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Chung MK, House JS, Akhtari FS, Makris KC, Langston MA, Islam KT, Holmes P, Chadeau-Hyam M, Smirnov AI, Du X, Thessen AE, Cui Y, Zhang K, Manrai AK, Motsinger-Reif A, Patel CJ. Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs). EXPOSOME 2024; 4:osae001. [PMID: 38344436 PMCID: PMC10857773 DOI: 10.1093/exposome/osae001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 10/16/2023] [Accepted: 11/20/2023] [Indexed: 03/07/2024]
Abstract
This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.
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Affiliation(s)
- Ming Kei Chung
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
- Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China
| | - John S House
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Farida S Akhtari
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Konstantinos C Makris
- Cyprus International Institute for Environmental and Public Health, School of Health Sciences, Cyprus University of Technology, Limassol, Cyprus
| | - Michael A Langston
- Department of Electrical Engineering and Computer Science, University of TN, Knoxville, TN, USA
| | - Khandaker Talat Islam
- Department of Population and Public Health Sciences, Keck School of Medicine of the University of Southern CA, Los Angeles, CA, USA
| | - Philip Holmes
- Department of Physics, Villanova University, Villanova, Philadelphia, USA
| | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Alex I Smirnov
- Department of Chemistry, NC State University, Raleigh, NC, USA
| | - Xiuxia Du
- Department of Bioinformatics and Genomics, College of Computing and Informatics, University of NC at Charlotte, Charlotte, NC, USA
| | - Anne E Thessen
- Department of Biomedical Informatics, University of CO Anschutz Medical Campus, Aurora, CO, USA
| | - Yuxia Cui
- Exposure, Response, and Technology Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kai Zhang
- Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of NY, Rensselaer, NY, USA
| | - Arjun K Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Alison Motsinger-Reif
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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2
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Towns C, Richer M, Jasaityte S, Stafford EJ, Joubert J, Antar T, Martinez-Carrasco A, Makarious MB, Casey B, Vitale D, Levine K, Leonard H, Pantazis CB, Screven LA, Hernandez DG, Wegel CE, Solle J, Nalls MA, Blauwendraat C, Singleton AB, Tan MMX, Iwaki H, Morris HR. Defining the causes of sporadic Parkinson's disease in the global Parkinson's genetics program (GP2). NPJ Parkinsons Dis 2023; 9:131. [PMID: 37699923 PMCID: PMC10497609 DOI: 10.1038/s41531-023-00533-w] [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: 11/25/2022] [Accepted: 05/22/2023] [Indexed: 09/14/2023] Open
Abstract
The Global Parkinson's Genetics Program (GP2) will genotype over 150,000 participants from around the world, and integrate genetic and clinical data for use in large-scale analyses to dramatically expand our understanding of the genetic architecture of PD. This report details the workflow for cohort integration into the complex arm of GP2, and together with our outline of the monogenic hub in a companion paper, provides a generalizable blueprint for establishing large scale collaborative research consortia.
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Affiliation(s)
- Clodagh Towns
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
| | - Madeleine Richer
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
| | - Simona Jasaityte
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
| | - Eleanor J Stafford
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
- University College London, London, UK
| | - Julie Joubert
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
| | - Tarek Antar
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Alejandro Martinez-Carrasco
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
- University College London, London, UK
| | - Mary B Makarious
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- National Institutes of Health, Bethesda, MD, USA
| | - Bradford Casey
- Department of Clinical Research, Michael J. Fox Foundation for Parkinson's Research, New York City, NY, USA
- The Michael J. Fox Foundation for Parkinson's Research, New York, NY, USA
| | - Dan Vitale
- National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International, Washington, DC, USA
| | - Kristin Levine
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International, Washington, DC, USA
| | - Hampton Leonard
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International, Washington, DC, USA
- National Institute on Aging/National Institutes of Health, Bethesda, MD, USA
| | - Caroline B Pantazis
- National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Laurel A Screven
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Dena G Hernandez
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Claire E Wegel
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Justin Solle
- Department of Clinical Research, Michael J. Fox Foundation for Parkinson's Research, New York City, NY, USA
| | - Mike A Nalls
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International, Washington, DC, USA
| | - Cornelis Blauwendraat
- National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
- Integrative Genomics Unit, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Andrew B Singleton
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
- National Institute on Aging, Bethesda, MD, USA
| | - Manuela M X Tan
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Hirotaka Iwaki
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International, Washington, DC, USA
| | - Huw R Morris
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London, UK.
- University College London, London, UK.
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3
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Divaris K, Haworth S, Shaffer J, Anttonen V, Beck J, Furuichi Y, Holtfreter B, Jönsson D, Kocher T, Levy S, Magnusson P, McNeil D, Michaëlsson K, North K, Palotie U, Papapanou P, Pussinen P, Porteous D, Reis K, Salminen A, Schaefer A, Sudo T, Sun Y, Suominen A, Tamahara T, Weinberg S, Lundberg P, Marazita M, Johansson I. Phenotype Harmonization in the GLIDE2 Oral Health Genomics Consortium. J Dent Res 2022; 101:1408-1416. [PMID: 36000800 PMCID: PMC9516613 DOI: 10.1177/00220345221109775] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Genetic risk factors play important roles in the etiology of oral, dental, and craniofacial diseases. Identifying the relevant risk loci and understanding their molecular biology could highlight new prevention and management avenues. Our current understanding of oral health genomics suggests that dental caries and periodontitis are polygenic diseases, and very large sample sizes and informative phenotypic measures are required to discover signals and adequately map associations across the human genome. In this article, we introduce the second wave of the Gene-Lifestyle Interactions and Dental Endpoints consortium (GLIDE2) and discuss relevant data analytics challenges, opportunities, and applications. In this phase, the consortium comprises a diverse, multiethnic sample of over 700,000 participants from 21 studies contributing clinical data on dental caries experience and periodontitis. We outline the methodological challenges of combining data from heterogeneous populations, as well as the data reduction problem in resolving detailed clinical examination records into tractable phenotypes, and describe a strategy that addresses this. Specifically, we propose a 3-tiered phenotyping approach aimed at leveraging both the large sample size in the consortium and the detailed clinical information available in some studies, wherein binary, severity-encompassing, and "precision," data-driven clinical traits are employed. As an illustration of the use of data-driven traits across multiple cohorts, we present an application of dental caries experience data harmonization in 8 participating studies (N = 55,143) using previously developed permanent dentition tooth surface-level dental caries pattern traits. We demonstrate that these clinical patterns are transferable across multiple cohorts, have similar relative contributions within each study, and thus are prime targets for genetic interrogation in the expanded and diverse multiethnic sample of GLIDE2. We anticipate that results from GLIDE2 will decisively advance the knowledge base of mechanisms at play in oral, dental, and craniofacial health and disease and further catalyze international collaboration and data and resource sharing in genomics research.
