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Chao HT, Davids M, Burke E, Pappas JG, Rosenfeld JA, McCarty AJ, Davis T, Wolfe L, Toro C, Tifft C, Xia F, Stong N, Johnson TK, Warr CG, Yamamoto S, Adams DR, Markello TC, Gahl WA, Bellen HJ, Wangler MF, Malicdan MCV, Adams DR, Adams CJ, Alejandro ME, Allard P, Ashley EA, Bacino CA, Balasubramanyam A, Barseghyan H, Beggs AH, Bellen HJ, Bernstein JA, Bick DP, Birch CL, Boone BE, Briere LC, Brown DM, Brush M, Burrage LC, Chao KR, Clark GD, Cogan JD, Cooper CM, Craigen WJ, Davids M, Dayal JG, Dell'Angelica EC, Dhar SU, Dipple KM, Donnell-Fink LA, Dorrani N, Dorset DC, Draper DD, Dries AM, Eckstein DJ, Emrick LT, Eng CM, Esteves C, Estwick T, Fisher PG, Frisby TS, Frost K, Gahl WA, Gartner V, Godfrey RA, Goheen M, Golas GA, Goldstein DB, Gordon M“GG, Gould SE, Gourdine JPF, Graham BH, Groden CA, Gropman AL, Hackbarth ME, Haendel M, Hamid R, Hanchard NA, Handley LH, Hardee I, Herzog MR, Holm IA, Howerton EM, Jacob HJ, Jain M, Jiang YH, Johnston JM, Jones AL, Koehler AE, Koeller DM, Kohane IS, Kohler JN, Krasnewich DM, Krieg EL, Krier JB, Kyle JE, Lalani SR, Latham L, Latour YL, Lau CC, Lazar J, Lee BH, Lee H, Lee PR, Levy SE, Levy DJ, Lewis RA, Liebendorder AP, Lincoln SA, Loomis CR, Loscalzo J, Maas RL, Macnamara EF, MacRae CA, Maduro VV, Malicdan MCV, Mamounas LA, Manolio TA, Markello TC, Mashid AS, Mazur P, McCarty AJ, McConkie-Rosell A, McCray AT, Metz TO, Might M, Moretti PM, Mulvihill JJ, Murphy JL, Muzny DM, Nehrebecky ME, Nelson SF, Newberry JS, Newman JH, Nicholas SK, Novacic D, Orange JS, Pallais JC, Palmer CG, Papp JC, Pena LD, Phillips JA, Posey JE, Postlethwait JH, Potocki L, Pusey BN, Ramoni RB, Rodan LH, Sadozai S, Schaffer KE, Schoch K, Schroeder MC, Scott DA, Sharma P, Shashi V, Silverman EK, Sinsheimer JS, Soldatos AG, Spillmann RC, Splinter K, Stoler JM, Stong N, Strong KA, Sullivan JA, Sweetser DA, Thomas SP, Tift CJ, Tolman NJ, Toro C, Tran AA, Valivullah ZM, Vilain E, Waggott DM, Wahl CE, Walley NM, Walsh CA, Wangler MF, Warburton M, Ward PA, Waters KM, Webb-Robertson BJM, Weech AA, Westerfield M, Wheeler MT, Wise AL, Worthe LA, Worthey EA, Yamamoto S, Yang Y, Yu G, Zornio PA. A Syndromic Neurodevelopmental Disorder Caused by De Novo Variants in EBF3. Am J Hum Genet 2017; 100:128-137. [PMID: 28017372 PMCID: PMC5223093 DOI: 10.1016/j.ajhg.2016.11.018] [Citation(s) in RCA: 76] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Accepted: 11/21/2016] [Indexed: 02/06/2023] Open
Abstract
Early B cell factor 3 (EBF3) is a member of the highly evolutionarily conserved Collier/Olf/EBF (COE) family of transcription factors. Prior studies on invertebrate and vertebrate animals have shown that EBF3 homologs are essential for survival and that loss-of-function mutations are associated with a range of nervous system developmental defects, including perturbation of neuronal development and migration. Interestingly, aristaless-related homeobox (ARX), a homeobox-containing transcription factor critical for the regulation of nervous system development, transcriptionally represses EBF3 expression. However, human neurodevelopmental disorders related to EBF3 have not been reported. Here, we describe three individuals who are affected by global developmental delay, intellectual disability, and expressive speech disorder and carry de novo variants in EBF3. Associated features seen in these individuals include congenital hypotonia, structural CNS malformations, ataxia, and genitourinary abnormalities. The de novo variants affect a single conserved residue in a zinc finger motif crucial for DNA binding and are deleterious in a fly model. Our findings indicate that mutations in EBF3 cause a genetic neurodevelopmental syndrome and suggest that loss of EBF3 function might mediate a subset of neurologic phenotypes shared by ARX-related disorders, including intellectual disability, abnormal genitalia, and structural CNS malformations.
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Affiliation(s)
- Andrew L Beam
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
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Nazeen S, Palmer NP, Berger B, Kohane IS. Integrative analysis of genetic data sets reveals a shared innate immune component in autism spectrum disorder and its co-morbidities. Genome Biol 2016; 17:228. [PMID: 27842596 PMCID: PMC5108086 DOI: 10.1186/s13059-016-1084-z] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2016] [Accepted: 10/12/2016] [Indexed: 12/22/2022] Open
Abstract
Background Autism spectrum disorder (ASD) is a common neurodevelopmental disorder that tends to co-occur with other diseases, including asthma, inflammatory bowel disease, infections, cerebral palsy, dilated cardiomyopathy, muscular dystrophy, and schizophrenia. However, the molecular basis of this co-occurrence, and whether it is due to a shared component that influences both pathophysiology and environmental triggering of illness, has not been elucidated. To address this, we deploy a three-tiered transcriptomic meta-analysis that functions at the gene, pathway, and disease levels across ASD and its co-morbidities. Results Our analysis reveals a novel shared innate immune component between ASD and all but three of its co-morbidities that were examined. In particular, we find that the Toll-like receptor signaling and the chemokine signaling pathways, which are key pathways in the innate immune response, have the highest shared statistical significance. Moreover, the disease genes that overlap these two innate immunity pathways can be used to classify the cases of ASD and its co-morbidities vs. controls with at least 70 % accuracy. Conclusions This finding suggests that a neuropsychiatric condition and the majority of its non-brain-related co-morbidities share a dysregulated signal that serves as not only a common genetic basis for the diseases but also as a link to environmental triggers. It also raises the possibility that treatment and/or prophylaxis used for disorders of innate immunity may be successfully used for ASD patients with immune-related phenotypes. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1084-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sumaiya Nazeen
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, MA, USA
| | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, 25 Shattuck Street, Boston, 02115, MA, USA
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, MA, USA. .,Department of Mathematics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, MA, USA.
