - John Connolly
- Center for Applied Genomics, Children’s Hospital of Philadelphia, Department of Pediatrics, Philadelphia, Pennsylvania 19104
| | - Patrick Sleiman
- Center for Applied Genomics, Children’s Hospital of Philadelphia, Department of Pediatrics, Philadelphia, Pennsylvania 19104
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Tesfaye B. Mersha
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, Ohio 45229
- Division of Asthma Research, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229
| | - Frank D Mentch
- Center for Applied Genomics, Children’s Hospital of Philadelphia, Department of Pediatrics, Philadelphia, Pennsylvania 19104
| | - Hakon Hakonarson
- Center for Applied Genomics, Children’s Hospital of Philadelphia, Department of Pediatrics, Philadelphia, Pennsylvania 19104
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Cynthia A. Prows
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, Ohio 45229
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229
- Department of Patient Services, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229
| | - Jocelyn M. Biagini
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, Ohio 45229
- Division of Asthma Research, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229
| | - Gurjit K. Khurana Hershey
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, Ohio 45229
- Division of Asthma Research, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229
- Division of Allergy & Immunology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229
| | - Lisa J. Martin
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, Ohio 45229
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229
| | - Leah Kottyan
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, Ohio 45229
- Division of Allergy & Immunology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229
| | - The eMERGE Network
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892
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14
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Poulton R, Guiney H, Ramrakha S, Moffitt TE. The Dunedin study after half a century: reflections on the past, and course for the future. J R Soc N Z 2022. [DOI: 10.1080/03036758.2022.2114508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, Division of Sciences, University of Otago, Dunedin, New Zealand
| | - Hayley Guiney
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, Division of Sciences, University of Otago, Dunedin, New Zealand
| | - Sandhya Ramrakha
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, Division of Sciences, University of Otago, Dunedin, New Zealand
| | - Terrie E. Moffitt
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- SGDP Centre, Kings College London, London, UK
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15
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Nur Husna SM, Md Shukri N, Mohd Ashari NS, Wong KK. IL-4/IL-13 axis as therapeutic targets in allergic rhinitis and asthma. PeerJ 2022; 10:e13444. [PMID: 35663523 PMCID: PMC9161813 DOI: 10.7717/peerj.13444] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 04/25/2022] [Indexed: 01/14/2023] Open
Abstract
Allergic rhinitis (AR) is a common disorder of the upper airway, while asthma is a disease affecting the lower airway and both diseases are usually comorbid. Interleukin (IL)-4 and IL-13 are critical cytokines in the induction of the pathogenic Th2 responses in AR and asthma. Targeting the IL-4/IL-13 axis at various levels of its signaling pathway has emerged as promising targeted therapy in both AR and asthma patient populations. In this review, we discuss the biological characteristics of IL-4 and IL-13, their signaling pathways, and therapeutic antibodies against each cytokine as well as their receptors. In particular, the pleiotropic roles of IL-4 and IL-13 in orchestrating Th2 responses in AR and asthma patients indicate that dual IL-4/IL-13 blockade is a promising therapeutic strategy for both diseases.
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Affiliation(s)
- Siti Muhamad Nur Husna
- Department of Immunology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia
| | - Norasnieda Md Shukri
- Department of Otorhinolaryngology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia
| | - Noor Suryani Mohd Ashari
- Department of Immunology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia
| | - Kah Keng Wong
- Department of Immunology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia
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16
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Development and validation of an RNA-seq-based transcriptomic risk score for asthma. Sci Rep 2022; 12:8643. [PMID: 35606385 PMCID: PMC9126925 DOI: 10.1038/s41598-022-12199-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 05/04/2022] [Indexed: 11/30/2022] Open
Abstract
Recent progress in RNA sequencing (RNA-seq) allows us to explore whole-genome gene expression profiles and to develop predictive model for disease risk. The objective of this study was to develop and validate an RNA-seq-based transcriptomic risk score (RSRS) for disease risk prediction that can simultaneously accommodate demographic information. We analyzed RNA-seq gene expression data from 441 asthmatic and 254 non-asthmatic samples. Logistic least absolute shrinkage and selection operator (Lasso) regression analysis in the training set identified 73 differentially expressed genes (DEG) to form a weighted RSRS that discriminated asthmatics from healthy subjects with area under the curve (AUC) of 0.80 in the testing set after adjustment for age and gender. The 73-gene RSRS was validated in three independent RNA-seq datasets and achieved AUCs of 0.70, 0.77 and 0.60, respectively. To explore their biological and molecular functions in asthma phenotype, we examined the 73 genes by enrichment pathway analysis and found that these genes were significantly (p < 0.0001) enriched for DNA replication, recombination, and repair, cell-to-cell signaling and interaction, and eumelanin biosynthesis and developmental disorder. Further in-silico analyses of the 73 genes using Connectivity map shows that drugs (mepacrine, dactolisib) and genetic perturbagens (PAK1, GSR, RBM15 and TNFRSF12A) were identified and could potentially be repurposed for treating asthma. These findings show the promise for RNA-seq risk scores to stratify and predict disease risk.
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17
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Bourassa KJ, Moffitt TE, Ambler A, Hariri AR, Harrington H, Houts RM, Ireland D, Knodt A, Poulton R, Ramrakha S, Caspi A. Association of Treatable Health Conditions During Adolescence With Accelerated Aging at Midlife. JAMA Pediatr 2022; 176:392-399. [PMID: 35188538 PMCID: PMC8861897 DOI: 10.1001/jamapediatrics.2021.6417] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
IMPORTANCE Biological aging is a distinct construct from health; however, people who age quickly are more likely to experience poor health. Identifying pediatric health conditions associated with accelerated aging could help develop treatment approaches to slow midlife aging and prevent poor health in later life. OBJECTIVE To examine the association between 4 treatable health conditions in adolescence and accelerated aging at midlife. DESIGN, SETTING, AND PARTICIPANTS This cohort study analyzed data from participants in the Dunedin Study, a longitudinal investigation of health and behavior among a birth cohort born between April 1, 1972, and March 31, 1973, in Dunedin, New Zealand, and followed up until age 45 years. Participants underwent an assessment at age 45 years and had data for at least 1 adolescent health condition (asthma, smoking, obesity, and psychological disorders) and outcome measure (pace of aging, gait speed, brain age, and facial age). Data analysis was performed from February 11 to September 27, 2021. EXPOSURES Asthma, cigarette smoking, obesity, and psychological disorders were assessed at age 11, 13, and 15 years. MAIN OUTCOMES AND MEASURES The outcome was a midlife aging factor composite score comprising 4 measures of biological aging: pace of aging, gait speed, brain age (specifically, BrainAGE score), and facial age. RESULTS A total of 910 participants (459 men [50.4%]) met the inclusion criteria, including an assessment at age 45 years. Participants who had smoked daily (0.61 [95% CI, 0.43-0.79] SD units), had obesity (0.82 [95% CI, 0.59-1.06] SD units), or had a psychological disorder diagnosis (0.43 [95% CI, 0.29-0.56] SD units) during adolescence were biologically older at midlife compared with participants without these conditions. Participants with asthma were not biologically older at midlife (0.02 [95% CI, -0.14 to 0.19] SD units) compared with those without asthma. These results remained unchanged after adjusting for childhood risk factors such as poor health, socioeconomic disadvantage, and adverse experiences. CONCLUSIONS AND RELEVANCE This study found that adolescent smoking, obesity, and psychological disorder diagnoses were associated with older biological age at midlife. These health conditions could be treated during adolescence to reduce the risk of accelerated biological aging later in life.
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Affiliation(s)
- Kyle J. Bourassa
- Center for the Study of Aging and Human Development, Duke University Medical Center, Durham, North Carolina,Geriatric Research, Education, and Clinical Center, Veterans Affairs Durham Healthcare System, Durham, North Carolina,Department of Psychology and Neuroscience, Duke University, Durham, North Carolina
| | - Terrie E. Moffitt
- Center for the Study of Aging and Human Development, Duke University Medical Center, Durham, North Carolina,Department of Psychology and Neuroscience, Duke University, Durham, North Carolina,Institute of Psychiatry, King’s College London, London, United Kingdom,Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina,Center for Genomic and Computational Biology, Duke University, Durham, North Carolina
| | - Antony Ambler
- Institute of Psychiatry, King’s College London, London, United Kingdom
| | - Ahmad R. Hariri
- Department of Psychology and Neuroscience, Duke University, Durham, North Carolina
| | - HonaLee Harrington
- Department of Psychology and Neuroscience, Duke University, Durham, North Carolina
| | - Renate M. Houts
- Department of Psychology and Neuroscience, Duke University, Durham, North Carolina
| | - David Ireland
- Department of Psychology, University of Otago, Otago, New Zealand
| | - Annchen Knodt
- Department of Psychology and Neuroscience, Duke University, Durham, North Carolina
| | - Richie Poulton
- Department of Psychology, University of Otago, Otago, New Zealand
| | - Sandhya Ramrakha
- Department of Psychology, University of Otago, Otago, New Zealand
| | - Avshalom Caspi
- Center for the Study of Aging and Human Development, Duke University Medical Center, Durham, North Carolina,Department of Psychology and Neuroscience, Duke University, Durham, North Carolina,Institute of Psychiatry, King’s College London, London, United Kingdom,Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina,Center for Genomic and Computational Biology, Duke University, Durham, North Carolina
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18
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Busse WW, Melén E, Menzies-Gow AN. Holy Grail: the journey towards disease modification in asthma. Eur Respir Rev 2022; 31:31/163/210183. [PMID: 35197266 PMCID: PMC9488532 DOI: 10.1183/16000617.0183-2021] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 11/28/2021] [Indexed: 12/12/2022] Open
Abstract
At present, there is no cure for asthma, and treatment typically involves therapies that prevent or reduce asthma symptoms, without modifying the underlying disease. A “disease-modifying” treatment can be classed as able to address the pathogenesis of a disease, preventing progression or leading to a long-term reduction in symptoms. Such therapies have been investigated and approved in other indications, e.g. rheumatoid arthritis and immunoglobulin E-mediated allergic disease. Asthma's heterogeneous nature has made the discovery of similar therapies in asthma more difficult, although novel therapies (e.g. biologics) may have the potential to exhibit disease-modifying properties. To investigate the disease-modifying potential of a treatment, study design considerations can be made, including: appropriate end-point selection, length of trial, age of study population (key differences between adults/children in physiology, pathology and drug metabolism) and comorbidities in the patient population. Potential future focus areas for disease-modifying treatments in asthma include early assessments (e.g. to detect patterns of remodelling) and interventions for patients genetically susceptible to asthma, interventions to prevent virally induced asthma and therapies to promote a healthy microbiome. This review explores the pathophysiology of asthma, the disease-modifying potential of current asthma therapies and the direction future research may take to achieve full disease remission or prevention. Asthma is a complex, heterogeneous disease, which currently has no cure; this review explores the disease-modifying potential of asthma therapies and the direction future research may take to achieve disease remission or prevention.https://bit.ly/31AxYou
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Affiliation(s)
- William W Busse
- Dept of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Erik Melén
- Dept of Clinical Science and Education Södersjukhuset, Karolinska Institutet and Sachs' Children's Hospital, Stockholm, Sweden
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19
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Leffa DT, Horta B, Barros FC, Menezes AMB, Martins-Silva T, Hutz MH, Bau CHD, Grevet EH, Rohde LA, Tovo-Rodrigues L. Association between Polygenic Risk Scores for ADHD and Asthma: A Birth Cohort Investigation. J Atten Disord 2022; 26:685-695. [PMID: 34078169 DOI: 10.1177/10870547211020111] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
OBJECTIVE Shared genetic mechanisms have been hypothesized to explain the comorbidity between ADHD and asthma. To evaluate their genetic overlap, we relied on data from the 1982 Pelotas birth cohort to test the association between polygenic risk scores (PRSs) for ADHD (ADHD-PRSs) and asthma, and PRSs for asthma (asthma-PRSs) and ADHD. METHOD We analyzed data collected at birth, 2, 22, and 30 years from 3,574 individuals. RESULTS Subjects with ADHD had increased risk of having asthma (OR 1.92, 95% CI 1.01-3.66). The association was stronger for females. Our results showed no evidence of association between ADHD-PRSs and asthma or asthma-PRSs and ADHD. However, an exploratory analysis suggested that adult ADHD might be genetically associated with asthma. CONCLUSION Our results do not support a shared genetic background between both conditions. Findings should be viewed in light of important limitations, particularly the sample size and the self-reported asthma diagnosis. Studies in larger datasets are required to better explore the genetic overlap between adult ADHD and asthma.