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Affiliation(s)
- K. Divaris
- Division of Pediatric and Public
Health, Adams School of Dentistry, University of North Carolina at Chapel Hill,
Chapel Hill, NC, USA
- Department of Epidemiology, Gillings
School of Global Public Health, University of North Carolina at Chapel Hill, Chapel
Hill, NC, USA
| | - S. Haworth
- Medical Research Council Integrative
Epidemiology United, Department of Population Health Sciences, Bristol Medical
School, University of Bristol, Bristol, UK
- Bristol Dental School, University of
Bristol, Bristol, UK
| | - J.R. Shaffer
- Department of Human Genetics, Graduate
School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Craniofacial and Dental
Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine,
University of Pittsburgh, Pittsburgh, PA, USA
| | - V. Anttonen
- Research Unit of Oral Health Sciences,
Faculty of Medicine, University of Oulu, Oulu, Finland
- Medical Research Center, Oulu
University Hospital and University of Oulu, Oulu, Finland
| | - J.D. Beck
- Division of Comprehensive Oral
Health–Periodontology, Adams School of Dentistry, University of North Carolina at
Chapel Hill, Chapel Hill, NC, USA
| | - Y. Furuichi
- Division of Endodontology and
Periodontology, Department of Oral Rehabilitation, Graduate School of Dentistry,
Health Sciences University of Hokkaido, Hokkaido, Japan
| | - B. Holtfreter
- Department of Restorative Dentistry,
Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University
Medicine Greifswald, Greifswald, Germany
| | - D. Jönsson
- Public Dental Service of Skåne, Lund,
Sweden
- Hypertension and Cardiovascular
Disease, Department of Clinical Sciences in Malmö, Lund University, Malmö,
Sweden
- Faculty of Odontology, Malmö
University, Malmö, Sweden
| | - T. Kocher
- Department of Restorative Dentistry,
Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University
Medicine Greifswald, Greifswald, Germany
| | - S.M. Levy
- Department of Preventive and
Community Dentistry, College of Dentistry, University of Iowa, Iowa City, IA,
USA
| | - P.K.E. Magnusson
- Department of Medical Epidemiology
and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - D.W. McNeil
- Center for Oral Health Research in
Appalachia, Appalachia, NY, USA
- Department of Psychology, West
Virginia University, Morgantown, WV, USA
- Department of Dental Public Health
& Professional Practice, West Virginia University, Morgantown, WV, USA
| | - K. Michaëlsson
- Department of Surgical Sciences, Unit
of Medical Epidemiology, Uppsala University, Uppsala, Sweden
| | - K.E. North
- Department of Epidemiology, Gillings
School of Global Public Health, University of North Carolina at Chapel Hill, Chapel
Hill, NC, USA
- Carolina Population Center,
University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - U. Palotie
- Oral and Maxillofacial Diseases,
University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - P.N. Papapanou
- Division of Periodontics, Section of
Oral, Diagnostic and Rehabilitation Sciences, Columbia University, College of Dental
Medicine, New York, NY, USA
| | - P.J. Pussinen
- Oral and Maxillofacial Diseases,
University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Institute of Dentistry, School on
Medicine, University of Eastern Finland, Kuopio, Finland
| | - D. Porteous
- Centre for Genomic and Experimental
Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh,
UK
| | - K. Reis
- Institute of Genomics, University of
Tartu, Tartu, Estonia
| | - A. Salminen
- Oral and Maxillofacial Diseases,
University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - A.S. Schaefer
- Department of Periodontology, Oral
Medicine and Oral Surgery, Institute for Dental and Craniofacial Sciences,
Charité–Universitätsmedizin Berlin, Berlin, Germany
| | - T. Sudo
- Institute of Education, Tokyo Medical
and Dental University, Tokyo, Japan
| | - Y.Q. Sun
- Center for Oral Health Services and
Research Mid-Norway (TkMidt), Trondheim, Norway
- Department of Clinical and Molecular
Medicine, NTNU, Norwegian University of Science and Technology, Trondheim,
Norway
| | - A.L. Suominen
- Institute of Dentistry, School on
Medicine, University of Eastern Finland, Kuopio, Finland
- Institute of Dentistry, School on
Medicine, University of Eastern Finland, Kuopio, Finland
- Department of Oral and Maxillofacial
Diseases, Kuopio University Hospital, Kuopio, Finland
- Public Health Evaluation and
Projection Unit, Finnish Institute for Health and Welfare (THL), Helsinki,
Finland
| | - T. Tamahara
- Department of Community Medical
Supports, Tohoku Medical Megabank Organization, Tohoku University, Sendai,
Japan
| | - S.M. Weinberg
- Department of Human Genetics, Graduate
School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Craniofacial and Dental
Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine,
University of Pittsburgh, Pittsburgh, PA, USA
| | - P. Lundberg
- Department of Odontology, Section of
Molecular Periodontology, Umeå University, Umeå, Sweden
| | - M.L. Marazita
- Department of Human Genetics, Graduate
School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Craniofacial and Dental
Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine,
University of Pittsburgh, Pittsburgh, PA, USA
| | - I. Johansson
- Department of Odontology, Section of
Cariology, Umeå University, Umeå, Sweden
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4
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Pan H, Bakalov V, Cox L, Engle ML, Erickson SW, Feolo M, Guo Y, Huggins W, Hwang S, Kimura M, Krzyzanowski M, Levy J, Phillips M, Qin Y, Williams D, Ramos EM, Hamilton CM. Identifying Datasets for Cross-Study Analysis in dbGaP using PhenX. Sci Data 2022; 9:532. [PMID: 36050327 PMCID: PMC9434066 DOI: 10.1038/s41597-022-01660-4] [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: 09/08/2021] [Accepted: 08/23/2022] [Indexed: 11/09/2022] Open
Abstract
Identifying relevant studies and harmonizing datasets are major hurdles for data reuse. Common Data Elements (CDEs) can help identify comparable study datasets and reduce the burden of retrospective data harmonization, but they have not been required, historically. The collaborative team at PhenX and dbGaP developed an approach to use PhenX variables as a set of CDEs to link phenotypic data and identify comparable studies in dbGaP. Variables were identified as either comparable or related, based on the data collection mode used to harmonize data across mapped datasets. We further added a CDE data field in the dbGaP data submission packet to indicate use of PhenX and annotate linkages in the future. Some 13,653 dbGaP variables from 521 studies were linked through PhenX variable mapping. These variable linkages have been made accessible for browsing and searching in the repository through dbGaP CDE-faceted search filter and the PhenX variable search tool. New features in dbGaP and PhenX enable investigators to identify variable linkages among dbGaP studies and reveal opportunities for cross-study analysis.
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Affiliation(s)
- Huaqin Pan
- RTI International, Research Triangle Park, NC, USA.
| | | | - Lisa Cox
- RTI International, Research Triangle Park, NC, USA
| | | | | | - Michael Feolo
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Yuelong Guo
- GeneCentric Therapeutics Inc., Durham, NC, USA
| | | | | | - Masato Kimura
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | | | - Josh Levy
- Levy Informatics, Chapel Hill, NC, USA
| | | | - Ying Qin
- RTI International, Research Triangle Park, NC, USA
| | | | - Erin M Ramos
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
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5
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Stilp AM, Emery LS, Broome JG, Buth EJ, Khan AT, Laurie CA, Wang FF, Wong Q, Chen D, D’Augustine CM, Heard-Costa NL, Hohensee CR, Johnson WC, Juarez LD, Liu J, Mutalik KM, Raffield LM, Wiggins KL, de Vries PS, Kelly TN, Kooperberg C, Natarajan P, Peloso GM, Peyser PA, Reiner AP, Arnett DK, Aslibekyan S, Barnes KC, Bielak LF, Bis JC, Cade BE, Chen MH, Correa A, Cupples LA, de Andrade M, Ellinor PT, Fornage M, Franceschini N, Gan W, Ganesh SK, Graffelman J, Grove ML, Guo X, Hawley NL, Hsu WL, Jackson RD, Jaquish CE, Johnson AD, Kardia SLR, Kelly S, Lee J, Mathias RA, McGarvey ST, Mitchell BD, Montasser ME, Morrison AC, North KE, Nouraie SM, Oelsner EC, Pankratz N, Rich SS, Rotter JI, Smith JA, Taylor KD, Vasan RS, Weeks DE, Weiss ST, Wilson CG, Yanek LR, Psaty BM, Heckbert SR, Laurie CC. A System for Phenotype Harmonization in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Program. Am J Epidemiol 2021; 190:1977-1992. [PMID: 33861317 PMCID: PMC8485147 DOI: 10.1093/aje/kwab115] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 04/09/2021] [Accepted: 04/13/2021] [Indexed: 11/12/2022] Open
Abstract
Genotype-phenotype association studies often combine phenotype data from multiple studies to increase statistical power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data-set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data-sharing mechanisms. This system was developed for the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other -omics data for more than 80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants (recruited in 1948-2012) from up to 17 studies per phenotype. Here we discuss challenges in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include 1) the software code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify, or extend these harmonizations to additional studies, and 2) the results of labeling thousands of phenotype variables with controlled vocabulary terms.
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Affiliation(s)
- Adrienne M Stilp
- Correspondence to Dr. Adrienne Stilp, Department of Biostatistics, School of Public Health, University of Washington, Box 359461, Seattle, WA 98195 (e-mail: )
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6
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Maurya R, Kanakan A, Vasudevan JS, Chattopadhyay P, Pandey R. Infection outcome needs two to tango: human host and the pathogen. Brief Funct Genomics 2021; 21:90-102. [PMID: 34402498 PMCID: PMC8385967 DOI: 10.1093/bfgp/elab037] [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: 06/15/2021] [Revised: 07/15/2021] [Accepted: 07/21/2021] [Indexed: 12/15/2022] Open
Abstract
Infectious diseases are potential drivers for human evolution, through a complex, continuous and dynamic interaction between the host and the pathogen/s. It is this dynamic interaction that contributes toward the clinical outcome of a pathogenic disease. These are modulated by contributions from the human genetic variants, transcriptional response (including noncoding RNA) and the pathogen’s genome architecture. Modern genomic tools and techniques have been crucial for the detection and genomic characterization of pathogens with respect to the emerging infectious diseases. Aided by next-generation sequencing (NGS), risk stratification of host population/s allows for the identification of susceptible subgroups and better disease management. Nevertheless, many challenges to a general understanding of host–pathogen interactions remain. In this review, we elucidate how a better understanding of the human host-pathogen interplay can substantially enhance, and in turn benefit from, current and future applications of multi-omics based approaches in infectious and rare diseases. This includes the RNA-level response, which modulates the disease severity and outcome. The need to understand the role of human genetic variants in disease severity and clinical outcome has been further highlighted during the Coronavirus disease 2019 (COVID-19) pandemic. This would enhance and contribute toward our future pandemic preparedness.