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, 25 Shattuck Street, Boston, 02115, MA, USA.
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104
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Abstract
BACKGROUND For more than a decade, risk stratification for hypertrophic cardiomyopathy has been enhanced by targeted genetic testing. Using sequencing results, clinicians routinely assess the risk of hypertrophic cardiomyopathy in a patient's relatives and diagnose the condition in patients who have ambiguous clinical presentations. However, the benefits of genetic testing come with the risk that variants may be misclassified. METHODS Using publicly accessible exome data, we identified variants that have previously been considered causal in hypertrophic cardiomyopathy and that are overrepresented in the general population. We studied these variants in diverse populations and reevaluated their initial ascertainments in the medical literature. We reviewed patient records at a leading genetic-testing laboratory for occurrences of these variants during the near-decade-long history of the laboratory. RESULTS Multiple patients, all of whom were of African or unspecified ancestry, received positive reports, with variants misclassified as pathogenic on the basis of the understanding at the time of testing. Subsequently, all reported variants were recategorized as benign. The mutations that were most common in the general population were significantly more common among black Americans than among white Americans (P<0.001). Simulations showed that the inclusion of even small numbers of black Americans in control cohorts probably would have prevented these misclassifications. We identified methodologic shortcomings that contributed to these errors in the medical literature. CONCLUSIONS The misclassification of benign variants as pathogenic that we found in our study shows the need for sequencing the genomes of diverse populations, both in asymptomatic controls and the tested patient population. These results expand on current guidelines, which recommend the use of ancestry-matched controls to interpret variants. As additional populations of different ancestry backgrounds are sequenced, we expect variant reclassifications to increase, particularly for ancestry groups that have historically been less well studied. (Funded by the National Institutes of Health.).
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Affiliation(s)
- Arjun K Manrai
- From the Departments of Biomedical Informatics (A.K.M., D.M.M., I.S.K.), Pathology (B.H.F.), and Medicine (B.A.M., J.L.), Harvard Medical School, the Departments of Pathology, Massachusetts General Hospital (B.H.F.), and the Department of Pathology (H.L.R.), Division of Cardiovascular Medicine (B.A.M.), and Department of Medicine (B.A.M., J.L.), Brigham and Women's Hospital, Boston, and the Division of Health Sciences and Technology, Harvard-Massachusetts Institute of Technology (MIT) (A.K.M., I.S.K.), the Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine (B.H.F., H.L.R.), and the Computer Science and Artificial Intelligence Laboratory, MIT (P.S.), Cambridge - all in Massachusetts; and the Laboratory of Molecular Cardiology, Department of Cardiology, the Heart Center, University Hospital of Copenhagen, Rigshospitalet, and the Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen (M.S.O.) - both in Copenhagen
| | - Birgit H Funke
- From the Departments of Biomedical Informatics (A.K.M., D.M.M., I.S.K.), Pathology (B.H.F.), and Medicine (B.A.M., J.L.), Harvard Medical School, the Departments of Pathology, Massachusetts General Hospital (B.H.F.), and the Department of Pathology (H.L.R.), Division of Cardiovascular Medicine (B.A.M.), and Department of Medicine (B.A.M., J.L.), Brigham and Women's Hospital, Boston, and the Division of Health Sciences and Technology, Harvard-Massachusetts Institute of Technology (MIT) (A.K.M., I.S.K.), the Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine (B.H.F., H.L.R.), and the Computer Science and Artificial Intelligence Laboratory, MIT (P.S.), Cambridge - all in Massachusetts; and the Laboratory of Molecular Cardiology, Department of Cardiology, the Heart Center, University Hospital of Copenhagen, Rigshospitalet, and the Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen (M.S.O.) - both in Copenhagen
| | - Heidi L Rehm
- From the Departments of Biomedical Informatics (A.K.M., D.M.M., I.S.K.), Pathology (B.H.F.), and Medicine (B.A.M., J.L.), Harvard Medical School, the Departments of Pathology, Massachusetts General Hospital (B.H.F.), and the Department of Pathology (H.L.R.), Division of Cardiovascular Medicine (B.A.M.), and Department of Medicine (B.A.M., J.L.), Brigham and Women's Hospital, Boston, and the Division of Health Sciences and Technology, Harvard-Massachusetts Institute of Technology (MIT) (A.K.M., I.S.K.), the Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine (B.H.F., H.L.R.), and the Computer Science and Artificial Intelligence Laboratory, MIT (P.S.), Cambridge - all in Massachusetts; and the Laboratory of Molecular Cardiology, Department of Cardiology, the Heart Center, University Hospital of Copenhagen, Rigshospitalet, and the Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen (M.S.O.) - both in Copenhagen
| | - Morten S Olesen
- From the Departments of Biomedical Informatics (A.K.M., D.M.M., I.S.K.), Pathology (B.H.F.), and Medicine (B.A.M., J.L.), Harvard Medical School, the Departments of Pathology, Massachusetts General Hospital (B.H.F.), and the Department of Pathology (H.L.R.), Division of Cardiovascular Medicine (B.A.M.), and Department of Medicine (B.A.M., J.L.), Brigham and Women's Hospital, Boston, and the Division of Health Sciences and Technology, Harvard-Massachusetts Institute of Technology (MIT) (A.K.M., I.S.K.), the Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine (B.H.F., H.L.R.), and the Computer Science and Artificial Intelligence Laboratory, MIT (P.S.), Cambridge - all in Massachusetts; and the Laboratory of Molecular Cardiology, Department of Cardiology, the Heart Center, University Hospital of Copenhagen, Rigshospitalet, and the Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen (M.S.O.) - both in Copenhagen
| | - Bradley A Maron
- From the Departments of Biomedical Informatics (A.K.M., D.M.M., I.S.K.), Pathology (B.H.F.), and Medicine (B.A.M., J.L.), Harvard Medical School, the Departments of Pathology, Massachusetts General Hospital (B.H.F.), and the Department of Pathology (H.L.R.), Division of Cardiovascular Medicine (B.A.M.), and Department of Medicine (B.A.M., J.L.), Brigham and Women's Hospital, Boston, and the Division of Health Sciences and Technology, Harvard-Massachusetts Institute of Technology (MIT) (A.K.M., I.S.K.), the Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine (B.H.F., H.L.R.), and the Computer Science and Artificial Intelligence Laboratory, MIT (P.S.), Cambridge - all in Massachusetts; and the Laboratory of Molecular Cardiology, Department of Cardiology, the Heart Center, University Hospital of Copenhagen, Rigshospitalet, and the Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen (M.S.O.) - both in Copenhagen