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Affiliation(s)
- Douglas Teixeira Leffa
- Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | | | | | | | | | - Mara Helena Hutz
- Department of Genetics, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Claiton Henrique Dotto Bau
- Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,Department of Genetics, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Eugenio Horacio Grevet
- Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Luis Augusto Rohde
- Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,National Institute of Developmental Psychiatry, Brazil
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20
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Kothalawala DM, Kadalayil L, Curtin JA, Murray CS, Simpson A, Custovic A, Tapper WJ, Arshad SH, Rezwan FI, Holloway JW. Integration of Genomic Risk Scores to Improve the Prediction of Childhood Asthma Diagnosis. J Pers Med 2022; 12:75. [PMID: 35055391 PMCID: PMC8777841 DOI: 10.3390/jpm12010075] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 12/18/2021] [Accepted: 12/31/2021] [Indexed: 01/24/2023] Open
Abstract
Genome-wide and epigenome-wide association studies have identified genetic variants and differentially methylated nucleotides associated with childhood asthma. Incorporation of such genomic data may improve performance of childhood asthma prediction models which use phenotypic and environmental data. Using genome-wide genotype and methylation data at birth from the Isle of Wight Birth Cohort (n = 1456), a polygenic risk score (PRS), and newborn (nMRS) and childhood (cMRS) methylation risk scores, were developed to predict childhood asthma diagnosis. Each risk score was integrated with two previously published childhood asthma prediction models (CAPE and CAPP) and were validated in the Manchester Asthma and Allergy Study. Individually, the genomic risk scores demonstrated modest-to-moderate discriminative performance (area under the receiver operating characteristic curve, AUC: PRS = 0.64, nMRS = 0.55, cMRS = 0.54), and their integration only marginally improved the performance of the CAPE (AUC: 0.75 vs. 0.71) and CAPP models (AUC: 0.84 vs. 0.82). The limited predictive performance of each genomic risk score individually and their inability to substantially improve upon the performance of the CAPE and CAPP models suggests that genetic and epigenetic predictors of the broad phenotype of asthma are unlikely to have clinical utility. Hence, further studies predicting specific asthma endotypes are warranted.
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Affiliation(s)
- Dilini M. Kothalawala
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK; (D.M.K.); (L.K.); (W.J.T.); (F.I.R.)
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton SO16 6YD, UK;
| | - Latha Kadalayil
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK; (D.M.K.); (L.K.); (W.J.T.); (F.I.R.)
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK
| | - John A. Curtin
- Division of Infection, Immunity, and Respiratory Medicine, School of Biological Sciences, Manchester University Hospital NHS Foundation Trust, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9PL, UK; (J.A.C.); (C.S.M.); (A.S.)
| | - Clare S. Murray
- Division of Infection, Immunity, and Respiratory Medicine, School of Biological Sciences, Manchester University Hospital NHS Foundation Trust, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9PL, UK; (J.A.C.); (C.S.M.); (A.S.)
| | - Angela Simpson
- Division of Infection, Immunity, and Respiratory Medicine, School of Biological Sciences, Manchester University Hospital NHS Foundation Trust, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9PL, UK; (J.A.C.); (C.S.M.); (A.S.)
| | - Adnan Custovic
- National Heart and Lung Institute, Imperial College of Science, Technology, and Medicine, London SW3 6LY, UK;
| | - William J. Tapper
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK; (D.M.K.); (L.K.); (W.J.T.); (F.I.R.)
| | - S. Hasan Arshad
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton SO16 6YD, UK;
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK
- The David Hide Asthma and Allergy Research Centre, St. Mary’s Hospital, Isle of Wight PO30 5TG, UK
| | - Faisal I. Rezwan
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK; (D.M.K.); (L.K.); (W.J.T.); (F.I.R.)
- Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK
| | - John W. Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK; (D.M.K.); (L.K.); (W.J.T.); (F.I.R.)
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton SO16 6YD, UK;
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21
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Li B, Sun WX, Zhang WY, Zheng Y, Qiao L, Hu YM, Li WQ, Liu D, Leng B, Liu JR, Jiang XF, Zhang Y. The Transcriptome Characteristics of Severe Asthma From the Prospect of Co-Expressed Gene Modules. Front Genet 2021; 12:765400. [PMID: 34759961 PMCID: PMC8573341 DOI: 10.3389/fgene.2021.765400] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 09/29/2021] [Indexed: 01/08/2023] Open
Abstract
Rationale: Severe asthma is a heterogeneous disease with multiple molecular mechanisms. Gene expression studies of asthmatic bronchial epithelial cells have provided biological insights and underscored possible pathological mechanisms; however, the molecular basis in severe asthma is still poorly understood. Objective: The objective of this study was to identify the features of asthma and uncover the molecular basis of severe asthma in distinct molecular phenotype. Methods: The k-means clustering and differentially expressed genes (DEGs) were performed in 129 asthma individuals in the Severe Asthma Research Program. The DEG profiles were analyzed by weighted gene co-expression network analysis (WGCNA), and the expression value of each gene module in each individual was annotated by gene set variation analysis (GSVA). Results: Expression analysis defined five stable asthma subtype (AS): 1) Phagocytosis-Th2, 2) Normal-like, 3) Neutrophils, 4) Mucin-Th2, and 5) Interferon-Th1 and 15 co-expressed gene modules. “Phagocytosis-Th2” enriched for receptor-mediated endocytosis, upregulation of Toll-like receptor signal, and myeloid leukocyte activation. “Normal-like” is most similar to normal samples. “Mucin-Th2” preferentially expressed genes involved in O-glycan biosynthesis and unfolded protein response. “Interferon-Th1” displayed upregulation of genes that regulate networks involved in cell cycle, IFN gamma response, and CD8 TCR. The dysregulation of neural signal, REDOX, apoptosis, and O-glycan process were related to the severity of asthma. In non-TH2 subtype (Neutrophils and Interferon-Th1) with severe asthma individuals, the neural signals and IL26-related co-expression module were dysregulated more significantly compared to that in non-severe asthma. These data infer differences in the molecular evolution of asthma subtypes and identify opportunities for therapeutic development. Conclusions: Asthma is a heterogeneous disease. The co-expression analysis provides new insights into the biological mechanisms related to its phenotypes and the severity.