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Affiliation(s)
- Ranjeet Maurya
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi-110007, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India
| | - Akshay Kanakan
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi-110007, India
| | - Janani Srinivasa Vasudevan
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi-110007, India
| | - Partha Chattopadhyay
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi-110007, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India
| | - Rajesh Pandey
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi-110007, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India
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7
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Cox LA, Hwang S, Haines J, Ramos EM, McCarty CA, Marazita ML, Engle ML, Hendershot T, Pan HH, Hamilton CM. Using the PhenX Toolkit to Select Standard Measurement Protocols for Your Research Study. Curr Protoc 2021; 1:e149. [PMID: 34038028 PMCID: PMC8251725 DOI: 10.1002/cpz1.149] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The goals of PhenX (consensus measures for Phenotypes and eXposures) are to promote the use of standard measurement protocols and to help investigators identify opportunities for collaborative research and cross‐study analysis, thus increasing the impact of individual studies. The PhenX Toolkit (https://www.phenxtoolkit.org/) offers high‐quality, well‐established measurement protocols to assess phenotypes and exposures in studies with human participants. The Toolkit contains protocols representing 29 research domains and 6 specialty collections of protocols that add depth to the Toolkit in specific research areas (e.g., COVID‐19, Social Determinants of Health [SDoH], Blood Sciences Research [BSR], Mental Health Research [MHR], Tobacco Regulatory Research [TRR], and Substance Abuse and Addiction [SAA]). Protocols are recommended for inclusion in the PhenX Toolkit by Working Groups of domain experts using a consensus process that includes input from the scientific community. For each PhenX protocol, the Toolkit provides a detailed description, the rationale for inclusion, and supporting documentation. Users can browse protocols in the Toolkit, search the Toolkit using keywords, or use Browse Protocols Tree to identify protocols of interest. The PhenX Toolkit provides data dictionaries compatible with the database of Genotypes and Phenotypes (dbGaP), Research Electronic Data Capture (REDCap) data submission compatibility, and data collection worksheets to help investigators incorporate PhenX protocols into their study design. The PhenX Toolkit provides resources to help users identify published studies that used PhenX protocols. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Using the PhenX Toolkit to support or extend study design
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Affiliation(s)
- Lisa Ann Cox
- RTI International, Research Triangle Park, North Carolina
| | - Stephen Hwang
- RTI International, Research Triangle Park, North Carolina
| | - Jonathan Haines
- Department of Population & Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio
| | - Erin M Ramos
- National Human Genome Research Institute (NHGRI), Bethesda, Maryland
| | | | - Mary L Marazita
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine; Department of Human Genetics, Graduate School of Public Health; and Clinical and Translational Science, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
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8
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George MN, Leavens KF, Gadue P. Genome Editing Human Pluripotent Stem Cells to Model β-Cell Disease and Unmask Novel Genetic Modifiers. Front Endocrinol (Lausanne) 2021; 12:682625. [PMID: 34149620 PMCID: PMC8206553 DOI: 10.3389/fendo.2021.682625] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 05/13/2021] [Indexed: 01/21/2023] Open
Abstract
A mechanistic understanding of the genetic basis of complex diseases such as diabetes mellitus remain elusive due in large part to the activity of genetic disease modifiers that impact the penetrance and/or presentation of disease phenotypes. In the face of such complexity, rare forms of diabetes that result from single-gene mutations (monogenic diabetes) can be used to model the contribution of individual genetic factors to pancreatic β-cell dysfunction and the breakdown of glucose homeostasis. Here we review the contribution of protein coding and non-protein coding genetic disease modifiers to the pathogenesis of diabetes subtypes, as well as how recent technological advances in the generation, differentiation, and genome editing of human pluripotent stem cells (hPSC) enable the development of cell-based disease models. Finally, we describe a disease modifier discovery platform that utilizes these technologies to identify novel genetic modifiers using induced pluripotent stem cells (iPSC) derived from patients with monogenic diabetes caused by heterozygous mutations.
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Affiliation(s)
- Matthew N. George
- Center for Cellular and Molecular Therapeutics, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Karla F. Leavens
- Center for Cellular and Molecular Therapeutics, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Division of Endocrinology and Diabetes, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Paul Gadue
- Center for Cellular and Molecular Therapeutics, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
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9
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Luningham JM, Hendriks AM, Krapohl E, Ip HF, van Beijsterveldt CE, Lundström S, Vuoksimaa E, Korhonen T, Lichtenstein P, Plomin R, Pulkkinen L, Rose RJ, Kaprio J, Bartels M, Boomsma DI, Lubke GH. Harmonizing behavioral outcomes across studies, raters, and countries: application to the genetic analysis of aggression in the ACTION Consortium. J Child Psychol Psychiatry 2020; 61:807-817. [PMID: 31950512 PMCID: PMC7363537 DOI: 10.1111/jcpp.13188] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/25/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND Aggression in children has genetic and environmental causes. Studies of aggression can pool existing datasets to include more complex models of social effects. Such analyses require large datasets with harmonized outcome measures. Here, we made use of a reference panel for phenotype data to harmonize multiple aggression measures in school-aged children to jointly analyze data from five large twin cohorts. METHODS Individual level aggression data on 86,559 children (42,468 twin pairs) were available in five European twin cohorts measured by different instruments. A phenotypic reference panel was collected which enabled a model-based phenotype harmonization approach. A bi-factor integration model in the integrative data analysis framework was developed to model aggression across studies while adjusting for rater, age, and sex. Finally, harmonized aggression scores were analyzed to estimate contributions of genes, environment, and social interaction to aggression. The large sample size allowed adequate power to test for sibling interaction effects, with unique dynamics permitted for opposite-sex twins. RESULTS The best-fitting model found a high level of overall heritability of aggression (~60%). Different heritability rates of aggression across sex were marginally significant, with heritability estimates in boys of ~64% and ~58% in girls. Sibling interaction effects were only significant in the opposite-sex twin pairs: the interaction effect of males on their female co-twin differed from the effect of females on their male co-twin. An aggressive female had a positive effect on male co-twin aggression, whereas more aggression in males had a negative influence on a female co-twin. CONCLUSIONS Opposite-sex twins displayed unique social dynamics of aggressive behaviors in a joint analysis of a large, multinational dataset. The integrative data analysis framework, applied in combination with a reference panel, has the potential to elucidate broad, generalizable results in the investigation of common psychological traits in children.
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Affiliation(s)
- Justin M. Luningham
- Department of Psychology, University of Notre Dame, Notre
Dame, IN;,Department of Population Health Sciences, School of Public
Health, Georgia State University, Atlanta, GA, USA
| | - Anne M. Hendriks
- Netherlands Twin Register, Department of Biological
Psychology, Vrije Universiteit Amsterdam, Amsterdam;,Amsterdam Public Health research institute, Faculty of
Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The
Netherlands
| | - Eva Krapohl
- Medical Research Council Social, Genetic, and Developmental
Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, UK
| | - Hill Fung Ip
- Netherlands Twin Register, Department of Biological
Psychology, Vrije Universiteit Amsterdam, Amsterdam;,Amsterdam Public Health research institute, Faculty of
Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The
Netherlands
| | - Catharina E.M. van Beijsterveldt
- Netherlands Twin Register, Department of Biological
Psychology, Vrije Universiteit Amsterdam, Amsterdam;,Amsterdam Public Health research institute, Faculty of
Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The
Netherlands
| | - Sebastian Lundström
- Gillberg Neuropsychiatry Centre, University of Gothenburg,
Gothenburg;,Centre for Ethics, Law and Mental Health (CELAM),
University of Gothenburg, Gothenburg, Sweden
| | - Eero Vuoksimaa
- Institute for Molecular Medicine Finland, University of
Helsinki, Helsinki, Finland
| | - Tellervo Korhonen
- Institute for Molecular Medicine Finland, University of
Helsinki, Helsinki, Finland
| | - Paul Lichtenstein
- Department of Medical Epidemiology and Biostatistics,
Karolinska Institutet, Stockholm, Sweden
| | - Robert Plomin
- Medical Research Council Social, Genetic, and Developmental
Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, UK
| | - Lea Pulkkinen
- Department of Psychology, University of Jyvaskyla,
Jyvaskyla, Finland
| | - Richard J. Rose
- Department of Psychological and Brain Sciences, Indiana
University, Bloomington, IN, USA
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland, University of
Helsinki, Helsinki, Finland;,Department of Public Health, University of Helsinki,
Helsinki, Finland
| | - Meike Bartels
- Netherlands Twin Register, Department of Biological
Psychology, Vrije Universiteit Amsterdam, Amsterdam;,Amsterdam Public Health research institute, Faculty of
Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The
Netherlands;,Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Dorret I. Boomsma
- Netherlands Twin Register, Department of Biological
Psychology, Vrije Universiteit Amsterdam, Amsterdam;,Amsterdam Public Health research institute, Faculty of
Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The
Netherlands;,Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Gitta H. Lubke
- Department of Psychology, University of Notre Dame, Notre
Dame, IN
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10
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Pan H, Edwards SW, Ives C, Covert H, Harville EW, Lichtveld MY, Wickliffe JK, Hamilton CM. An Assessment of Environmental Health Measures in the Deepwater Horizon Research Consortia. CURRENT OPINION IN TOXICOLOGY 2020; 16:75-82. [PMID: 32457927 DOI: 10.1016/j.cotox.2019.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Research consortia play a key role in our understanding of how environmental exposures influence health and wellbeing, especially in the case of catastrophic events such as the Deepwater Horizon oil spill. A common challenge that prevents the optimal use of these data is the difficulty of harmonizing data regarding the environmental exposures and health effects across the studies within and among consortia. A review of the measures used by members of the Deepwater Horizon Research Consortia highlights the challenges associated with balancing timely implementation of a study to support disaster relief with optimizing the long-term value of the data. The inclusion of common, standard measures at the study design phase and a priori discussions regarding harmonization of study-specific measures among consortia members are key to overcoming this challenge. As more resources become available to support the use of standard measures, researchers now have the tools needed to rapidly coordinate their studies without compromising research focus or timely completion of the original study goals.