| | - Peter Szolovits
- From the Departments of Biomedical Informatics (A.K.M., D.M.M., I.S.K.), Pathology (B.H.F.), and Medicine (B.A.M., J.L.), Harvard Medical School, the Departments of Pathology, Massachusetts General Hospital (B.H.F.), and the Department of Pathology (H.L.R.), Division of Cardiovascular Medicine (B.A.M.), and Department of Medicine (B.A.M., J.L.), Brigham and Women's Hospital, Boston, and the Division of Health Sciences and Technology, Harvard-Massachusetts Institute of Technology (MIT) (A.K.M., I.S.K.), the Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine (B.H.F., H.L.R.), and the Computer Science and Artificial Intelligence Laboratory, MIT (P.S.), Cambridge - all in Massachusetts; and the Laboratory of Molecular Cardiology, Department of Cardiology, the Heart Center, University Hospital of Copenhagen, Rigshospitalet, and the Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen (M.S.O.) - both in Copenhagen
| | - David M Margulies
- From the Departments of Biomedical Informatics (A.K.M., D.M.M., I.S.K.), Pathology (B.H.F.), and Medicine (B.A.M., J.L.), Harvard Medical School, the Departments of Pathology, Massachusetts General Hospital (B.H.F.), and the Department of Pathology (H.L.R.), Division of Cardiovascular Medicine (B.A.M.), and Department of Medicine (B.A.M., J.L.), Brigham and Women's Hospital, Boston, and the Division of Health Sciences and Technology, Harvard-Massachusetts Institute of Technology (MIT) (A.K.M., I.S.K.), the Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine (B.H.F., H.L.R.), and the Computer Science and Artificial Intelligence Laboratory, MIT (P.S.), Cambridge - all in Massachusetts; and the Laboratory of Molecular Cardiology, Department of Cardiology, the Heart Center, University Hospital of Copenhagen, Rigshospitalet, and the Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen (M.S.O.) - both in Copenhagen
| | - Joseph Loscalzo
- From the Departments of Biomedical Informatics (A.K.M., D.M.M., I.S.K.), Pathology (B.H.F.), and Medicine (B.A.M., J.L.), Harvard Medical School, the Departments of Pathology, Massachusetts General Hospital (B.H.F.), and the Department of Pathology (H.L.R.), Division of Cardiovascular Medicine (B.A.M.), and Department of Medicine (B.A.M., J.L.), Brigham and Women's Hospital, Boston, and the Division of Health Sciences and Technology, Harvard-Massachusetts Institute of Technology (MIT) (A.K.M., I.S.K.), the Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine (B.H.F., H.L.R.), and the Computer Science and Artificial Intelligence Laboratory, MIT (P.S.), Cambridge - all in Massachusetts; and the Laboratory of Molecular Cardiology, Department of Cardiology, the Heart Center, University Hospital of Copenhagen, Rigshospitalet, and the Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen (M.S.O.) - both in Copenhagen
| | - Isaac S Kohane
- From the Departments of Biomedical Informatics (A.K.M., D.M.M., I.S.K.), Pathology (B.H.F.), and Medicine (B.A.M., J.L.), Harvard Medical School, the Departments of Pathology, Massachusetts General Hospital (B.H.F.), and the Department of Pathology (H.L.R.), Division of Cardiovascular Medicine (B.A.M.), and Department of Medicine (B.A.M., J.L.), Brigham and Women's Hospital, Boston, and the Division of Health Sciences and Technology, Harvard-Massachusetts Institute of Technology (MIT) (A.K.M., I.S.K.), the Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine (B.H.F., H.L.R.), and the Computer Science and Artificial Intelligence Laboratory, MIT (P.S.), Cambridge - all in Massachusetts; and the Laboratory of Molecular Cardiology, Department of Cardiology, the Heart Center, University Hospital of Copenhagen, Rigshospitalet, and the Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen (M.S.O.) - both in Copenhagen
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Beauchemin KJ, Wells JM, Kho AT, Philip VM, Kamir D, Kohane IS, Graber JH, Bult CJ. Temporal dynamics of the developing lung transcriptome in three common inbred strains of laboratory mice reveals multiple stages of postnatal alveolar development. PeerJ 2016; 4:e2318. [PMID: 27602285 PMCID: PMC4991849 DOI: 10.7717/peerj.2318] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Accepted: 07/12/2016] [Indexed: 12/12/2022] Open
Abstract
To characterize temporal patterns of transcriptional activity during normal lung development, we generated genome wide gene expression data for 26 pre- and post-natal time points in three common inbred strains of laboratory mice (C57BL/6J, A/J, and C3H/HeJ). Using Principal Component Analysis and least squares regression modeling, we identified both strain-independent and strain-dependent patterns of gene expression. The 4,683 genes contributing to the strain-independent expression patterns were used to define a murine Developing Lung Characteristic Subtranscriptome (mDLCS). Regression modeling of the Principal Components supported the four canonical stages of mammalian embryonic lung development (embryonic, pseudoglandular, canalicular, saccular) defined previously by morphology and histology. For postnatal alveolar development, the regression model was consistent with four stages of alveolarization characterized by episodic transcriptional activity of genes related to pulmonary vascularization. Genes expressed in a strain-dependent manner were enriched for annotations related to neurogenesis, extracellular matrix organization, and Wnt signaling. Finally, a comparison of mouse and human transcriptomics from pre-natal stages of lung development revealed conservation of pathways associated with cell cycle, axon guidance, immune function, and metabolism as well as organism-specific expression of genes associated with extracellular matrix organization and protein modification. The mouse lung development transcriptome data generated for this study serves as a unique reference set to identify genes and pathways essential for normal mammalian lung development and for investigations into the developmental origins of respiratory disease and cancer. The gene expression data are available from the Gene Expression Omnibus (GEO) archive (GSE74243). Temporal expression patterns of mouse genes can be investigated using a study specific web resource (http://lungdevelopment.jax.org).