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Affiliation(s)
- Bin Li
- Department of Clinical Laboratory, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.,School of Life Science and Technology, Computational Biology Research Center, Harbin Institute of Technology, Harbin, China.,Heilongjiang Longwei Precision Medical Laboratory Center, Harbin, China
| | - Wen-Xuan Sun
- Department of Clinical Laboratory, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Wan-Ying Zhang
- Department of Clinical Laboratory, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.,Heilongjiang Longwei Precision Medical Laboratory Center, Harbin, China
| | - Ye Zheng
- Department of Clinical Laboratory, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lu Qiao
- Department of Clinical Laboratory, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yue-Ming Hu
- Department of Clinical Laboratory, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Wei-Qiang Li
- Department of Clinical Laboratory, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Di Liu
- Department of Clinical Laboratory, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Bing Leng
- Department of Clinical Laboratory, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Jia-Ren Liu
- Department of Clinical Laboratory, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.,Heilongjiang Longwei Precision Medical Laboratory Center, Harbin, China
| | - Xiao-Feng Jiang
- Department of Clinical Laboratory, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yan Zhang
- School of Life Science and Technology, Computational Biology Research Center, Harbin Institute of Technology, Harbin, China
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22
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Sordillo JE, Lutz SM, Jorgenson E, Iribarren C, McGeachie M, Dahlin A, Tantisira K, Kelly R, Lasky-Su J, Sakornsakolpat P, Moll M, Cho MH, Wu AC. A polygenic risk score for asthma in a large racially diverse population. Clin Exp Allergy 2021; 51:1410-1420. [PMID: 34459047 DOI: 10.1111/cea.14007] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 03/02/2021] [Accepted: 08/27/2021] [Indexed: 02/05/2023]
Abstract
BACKGROUND Polygenic risk scores (PRSs) will have important utility for asthma and other chronic diseases as a tool for predicting disease incidence and subphenotypes. OBJECTIVE We utilized findings from a large multiancestry GWAS of asthma to compute a PRS for asthma with relevance for racially diverse populations. METHODS We derived two PRSs for asthma using a standard approach (based on genome-wide significant variants) and a lasso sum regression approach (allowing all genetic variants to potentially contribute). We used data from the racially diverse Kaiser Permanente GERA cohort (68 638 non-Hispanic Whites, 5874 Hispanics, 6870 Asians and 2760 Blacks). Race was self-reported by questionnaire. RESULTS For the standard PRS, non-Hispanic Whites showed the highest odds ratio for a standard deviation increase in PRS for asthma (OR = 1.16 (95% CI 1.14-1.18)). The standard PRS was also associated with asthma in Hispanic (OR = 1.12 (95% CI 1.05-1.19)) and Asian (OR = 1.10 (95% CI 1.04-1.17)) subjects, with a trend towards increased risk in Blacks (OR = 1.05 (95% CI 0.97-1.15)). We detected an interaction by sex, with men showing a higher risk of asthma with an increase in PRS as compared to women. The lasso sum regression-derived PRS showed stronger associations with asthma in non-Hispanic White subjects (OR = 1.20 (95% CI 1.18-1.23)), Hispanics (OR = 1.17 (95% 1.10-1.26)), Asians (OR = 1.18 (95% CI 1.10-1.27)) and Blacks (OR = 1.10 (95% CI 0.99-1.22)). CONCLUSION Polygenic risk scores across multiple racial/ethnic groups were associated with increased asthma risk, suggesting that PRSs have potential as a tool for predicting disease development.
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Affiliation(s)
- Joanne E Sordillo
- PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care, Boston, Massachusetts, USA
| | - Sharon M Lutz
- PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care, Boston, Massachusetts, USA
| | - Eric Jorgenson
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
| | - Carlos Iribarren
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
| | - Michael McGeachie
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Amber Dahlin
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Kelan Tantisira
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Rachel Kelly
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Phuwanat Sakornsakolpat
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Matthew Moll
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Ann Chen Wu
- PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care, Boston, Massachusetts, USA
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23
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Zuluaga G, Sarmiento I, Pimentel J, Correal C, Andersson N. [Cultivation and use of medicinal plants and association with reporting of childhood asthma: A case-control study in the Bogotá savanna]. Medwave 2021; 21:e8196. [PMID: 34037578 DOI: 10.5867/medwave.2021.04.8196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 04/13/2021] [Indexed: 11/27/2022] Open
Abstract
Introduction The prevalence of childhood asthma has increased in recent years. The World Health Organization has called for conducting research exploring the role of traditional medicine and medicinal plants in respiratory disease control. Objective To identify the relationship between the prevalence of childhood asthma and traditional care of the respiratory system, including cultivation and use of medicinal plants. Methods We conducted an observational, analytic, case-control study that included children 2 to 14 years old who used official health services in eight municipalities near Bogota between 2014 and 2015. Cases were children diagnosed with asthma. We randomly selected the controls among the remaining patients of the same healthcare facilities. We applied an 18-question survey. The Mantel-Haenszel procedure identified significant associations using 95% confidence intervals. Results We surveyed the caretakers of 97 cases and 279 controls in eight municipalities. Some 23.4% (88/376) and 37.9% (142/375) reported using traditional remedies for fever control and common cold management, respectively. 8.8% (33/376) reported following traditional care during a common cold, 30.4% (114/375) reported growing medicinal plants at home, and 45% (166/369) reported using medicinal plants for health purposes in their household. Multivariate analysis showed that having and using medicinal plants at home is associated with a lower reporting of asthma (odds ratio 0.49; 95% confidence interval: 0.25 to 0.99). Conclusions Cultivating and using medicinal plants at home is associated with a lower reporting of childhood asthma. Researchers should consider the therapeutic, environmental, and cultural properties of medicinal plants to prevent respiratory diseases.
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Affiliation(s)
- Germán Zuluaga
- Grupo de Estudios en Sistemas Tradicionales de Salud, Escuela de Medicina y Ciencias de la Salud, Universidad del Rosario, Bogotá, Colombia. Dirección: Calle 12 # 3A 21, Cota, Cundinamarca, Colombia. . ORCID: 0000-0001-5715-9133
| | - Iván Sarmiento
- Grupo de Estudios en Sistemas Tradicionales de Salud, Escuela de Medicina y Ciencias de la Salud, Universidad del Rosario, Bogotá, Colombia. ORCID: 0000-0003-2871-1464
| | - Juan Pimentel
- Grupo de Estudios en Sistemas Tradicionales de Salud, Escuela de Medicina y Ciencias de la Salud, Universidad del Rosario, Bogotá, Colombia. ORCID: 0000-0002-6842-3064
| | - Camilo Correal
- Universidad de La Sabana, Departamento de Medicina Familiar y Salud Pública, Chía, Colombia. ORCID: 0000-0002-4252-326X
| | - Neil Andersson
- CIET-PRAM, Departamento de Medicina Familiar, Universidad de McGill, Montreal, Quebec, Canadá. ORCID: 0000-0003-1121-6918
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The intersect of genetics, environment, and microbiota in asthma-perspectives and challenges. J Allergy Clin Immunol 2021; 147:781-793. [PMID: 33678251 DOI: 10.1016/j.jaci.2020.08.026] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 07/07/2020] [Accepted: 08/05/2020] [Indexed: 02/07/2023]
Abstract
In asthma, a significant portion of the interaction between genetics and environment occurs through microbiota. The proposed mechanisms behind this interaction are complex and at times contradictory. This review covers recent developments in our understanding of this interaction: the "microbial hypothesis" and the "farm effect"; the role of endotoxin and genetic variation in pattern recognition systems; the interaction with allergen exposure; the additional involvement of host gut and airway microbiota; the role of viral respiratory infections in interaction with the 17q21 and CDHR3 genetic loci; and the importance of in utero and early-life timing of exposures. We propose a unified framework for understanding how all these phenomena interact to drive asthma pathogenesis. Finally, we point out some future challenges for continued research in this field, in particular the need for multiomic integration, as well as the potential utility of asthma endotyping.
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Zhu Z, Hasegawa K, Camargo CA, Liang L. Investigating asthma heterogeneity through shared and distinct genetics: Insights from genome-wide cross-trait analysis. J Allergy Clin Immunol 2020; 147:796-807. [PMID: 32693092 PMCID: PMC7368660 DOI: 10.1016/j.jaci.2020.07.004] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 07/03/2020] [Accepted: 07/09/2020] [Indexed: 12/17/2022]
Abstract
Asthma is a heterogeneous respiratory disease reflecting distinct pathobiologic mechanisms. These mechanisms are based, at least partly, on different genetic factors shared by many other conditions, such as allergic diseases and obesity. Investigating the shared genetic effects enables better understanding of the mechanisms of phenotypic correlations and is less subject to confounding by environmental factors. The increasing availability of large-scale genome-wide association study (GWAS) for asthma has enabled researchers to examine the genetic contributions to the epidemiologic associations between asthma subtypes and those between coexisting diseases and/or traits and asthma. Studies have found not only shared but also distinct genetic components between asthma subtypes, indicating that the heterogeneity is related to distinct genetics. This review summarizes a recently compiled analytic approach-genome-wide cross-trait analysis-to determine shared and distinct genetic architecture. The genome-wide cross-trait analysis features in several analytic aspects: genetic correlation, cross-trait meta-analysis, Mendelian randomization, polygenic risk score, and functional analysis. In this article, we discuss in detail the scientific goals that can be achieved by these analyses, their advantages, and their limitations. We also make recommendations for future directions: (1) ethnicity-specific asthma GWASs and (2) application of cross-trait methods to multiomics data to dissect the heritability found in GWASs. Finally, these analytic approaches are also applicable to complex and heterogeneous traits beyond asthma.
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Affiliation(s)
- Zhaozhong Zhu
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Mass; Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Mass; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Mass
| | - Kohei Hasegawa
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
| | - Carlos A Camargo
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Mass; Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
| | - Liming Liang
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Mass; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Mass.
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Age-of-onset information helps identify 76 genetic variants associated with allergic disease. PLoS Genet 2020; 16:e1008725. [PMID: 32603359 PMCID: PMC7367489 DOI: 10.1371/journal.pgen.1008725] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 07/17/2020] [Accepted: 03/19/2020] [Indexed: 12/18/2022] Open
Abstract
Risk factors that contribute to inter-individual differences in the age-of-onset of allergic diseases are poorly understood. The aim of this study was to identify genetic risk variants associated with the age at which symptoms of allergic disease first develop, considering information from asthma, hay fever and eczema. Self-reported age-of-onset information was available for 117,130 genotyped individuals of European ancestry from the UK Biobank study. For each individual, we identified the earliest age at which asthma, hay fever and/or eczema was first diagnosed and performed a genome-wide association study (GWAS) of this combined age-of-onset phenotype. We identified 50 variants with a significant independent association (P<3x10-8) with age-of-onset. Forty-five variants had comparable effects on the onset of the three individual diseases and 38 were also associated with allergic disease case-control status in an independent study (n = 222,484). We observed a strong negative genetic correlation between age-of-onset and case-control status of allergic disease (rg = -0.63, P = 4.5x10-61), indicating that cases with early disease onset have a greater burden of allergy risk alleles than those with late disease onset. Subsequently, a multivariate GWAS of age-of-onset and case-control status identified a further 26 associations that were missed by the univariate analyses of age-of-onset or case-control status only. Collectively, of the 76 variants identified, 18 represent novel associations for allergic disease. We identified 81 likely target genes of the 76 associated variants based on information from expression quantitative trait loci (eQTL) and non-synonymous variants, of which we highlight ADAM15, FOSL2, TRIM8, BMPR2, CD200R1, PRKCQ, NOD2, SMAD4, ABCA7 and UBE2L3. Our results support the notion that early and late onset allergic disease have partly distinct genetic architectures, potentially explaining known differences in pathophysiology between individuals. So far, genetic studies of allergic disease have investigated the presence of the disease rather than the age at which the first allergic symptoms develop. We aimed to identify genetic risk variants associated with the age at which symptoms of allergic disease first develop, considering information from asthma, hay fever and eczema by examining 117,130 genotyped individuals of European ancestry from the UK Biobank study. We identified 50 variants with a significant independent association (P<3x10-8) with age-of-onset. Forty-five variants had comparable effects on the onset of the three individual diseases and 38 were also associated with allergic disease case-control status in an independent study (n = 222,484). We then performed a multivariate GWAS of age-of-onset and case-control status identified a further 26 associations that were missed by the univariate analyses of age-of-onset or case-control status only. 18 of 76 variants identified represent novel associations for allergic disease. We identified 81 likely target genes of the 76 genetic variants, including ADAM15, FOSL2, TRIM8, BMPR2, CD200R1, PRKCQ, NOD2, SMAD4, ABCA7 and UBE2L3. Our results support the notion that early and late onset allergic disease have partly distinct genetic architectures, potentially explaining known differences in pathophysiology between individuals.