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Affiliation(s)
- Huaqin Pan
- RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC 27709
| | - Stephen W Edwards
- RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC 27709
| | - Cataia Ives
- RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC 27709
| | - Hannah Covert
- Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, New Orleans, LA 70112
| | - Emily W Harville
- Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, New Orleans, LA 70112
| | - Maureen Y Lichtveld
- Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, New Orleans, LA 70112
| | - Jeffrey K Wickliffe
- Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, New Orleans, LA 70112
| | - Carol M Hamilton
- RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC 27709
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11
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Luningham JM, McArtor DB, Hendriks AM, van Beijsterveldt CEM, Lichtenstein P, Lundström S, Larsson H, Bartels M, Boomsma DI, Lubke GH. Data Integration Methods for Phenotype Harmonization in Multi-Cohort Genome-Wide Association Studies With Behavioral Outcomes. Front Genet 2020; 10:1227. [PMID: 31921287 PMCID: PMC6914843 DOI: 10.3389/fgene.2019.01227] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 11/05/2019] [Indexed: 01/03/2023] Open
Abstract
Parallel meta-analysis is a popular approach for increasing the power to detect genetic effects in genome-wide association studies across multiple cohorts. Consortia studying the genetics of behavioral phenotypes are oftentimes faced with systematic differences in phenotype measurement across cohorts, introducing heterogeneity into the meta-analysis and reducing statistical power. This study investigated integrative data analysis (IDA) as an approach for jointly modeling the phenotype across multiple datasets. We put forth a bi-factor integration model (BFIM) that provides a single common phenotype score and accounts for sources of study-specific variability in the phenotype. In order to capitalize on this modeling strategy, a phenotype reference panel was utilized as a supplemental sample with complete data on all behavioral measures. A simulation study showed that a mega-analysis of genetic variant effects in a BFIM were more powerful than meta-analysis of genetic effects on a cohort-specific sum score of items. Saving the factor scores from the BFIM and using those as the outcome in meta-analysis was also more powerful than the sum score in most simulation conditions, but a small degree of bias was introduced by this approach. The reference panel was necessary to realize these power gains. An empirical demonstration used the BFIM to harmonize aggression scores in 9-year old children across the Netherlands Twin Register and the Child and Adolescent Twin Study in Sweden, providing a template for application of the BFIM to a range of different phenotypes. A supplemental data collection in the Netherlands Twin Register served as a reference panel for phenotype modeling across both cohorts. Our results indicate that model-based harmonization for the study of complex traits is a useful step within genetic consortia.
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Affiliation(s)
- Justin M Luningham
- Department of Psychology, University of Notre Dame, Notre Dame, IN, United States
| | - Daniel B McArtor
- Department of Psychology, University of Notre Dame, Notre Dame, IN, United States
| | - Anne M Hendriks
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Faculty of Behavioural and Movement Sciences, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Catharina E M van Beijsterveldt
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Faculty of Behavioural and Movement Sciences, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Paul Lichtenstein
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Sebastian Lundström
- Gillberg Neuropsychiatry Centre, University of Gothenburg, Gothenburg, Sweden
| | - Henrik Larsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Meike Bartels
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Faculty of Behavioural and Movement Sciences, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
| | - Dorret I Boomsma
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Faculty of Behavioural and Movement Sciences, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
| | - Gitta H Lubke
- Department of Psychology, University of Notre Dame, Notre Dame, IN, United States
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12
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Cates HM, Benca-Bachman CE, de Guglielmo G, Schoenrock SA, Shu C, Kallupi M. National Institute on Drug Abuse genomics consortium white paper: Coordinating efforts between human and animal addiction studies. GENES BRAIN AND BEHAVIOR 2019; 18:e12577. [PMID: 31012252 DOI: 10.1111/gbb.12577] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 04/15/2019] [Accepted: 04/15/2019] [Indexed: 12/14/2022]
Abstract
The National Institute on Drug Abuse Genetics and Epigenetics Cross-Cutting Research Team convened a diverse group of researchers, clinicians, and healthcare providers on the campus of the University of California, San Diego, in June 2018. The goal was to develop strategies to integrate genetics and phenotypes across species to achieve a better understanding of substance use disorders through associations between genotypes and addictive behaviors. This conference (a) discussed progress in harmonizing large opioid genetics cohorts, (b) discussed phenotypes that are used for genetics studies in humans, (c) examined phenotypes that are used for genetics studies in animal models, (d) identified synergies and gaps in phenotypic analyses of human and animal models and (e) identified strategies to integrate genetics and genomics data with phenotypes across species. The meeting consisted of panels that focused on phenotype harmonization (Dr. Laura Bierut, Dr. Olivier George, Dr. Dan Larach and Dr. Sesh Mudumbai), translating genetic findings between species (Dr. Elissa Chesler, Dr. Gary Peltz and Dr. Abraham Palmer), interpreting and understanding allelic variations (Dr. Vanessa Troiani and Dr. Tamara Richards) and pathway conservation in animal models and human studies (Dr. Robert Hitzemann, Dr. Huda Akil and Dr. Laura Saba). There were also updates that were provided by large consortia (Dr. Susan Tapert, Dr. Danielle Dick, Dr. Howard Edenberg and Dr. Eric Johnson). Collectively, the conference was convened to discuss progress and changes in genome-wide association studies.
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Affiliation(s)
- Hannah M Cates
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | | | | | - Sarah A Schoenrock
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Chang Shu
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
| | - Marsida Kallupi
- Department of Neuroscience, The Scripps Research Institute, La Jolla, California
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13
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Shotelersuk V, Tongsima S, Pithukpakorn M, Eu‐ahsunthornwattana J, Mahasirimongkol S. Precision medicine in Thailand. AMERICAN JOURNAL OF MEDICAL GENETICS PART C-SEMINARS IN MEDICAL GENETICS 2019; 181:245-253. [DOI: 10.1002/ajmg.c.31694] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 02/12/2019] [Accepted: 02/13/2019] [Indexed: 12/17/2022]
Affiliation(s)
- Vorasuk Shotelersuk
- Center of Excellence for Medical Genomics, Department of Pediatrics, Faculty of MedicineChulalongkorn University Bangkok Thailand
- Excellence Center for Medical GeneticsKing Chulalongkorn Memorial Hospital, the Thai Red Cross Society Bangkok Thailand
| | - Sissades Tongsima
- National Center for Genetic Engineering and BiotechnologyNational Science and Technology Development Agency Pathum Thani Thailand
| | - Manop Pithukpakorn
- Division of Medical Genetics, Department of MedicineFaculty of Medicine Siriraj Hospital, Mahidol University Bangkok Thailand
- Siriraj Center of Research Excellence in Precision MedicineFaculty of Medicine Siriraj Hospital, Mahidol University Bangkok Thailand
| | - Jakris Eu‐ahsunthornwattana
- Department of Community MedicineFaculty of Medicine Ramathibodi Hospital, Mahidol University Bangkok Thailand
- Division of Medical Genetics and Molecular Medicine, Department of Internal Medicine, Faculty of Medicine Ramathibodi HospitalMahidol University Bangkok Thailand
| | - Surakameth Mahasirimongkol
- Medical Genetics Center, Medical Life Sciences Institute, Department of Medical SciencesMinistry of Public Health Nonthaburi Thailand
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14
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Kourou KD, Pezoulas VC, Georga EI, Exarchos TP, Tsanakas P, Tsiknakis M, Varvarigou T, De Vita S, Tzioufas A, Fotiadis DI. Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective. IEEE Rev Biomed Eng 2019; 12:303-318. [DOI: 10.1109/rbme.2018.2855055] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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15
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Mozzi A, Pontremoli C, Sironi M. Genetic susceptibility to infectious diseases: Current status and future perspectives from genome-wide approaches. INFECTION GENETICS AND EVOLUTION 2017; 66:286-307. [PMID: 28951201 PMCID: PMC7106304 DOI: 10.1016/j.meegid.2017.09.028] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Revised: 09/20/2017] [Accepted: 09/21/2017] [Indexed: 02/06/2023]
Abstract
Genome-wide association studies (GWASs) have been widely applied to identify genetic factors that affect complex diseases or traits. Presently, the GWAS Catalog includes > 2800 human studies. Of these, only a minority have investigated the susceptibility to infectious diseases or the response to therapies for the treatment or prevention of infections. Despite their limited application in the field, GWASs have provided valuable insights by pinpointing associations to both innate and adaptive immune response loci, as well as novel unexpected risk factors for infection susceptibility. Herein, we discuss some issues and caveats of GWASs for infectious diseases, we review the most recent findings ensuing from these studies, and we provide a brief summary of selected GWASs for infections in non-human mammals. We conclude that, although the general trend in the field of complex traits is to shift from GWAS to next-generation sequencing, important knowledge on infectious disease-related traits can be still gained by GWASs, especially for those conditions that have never been investigated using this approach. We suggest that future studies will benefit from the leveraging of information from the host's and pathogen's genomes, as well as from the exploration of models that incorporate heterogeneity across populations and phenotypes. Interactions within HLA genes or among HLA variants and polymorphisms located outside the major histocompatibility complex may also play an important role in shaping the susceptibility and response to invading pathogens. Relatively few GWASs for infectious diseases were performed. Phenotype heterogeneity and case/control misclassification can affect GWAS power. Adaptive and innate immunity loci were identified in several infectious disease GWASs. Unexpected loci (e.g., lncRNAs) were also associated with infection susceptibility. GWASs should integrate host and pathogen diversity and use complex association models.