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Affiliation(s)
- Kyle J. Beauchemin
- The Jackson Laboratory, Bar Harbor, ME, United States
- Graduate School of Biomedical Sciences and Engineering, The University of Maine, Orono, ME, United States
| | | | - Alvin T. Kho
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, United States
| | | | - Daniela Kamir
- The Jackson Laboratory, Bar Harbor, ME, United States
| | - Isaac S. Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | | | - Carol J. Bult
- The Jackson Laboratory, Bar Harbor, ME, United States
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106
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Lingren T, Thaker V, Brady C, Namjou B, Kennebeck S, Bickel J, Patibandla N, Ni Y, Van Driest SL, Chen L, Roach A, Cobb B, Kirby J, Denny J, Bailey-Davis L, Williams MS, Marsolo K, Solti I, Holm IA, Harley J, Kohane IS, Savova G, Crimmins N. Developing an Algorithm to Detect Early Childhood Obesity in Two Tertiary Pediatric Medical Centers. Appl Clin Inform 2016; 7:693-706. [PMID: 27452794 DOI: 10.4338/aci-2016-01-ra-0015] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Accepted: 06/15/2016] [Indexed: 01/12/2023] Open
Abstract
OBJECTIVE The objective of this study is to develop an algorithm to accurately identify children with severe early onset childhood obesity (ages 1-5.99 years) using structured and unstructured data from the electronic health record (EHR). INTRODUCTION Childhood obesity increases risk factors for cardiovascular morbidity and vascular disease. Accurate definition of a high precision phenotype through a standardize tool is critical to the success of large-scale genomic studies and validating rare monogenic variants causing severe early onset obesity. DATA AND METHODS Rule based and machine learning based algorithms were developed using structured and unstructured data from two EHR databases from Boston Children's Hospital (BCH) and Cincinnati Children's Hospital and Medical Center (CCHMC). Exclusion criteria including medications or comorbid diagnoses were defined. Machine learning algorithms were developed using cross-site training and testing in addition to experimenting with natural language processing features. RESULTS Precision was emphasized for a high fidelity cohort. The rule-based algorithm performed the best overall, 0.895 (CCHMC) and 0.770 (BCH). The best feature set for machine learning employed Unified Medical Language System (UMLS) concept unique identifiers (CUIs), ICD-9 codes, and RxNorm codes. CONCLUSIONS Detecting severe early childhood obesity is essential for the intervention potential in children at the highest long-term risk of developing comorbidities related to obesity and excluding patients with underlying pathological and non-syndromic causes of obesity assists in developing a high-precision cohort for genetic study. Further such phenotyping efforts inform future practical application in health care environments utilizing clinical decision support.
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Affiliation(s)
- Todd Lingren
- Todd Lingren, Cincinnati Children's Hospital Medical Center, Biomedical Informatics, 3333 Burnet Avenue, MLC 7024 Cincinnati, OH 45229-3039, Phone: 513-803-9032, Fax: 513-636-2056,
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107
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Warner JL, Rioth MJ, Mandl KD, Mandel JC, Kreda DA, Kohane IS, Carbone D, Oreto R, Wang L, Zhu S, Yao H, Alterovitz G. SMART precision cancer medicine: a FHIR-based app to provide genomic information at the point of care. J Am Med Inform Assoc 2016; 23:701-10. [PMID: 27018265 DOI: 10.1093/jamia/ocw015] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2015] [Accepted: 01/26/2016] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Precision cancer medicine (PCM) will require ready access to genomic data within the clinical workflow and tools to assist clinical interpretation and enable decisions. Since most electronic health record (EHR) systems do not yet provide such functionality, we developed an EHR-agnostic, clinico-genomic mobile app to demonstrate several features that will be needed for point-of-care conversations. METHODS Our prototype, called Substitutable Medical Applications and Reusable Technology (SMART)® PCM, visualizes genomic information in real time, comparing a patient's diagnosis-specific somatic gene mutations detected by PCR-based hotspot testing to a population-level set of comparable data. The initial prototype works for patient specimens with 0 or 1 detected mutation. Genomics extensions were created for the Health Level Seven® Fast Healthcare Interoperability Resources (FHIR)® standard; otherwise, the prototype is a normal SMART on FHIR app. RESULTS The PCM prototype can rapidly present a visualization that compares a patient's somatic genomic alterations against a distribution built from more than 3000 patients, along with context-specific links to external knowledge bases. Initial evaluation by oncologists provided important feedback about the prototype's strengths and weaknesses. We added several requested enhancements and successfully demonstrated the app at the inaugural American Society of Clinical Oncology Interoperability Demonstration; we have also begun to expand visualization capabilities to include cancer specimens with multiple mutations. DISCUSSION PCM is open-source software for clinicians to present the individual patient within the population-level spectrum of cancer somatic mutations. The app can be implemented on any SMART on FHIR-enabled EHRs, and future versions of PCM should be able to evolve in parallel with external knowledge bases.