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Abstract
PURPOSE OF REVIEW Allergic diseases are prototypic examples for gene × environment-wide interactions. This review considers the current evidence for genetic and epigenetic mechanisms in allergic diseases and highlights barriers and facilitators for the implementation of these novel tools both for research and clinical practice. RECENT FINDINGS The value of whole-genome sequencing studies and the use of polygenic risk score analysis in homogeneous well characterized populations are currently being tested. Epigenetic mechanisms are known to play a crucial role in the pathogenesis of allergic disorders, especially through mediating the effects of the environmental factors, well recognized risk modifiers. There is emerging evidence for the immune-modulatory role of probiotics through epigenetic changes. Direct or indirect targeting of epigenetic mechanisms affect expression of the genes favouring the development of allergic diseases and can improve tissue biology. The ability to specifically edit the epigenome, especially using the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 technology, holds the promise of enhancing understanding of how epigenetic modifications function and enabling manipulation of cell phenotype for research or therapeutic purposes. SUMMARY Additional research in the role of genetic and epigenetic mechanisms in relation to allergic diseases' endotypes is needed. An international project characterizing the human epigenome in relation to allergic diseases is warranted.
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Pharmacological enrichment of polygenic risk for precision medicine in complex disorders. Sci Rep 2020; 10:879. [PMID: 31964963 PMCID: PMC6972917 DOI: 10.1038/s41598-020-57795-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Accepted: 01/03/2020] [Indexed: 12/29/2022] Open
Abstract
Individuals with complex disorders typically have a heritable burden of common variation that can be expressed as a polygenic risk score (PRS). While PRS has some predictive utility, it lacks the molecular specificity to be directly informative for clinical interventions. We therefore sought to develop a framework to quantify an individual’s common variant enrichment in clinically actionable systems responsive to existing drugs. This was achieved with a metric designated the pharmagenic enrichment score (PES), which we demonstrate for individual SNP profiles in a cohort of cases with schizophrenia. A large proportion of these had elevated PES in one or more of eight clinically actionable gene-sets enriched with schizophrenia associated common variation. Notable candidates targeting these pathways included vitamins, antioxidants, insulin modulating agents, and cholinergic drugs. Interestingly, elevated PES was also observed in individuals with otherwise low common variant burden. The biological saliency of PES profiles were observed directly through their impact on gene expression in a subset of the cohort with matched transcriptomic data, supporting our assertion that this gene-set orientated approach could integrate an individual’s common variant risk to inform personalised interventions, including drug repositioning, for complex disorders such as schizophrenia.
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Reworking GWAS Data to Understand the Role of Nongenetic Factors in MS Etiopathogenesis. Genes (Basel) 2020; 11:genes11010097. [PMID: 31947683 PMCID: PMC7017269 DOI: 10.3390/genes11010097] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 01/03/2020] [Accepted: 01/10/2020] [Indexed: 12/13/2022] Open
Abstract
Genome-wide association studies have identified more than 200 multiple sclerosis (MS)-associated loci across the human genome over the last decade, suggesting complexity in the disease etiology. This complexity poses at least two challenges: the definition of an etiological model including the impact of nongenetic factors, and the clinical translation of genomic data that may be drivers for new druggable targets. We reviewed studies dealing with single genes of interest, to understand how MS-associated single nucleotide polymorphism (SNP) variants affect the expression and the function of those genes. We then surveyed studies on the bioinformatic reworking of genome-wide association studies (GWAS) data, with aggregate analyses of many GWAS loci, each contributing with a small effect to the overall disease predisposition. These investigations uncovered new information, especially when combined with nongenetic factors having possible roles in the disease etiology. In this context, the interactome approach, defined as “modules of genes whose products are known to physically interact with environmental or human factors with plausible relevance for MS pathogenesis”, will be reported in detail. For a future perspective, a polygenic risk score, defined as a cumulative risk derived from aggregating the contributions of many DNA variants associated with a complex trait, may be integrated with data on environmental factors affecting the disease risk or protection.
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Tang HHF, Sly PD, Holt PG, Holt KE, Inouye M. Systems biology and big data in asthma and allergy: recent discoveries and emerging challenges. Eur Respir J 2020; 55:13993003.00844-2019. [PMID: 31619470 DOI: 10.1183/13993003.00844-2019] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 09/12/2019] [Indexed: 12/15/2022]
Abstract
Asthma is a common condition caused by immune and respiratory dysfunction, and it is often linked to allergy. A systems perspective may prove helpful in unravelling the complexity of asthma and allergy. Our aim is to give an overview of systems biology approaches used in allergy and asthma research. Specifically, we describe recent "omic"-level findings, and examine how these findings have been systematically integrated to generate further insight.Current research suggests that allergy is driven by genetic and epigenetic factors, in concert with environmental factors such as microbiome and diet, leading to early-life disturbance in immunological development and disruption of balance within key immuno-inflammatory pathways. Variation in inherited susceptibility and exposures causes heterogeneity in manifestations of asthma and other allergic diseases. Machine learning approaches are being used to explore this heterogeneity, and to probe the pathophysiological patterns or "endotypes" that correlate with subphenotypes of asthma and allergy. Mathematical models are being built based on genomic, transcriptomic and proteomic data to predict or discriminate disease phenotypes, and to describe the biomolecular networks behind asthma.The use of systems biology in allergy and asthma research is rapidly growing, and has so far yielded fruitful results. However, the scale and multidisciplinary nature of this research means that it is accompanied by new challenges. Ultimately, it is hoped that systems medicine, with its integration of omics data into clinical practice, can pave the way to more precise, personalised and effective management of asthma.
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Affiliation(s)
- Howard H F Tang
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia .,Cambridge Baker Systems Genomics Initiative, Dept of Public Health and Primary Care, University of Cambridge, Cambridge, UK.,School of BioSciences, The University of Melbourne, Parkville, Australia
| | - Peter D Sly
- Queensland Children's Medical Research Institute, The University of Queensland, Brisbane, Australia.,Telethon Kids Institute, University of Western Australia, Perth, Australia
| | - Patrick G Holt
- Queensland Children's Medical Research Institute, The University of Queensland, Brisbane, Australia.,Telethon Kids Institute, University of Western Australia, Perth, Australia
| | - Kathryn E Holt
- Dept of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Australia.,London School of Hygiene and Tropical Medicine, London, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia.,Cambridge Baker Systems Genomics Initiative, Dept of Public Health and Primary Care, University of Cambridge, Cambridge, UK.,School of BioSciences, The University of Melbourne, Parkville, Australia.,The Alan Turing Institute, London, UK
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31
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Zhorina Y, Abramovskikh S, Ignatova G, Ploshchanskay O. Analysis of associations of polymorphisms in the genes coding for L4, IL10, IL13 with the development of atopic bronchial asthma and its remission. BULLETIN OF RUSSIAN STATE MEDICAL UNIVERSITY 2019. [DOI: 10.24075/brsmu.2019.067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Bronchial asthma is a multifactorial disease underpinned by chronic inflammation. The atopic phenotype of BA implies the presence of similar molecular mechanisms of pathogenesis between the patients. The aim of this study was to analyze the associations between the development of atopic BA/its remission and the following polymorphisms of interleukin genes: IL4 (rs2243250; C-589T), IL10 (rs1800896; G-1082A; rs1800872; C-592A), and IL13 (rs20541; Arg130Gln). Using allele-specific polymerase chain reaction (PCR), we studied the listed SNPs in the mixed urban sample of patients with BA (n = 53) and the controls (n = 30) residing in South Ural. The analysis revealed that genotype АА of IL10 (rs1800872) occurred more frequently in the control group (23.3%) than in the patients with atopic BA (5.7%) (OR = 0.197; 95% CI [0.047–0.832]; р = 0.031). No differences in genotype frequencies were observed between the patients with atopic BA and the controls for other studied polymorphisms. Our study failed to demonstrate the association of the listed polymorphisms and BA remission.
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Affiliation(s)
- Yu.V. Zhorina
- South Ural State Medical University, Chelyabinsk, Russia
| | | | - G.L. Ignatova
- South Ural State Medical University, Chelyabinsk, Russia
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Weathington N, O’Brien ME, Radder J, Whisenant TC, Bleecker ER, Busse WW, Erzurum SC, Gaston B, Hastie AT, Jarjour NN, Meyers DA, Milosevic J, Moore WC, Tedrow JR, Trudeau JB, Wong HP, Wu W, Kaminski N, Wenzel SE, Modena BD. BAL Cell Gene Expression in Severe Asthma Reveals Mechanisms of Severe Disease and Influences of Medications. Am J Respir Crit Care Med 2019; 200:837-856. [PMID: 31161938 PMCID: PMC6812436 DOI: 10.1164/rccm.201811-2221oc] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 06/03/2019] [Indexed: 01/16/2023] Open
Abstract
Rationale: Gene expression of BAL cells, which samples the cellular milieu within the lower respiratory tract, has not been well studied in severe asthma.Objectives: To identify new biomolecular mechanisms underlying severe asthma by an unbiased, detailed interrogation of global gene expression.Methods: BAL cell expression was profiled in 154 asthma and control subjects. Of these participants, 100 had accompanying airway epithelial cell gene expression. BAL cell expression profiles were related to participant (age, sex, race, and medication) and sample traits (cell proportions), and then severity-related gene expression determined by correlating transcripts and coexpression networks to lung function, emergency department visits or hospitalizations in the last year, medication use, and quality-of-life scores.Measurements and Main Results: Age, sex, race, cell proportions, and medications strongly influenced BAL cell gene expression, but leading severity-related genes could be determined by carefully identifying and accounting for these influences. A BAL cell expression network enriched for cAMP signaling components most differentiated subjects with severe asthma from other subjects. Subsequently, an in vitro cellular model showed this phenomenon was likely caused by a robust upregulation in cAMP-related expression in nonsevere and β-agonist-naive subjects given a β-agonist before cell collection. Interestingly, ELISAs performed on BAL lysates showed protein levels may partly disagree with expression changes.Conclusions: Gene expression in BAL cells is influenced by factors seldomly considered. Notably, β-agonist exposure likely had a strong and immediate impact on cellular gene expression, which may not translate to important disease mechanisms or necessarily match protein levels. Leading severity-related genes were discovered in an unbiased, system-wide analysis, revealing new targets that map to asthma susceptibility loci.