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Affiliation(s)
- Alessandra Mozzi
- Bioinformatics, Scientific Institute IRCCS E.MEDEA, 23842 Bosisio Parini, Italy
| | - Chiara Pontremoli
- Bioinformatics, Scientific Institute IRCCS E.MEDEA, 23842 Bosisio Parini, Italy
| | - Manuela Sironi
- Bioinformatics, Scientific Institute IRCCS E.MEDEA, 23842 Bosisio Parini, Italy.
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16
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Extracting Country-of-Origin from Electronic Health Records for Gene- Environment Studies as Part of the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) Study. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2017; 2017:50-57. [PMID: 28815105 PMCID: PMC5543359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
We describe here the extraction of country-of-origin, an acculturation variable relevant for gene-environment studies, in a biorepository linked to de-identified electronic health records (EHRs) assessed by the Epidemiologic Architecture for Genes Linked to Environment (EAGLE), a study site of the Population Architecture using Genomics and Epidemiology (PAGE) I study. We extracted country-of-origin from the unstructured clinical free text using regular expressions within the MySQL relational database system in a cohort of 15,863 subjects of mostly non-European descent (including 11,519 African Americans, 1,702 Hispanics, and 1,118 Asians). We performed searches for 231 world countries (including independent sovereign states, dependent areas, and disputed territories) and common misspellings in >14 gigabytes of data including >13 billion characters of clinical text. Manual review of a fraction of the initial country-of-origin assignments established rules for data cleaning and quality control to achieve final country-of-origin status for each subject. After data cleaning, a total of 1,911/15,893 (12.02%) subjects were assigned to a country-of-origin outside of the United States. Mexico was the most commonly assigned country outside of the United States (264 subjects; 13.8% of subjects with a foreign country-of-origin assignment). The distribution of the countries assigned followed expectations based on known migration patterns to the United States with an emphasis on the southeastern region. These data suggest country-of-origin can be successfully extracted from unstructured clinical text for downstream genetic association studies.
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17
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Fortier I, Raina P, Van den Heuvel ER, Griffith LE, Craig C, Saliba M, Doiron D, Stolk RP, Knoppers BM, Ferretti V, Granda P, Burton P. Maelstrom Research guidelines for rigorous retrospective data harmonization. Int J Epidemiol 2017; 46:103-105. [PMID: 27272186 PMCID: PMC5407152 DOI: 10.1093/ije/dyw075] [Citation(s) in RCA: 83] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/16/2016] [Indexed: 12/26/2022] Open
Abstract
Background It is widely accepted and acknowledged that data harmonization is crucial: in its absence, the co-analysis of major tranches of high quality extant data is liable to inefficiency or error. However, despite its widespread practice, no formalized/systematic guidelines exist to ensure high quality retrospective data harmonization. Methods To better understand real-world harmonization practices and facilitate development of formal guidelines, three interrelated initiatives were undertaken between 2006 and 2015. They included a phone survey with 34 major international research initiatives, a series of workshops with experts, and case studies applying the proposed guidelines. Results A wide range of projects use retrospective harmonization to support their research activities but even when appropriate approaches are used, the terminologies, procedures, technologies and methods adopted vary markedly. The generic guidelines outlined in this article delineate the essentials required and describe an interdependent step-by-step approach to harmonization: 0) define the research question, objectives and protocol; 1) assemble pre-existing knowledge and select studies; 2) define targeted variables and evaluate harmonization potential; 3) process data; 4) estimate quality of the harmonized dataset(s) generated; and 5) disseminate and preserve final harmonization products. Conclusions This manuscript provides guidelines aiming to encourage rigorous and effective approaches to harmonization which are comprehensively and transparently documented and straightforward to interpret and implement. This can be seen as a key step towards implementing guiding principles analogous to those that are well recognised as being essential in securing the foundational underpinning of systematic reviews and the meta-analysis of clinical trials.
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Affiliation(s)
- Isabel Fortier
- Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Parminder Raina
- McMaster University, Department of Clinical Epidemiology and Biostatistics, Hamilton, ON, Canada
| | - Edwin R Van den Heuvel
- Eindhoven University of Technology, Department of Mathematics and Computer Science, Eindhoven, The Netherlands
| | - Lauren E Griffith
- McMaster University, Department of Clinical Epidemiology and Biostatistics, Hamilton, ON, Canada
| | - Camille Craig
- Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Matilda Saliba
- Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Dany Doiron
- Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Ronald P Stolk
- University Medical Center Groningen, Department of Epidemiology, Groningen, Groningen, The Netherlands
| | - Bartha M Knoppers
- McGill University, Centre of Genomics and Policy, Montreal, Montrreal, QC, Canada
| | - Vincent Ferretti
- Ontario Institute for Cancer Research, MaRS Centre, Toronto, ON, Canada
| | - Peter Granda
- University of Michigan, Inter-university Consortium for Political and Social Research (ICPSR), Ann Arbor, MI, USA
| | - Paul Burton
- University of Bristol, D2K Research Group, School of Social and Community Medicine, Bristol, UK
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18
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Windle M, Kogan SM, Lee S, Chen YF, Lei KM, Brody GH, Beach SRH, Yu T. Neighborhood × Serotonin Transporter Linked Polymorphic Region (5-HTTLPR) interactions for substance use from ages 10 to 24 years using a harmonized data set of African American children. Dev Psychopathol 2016; 28:415-31. [PMID: 26073189 PMCID: PMC4881837 DOI: 10.1017/s095457941500053x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
This study investigated the influences of neighborhood factors (residential stability and neighborhood disadvantage) and variants of the serotonin transporter linked polymorphic region (5-HTTLPR) genotype on the development of substance use among African American children aged 10-24 years. To accomplish this, a harmonized data set of five longitudinal studies was created via pooling overlapping age cohorts to establish a database with 2,689 children and 12,474 data points to span ages 10-24 years. A description of steps used in the development of the harmonized data set is provided, including how issues such as the measurement equivalence of constructs were addressed. A sequence of multilevel models was specified to evaluate Gene × Environment effects on growth of substance use across time. Findings indicated that residential instability was associated with higher levels and a steeper gradient of growth in substance use across time. The inclusion of the 5-HTTLPR genotype provided greater precision to the relationships in that higher residential instability, in conjunction with the risk variant of 5-HTTLPR (i.e., the short allele), was associated with the highest level and steepest gradient of growth in substance use across ages 10-24 years. The findings demonstrated how the creation of a harmonized data set increased statistical power to test Gene × Environment interactions for an under studied sample.
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Nelson EC, Agrawal A, Heath AC, Bogdan R, Sherva R, Zhang B, Al-Hasani R, Bruchas MR, Chou YL, Demers CH, Carey CE, Conley ED, Fakira AK, Farrer LA, Goate A, Gordon S, Henders AK, Hesselbrock V, Kapoor M, Lynskey MT, Madden PA, Moron JA, Rice JP, Saccone NL, Schwab SG, Shand FL, Todorov AA, Wallace L, Wang T, Wray NR, Zhou X, Degenhardt L, Martin NG, Hariri AR, Kranzler HR, Gelernter J, Bierut LJ, Clark DJ, Montgomery GW. Evidence of CNIH3 involvement in opioid dependence. Mol Psychiatry 2016; 21:608-14. [PMID: 26239289 PMCID: PMC4740268 DOI: 10.1038/mp.2015.102] [Citation(s) in RCA: 86] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2015] [Revised: 05/12/2015] [Accepted: 06/16/2015] [Indexed: 01/28/2023]
Abstract
Opioid dependence, a severe addictive disorder and major societal problem, has been demonstrated to be moderately heritable. We conducted a genome-wide association study in Comorbidity and Trauma Study data comparing opioid-dependent daily injectors (N=1167) with opioid misusers who never progressed to daily injection (N=161). The strongest associations, observed for CNIH3 single-nucleotide polymorphisms (SNPs), were confirmed in two independent samples, the Yale-Penn genetic studies of opioid, cocaine and alcohol dependence and the Study of Addiction: Genetics and Environment, which both contain non-dependent opioid misusers and opioid-dependent individuals. Meta-analyses found five genome-wide significant CNIH3 SNPs. The A allele of rs10799590, the most highly associated SNP, was robustly protective (P=4.30E-9; odds ratio 0.64 (95% confidence interval 0.55-0.74)). Epigenetic annotation predicts that this SNP is functional in fetal brain. Neuroimaging data from the Duke Neurogenetics Study (N=312) provide evidence of this SNP's in vivo functionality; rs10799590 A allele carriers displayed significantly greater right amygdala habituation to threat-related facial expressions, a phenotype associated with resilience to psychopathology. Computational genetic analyses of physical dependence on morphine across 23 mouse strains yielded significant correlations for haplotypes in CNIH3 and functionally related genes. These convergent findings support CNIH3 involvement in the pathophysiology of opioid dependence, complementing prior studies implicating the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) glutamate system.