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Affiliation(s)
- Jeremy L Warner
- Department of Medicine, Division of Hematology and Oncology, Vanderbilt University, Nashville, TN, USA Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
| | - Matthew J Rioth
- Department of Medicine, Division of Hematology and Oncology, Vanderbilt University, Nashville, TN, USA Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
| | - Kenneth D Mandl
- Boston Children's Hospital Computational Health Informatics Program, Boston, MA, USA Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Joshua C Mandel
- Boston Children's Hospital Computational Health Informatics Program, Boston, MA, USA Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | - Isaac S Kohane
- Boston Children's Hospital Computational Health Informatics Program, Boston, MA, USA Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA Department of Pediatric Endocrinology, Boston Children's Hospital, Boston, MA, USA
| | - Daniel Carbone
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ross Oreto
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lucy Wang
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shilin Zhu
- Department of Electrical Engineering and Information Science, University of Science and Technology of China, Hefei, China
| | - Heming Yao
- Department of Medicine, Division of Hematology and Oncology, Vanderbilt University, Nashville, TN, USA
| | - Gil Alterovitz
- Boston Children's Hospital Computational Health Informatics Program, Boston, MA, USA Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
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108
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109
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Affiliation(s)
- Arjun K Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - John P A Ioannidis
- Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, California3Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
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Abstract
Applying federalist principles to networked health record data could facilitate realization of the potential of shared health data.
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Affiliation(s)
- Kenneth D Mandl
- Boston Children's Hospital, Boston and Harvard Medical School, Boston, Massachusetts, USA
| | - Isaac S Kohane
- Boston Children's Hospital, Boston and Harvard Medical School, Boston, Massachusetts, USA
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Mandel JC, Kreda DA, Mandl KD, Kohane IS, Ramoni RB. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J Am Med Inform Assoc 2016; 23:899-908. [PMID: 26911829 PMCID: PMC4997036 DOI: 10.1093/jamia/ocv189] [Citation(s) in RCA: 324] [Impact Index Per Article: 40.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2015] [Accepted: 11/07/2015] [Indexed: 11/12/2022] Open
Abstract
Objective In early 2010, Harvard Medical School and Boston Children’s Hospital began an interoperability project with the distinctive goal of developing a platform to enable medical applications to be written once and run unmodified across different healthcare IT systems. The project was called Substitutable Medical Applications and Reusable Technologies (SMART). Methods We adopted contemporary web standards for application programming interface transport, authorization, and user interface, and standard medical terminologies for coded data. In our initial design, we created our own openly licensed clinical data models to enforce consistency and simplicity. During the second half of 2013, we updated SMART to take advantage of the clinical data models and the application-programming interface described in a new, openly licensed Health Level Seven draft standard called Fast Health Interoperability Resources (FHIR). Signaling our adoption of the emerging FHIR standard, we called the new platform SMART on FHIR. Results We introduced the SMART on FHIR platform with a demonstration that included several commercial healthcare IT vendors and app developers showcasing prototypes at the Health Information Management Systems Society conference in February 2014. This established the feasibility of SMART on FHIR, while highlighting the need for commonly accepted pragmatic constraints on the base FHIR specification. Conclusion In this paper, we describe the creation of SMART on FHIR, relate the experience of the vendors and developers who built SMART on FHIR prototypes, and discuss some challenges in going from early industry prototyping to industry-wide production use.
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Affiliation(s)
- Joshua C Mandel
- Computational Health Informatics Program at Harvard-MIT Health Sciences and Technology, Boston, MA, USA Department of Pediatrics, Harvard Medical School, Boston, MA, USA Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - David A Kreda
- SMART Health IT Project, Center for Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kenneth D Mandl
- Computational Health Informatics Program at Harvard-MIT Health Sciences and Technology, Boston, MA, USA Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Isaac S Kohane
- Computational Health Informatics Program at Harvard-MIT Health Sciences and Technology, Boston, MA, USA Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Rachel B Ramoni
- SMART Health IT Project, Center for Biomedical Informatics, Harvard Medical School, Boston, MA, USA Department of Oral Health Policy and Epidemiology, Harvard School of Dental Medicine, Boston, MA, USA
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112
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Brown AS, Kong SW, Kohane IS, Patel CJ. ksRepo: a generalized platform for computational drug repositioning. BMC Bioinformatics 2016; 17:78. [PMID: 26860211 PMCID: PMC4746802 DOI: 10.1186/s12859-016-0931-y] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Accepted: 01/29/2016] [Indexed: 01/22/2023] Open
Abstract
Background Repositioning approved drug and small molecules in novel therapeutic areas is of key interest to the pharmaceutical industry. A number of promising computational techniques have been developed to aid in repositioning, however, the majority of available methodologies require highly specific data inputs that preclude the use of many datasets and databases. There is a clear unmet need for a generalized methodology that enables the integration of multiple types of both gene expression data and database schema. Results ksRepo eliminates the need for a single microarray platform as input and allows for the use of a variety of drug and chemical exposure databases. We tested ksRepo’s performance on a set of five prostate cancer datasets using the Comparative Toxicogenomics Database (CTD) as our database of gene-compound interactions. ksRepo successfully predicted significance for five frontline prostate cancer therapies, representing a significant enrichment from over 7000 CTD compounds, and achieved specificity similar to other repositioning methods. Conclusions We present ksRepo, which enables investigators to use any data inputs for computational drug repositioning. ksRepo is implemented in a series of four functions in the R statistical environment under a BSD3 license. Source code is freely available at http://github.com/adam-sam-brown/ksRepo. A vignette is provided to aid users in performing ksRepo analysis.
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Affiliation(s)
- Adam S Brown
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA.
| | - Sek Won Kong
- Boston Children's Hospital, Boston, MA, 02115, USA.
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA.
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA.
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113
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Affiliation(s)
- Kenneth D Mandl
- From the Computational Health Informatics Program, Boston Children's Hospital, and the Department of Biomedical Informatics, Harvard Medical School - both in Boston
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114
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Manrai AK, Wang BL, Patel CJ, Kohane IS. REPRODUCIBLE AND SHAREABLE QUANTIFICATIONS OF PATHOGENICITY. Pac Symp Biocomput 2016; 21:231-242. [PMID: 26776189 PMCID: PMC4720982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
There are now hundreds of thousands of pathogenicity assertions that relate genetic variation to disease, but most of this clinically utilized variation has no accepted quantitative disease risk estimate. Recent disease-specific studies have used control sequence data to reclassify large amounts of prior pathogenic variation, but there is a critical need to scale up both the pace and feasibility of such pathogenicity reassessments across human disease. In this manuscript we develop a shareable computational framework to quantify pathogenicity assertions. We release a reproducible "digital notebook" that integrates executable code, text annotations, and mathematical expressions in a freely accessible statistical environment. We extend previous disease-specific pathogenicity assessments to over 6,000 diseases and 160,000 assertions in the ClinVar database. Investigators can use this platform to prioritize variants for reassessment and tailor genetic model parameters (such as prevalence and heterogeneity) to expose the uncertainty underlying pathogenicity-based risk assessments. Finally, we release a website that links users to pathogenic variation for a queried disease, supporting literature, and implied disease risk calculations subject to user-defined and disease-specific genetic risk models in order to facilitate variant reassessments.