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Affiliation(s)
- Nathaniel Weathington
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Michael E. O’Brien
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Josiah Radder
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Thomas C. Whisenant
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, California
| | - Eugene R. Bleecker
- Division of Genetics, Genomics and Precision Medicine, University of Arizona, Tucson, Arizona
| | - William W. Busse
- Division of Allergy, Pulmonary, and Critical Care Medicine, University of Wisconsin, Madison, Wisconsin
| | - Serpil C. Erzurum
- Lerner Research Institute, Respiratory Institute, Cleveland Clinic, Cleveland, Ohio
| | - Benjamin Gaston
- Division of Pediatric Pulmonary, Allergy and Immunology, Case Western Reserve University and Rainbow Babies Children’s Hospital, Cleveland, Ohio
| | - Annette T. Hastie
- Section on Pulmonary, Critical Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Nizar N. Jarjour
- Division of Allergy, Pulmonary, and Critical Care Medicine, University of Wisconsin, Madison, Wisconsin
| | - Deborah A. Meyers
- Division of Genetics, Genomics and Precision Medicine, University of Arizona, Tucson, Arizona
| | - Jadranka Milosevic
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Wendy C. Moore
- Section on Pulmonary, Critical Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - John R. Tedrow
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - John B. Trudeau
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Hesper P. Wong
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Wei Wu
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Naftali Kaminski
- Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Sally E. Wenzel
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Environmental and Occupational Health, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania; and
| | - Brian D. Modena
- Division of Allergy, National Jewish Hospital, Denver, Colorado
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Melén E, Guerra S, Hallberg J, Jarvis D, Stanojevic S. Linking COPD epidemiology with pediatric asthma care: Implications for the patient and the physician. Pediatr Allergy Immunol 2019; 30:589-597. [PMID: 30968967 DOI: 10.1111/pai.13054] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 03/22/2019] [Indexed: 12/25/2022]
Abstract
What are the implications of a lower than expected forced expiratory volume in one second (FEV1) in childhood on respiratory health later in adulthood? Lung function is known to track with age, and there is evidence from recent epidemiologic studies that impaired lung function early in life is associated with later chronic airflow limitation, or even chronic obstructive pulmonary disease, COPD. This risk seems particularly strong in subjects with persistent and severe forms of childhood asthma. Can we translate findings from longitudinal cohort studies to individual risk predictions and preventive guidelines in our pediatric care? In this review, we discuss the clinical implementations of recent epidemiological respiratory studies and the importance of preserved lung health across the life course. Also, we evaluate available clinical tools, primarily lung function measures, and profiles of risk factors, including biomarkers, that may help identifying children at risk of chronic airway disease in adulthood. We conclude that translating population level results to the individual patient in the pediatric care setting is not straight forward, and that there is a need for studies specifically designed to evaluate performance of prediction of risk profiles for long-term sequelae of childhood asthma and lung function impairment.
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Affiliation(s)
- Erik Melén
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden.,Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.,Sachs' Children and Youth Hospital, Södersjukhuset, Stockholm, Sweden
| | - Stefano Guerra
- Asthma and Airway Disease Research Center, University of Arizona, Tucson, Arizona.,ISGlobal, Barcelona, Spain
| | - Jenny Hallberg
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden.,Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.,Sachs' Children and Youth Hospital, Södersjukhuset, Stockholm, Sweden
| | - Deborah Jarvis
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Sanja Stanojevic
- Translational Medicine, Hospital for Sick Children, Toronto, Ontario, Canada
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Borna E, Nwaru BI, Bjerg A, Mincheva R, Rådinger M, Lundbäck B, Ekerljung L. Changes in the prevalence of asthma and respiratory symptoms in western Sweden between 2008 and 2016. Allergy 2019; 74:1703-1715. [PMID: 31021427 DOI: 10.1111/all.13840] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 02/06/2019] [Accepted: 02/18/2019] [Indexed: 01/22/2023]
Abstract
BACKGROUND Asthma is a common chronic inflammatory disease of the airways, with a noticeable increase in prevalence during the second half of the 20th century. Recent studies assessing the prevalence trends among adults have been inconsistent. We investigated the changes in the prevalence of asthma, respiratory symptoms, and risk factors between 2008 and 2016 in western Sweden. METHODS The West Sweden Asthma Study (WSAS) is a population-based study which started in 2008 (WSAS I) and then repeated in 2016 (WSAS II) in western Sweden. Randomly selected individuals aged 16-75 years (N = 18 087 in 2008 and N = 24 534 in 2016) completed a questionnaire regarding obstructive lung diseases, respiratory symptoms, potential risk factors, and also questions from the GA2 LEN survey. RESULTS The prevalence of reported ever asthma, physician-diagnosed asthma, use of asthma medication, and current asthma increased significantly from 9.6% to 11%, 8.3% to 10%, 8.6% to 9.8%, and 8.1% to 9.1%, respectively, between 2008 and 2016. There were also increases in the prevalence of respiratory symptoms during the same period. The greatest increase occurred in young adults aged 16-25 years. Female gender, allergic rhinitis, obesity, and family history of asthma remained the strongest risk factors for asthma in 2016 as it was in 2008. CONCLUSION There were moderate increases in asthma and respiratory symptoms in adults in western Sweden between 2008 and 2016, the greatest increase occurring in younger adults. The potential risk factors for asthma remained the same during the study period.
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Affiliation(s)
- Eivind Borna
- Department of Internal Medicine and Clinical Nutrition, Krefting Research Centre, Institute of Medicine Sahlgrenska Academy, University of Gothenburg Gothenburg Sweden
| | - Bright I. Nwaru
- Department of Internal Medicine and Clinical Nutrition, Krefting Research Centre, Institute of Medicine Sahlgrenska Academy, University of Gothenburg Gothenburg Sweden
- Wallenberg Center for Molecular and Translational Medicine University of Gothenburg Gothenburg Sweden
| | - Anders Bjerg
- Department of Internal Medicine and Clinical Nutrition, Krefting Research Centre, Institute of Medicine Sahlgrenska Academy, University of Gothenburg Gothenburg Sweden
- Astrid Lindgren Children’s Hospital Karolinska University Hospital Stockholm Sweden
- Department of Women´s and Children´s Health Karolinska Institutet Stockholm Sweden
| | - Roxana Mincheva
- Department of Internal Medicine and Clinical Nutrition, Krefting Research Centre, Institute of Medicine Sahlgrenska Academy, University of Gothenburg Gothenburg Sweden
- Respiratory Medicine & Allergology Department Sahlgrenska University Hospital Gothenburg Sweden
| | - Madeleine Rådinger
- Department of Internal Medicine and Clinical Nutrition, Krefting Research Centre, Institute of Medicine Sahlgrenska Academy, University of Gothenburg Gothenburg Sweden
| | - Bo Lundbäck
- Department of Internal Medicine and Clinical Nutrition, Krefting Research Centre, Institute of Medicine Sahlgrenska Academy, University of Gothenburg Gothenburg Sweden
| | - Linda Ekerljung
- Department of Internal Medicine and Clinical Nutrition, Krefting Research Centre, Institute of Medicine Sahlgrenska Academy, University of Gothenburg Gothenburg Sweden
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Chasioti D, Yan J, Nho K, Saykin AJ. Progress in Polygenic Composite Scores in Alzheimer's and Other Complex Diseases. Trends Genet 2019; 35:371-382. [PMID: 30922659 PMCID: PMC6475476 DOI: 10.1016/j.tig.2019.02.005] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 02/12/2019] [Accepted: 02/22/2019] [Indexed: 11/25/2022]
Abstract
Advances in high-throughput genotyping and next-generation sequencing (NGS) coupled with larger sample sizes brings the realization of precision medicine closer than ever. Polygenic approaches incorporating the aggregate influence of multiple genetic variants can contribute to a better understanding of the genetic architecture of many complex diseases and facilitate patient stratification. This review addresses polygenic concepts, methodological developments, hypotheses, and key issues in study design. Polygenic risk scores (PRSs) have been applied to many complex diseases and here we focus on Alzheimer's disease (AD) as a primary exemplar. This review was designed to serve as a starting point for investigators wishing to use PRSs in their research and those interested in enhancing clinical study designs through enrichment strategies.