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Affiliation(s)
| | | | | | | | | | - Bo Zhang
- Washington University, St. Louis, MO
| | | | | | | | | | | | | | - Amanda K. Fakira
- Columbia University College of Physicians and Surgeons, New York, NY
| | | | - Alison Goate
- Icahn School of Medicine at Mount Sinai, New York, NY
| | - Scott Gordon
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Anjali K. Henders
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | | | - Manav Kapoor
- Icahn School of Medicine at Mount Sinai, New York, NY
| | | | | | - Jose A. Moron
- Columbia University College of Physicians and Surgeons, New York, NY
| | | | | | - Sibylle G. Schwab
- Faculty of Science Medicine & Health, University of Wollongong, Wollongong Australia
| | | | | | - Leanne Wallace
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Ting Wang
- Washington University, St. Louis, MO
| | - Naomi R. Wray
- The University of Queensland, Queensland Brain Institute, Brisbane, Queensland, Australia
| | - Xin Zhou
- St. Jude Children’s Research Hospital, Memphis, TN
| | - Louisa Degenhardt
- National Drug and Alcohol Research Centre, University of New South Wales, Sydney, Australia
| | - Nicholas G. Martin
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | | | - Henry R. Kranzler
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
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20
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Unravelling the human genome-phenome relationship using phenome-wide association studies. Nat Rev Genet 2016; 17:129-45. [PMID: 26875678 DOI: 10.1038/nrg.2015.36] [Citation(s) in RCA: 166] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Advances in genotyping technology have, over the past decade, enabled the focused search for common genetic variation associated with human diseases and traits. With the recently increased availability of detailed phenotypic data from electronic health records and epidemiological studies, the impact of one or more genetic variants on the phenome is starting to be characterized both in clinical and population-based settings using phenome-wide association studies (PheWAS). These studies reveal a number of challenges that will need to be overcome to unlock the full potential of PheWAS for the characterization of the complex human genome-phenome relationship.
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21
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Kim Y, Park T. Robust Gene-Gene Interaction Analysis in Genome Wide Association Studies. PLoS One 2015; 10:e0135016. [PMID: 26267341 PMCID: PMC4534386 DOI: 10.1371/journal.pone.0135016] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2014] [Accepted: 07/17/2015] [Indexed: 11/19/2022] Open
Abstract
Genome-wide association studies (GWAS) have successfully discovered hundreds of associations between genetic variants and complex traits. Most GWAS have focused on the identification of single variants. It has been shown that most of the variants that were discovered by GWAS could only partially explain disease heritability. The explanation for this missing heritability is generally believed to be gene-gene (GG) or gene-environment (GE) interactions and other structural variants. Generalized multifactor dimensionality reduction (GMDR) has been proven to be reasonably powerful in detecting GG and GE interactions; however, its performance has been found to decline when outlying quantitative traits are present. This paper proposes a robust GMDR estimation method (based on the L-estimator and M-estimator estimation methods) in an attempt to reduce the effects caused by outlying traits. A comparison of robust GMDR with the original MDR based on simulation studies showed the former method to outperform the latter. The performance of robust GMDR is illustrated through a real GWA example consisting of 8,577 samples from the Korean population using the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) level as a phenotype. Robust GMDR identified the KCNH1 gene to have strong interaction effects with other genes on the function of insulin secretion.
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Affiliation(s)
- Yongkang Kim
- Department of Statistics, Seoul National University, Seoul, South Korea
| | - Taesung Park
- Department of Statistics, Seoul National University, Seoul, South Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151–741, South Korea
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Hendershot T, Pan H, Haines J, Harlan WR, Marazita ML, McCarty CA, Ramos EM, Hamilton CM. Using the PhenX Toolkit to Add Standard Measures to a Study. ACTA ACUST UNITED AC 2015; 86:1.21.1-1.21.17. [PMID: 26132000 DOI: 10.1002/0471142905.hg0121s86] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The PhenX (consensus measures for Phenotypes and eXposures) Toolkit (https://www.phenxtoolkit.org/) offers high-quality, well-established measures of phenotypes and exposures for use by the scientific community. The goal is to promote the use of standard measures, enhance data interoperability, and help investigators identify opportunities for collaborative and translational research. The Toolkit contains 395 measures drawn from 22 research domains (fields of research), along with additional collections of measures for Substance Abuse and Addiction (SAA) research, Mental Health Research (MHR), and Tobacco Regulatory Research (TRR). Additional measures for TRR that are expected to be released in 2015 include Obesity, Eating Disorders, and Sickle Cell Disease. Measures are selected by working groups of domain experts using a consensus process that includes input from the scientific community. The Toolkit provides a description of each PhenX measure, the rationale for including it in the Toolkit, protocol(s) for collecting the measure, and supporting documentation. Users can browse measures in the Toolkit or can search the Toolkit using the Smart Query Tool or a full text search. PhenX Toolkit users select measures of interest to add to their Toolkit. Registered Toolkit users can save their Toolkit and return to it later to revise or complete. They then have options to download a customized Data Collection Worksheet that specifies the data to be collected, and a Data Dictionary that describes each variable included in the Data Collection Worksheet. The Toolkit also has a Register Your Study feature that facilitates cross-study collaboration by allowing users to find other investigators using the same PhenX measures.
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Affiliation(s)
| | - Huaqin Pan
- RTI International, Research Triangle Park, North Carolina
| | | | - William R Harlan
- Retired, Office of the Director, National Institutes of Health, Bethesda, Maryland
| | | | | | - Erin M Ramos
- National Human Genome Research Institute, Bethesda, Maryland
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23
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Gross AL, Sherva R, Mukherjee S, Newhouse S, Kauwe JSK, Munsie LM, Waterston LB, Bennett DA, Jones RN, Green RC, Crane PK. Calibrating longitudinal cognition in Alzheimer's disease across diverse test batteries and datasets. Neuroepidemiology 2014; 43:194-205. [PMID: 25402421 PMCID: PMC4297570 DOI: 10.1159/000367970] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2014] [Accepted: 08/23/2014] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND We sought to identify optimal approaches by calibrating longitudinal cognitive performance across studies with different neuropsychological batteries. METHODS We examined four approaches to calibrate cognitive performance in nine longitudinal studies of Alzheimer's disease (AD) (n = 10,875): (1) common test, (2) standardize and average available tests, (3) confirmatory factor analysis (CFA) with continuous indicators, and (4) CFA with categorical indicators. To compare precision, we determined the minimum sample sizes needed to detect 25% cognitive decline with 80% power. To compare criterion validity, we correlated cognitive change from each approach with 6-year changes in average cortical thickness and hippocampal volume using available MRI data from the AD Neuroimaging Initiative. RESULTS CFA with categorical indicators required the smallest sample size to detect 25% cognitive decline with 80% power (n = 232) compared to common test (n = 277), standardize-and-average (n = 291), and CFA with continuous indicators (n = 315) approaches. Associations with changes in biomarkers changes were the strongest for CFA with categorical indicators. CONCLUSIONS CFA with categorical indicators demonstrated greater power to detect change and superior criterion validity compared to other approaches. It has wide applicability to directly compare cognitive performance across studies, making it a good way to obtain operational phenotypes for genetic analyses of cognitive decline among people with AD.
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Affiliation(s)
- Alden L Gross
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md., USA
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24
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Data compatibility in the addiction sciences: an examination of measure commonality. Drug Alcohol Depend 2014; 141:153-8. [PMID: 24954640 PMCID: PMC4096981 DOI: 10.1016/j.drugalcdep.2014.04.029] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2013] [Revised: 04/28/2014] [Accepted: 04/30/2014] [Indexed: 11/23/2022]
Abstract
The need for comprehensive analysis to compare and combine data across multiple studies in order to validate and extend results is widely recognized. This paper aims to assess the extent of data compatibility in the substance abuse and addiction (SAA) sciences through an examination of measure commonality, defined as the use of similar measures, across grants funded by the National Institute on Drug Abuse (NIDA) and the National Institute on Alcohol Abuse and Alcoholism (NIAAA). Data were extracted from applications of funded, active grants involving human-subjects research in four scientific areas (epidemiology, prevention, services, and treatment) and six frequently assessed scientific domains. A total of 548 distinct measures were cited across 141 randomly sampled applications. Commonality, as assessed by density (range of 0-1) of shared measurement, was examined. Results showed that commonality was low and varied by domain/area. Commonality was most prominent for (1) diagnostic interviews (structured and semi-structured) for substance use disorders and psychopathology (density of 0.88), followed by (2) scales to assess dimensions of substance use problems and disorders (0.70), (3) scales to assess dimensions of affect and psychopathology (0.69), (4) measures of substance use quantity and frequency (0.62), (5) measures of personality traits (0.40), and (6) assessments of cognitive/neurologic ability (0.22). The areas of prevention (density of 0.41) and treatment (0.42) had greater commonality than epidemiology (0.36) and services (0.32). To address the lack of measure commonality, NIDA and its scientific partners recommend and provide common measures for SAA researchers within the PhenX Toolkit.