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Affiliation(s)
- Arjun K Manrai
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St., Boston, MA, 02115, USA,
| | - Brice L Wang
- Illinois Mathematics and Science Academy, 1500 Sullivan Rd., Aurora, IL 60506,
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St., Boston, MA, 02115, USA,
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St., Boston, MA, 02115, USA,
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115
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Manrai AK, Patel CJ, Gehlenborg N, Tatonetti NP, Ioannidis JPA, Kohane IS. METHODS TO ENHANCE THE REPRODUCIBILITY OF PRECISION MEDICINE. Pac Symp Biocomput 2016; 21:180-182. [PMID: 28004011 PMCID: PMC5167531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Affiliation(s)
- Arjun K Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | | | - John P A Ioannidis
- Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
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116
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Wang F, Remke M, Bhat K, Wong ET, Zhou S, Ramaswamy V, Dubuc A, Fonkem E, Salem S, Zhang H, Hsieh TC, O'Rourke ST, Wu L, Li DW, Hawkins C, Kohane IS, Wu JM, Wu M, Taylor MD, Wu E. A microRNA-1280/JAG2 network comprises a novel biological target in high-risk medulloblastoma. Oncotarget 2015; 6:2709-24. [PMID: 25576913 PMCID: PMC4413612 DOI: 10.18632/oncotarget.2779] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2014] [Accepted: 11/19/2014] [Indexed: 01/23/2023] Open
Abstract
Over-expression of PDGF receptors (PDGFRs) has been previously implicated in high-risk medulloblastoma (MB) pathogenesis. However, the exact biological functions of PDGFRα and PDGFRβ signaling in MB biology remain poorly understood. Here, we report the subgroup specific expression of PDGFRα and PDGFRβ and their associated biological pathways in MB tumors. c-MYC, a downstream target of PDGFRβ but not PDGFRα, is involved in PDGFRβ signaling associated with cell proliferation, cell death, and invasion. Concurrent inhibition of PDGFRβ and c-MYC blocks MB cell proliferation and migration synergistically. Integrated analysis of miRNA and miRNA targets regulated by both PDGFRβ and c-MYC reveals that increased expression of JAG2, a target of miR-1280, is associated with high metastatic dissemination at diagnosis and a poor outcome in MB patients. Our study may resolve the controversy on the role of PDGFRs in MB and unveils JAG2 as a key downstream effector of a PDGFRβ-driven signaling cascade and a potential therapeutic target.
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Affiliation(s)
- Fengfei Wang
- Department of Pharmaceutical Sciences, North Dakota State University, Fargo, ND 58105, USA
| | - Marc Remke
- Arthur and Sonia Labatt Brain Tumor Research Centre, Program in Developmental and Stem Cell Biology, Hospital for Sick Children, University of Toronto, Toronto, ON M5G 1X8, Canada
| | - Kruttika Bhat
- Department of Pharmaceutical Sciences, North Dakota State University, Fargo, ND 58105, USA
| | - Eric T Wong
- Brain Tumor Center & Neuro-Oncology Unit, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, USA
| | - Shuang Zhou
- Department of Pharmaceutical Sciences, North Dakota State University, Fargo, ND 58105, USA
| | - Vijay Ramaswamy
- Arthur and Sonia Labatt Brain Tumor Research Centre, Program in Developmental and Stem Cell Biology, Hospital for Sick Children, University of Toronto, Toronto, ON M5G 1X8, Canada
| | - Adrian Dubuc
- Arthur and Sonia Labatt Brain Tumor Research Centre, Program in Developmental and Stem Cell Biology, Hospital for Sick Children, University of Toronto, Toronto, ON M5G 1X8, Canada
| | - Ekokobe Fonkem
- Scott & White Neuroscience Institute, Texas A & M Health Science Center, Temple, TX 76508, USA
| | - Saeed Salem
- Department of Computer Sciences, North Dakota State University, Fargo, ND 58105, USA
| | - Hongbing Zhang
- Department of Physiology, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100073, China
| | - Tze-Chen Hsieh
- Department of Biochemistry and Molecular Biology, New York Medical College, Valhalla, NY 10595, USA
| | - Stephen T O'Rourke
- Department of Pharmaceutical Sciences, North Dakota State University, Fargo, ND 58105, USA
| | - Lizi Wu
- Department of Molecular Genetics and Microbiology, Shands Cancer Center, University of Florida, Gainesville, FL 32610, USA
| | - David W Li
- Department of Ophthalmology & Visual Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Cynthia Hawkins
- Division of Pathology, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Isaac S Kohane
- Informatics Program, Children's Hospital Boston, Harvard Medical School, Boston 02115, MA, USA
| | - Joseph M Wu
- Department of Biochemistry and Molecular Biology, New York Medical College, Valhalla, NY 10595, USA
| | - Min Wu
- Department of Biochemistry and Molecular Biology, University of North Dakota, Grand Forks, ND 58202, USA
| | - Michael D Taylor
- Arthur and Sonia Labatt Brain Tumor Research Centre, Program in Developmental and Stem Cell Biology, Hospital for Sick Children, University of Toronto, Toronto, ON M5G 1X8, Canada
| | - Erxi Wu
- Department of Pharmaceutical Sciences, North Dakota State University, Fargo, ND 58105, USA
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117
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Herr TM, Bielinski SJ, Bottinger E, Brautbar A, Brilliant M, Chute CG, Cobb BL, Denny JC, Hakonarson H, Hartzler AL, Hripcsak G, Kannry J, Kohane IS, Kullo IJ, Lin S, Manzi S, Marsolo K, Overby CL, Pathak J, Peissig P, Pulley J, Ralston J, Rasmussen L, Roden DM, Tromp G, Uphoff T, Weng C, Wolf W, Williams MS, Starren J. Practical considerations in genomic decision support: The eMERGE experience. J Pathol Inform 2015; 6:50. [PMID: 26605115 PMCID: PMC4629307 DOI: 10.4103/2153-3539.165999] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 07/23/2015] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND Genomic medicine has the potential to improve care by tailoring treatments to the individual. There is consensus in the literature that pharmacogenomics (PGx) may be an ideal starting point for real-world implementation, due to the presence of well-characterized drug-gene interactions. Clinical Decision Support (CDS) is an ideal avenue by which to implement PGx at the bedside. Previous literature has established theoretical models for PGx CDS implementation and discussed a number of anticipated real-world challenges. However, work detailing actual PGx CDS implementation experiences has been limited. Anticipated challenges include data storage and management, system integration, physician acceptance, and more. METHODS In this study, we analyzed the experiences of ten members of the Electronic Medical Records and Genomics (eMERGE) Network, and one affiliate, in their attempts to implement PGx CDS. We examined the resulting PGx CDS system characteristics and conducted a survey to understand the unanticipated implementation challenges sites encountered. RESULTS Ten sites have successfully implemented at least one PGx CDS rule in the clinical setting. The majority of sites elected to create an Omic Ancillary System (OAS) to manage genetic and genomic data. All sites were able to adapt their existing CDS tools for PGx knowledge. The most common and impactful delays were not PGx-specific issues. Instead, they were general IT implementation problems, with top challenges including team coordination/communication and staffing. The challenges encountered caused a median total delay in system go-live of approximately two months. CONCLUSIONS These results suggest that barriers to PGx CDS implementations are generally surmountable. Moreover, PGx CDS implementation may not be any more difficult than other healthcare IT projects of similar scope, as the most significant delays encountered were not unique to genomic medicine. These are encouraging results for any institution considering implementing a PGx CDS tool, and for the advancement of genomic medicine.