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Affiliation(s)
- Danai Chasioti
- Department of BioHealth Informatics, Indiana University-Purdue University, Indianapolis, IN 46202, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
| | - Jingwen Yan
- Department of BioHealth Informatics, Indiana University-Purdue University, Indianapolis, IN 46202, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
| | - Kwangsik Nho
- Department of BioHealth Informatics, Indiana University-Purdue University, Indianapolis, IN 46202, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
| | - Andrew J Saykin
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
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Shared and distinct genetic risk factors for childhood-onset and adult-onset asthma: genome-wide and transcriptome-wide studies. THE LANCET RESPIRATORY MEDICINE 2019; 7:509-522. [PMID: 31036433 DOI: 10.1016/s2213-2600(19)30055-4] [Citation(s) in RCA: 185] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 12/20/2018] [Accepted: 01/07/2019] [Indexed: 02/07/2023]
Abstract
BACKGROUND Childhood-onset and adult-onset asthma differ with respect to severity and comorbidities. Whether they also differ with respect to genetic risk factors has not been previously investigated in large samples. The goals of this study were to identify shared and distinct genetic risk loci for childhood-onset and adult-onset asthma, and to identify the genes that might mediate the effects of associated variation. METHODS We did genome-wide and transcriptome-wide studies, using data from the UK Biobank, in individuals with asthma, including adults with childhood-onset asthma (onset before 12 years of age), adults with adult-onset asthma (onset between 26 and 65 years of age), and adults without asthma (controls; aged older than 38 years). We did genome-wide association studies (GWAS) for childhood-onset asthma and adult-onset asthma each compared with shared controls, and for age of asthma onset in all asthma cases, with a genome-wide significance threshold of p<5 × 10-8. Enrichment studies determined the tissues in which genes at GWAS loci were most highly expressed, and PrediXcan, a transcriptome-wide gene-based test, was used to identify candidate risk genes. FINDINGS Of 376 358 British white individuals from the UK Biobank, we included 37 846 with self-reports of doctor-diagnosed asthma: 9433 adults with childhood-onset asthma; 21 564 adults with adult-onset asthma; and an additional 6849 young adults with asthma with onset between 12 and 25 years of age. For the first and second GWAS analyses, 318 237 individuals older than 38 years without asthma were used as controls. We detected 61 independent asthma loci: 23 were childhood-onset specific, one was adult-onset specific, and 37 were shared. 19 loci were associated with age of asthma onset. The most significant asthma-associated locus was at 17q12 (odds ratio 1·406, 95% CI 1·365-1·448; p=1·45 × 10-111) in the childhood-onset GWAS. Genes at the childhood onset-specific loci were most highly expressed in skin, blood, and small intestine; genes at the adult onset-specific loci were most highly expressed in lung, blood, small intestine, and spleen. PrediXcan identified 113 unique candidate genes at 22 of the 61 GWAS loci. Single-nucleotide polymorphism-based heritability estimates were more than three times larger for childhood-onset asthma (0·327) than for adult-onset disease (0·098). The onset of disease in childhood was associated with additional genes with relatively large effect sizes, with the largest odds ratio observed at the FLG locus at 1q21.3 (1·970, 95% CI 1·823-2·129). INTERPRETATION Genetic risk factors for adult-onset asthma are largely a subset of the genetic risk for childhood-onset asthma but with overall smaller effects, suggesting a greater role for non-genetic risk factors in adult-onset asthma. Combined with gene expression and tissue enrichment patterns, we suggest that the establishment of disease in children is driven more by dysregulated allergy and epithelial barrier function genes, whereas the cause of adult-onset asthma is more lung-centred and environmentally determined, but with immune-mediated mechanisms driving disease progression in both children and adults. FUNDING US National Institutes of Health.
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Genetic Architectures of Childhood- and Adult-Onset Asthma Are Partly Distinct. Am J Hum Genet 2019; 104:665-684. [PMID: 30929738 DOI: 10.1016/j.ajhg.2019.02.022] [Citation(s) in RCA: 148] [Impact Index Per Article: 29.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 02/20/2019] [Indexed: 12/13/2022] Open
Abstract
The extent to which genetic risk factors are shared between childhood-onset (COA) and adult-onset (AOA) asthma has not been estimated. On the basis of data from the UK Biobank study (n = 447,628), we found that the variance in disease liability explained by common variants is higher for COA (onset at ages between 0 and 19 years; h2g = 25.6%) than for AOA (onset at ages between 20 and 60 years; h2g = 10.6%). The genetic correlation (rg) between COA and AOA was 0.67. Variation in age of onset among COA-affected individuals had a low heritability (h2g = 5%), which we confirmed in independent studies and also among AOA-affected individuals. To identify subtype-specific genetic associations, we performed a genome-wide association study (GWAS) in the UK Biobank for COA (13,962 affected individuals) and a separate GWAS for AOA (26,582 affected individuals) by using a common set of 300,671 controls for both studies. We identified 123 independent associations for COA and 56 for AOA (37 overlapped); of these, 98 and 34, respectively, were reproducible in an independent study (n = 262,767). Collectively, 28 associations were not previously reported. For 96 COA-associated variants, including five variants that represent COA-specific risk factors, the risk allele was more common in COA- than in AOA-affected individuals. Conversely, we identified three variants that are stronger risk factors for AOA. Variants associated with obesity and smoking had a stronger contribution to the risk of AOA than to the risk of COA. Lastly, we identified 109 likely target genes of the associated variants, primarily on the basis of correlated expression quantitative trait loci (up to n = 31,684). GWAS informed by age of onset can identify subtype-specific risk variants, which can help us understand differences in pathophysiology between COA and AOA and so can be informative for drug development.
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Colicino S, Munblit D, Minelli C, Custovic A, Cullinan P. Validation of childhood asthma predictive tools: A systematic review. Clin Exp Allergy 2019; 49:410-418. [PMID: 30657220 DOI: 10.1111/cea.13336] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 01/09/2019] [Accepted: 12/06/2018] [Indexed: 02/06/2023]
Abstract
BACKGROUND There is uncertainty about the clinical usefulness of currently available asthma predictive tools. Validation of predictive tools in different populations and clinical settings is an essential requirement for the assessment of their predictive performance, reproducibility and generalizability. We aimed to critically appraise asthma predictive tools which have been validated in external studies. METHODS We searched MEDLINE and EMBASE (1946-2017) for all available childhood asthma prediction models and focused on externally validated predictive tools alongside the studies in which they were originally developed. We excluded non-English and non-original studies. PROSPERO registration number is CRD42016035727. RESULTS From 946 screened papers, eight were included in the review. Statistical approaches for creation of prediction tools included chi-square tests, logistic regression models and the least absolute shrinkage and selection operator. Predictive models were developed and validated in general and high-risk populations. Only three prediction tools were externally validated: the Asthma Predictive Index, the PIAMA and the Leicester asthma prediction tool. A variety of predictors has been tested, but no studies examined the same combination. There was heterogeneity in definition of the primary outcome among development and validation studies, and no objective measurements were used for asthma diagnosis. The performance of tools varied at different ages of outcome assessment. We observed a discrepancy between the development and validation studies in the tools' predictive performance in terms of sensitivity and positive predictive values. CONCLUSIONS Validated asthma predictive tools, reviewed in this paper, provided poor predictive accuracy with performance variation in sensitivity and positive predictive value.
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Affiliation(s)
- Silvia Colicino
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Daniel Munblit
- Department of Paediatrics, Imperial College London, London, UK
- Department of Paediatrics, Faculty of Paediatrics, Sechenov University, Moscow, Russia
- The In-VIVO Global Network, An Affiliate of the World Universities Network, New York, New York
- Solov'ev Research and Clinical Center for Neuropsychiatry, Moscow, Russia
| | - Cosetta Minelli
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Adnan Custovic
- Department of Paediatrics, Imperial College London, London, UK
| | - Paul Cullinan
- National Heart and Lung Institute, Imperial College London, London, UK
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Hernandez-Pacheco N, Pino-Yanes M, Flores C. Genomic Predictors of Asthma Phenotypes and Treatment Response. Front Pediatr 2019; 7:6. [PMID: 30805318 PMCID: PMC6370703 DOI: 10.3389/fped.2019.00006] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Accepted: 01/10/2019] [Indexed: 12/11/2022] Open
Abstract
Asthma is a complex respiratory disease considered as the most common chronic condition in children. A large genetic contribution to asthma susceptibility is predicted by the clustering of asthma and allergy symptoms among relatives and the large disease heritability estimated from twin studies, ranging from 55 to 90%. Genetic basis of asthma has been extensively investigated in the past 40 years using linkage analysis and candidate-gene association studies. However, the development of dense arrays for polymorphism genotyping has enabled the transition toward genome-wide association studies (GWAS), which have led the discovery of several unanticipated asthma genes in the last 11 years. Despite this, currently known risk variants identified using many thousand samples from distinct ethnicities only explain a small proportion of asthma heritability. This review examines the main findings of the last 2 years in genomic studies of asthma using GWAS and admixture mapping studies, as well as the direction of studies fostering integrative perspectives involving omics data. Additionally, we discuss the need for assessing the whole spectrum of genetic variation in association studies of asthma susceptibility, severity, and treatment response in order to further improve our knowledge of asthma genes and predictive biomarkers. Leveraging the individual's genetic information will allow a better understanding of asthma pathogenesis and will facilitate the transition toward a more precise diagnosis and treatment.
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Affiliation(s)
- Natalia Hernandez-Pacheco
- Research Unit, Hospital Universitario N.S. de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain.,Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, Santa Cruz de Tenerife, Spain
| | - Maria Pino-Yanes
- Research Unit, Hospital Universitario N.S. de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain.,Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, Santa Cruz de Tenerife, Spain.,CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
| | - Carlos Flores
- Research Unit, Hospital Universitario N.S. de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain.,CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain.,Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
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40
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Zhu Z, Lee PH, Chaffin MD, Chung W, Loh PR, Lu Q, Christiani DC, Liang L. A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases. Nat Genet 2018; 50:857-864. [PMID: 29785011 PMCID: PMC5980765 DOI: 10.1038/s41588-018-0121-0] [Citation(s) in RCA: 155] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Accepted: 03/27/2018] [Indexed: 01/10/2023]
Abstract
Clinical and epidemiological data suggest that asthma and allergic
diseases are associated and may share a common genetic etiology. We analyzed
genome-wide single-nucleotide polymorphism (SNP) data for asthma and allergic
diseases in 33,593 cases and 76,768 controls of European ancestry from the UK
Biobank. Two publicly available independent genome wide association studies
(GWAS) were used for replication. We have found a strong genome-wide genetic
correlation between asthma and allergic diseases (rg
= 0.75, P =
6.84×10−62). Cross trait analysis identified 38
genome-wide significant loci, including 7 novel shared loci. Computational
analysis showed that shared genetic loci are enriched in immune/inflammatory
systems and tissues with epithelium cells. Our work identifies common genetic
architectures shared between asthma and allergy and will help to advance our
understanding of the molecular mechanisms underlying co-morbid asthma and
allergic diseases.