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25
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Genome-wide diet-gene interaction analyses for risk of colorectal cancer. PLoS Genet 2014; 10:e1004228. [PMID: 24743840 PMCID: PMC3990510 DOI: 10.1371/journal.pgen.1004228] [Citation(s) in RCA: 73] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2013] [Accepted: 01/20/2014] [Indexed: 12/14/2022] Open
Abstract
Dietary factors, including meat, fruits, vegetables and fiber, are associated with colorectal cancer; however, there is limited information as to whether these dietary factors interact with genetic variants to modify risk of colorectal cancer. We tested interactions between these dietary factors and approximately 2.7 million genetic variants for colorectal cancer risk among 9,287 cases and 9,117 controls from ten studies. We used logistic regression to investigate multiplicative gene-diet interactions, as well as our recently developed Cocktail method that involves a screening step based on marginal associations and gene-diet correlations and a testing step for multiplicative interactions, while correcting for multiple testing using weighted hypothesis testing. Per quartile increment in the intake of red and processed meat were associated with statistically significant increased risks of colorectal cancer and vegetable, fruit and fiber intake with lower risks. From the case-control analysis, we detected a significant interaction between rs4143094 (10p14/near GATA3) and processed meat consumption (OR = 1.17; p = 8.7E-09), which was consistently observed across studies (p heterogeneity = 0.78). The risk of colorectal cancer associated with processed meat was increased among individuals with the rs4143094-TG and -TT genotypes (OR = 1.20 and OR = 1.39, respectively) and null among those with the GG genotype (OR = 1.03). Our results identify a novel gene-diet interaction with processed meat for colorectal cancer, highlighting that diet may modify the effect of genetic variants on disease risk, which may have important implications for prevention.
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26
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Dang Do AN, Munchhof AM, Terry C, Emmett T, Kara A. Research and publication trends in hospital medicine. J Hosp Med 2014; 9:148-54. [PMID: 24591288 DOI: 10.1002/jhm.2148] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2013] [Revised: 11/20/2013] [Accepted: 12/17/2013] [Indexed: 11/08/2022]
Abstract
BACKGROUND Research by hospitalists may aid the evolution of hospital medicine into an academic specialty. OBJECTIVE To describe the factors associated with research and publication activities among hospitalists and describe trends in hospitalist-led publications. METHODS We surveyed members of the Society of Hospital Medicine in June 2012 and conducted univariate analyses on their responses to determine predictors of successful authorship and to describe factors associated with research engagement. We searched PubMed from the database inception to October 2013 for publications with "hospitalist" or "hospital medicine" affiliated authors. Original research articles were reviewed for methodology and funding sources. RESULTS Of the 645 respondents (5.8% response rate), 277 (43%) had authored peer-reviewed publications, 126 (19%) had access to mentorship, and 68 (11%) reported funding support. There were 213 (33%) who were engaged in research, with the majority conducting quality improvement (QI) research (n = 152, 24%). Completion of a fellowship, pediatrics training, the presence of a mentor, funding, and >25% protected time for research were each individually associated with an increased likelihood of authoring publications. Hospitalist-led publications in PubMed have been increasing from 36 in 2006 to 179 in the first 10 months of 2013. Of the original research publications (n = 317), the majority were clinical (n = 129, 41%), and 58 (18%) were QI. Thirty-nine (22%) authors reported funding support. CONCLUSIONS Peer-reviewed publications by hospitalists are increasing, suggesting the academic maturation of hospital medicine. Provision of mentorship for hospitalists specifically in QI and guidance toward funding resources may assist in supporting this trend.
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Affiliation(s)
- An N Dang Do
- Internal Medicine-Pediatric Residency Program, Indiana University School of Medicine, Indianapolis, Indiana
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27
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Data harmonization and federated analysis of population-based studies: the BioSHaRE project. Emerg Themes Epidemiol 2013; 10:12. [PMID: 24257327 PMCID: PMC4175511 DOI: 10.1186/1742-7622-10-12] [Citation(s) in RCA: 91] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2013] [Accepted: 11/11/2013] [Indexed: 01/08/2023] Open
Abstract
Abstracts
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28
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Data sharing in large research consortia: experiences and recommendations from ENGAGE. Eur J Hum Genet 2013; 22:317-21. [PMID: 23778872 PMCID: PMC3925260 DOI: 10.1038/ejhg.2013.131] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2013] [Accepted: 05/11/2013] [Indexed: 11/24/2022] Open
Abstract
Data sharing is essential for the conduct of cutting-edge research and is increasingly required by funders concerned with maximising the scientific yield from research data collections. International research consortia are encouraged to share data intra-consortia, inter-consortia and with the wider scientific community. Little is reported regarding the factors that hinder or facilitate data sharing in these different situations. This paper provides results from a survey conducted in the European Network for Genetic and Genomic Epidemiology (ENGAGE) that collected information from its participating institutions about their data-sharing experiences. The questionnaire queried about potential hurdles to data sharing, concerns about data sharing, lessons learned and recommendations for future collaborations. Overall, the survey results reveal that data sharing functioned well in ENGAGE and highlight areas that posed the most frequent hurdles for data sharing. Further challenges arise for international data sharing beyond the consortium. These challenges are described and steps to help address these are outlined.
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Idaghdour Y, Awadalla P. Exploiting gene expression variation to capture gene-environment interactions for disease. Front Genet 2013; 3:228. [PMID: 23755064 PMCID: PMC3668192 DOI: 10.3389/fgene.2012.00228] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2012] [Accepted: 10/10/2012] [Indexed: 12/18/2022] Open
Abstract
Gene-environment interactions have long been recognized as a fundamental concept in evolutionary, quantitative, and medical genetics. In the genomics era, study of how environment and genome interact to shape gene expression variation is relevant to understanding the genetic architecture of complex phenotypes. While genetic analysis of gene expression variation focused on main effects, little is known about the extent of interaction effects implicating regulatory variants and their consequences on transcriptional variation. Here we survey the current state of the concept of transcriptional gene-environment interactions and discuss its utility for mapping disease phenotypes in light of the insights gained from genome-wide association studies of gene expression.
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Affiliation(s)
- Youssef Idaghdour
- Sainte-Justine Research Center, University of Montreal Montreal, QC, Canada
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30
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Evangelou E, Ioannidis JPA. Meta-analysis methods for genome-wide association studies and beyond. Nat Rev Genet 2013; 14:379-89. [PMID: 23657481 DOI: 10.1038/nrg3472] [Citation(s) in RCA: 382] [Impact Index Per Article: 34.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Meta-analysis of genome-wide association studies (GWASs) has become a popular method for discovering genetic risk variants. Here, we overview both widely applied and newer statistical methods for GWAS meta-analysis, including issues of interpretation and assessment of sources of heterogeneity. We also discuss extensions of these meta-analysis methods to complex data. Where possible, we provide guidelines for researchers who are planning to use these methods. Furthermore, we address special issues that may arise for meta-analysis of sequencing data and rare variants. Finally, we discuss challenges and solutions surrounding the goals of making meta-analysis data publicly available and building powerful consortia.
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Affiliation(s)
- Evangelos Evangelou
- Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina 45110, Greece
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31
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Wang X, Shaffer JR, Zeng Z, Begum F, Vieira AR, Noel J, Anjomshoaa I, Cuenco KT, Lee MK, Beck J, Boerwinkle E, Cornelis MC, Hu FB, Crosslin DR, Laurie CC, Nelson SC, Doheny KF, Pugh EW, Polk DE, Weyant RJ, Crout R, McNeil DW, Weeks DE, Feingold E, Marazita ML. Genome-wide association scan of dental caries in the permanent dentition. BMC Oral Health 2012; 12:57. [PMID: 23259602 PMCID: PMC3574042 DOI: 10.1186/1472-6831-12-57] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2012] [Accepted: 11/28/2012] [Indexed: 01/05/2023] Open
Abstract
UNLABELLED BACKGROUND Over 90% of adults aged 20 years or older with permanent teeth have suffered from dental caries leading to pain, infection, or even tooth loss. Although caries prevalence has decreased over the past decade, there are still about 23% of dentate adults who have untreated carious lesions in the US. Dental caries is a complex disorder affected by both individual susceptibility and environmental factors. Approximately 35-55% of caries phenotypic variation in the permanent dentition is attributable to genes, though few specific caries genes have been identified. Therefore, we conducted the first genome-wide association study (GWAS) to identify genes affecting susceptibility to caries in adults. METHODS Five independent cohorts were included in this study, totaling more than 7000 participants. For each participant, dental caries was assessed and genetic markers (single nucleotide polymorphisms, SNPs) were genotyped or imputed across the entire genome. Due to the heterogeneity among the five cohorts regarding age, genotyping platform, quality of dental caries assessment, and study design, we first conducted genome-wide association (GWA) analyses on each of the five independent cohorts separately. We then performed three meta-analyses to combine results for: (i) the comparatively younger, Appalachian cohorts (N = 1483) with well-assessed caries phenotype, (ii) the comparatively older, non-Appalachian cohorts (N = 5960) with inferior caries phenotypes, and (iii) all five cohorts (N = 7443). Top ranking genetic loci within and across meta-analyses were scrutinized for biologically plausible roles on caries. RESULTS Different sets of genes were nominated across the three meta-analyses, especially between the younger and older age cohorts. In general, we identified several suggestive loci (P-value ≤ 10E-05) within or near genes with plausible biological roles for dental caries, including RPS6KA2 and PTK2B, involved in p38-depenedent MAPK signaling, and RHOU and FZD1, involved in the Wnt signaling cascade. Both of these pathways have been implicated in dental caries. ADMTS3 and ISL1 are involved in tooth development, and TLR2 is involved in immune response to oral pathogens. CONCLUSIONS As the first GWAS for dental caries in adults, this study nominated several novel caries genes for future study, which may lead to better understanding of cariogenesis, and ultimately, to improved disease predictions, prevention, and/or treatment.
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Affiliation(s)
- Xiaojing Wang
- Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, 15219, USA.