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Affiliation(s)
- Timothy M Herr
- Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | | | - Erwin Bottinger
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine, Mount Sinai, New York, USA
| | - Ariel Brautbar
- Division of Genetics and Endocrinology, Cook Children's Medical Center, Fort Worth, Texas, USA
| | - Murray Brilliant
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA
| | - Christopher G Chute
- Division of General Internal Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Beth L Cobb
- Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University, Baltimore, MD, USA
| | - Hakon Hakonarson
- Department of Pediatrics, The Children's Hospital of Philadelphia, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | | | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Medical Center, New York, USA
| | - Joseph Kannry
- Icahn School of Medicine, Mount Sinai, New York, USA
| | - Isaac S Kohane
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Iftikhar J Kullo
- Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | - Simon Lin
- Nationwide Children's Hospital, Columbus, Ohio, USA
| | - Shannon Manzi
- Department of Pharmacy, Division of Genetics and Genomics, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Keith Marsolo
- Department of Pediatrics, University of Cincinnati College of Medicine, Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | | | - Jyotishman Pathak
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Peggy Peissig
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA
| | - Jill Pulley
- Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - James Ralston
- Group Health Research Institute, Seattle, Washington, USA
| | - Luke Rasmussen
- Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Dan M Roden
- Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Gerard Tromp
- Weis Center for Research, Geisinger Clinic, Danville, Pennsylvania, USA
| | - Timothy Uphoff
- Molecular Pathology, Mashfield Labs, Marshfield, Wisconsin, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, USA
| | - Wendy Wolf
- Department of Pediatrics, Harvard Medical School, Division of Genetics and Genomics, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Marc S Williams
- Genomic Medicine Institute, Geisinger Health System, Danville, Pennsylvania, USA
| | - Justin Starren
- Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
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118
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Herr TM, Bielinski SJ, Bottinger E, Brautbar A, Brilliant M, Chute CG, Denny J, Freimuth RR, Hartzler A, Kannry J, Kohane IS, Kullo IJ, Lin S, Pathak J, Peissig P, Pulley J, Ralston J, Rasmussen L, Roden D, Tromp G, Williams MS, Starren J. A conceptual model for translating omic data into clinical action. J Pathol Inform 2015; 6:46. [PMID: 26430534 PMCID: PMC4584438 DOI: 10.4103/2153-3539.163985] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 07/01/2015] [Indexed: 01/27/2023] Open
Abstract
Genomic, proteomic, epigenomic, and other “omic” data have the potential to enable precision medicine, also commonly referred to as personalized medicine. The volume and complexity of omic data are rapidly overwhelming human cognitive capacity, requiring innovative approaches to translate such data into patient care. Here, we outline a conceptual model for the application of omic data in the clinical context, called “the omic funnel.” This model parallels the classic “Data, Information, Knowledge, Wisdom pyramid” and adds context for how to move between each successive layer. Its goal is to allow informaticians, researchers, and clinicians to approach the problem of translating omic data from bench to bedside, by using discrete steps with clearly defined needs. Such an approach can facilitate the development of modular and interoperable software that can bring precision medicine into widespread practice.
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Affiliation(s)
- Timothy M Herr
- Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Suzette J Bielinski
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Erwin Bottinger
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine, Mount Sinai, New York, USA
| | - Ariel Brautbar
- Division of Genetics and Endocrinology, Cook Children's Medical Center, Fort Worth, Texas, USA
| | - Murray Brilliant
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA
| | - Christopher G Chute
- Division of General Internal Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Joshua Denny
- Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA
| | - Robert R Freimuth
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Joseph Kannry
- Icahn School of Medicine, Mount Sinai, New York, USA
| | - Isaac S Kohane
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Iftikhar J Kullo
- Division of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota, USA
| | - Simon Lin
- Nationwide Children's Hospital, Columbus, Ohio, USA
| | - Jyotishman Pathak
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Peggy Peissig
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA
| | - Jill Pulley
- Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - James Ralston
- Group Health Research Institute, Seattle, Washington, USA
| | - Luke Rasmussen
- Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Dan Roden
- Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Gerard Tromp
- Weis Center for Research, Danville, Pennsylvania, USA
| | - Marc S Williams
- Genomic Medicine Institute, Geisinger Health System, Danville, Pennsylvania, USA
| | - Justin Starren
- Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
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119
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Alterovitz G, Warner J, Zhang P, Chen Y, Ullman-Cullere M, Kreda D, Kohane IS. SMART on FHIR Genomics: facilitating standardized clinico-genomic apps. J Am Med Inform Assoc 2015. [PMID: 26198304 DOI: 10.1093/jamia/ocv045] [Citation(s) in RCA: 95] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Supporting clinical decision support for personalized medicine will require linking genome and phenome variants to a patient's electronic health record (EHR), at times on a vast scale. Clinico-genomic data standards will be needed to unify how genomic variant data are accessed from different sequencing systems. METHODS A specification for the basis of a clinic-genomic standard, building upon the current Health Level Seven International Fast Healthcare Interoperability Resources (FHIR®) standard, was developed. An FHIR application protocol interface (API) layer was attached to proprietary sequencing platforms and EHRs in order to expose gene variant data for presentation to the end-user. Three representative apps based on the SMART platform were built to test end-to-end feasibility, including integration of genomic and clinical data. RESULTS Successful design, deployment, and use of the API was demonstrated and adopted by HL7 Clinical Genomics Workgroup. Feasibility was shown through development of three apps by various types of users with background levels and locations. CONCLUSION This prototyping work suggests that an entirely data (and web) standards-based approach could prove both effective and efficient for advancing personalized medicine.