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Affiliation(s)
- Zhaozhong Zhu
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.,Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.,Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Phil H Lee
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Mark D Chaffin
- Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Wonil Chung
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Po-Ru Loh
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.,Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Quan Lu
- Program in Molecular and Integrative Physiological Sciences, Departments of Environmental Health and Genetics & Complex Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - David C Christiani
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.,Pulmonary and Critical Care Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Liming Liang
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA. .,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
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41
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Lu Y, Pouget JG, Andreassen OA, Djurovic S, Esko T, Hultman CM, Metspalu A, Milani L, Werge T, Sullivan PF. Genetic risk scores and family history as predictors of schizophrenia in Nordic registers. Psychol Med 2018; 48:1201-1208. [PMID: 28942743 PMCID: PMC6953171 DOI: 10.1017/s0033291717002665] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Family history is a long-standing and readily obtainable risk factor for schizophrenia (SCZ). Low-cost genotyping technologies have enabled large genetic studies of SCZ, and the results suggest the utility of genetic risk scores (GRS, direct assessments of inherited common variant risk). Few studies have evaluated family history and GRS simultaneously to ask whether one can explain away the other. METHODS We studied 5959 SCZ cases and 8717 controls from four Nordic countries. All subjects had family history data from national registers and genome-wide genotypes that were processed through the quality control procedures used by the Psychiatric Genomics Consortium. Using external training data, GRS were estimated for SCZ, bipolar disorder (BIP), major depression, autism, educational attainment, and body mass index. Multivariable modeling was used to estimate effect sizes. RESULTS Using harmonized genomic and national register data from Denmark, Estonia, Norway, and Sweden, we confirmed that family history of SCZ and GRS for SCZ and BIP were risk factors for SCZ. In a joint model, the effects of GRS for SCZ and BIP were essentially unchanged, and the effect of family history was attenuated but remained significant. The predictive capacity of a model including GRS and family history neared the minimum for clinical utility. CONCLUSIONS Combining national register data with measured genetic risk factors represents an important investigative approach for psychotic disorders. Our findings suggest the potential clinical utility of combining GRS and family history for early prediction and diagnostic improvements.
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Affiliation(s)
- Y Lu
- Department of Medical Epidemiology and Biostatistics,Karolinska Institutet,SE-17177 Stockholm,Sweden
| | - J G Pouget
- Campbell Family Mental Health Research Institute,Centre for Addiction and Mental Health,Toronto,Ontario,Canada
| | - O A Andreassen
- NORMENT,KG Jebsen Centre for Psychosis Research,Institute of Clinical Medicine,University of Oslo and Oslo University Hospital,0424 Oslo,Norway
| | - S Djurovic
- Department of Medical Genetics,Oslo University Hospital,Oslo,Norway
| | - T Esko
- Estonian Genome Center,University of Tartu,Tartu,Estonia
| | - C M Hultman
- Department of Medical Epidemiology and Biostatistics,Karolinska Institutet,SE-17177 Stockholm,Sweden
| | - A Metspalu
- Estonian Genome Center,University of Tartu,Tartu,Estonia
| | - L Milani
- Estonian Genome Center,University of Tartu,Tartu,Estonia
| | - T Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research,iPSYCH,Denmark
| | - P F Sullivan
- Department of Medical Epidemiology and Biostatistics,Karolinska Institutet,SE-17177 Stockholm,Sweden
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42
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Chronic airway obstruction in a population-based adult asthma cohort: Prevalence, incidence and prognostic factors. Respir Med 2018; 138:115-122. [PMID: 29724382 DOI: 10.1016/j.rmed.2018.03.036] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Revised: 03/06/2018] [Accepted: 03/31/2018] [Indexed: 12/13/2022]
Abstract
BACKGROUND Asthma and COPD may overlap (ACO) but information about incidence and risk factors are lacking. This study aimed to estimate prevalence, incidence and risk factors of chronic airway obstruction (CAO) in a population-based adult asthma cohort. METHODS During 1986-2001 a large population-based asthma cohort was identified (n = 2055, 19-72y). Subsamples have participated in clinical follow-ups during the subsequent years. The entire cohort was invited to a clinical follow-up including interview, spirometry, and blood sampling in 2012-2014 when n = 983 subjects performed adequate spirometry. CAO was defined as post-bronchodilator FEV1/FVC<0.7. RESULTS At study entry, asthmatics with prevalent CAO (11.4%) reported more respiratory symptoms, asthma medication use, and ischemic heart disease than asthmatics without CAO (asthma only). Subjects who developed CAO during follow-up (17.6%; incidence rate of 16/1000/year) had a more rapid FEV1 decline and higher levels of neutrophils than asthma only. Smoking, older age and male sex were independently associated with increased risk for both prevalent and incident CAO, while obesity had a protective effect. CONCLUSIONS In this prospective adult asthma cohort, the majority did not develop CAO. Smoking, older age and male sex were risk factors for prevalent and incident CAO, similar to risk factors described for COPD in the general population.
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43
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Modena BD, Wenzel SE. Consistency of T2 Gene Signatures in Severe Asthma. Key to Effective Treatments or Merely the Tip of the Iceberg? Am J Respir Crit Care Med 2017; 195:411-412. [PMID: 28199166 DOI: 10.1164/rccm.201609-1854ed] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- Brian D Modena
- 1 Department of Molecular and Experimental Medicine The Scripps Research Institute La Jolla, California.,2 Division of Allergy, Asthma, and Immunology Scripps Health San Diego, California and
| | - Sally E Wenzel
- 3 Division of Pulmonary, Allergy, and Critical Care Medicine University of Pittsburgh Asthma Institute at University of Pittsburgh Medical Center Pittsburgh, Pennsylvania
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44
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Abstract
Over the past decade, precision medicine (PM) approaches have received significant investment to create new therapies, learn more about disease processes, and potentially prevent diseases before they arise. However, in many ways, PM investments may come at the expense of existing public health measures that could have a greater impact on population health. As we tackle burgeoning public health concerns, such as obesity, and chronic diseases, such as cancer, it is not clear whether PM is aligned with public health or in conflict with its goals. We summarize the areas of promise demonstrated by PM, discuss the limitations of each of these areas from a population health perspective, and discuss how we can approach PM in a manner that is congruent with the core aims of public health.
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Affiliation(s)
- Ramya Ramaswami
- Imperial College NHS Healthcare Trust, Hammersmith Hospital, London W12 0HS, United Kingdom;
| | - Ronald Bayer
- Mailman School of Public Health, Columbia University, New York, NY 10032, USA;
| | - Sandro Galea
- Boston University School of Public Health, Boston, Massachusetts 02118, USA;
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45
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Modena BD, Bleecker ER, Busse WW, Erzurum SC, Gaston BM, Jarjour NN, Meyers DA, Milosevic J, Tedrow JR, Wu W, Kaminski N, Wenzel SE. Gene Expression Correlated with Severe Asthma Characteristics Reveals Heterogeneous Mechanisms of Severe Disease. Am J Respir Crit Care Med 2017; 195:1449-1463. [PMID: 27984699 DOI: 10.1164/rccm.201607-1407oc] [Citation(s) in RCA: 105] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
RATIONALE Severe asthma (SA) is a heterogeneous disease with multiple molecular mechanisms. Gene expression studies of bronchial epithelial cells in individuals with asthma have provided biological insight and underscored possible mechanistic differences between individuals. OBJECTIVES Identify networks of genes reflective of underlying biological processes that define SA. METHODS Airway epithelial cell gene expression from 155 subjects with asthma and healthy control subjects in the Severe Asthma Research Program was analyzed by weighted gene coexpression network analysis to identify gene networks and profiles associated with SA and its specific characteristics (i.e., pulmonary function tests, quality of life scores, urgent healthcare use, and steroid use), which potentially identified underlying biological processes. A linear model analysis confirmed these findings while adjusting for potential confounders. MEASUREMENTS AND MAIN RESULTS Weighted gene coexpression network analysis constructed 64 gene network modules, including modules corresponding to T1 and T2 inflammation, neuronal function, cilia, epithelial growth, and repair mechanisms. Although no network selectively identified SA, genes in modules linked to epithelial growth and repair and neuronal function were markedly decreased in SA. Several hub genes of the epithelial growth and repair module were found located at the 17q12-21 locus, near a well-known asthma susceptibility locus. T2 genes increased with severity in those treated with corticosteroids but were also elevated in untreated, mild-to-moderate disease compared with healthy control subjects. T1 inflammation, especially when associated with increased T2 gene expression, was elevated in a subgroup of younger patients with SA. CONCLUSIONS In this hypothesis-generating analysis, gene expression networks in relation to asthma severity provided potentially new insight into biological mechanisms associated with the development of SA and its phenotypes.
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Affiliation(s)
- Brian D Modena
- 1 Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh Asthma Institute at UPMC, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.,2 Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, California
| | - Eugene R Bleecker
- 3 Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - William W Busse
- 4 Division of Allergy, Pulmonary, and Critical Care Medicine, University of Wisconsin, Madison, Wisconsin
| | - Serpil C Erzurum
- 5 Department of Pathobiology, Lerner Research Institute, The Cleveland Clinic Foundation, Cleveland, Ohio
| | - Benjamin M Gaston
- 6 Division of Pediatric Pulmonary, Allergy and Immunology, Case Western Reserve University, Cleveland, Ohio.,7 Rainbow Babies and Children's Hospital, Cleveland, Ohio
| | - Nizar N Jarjour
- 4 Division of Allergy, Pulmonary, and Critical Care Medicine, University of Wisconsin, Madison, Wisconsin
| | - Deborah A Meyers
- 3 Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Jadranka Milosevic
- 1 Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh Asthma Institute at UPMC, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - John R Tedrow
- 1 Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh Asthma Institute at UPMC, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Wei Wu
- 8 Lane Center for Computational Biology School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania; and
| | - Naftali Kaminski
- 9 Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Sally E Wenzel
- 1 Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh Asthma Institute at UPMC, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
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Miller RL, Zhang H, Jezioro J, De Planell Saguer M, Lovinsky-Desir S, Liu X, Perzanowski M, Divjan A, Phipatanakul W, Matsui EC. Reduced mouse allergen is associated with epigenetic changes in regulatory genes, but not mouse sensitization, in asthmatic children. ENVIRONMENTAL RESEARCH 2017; 156:619-624. [PMID: 28454014 PMCID: PMC5503684 DOI: 10.1016/j.envres.2017.04.025] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Revised: 03/06/2017] [Accepted: 04/20/2017] [Indexed: 06/07/2023]
Abstract
Chronic exposure to mouse allergen may contribute greatly to the inner-city asthma burden. We hypothesized that reducing mouse allergen exposure may modulate the immunopathology underlying symptomatic pediatric allergic asthma, and that this occurs through epigenetic regulation. To test this hypothesis, we studied a cohort of mouse sensitized, persistent asthmatic inner-city children undergoing mouse allergen-targeted integrated pest management (IPM) vs education in a randomized controlled intervention trial. We found that decreasing mouse allergen exposure, but not cockroach, was associated with reduced FOXP3 buccal DNA promoter methylation, but this was unrelated to mouse specific IgE production. This finding suggests that the environmental epigenetic regulation of an immunomodulatory gene may occur following changing allergen exposures in some highly exposed cohorts. Given the clinical and public health importance of inner-city pediatric asthma and the potential impact of environmental interventions, further studies will be needed to corroborate changes in epigenetic regulation following changing exposures over time, and determine their impact on asthma morbidity in susceptible children.