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Austin MA, Hair MS, Fullerton SM. Research guidelines in the era of large-scale collaborations: an analysis of Genome-wide Association Study Consortia. Am J Epidemiol 2012; 175:962-9. [PMID: 22491085 DOI: 10.1093/aje/kwr441] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Scientific research has shifted from studies conducted by single investigators to the creation of large consortia. Genetic epidemiologists, for example, now collaborate extensively for genome-wide association studies (GWAS). The effect has been a stream of confirmed disease-gene associations. However, effects on human subjects oversight, data-sharing, publication and authorship practices, research organization and productivity, and intellectual property remain to be examined. The aim of this analysis was to identify all research consortia that had published the results of a GWAS analysis since 2005, characterize them, determine which have publicly accessible guidelines for research practices, and summarize the policies in these guidelines. A review of the National Human Genome Research Institute's Catalog of Published Genome-Wide Association Studies identified 55 GWAS consortia as of April 1, 2011. These consortia were comprised of individual investigators, research centers, studies, or other consortia and studied 48 different diseases or traits. Only 14 (25%) were found to have publicly accessible research guidelines on consortia websites. The available guidelines provide information on organization, governance, and research protocols; half address institutional review board approval. Details of publication, authorship, data-sharing, and intellectual property vary considerably. Wider access to consortia guidelines is needed to establish appropriate research standards with broad applicability to emerging forms of large-scale collaboration.
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Affiliation(s)
- Melissa A Austin
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, USA.
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Hutter CM, Chang-Claude J, Slattery ML, Pflugeisen BM, Lin Y, Duggan D, Nan H, Lemire M, Rangrej J, Figueiredo JC, Jiao S, Harrison TA, Liu Y, Chen LS, Stelling DL, Warnick GS, Hoffmeister M, Küry S, Fuchs CS, Giovannucci E, Hazra A, Kraft P, Hunter DJ, Gallinger S, Zanke BW, Brenner H, Frank B, Ma J, Ulrich CM, White E, Newcomb PA, Kooperberg C, LaCroix AZ, Prentice RL, Jackson RD, Schoen RE, Chanock SJ, Berndt SI, Hayes RB, Caan BJ, Potter JD, Hsu L, Bézieau S, Chan AT, Hudson TJ, Peters U. Characterization of gene-environment interactions for colorectal cancer susceptibility loci. Cancer Res 2012; 72:2036-44. [PMID: 22367214 PMCID: PMC3374720 DOI: 10.1158/0008-5472.can-11-4067] [Citation(s) in RCA: 123] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Genome-wide association studies (GWAS) have identified more than a dozen loci associated with colorectal cancer (CRC) risk. Here, we examined potential effect-modification between single-nucleotide polymorphisms (SNP) at 10 of these loci and probable or established environmental risk factors for CRC in 7,016 CRC cases and 9,723 controls from nine cohort and case-control studies. We used meta-analysis of an efficient empirical-Bayes estimator to detect potential multiplicative interactions between each of the SNPs [rs16892766 at 8q23.3 (EIF3H/UTP23), rs6983267 at 8q24 (MYC), rs10795668 at 10p14 (FLJ3802842), rs3802842 at 11q23 (LOC120376), rs4444235 at 14q22.2 (BMP4), rs4779584 at 15q13 (GREM1), rs9929218 at 16q22.1 (CDH1), rs4939827 at 18q21 (SMAD7), rs10411210 at 19q13.1 (RHPN2), and rs961253 at 20p12.3 (BMP2)] and select major CRC risk factors (sex, body mass index, height, smoking status, aspirin/nonsteroidal anti-inflammatory drug use, alcohol use, and dietary intake of calcium, folate, red meat, processed meat, vegetables, fruit, and fiber). The strongest statistical evidence for a gene-environment interaction across studies was for vegetable consumption and rs16892766, located on chromosome 8q23.3, near the EIF3H and UTP23 genes (nominal P(interaction) = 1.3 × 10(-4); adjusted P = 0.02). The magnitude of the main effect of the SNP increased with increasing levels of vegetable consumption. No other interactions were statistically significant after adjusting for multiple comparisons. Overall, the association of most CRC susceptibility loci identified in initial GWAS seems to be invariant to the other risk factors considered; however, our results suggest potential modification of the rs16892766 effect by vegetable consumption.
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Affiliation(s)
- Carolyn M Hutter
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
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Pan H, Tryka KA, Vreeman DJ, Huggins W, Phillips MJ, Mehta JP, Phillips JH, McDonald CJ, Junkins HA, Ramos EM, Hamilton CM. Using PhenX measures to identify opportunities for cross-study analysis. Hum Mutat 2012; 33:849-57. [PMID: 22415805 DOI: 10.1002/humu.22074] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2011] [Accepted: 02/28/2012] [Indexed: 11/11/2022]
Abstract
The PhenX Toolkit provides researchers with recommended, well-established, low-burden measures suitable for human subject research. The database of Genotypes and Phenotypes (dbGaP) is the data repository for a variety of studies funded by the National Institutes of Health, including genome-wide association studies. The dbGaP requires that investigators provide a data dictionary of study variables as part of the data submission process. Thus, dbGaP is a unique resource that can help investigators identify studies that share the same or similar variables. As a proof of concept, variables from 16 studies deposited in dbGaP were mapped to PhenX measures. Soon, investigators will be able to search dbGaP using PhenX variable identifiers and find comparable and related variables in these 16 studies. To enhance effective data exchange, PhenX measures, protocols, and variables were modeled in Logical Observation Identifiers Names and Codes (LOINC® ). PhenX domains and measures are also represented in the Cancer Data Standards Registry and Repository (caDSR). Associating PhenX measures with existing standards (LOINC® and caDSR) and mapping to dbGaP study variables extends the utility of these measures by revealing new opportunities for cross-study analysis.
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Affiliation(s)
- Huaqin Pan
- RTI International, Research Triangle Park, NC 27709, USA.
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Agrawal A, Freedman ND, Cheng YC, Lin P, Shaffer JR, Sun Q, Taylor K, Yaspan B, Cole JW, Cornelis MC, DeSensi RS, Fitzpatrick A, Heiss G, Kang JH, O'Connell J, Bennett S, Bookman E, Bucholz KK, Caporaso N, Crout R, Dick DM, Edenberg HJ, Goate A, Hesselbrock V, Kittner S, Kramer J, Nurnberger JI, Qi L, Rice JP, Schuckit M, van Dam RM, Boerwinkle E, Hu F, Levy S, Marazita M, Mitchell BD, Pasquale LR, Bierut LJ. Measuring alcohol consumption for genomic meta-analyses of alcohol intake: opportunities and challenges. Am J Clin Nutr 2012; 95:539-47. [PMID: 22301922 PMCID: PMC3278237 DOI: 10.3945/ajcn.111.015545] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2011] [Accepted: 12/21/2011] [Indexed: 01/09/2023] Open
Abstract
Whereas moderate drinking may have health benefits, excessive alcohol consumption causes many important acute and chronic diseases and is the third leading contributor to preventable death in the United States. Twin studies suggest that alcohol-consumption patterns are heritable (50%); however, multiple genetic variants of modest effect size are likely to contribute to this heritable variation. Genome-wide association studies provide a tool for discovering genetic loci that contribute to variations in alcohol consumption. Opportunities exist to identify susceptibility loci with modest effect by meta-analyzing together multiple studies. However, existing studies assessed many different aspects of alcohol use, such as typical compared with heavy drinking, and these different assessments can be difficult to reconcile. In addition, many studies lack the ability to distinguish between lifetime and recent abstention or to assess the pattern of drinking during the week, and a variety of such concerns surround the appropriateness of developing a common summary measure of alcohol intake. Combining such measures of alcohol intake can cause heterogeneity and exposure misclassification, cause a reduction in power, and affect the magnitude of genetic association signals. In this review, we discuss the challenges associated with harmonizing alcohol-consumption data from studies with widely different assessment instruments, with a particular focus on large-scale genetic studies.
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Affiliation(s)
- Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63110, USA.
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Hendershot T, Pan H, Haines J, Harlan WR, Junkins HA, Ramos EM, Hamilton CM. Using the PhenX Toolkit to Add Standard Measures to a Study. ACTA ACUST UNITED AC 2012; Chapter 1:Unit1.21. [PMID: 21975939 DOI: 10.1002/0471142905.hg0121s71] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The PhenX (consensus measures for Phenotypes and eXposures) Toolkit (https://www.phenxtoolkit.org/) offers high-quality, well-established measures of phenotypes and exposures for use by the scientific community. The Toolkit contains 295 measures drawn from 21 research domains (fields of research). The measures were selected by Working Groups of domain experts using a consensus process that included input from the scientific community. The Toolkit provides a description of each PhenX measure, the rationale for including it in the Toolkit, protocol(s) for collecting the measure, and supporting documentation. Users can browse by measures, domains, or collections, or can search the Toolkit using the Smart Query Tool. Once users have selected some measures, they can download a customized Data Collection Worksheet that specifies what information needs to be collected, and a Data Dictionary that describes each variable included in their Data Collection Worksheet. To help researchers find studies with comparable data, PhenX measures and variables are being mapped to studies in the database of Genotypes and Phenotypes (dbGaP).
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Agrawal A, Freedman ND, Bierut LJ. Genome-wide association studies of alcohol intake--a promising cocktail? Am J Clin Nutr 2011; 93:681-3. [PMID: 21367945 PMCID: PMC3057543 DOI: 10.3945/ajcn.111.012641] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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