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Affiliation(s)
- Rachel L Miller
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, Columbia University Medical Center, PH8E-101B, 630 W. 168th St., New York City, NY 10032, USA; Division of Allergy, Immunology, and Rheumatology, Department of Pediatrics, Columbia University Medical Center, PH8E-101B, 630 W. 168th St., New York City, NY 10032, USA; Department of Environmental Health Sciences, Columbia University, 722 W 168th St, 11th Floor, New York City, NY, 10032, USA.
| | - Hanjie Zhang
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, Columbia University Medical Center, PH8E-101B, 630 W. 168th St., New York City, NY 10032, USA
| | - Jacqueline Jezioro
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, Columbia University Medical Center, PH8E-101B, 630 W. 168th St., New York City, NY 10032, USA
| | - Mariangels De Planell Saguer
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, Columbia University Medical Center, PH8E-101B, 630 W. 168th St., New York City, NY 10032, USA
| | - Stephanie Lovinsky-Desir
- Division of Pulmonary, Department of Pediatrics, Columbia University Medical Center, 3959 Broadway, CHC 7-701, New York City, NY 10032, USA
| | - Xinhua Liu
- Department of Biostatistics, Columbia University Medical Center, 722 W 168 St, 6 Floor, New York City, NY, 10032, USA
| | - Matthew Perzanowski
- Department of Environmental Health Sciences, Columbia University, 722 W 168th St, 11th Floor, New York City, NY, 10032, USA
| | - Adnan Divjan
- Department of Environmental Health Sciences, Columbia University, 722 W 168th St, 11th Floor, New York City, NY, 10032, USA
| | - Wanda Phipatanakul
- Division of Pediatric Allergy/Immunology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, USA
| | - Elizabeth C Matsui
- Division of Pediatric Allergy/Immunology, Johns Hopkins School of Medicine, CMSC 1102, 600 N. Wolfe Street, Baltimore, MD 21287, USA
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47
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Modena BD, Bleecker ER, Busse WW, Erzurum SC, Gaston BM, Jarjour NN, Meyers DA, Milosevic J, Tedrow JR, Wu W, Kaminski N, Wenzel SE. Gene Expression Correlated with Severe Asthma Characteristics Reveals Heterogeneous Mechanisms of Severe Disease. Am J Respir Crit Care Med 2017. [PMID: 27984699 DOI: 10.1164/rccm.201607-1407oc 10.1164/rccm.201607-1407oc] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
RATIONALE Severe asthma (SA) is a heterogeneous disease with multiple molecular mechanisms. Gene expression studies of bronchial epithelial cells in individuals with asthma have provided biological insight and underscored possible mechanistic differences between individuals. OBJECTIVES Identify networks of genes reflective of underlying biological processes that define SA. METHODS Airway epithelial cell gene expression from 155 subjects with asthma and healthy control subjects in the Severe Asthma Research Program was analyzed by weighted gene coexpression network analysis to identify gene networks and profiles associated with SA and its specific characteristics (i.e., pulmonary function tests, quality of life scores, urgent healthcare use, and steroid use), which potentially identified underlying biological processes. A linear model analysis confirmed these findings while adjusting for potential confounders. MEASUREMENTS AND MAIN RESULTS Weighted gene coexpression network analysis constructed 64 gene network modules, including modules corresponding to T1 and T2 inflammation, neuronal function, cilia, epithelial growth, and repair mechanisms. Although no network selectively identified SA, genes in modules linked to epithelial growth and repair and neuronal function were markedly decreased in SA. Several hub genes of the epithelial growth and repair module were found located at the 17q12-21 locus, near a well-known asthma susceptibility locus. T2 genes increased with severity in those treated with corticosteroids but were also elevated in untreated, mild-to-moderate disease compared with healthy control subjects. T1 inflammation, especially when associated with increased T2 gene expression, was elevated in a subgroup of younger patients with SA. CONCLUSIONS In this hypothesis-generating analysis, gene expression networks in relation to asthma severity provided potentially new insight into biological mechanisms associated with the development of SA and its phenotypes.
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Affiliation(s)
- Brian D Modena
- 1 Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh Asthma Institute at UPMC, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.,2 Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, California
| | - Eugene R Bleecker
- 3 Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - William W Busse
- 4 Division of Allergy, Pulmonary, and Critical Care Medicine, University of Wisconsin, Madison, Wisconsin
| | - Serpil C Erzurum
- 5 Department of Pathobiology, Lerner Research Institute, The Cleveland Clinic Foundation, Cleveland, Ohio
| | - Benjamin M Gaston
- 6 Division of Pediatric Pulmonary, Allergy and Immunology, Case Western Reserve University, Cleveland, Ohio.,7 Rainbow Babies and Children's Hospital, Cleveland, Ohio
| | - Nizar N Jarjour
- 4 Division of Allergy, Pulmonary, and Critical Care Medicine, University of Wisconsin, Madison, Wisconsin
| | - Deborah A Meyers
- 3 Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Jadranka Milosevic
- 1 Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh Asthma Institute at UPMC, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - John R Tedrow
- 1 Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh Asthma Institute at UPMC, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Wei Wu
- 8 Lane Center for Computational Biology School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania; and
| | - Naftali Kaminski
- 9 Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Sally E Wenzel
- 1 Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh Asthma Institute at UPMC, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
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48
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Phua DY, Meaney MJ, Khor CC, Lau IYM, Hong YY. Effects of bonding with parents and home culture on intercultural adaptations and the moderating role of genes. Behav Brain Res 2017; 325:223-236. [PMID: 28202409 DOI: 10.1016/j.bbr.2017.02.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Revised: 02/08/2017] [Accepted: 02/09/2017] [Indexed: 01/07/2023]
Abstract
In the current age of globalization, living abroad is becoming an increasingly common and highly sought after experience. Sojourners' ability to adjust to a new culture can be affected by their existing attachments, internalized as intrapsychic environment, as well as their biological sensitivity to environment. This sensitivity can be partly attributed to one's genomic endowments. As such, this prospective study sought to examine the differential effects of early experiences with parents and affection for home culture on young adults' ability to adapt to a foreign culture (n=305, students who studied overseas for a semester) - specifically, the difficulties they experience - moderated by genetic susceptibility. An additional 258 students who did not travel overseas were included as a comparison group to demonstrate the uniqueness of intercultural adaptation. Current findings suggest that the maternal, paternal and cultural bondings or affections affect different aspects of intercultural adjustment. Maternal bonding affected sojourners' relationships with host nationals, while paternal bonding affected sojourners' adjustment to a new physical environment. Moreover, individuals' genetic predispositions significantly moderate these main effects regarding how much difficulty the sojourners experienced overseas.
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Affiliation(s)
| | - Michael J Meaney
- Singapore Institute for Clinical Sciences, Singapore; Sackler Program for Epigenetics and Psychobiology at McGill University, Canada
| | - Chiea Chuen Khor
- Genome Institute of Singapore, Singapore; Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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49
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Abstract
Recent studies show that subtle variations in thyroid function, including subclinical thyroid dysfunction, and even variation in thyroid function within the normal range, are associated with morbidity and mortality. It is estimated that 40-65% of the inter-individual variation in serum TSH and FT4 levels is determined by genetic factors. To identify these factors, various linkage and candidate gene studies have been performed in the past, which have identified only a few genes. In the last decade, genome-wide association studies identified many new genes, while recent whole-genome sequencing efforts have also been proven to be effective. In the current review, we provide a systematic overview of these studies, including strengths and limitations. We discuss new techniques which will further clarify the genetic basis of thyroid function in the near future, as well as the potential use of these genetic markers in personalizing the management of thyroid disease patients.
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Affiliation(s)
- Marco Medici
- Department of Internal Medicine and Rotterdam Thyroid Center, Erasmus Medical Center, Rotterdam, The Netherlands.
| | - Theo J Visser
- Department of Internal Medicine and Rotterdam Thyroid Center, Erasmus Medical Center, Rotterdam, The Netherlands.
| | - Robin P Peeters
- Department of Internal Medicine and Rotterdam Thyroid Center, Erasmus Medical Center, Rotterdam, The Netherlands.
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50
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Liu H, Guo G. Opportunities and challenges of big data for the social sciences: The case of genomic data. SOCIAL SCIENCE RESEARCH 2016; 59:13-22. [PMID: 27480368 PMCID: PMC5480284 DOI: 10.1016/j.ssresearch.2016.04.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Revised: 04/08/2016] [Accepted: 04/13/2016] [Indexed: 05/04/2023]
Abstract
In this paper, we draw attention to one unique and valuable source of big data, genomic data, by demonstrating the opportunities they provide to social scientists. We discuss different types of large-scale genomic data and recent advances in statistical methods and computational infrastructure used to address challenges in managing and analyzing such data. We highlight how these data and methods can be used to benefit social science research.
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Affiliation(s)
- Hexuan Liu
- Department of Sociology, The University of North Carolina at Chapel Hill, USA; Carolina Population Center, The University of North Carolina at Chapel Hill, USA; School of Criminal Justice, The University of Cincinnati, USA.
| | - Guang Guo
- Department of Sociology, The University of North Carolina at Chapel Hill, USA; Carolina Center for Genome Sciences, The University of North Carolina at Chapel Hill, USA; Carolina Population Center, The University of North Carolina at Chapel Hill, USA
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