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Affiliation(s)
- Gil Alterovitz
- Children's Hospital Informatics Program, Boston, MA Center for Biomedical Informatics, Harvard Medical School, Boston, MA Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
| | - Jeremy Warner
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN Department of Medicine, Division of Hematology/Oncology, Vanderbilt University, Nashville, TN
| | - Peijin Zhang
- Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA
| | - Yishen Chen
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL
| | | | - David Kreda
- Center for Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Isaac S Kohane
- Children's Hospital Informatics Program, Boston, MA Center for Biomedical Informatics, Harvard Medical School, Boston, MA Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
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120
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Affiliation(s)
- Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.
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121
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Abstract
Healthcare data will soon be accessible using standard, open software interfaces. Here, we describe how these interfaces could lead to improved healthcare by facilitating the development of software applications (apps) that can be shared across physicians, health care organizations, translational researchers, and patients. We provide recommendations for next steps and resources for the myriad stakeholders. If challenges related to efficacy, accuracy, utility, safety, privacy, and security can be met, this emerging apps model for health information technology will open up the point of care for innovation and connect patients at home to their healthcare data.
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Affiliation(s)
- Kenneth D. Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Joshua C. Mandel
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Isaac S. Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
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122
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Clements CC, Castro VM, Blumenthal SR, Rosenfield HR, Murphy SN, Fava M, Erb JL, Churchill SE, Kaimal AJ, Doyle AE, Robinson EB, Smoller JW, Kohane IS, Perlis RH. Prenatal antidepressant exposure is associated with risk for attention-deficit hyperactivity disorder but not autism spectrum disorder in a large health system. Mol Psychiatry 2015; 20:727-34. [PMID: 25155880 PMCID: PMC4427538 DOI: 10.1038/mp.2014.90] [Citation(s) in RCA: 142] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2014] [Revised: 04/29/2014] [Accepted: 06/23/2014] [Indexed: 12/19/2022]
Abstract
Previous studies suggested that risk for Autism Spectrum Disorder (ASD) may be increased in children exposed to antidepressants during the prenatal period. The disease specificity of this risk has not been addressed and the possibility of confounding has not been excluded. Children with ASD or attention-deficit hyperactivity disorder (ADHD) delivered in a large New England health-care system were identified from electronic health records (EHR), and each diagnostic group was matched 1:3 with children without ASD or ADHD. All children were linked with maternal health data using birth certificates and EHRs to determine prenatal medication exposures. Multiple logistic regression was used to examine association between prenatal antidepressant exposures and ASD or ADHD risk. A total of 1377 children diagnosed with ASD and 2243 with ADHD were matched with healthy controls. In models adjusted for sociodemographic features, antidepressant exposure prior to and during pregnancy was associated with ASD risk, but risk associated with exposure during pregnancy was no longer significant after controlling for maternal major depression (odds ratio (OR) 1.10 (0.70-1.70)). Conversely, antidepressant exposure during but not prior to pregnancy was associated with ADHD risk, even after adjustment for maternal depression (OR 1.81 (1.22-2.70)). These results suggest that the risk of autism observed with prenatal antidepressant exposure is likely confounded by severity of maternal illness, but further indicate that such exposure may still be associated with ADHD risk. This risk, modest in absolute terms, may still be a result of residual confounding and must be balanced against the substantial consequences of untreated maternal depression.
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Affiliation(s)
- Caitlin C. Clements
- Center for Experimental Drugs and Diagnostics, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 02114,Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 02114
| | - Victor M. Castro
- Center for Experimental Drugs and Diagnostics, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 02114,Partners Research Computing, Partners HealthCare System, One Constitution Center, Boston, MA 02129,Laboratory of Computer Science and Department of Neurology, Massachusetts General Hospital, Boston, MA 02114
| | - Sarah R. Blumenthal
- Center for Experimental Drugs and Diagnostics, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 02114,Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 02114
| | - Hannah R. Rosenfield
- Center for Experimental Drugs and Diagnostics, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 02114,Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 02114
| | - Shawn N. Murphy
- Partners Research Computing, Partners HealthCare System, One Constitution Center, Boston, MA 02129,Laboratory of Computer Science and Department of Neurology, Massachusetts General Hospital, Boston, MA 02114
| | - Maurizio Fava
- Depression Clinic and Research Program, Department of Psychiatry, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114
| | - Jane L. Erb
- Department of Psychiatry, Brigham and Women’s Hospital, 221 Longwood Avenue, Boston, MA 02115
| | - Susanne E. Churchill
- Information Systems, Partners HealthCare System, New Research Building 255, 77 Avenue Louis Pasteur, Boston, MA 02115
| | - Anjali J. Kaimal
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114
| | - Alysa E. Doyle
- Center for Experimental Drugs and Diagnostics, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 02114,Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 02114
| | - Elise B. Robinson
- Center for Experimental Drugs and Diagnostics, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 02114,Analytic and Translational Genomics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 02114
| | - Jordan W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 02114
| | - Isaac S. Kohane
- Department of Medicine, Brigham and Women’s Hospital, Suite 255, New Research Building, 77 Avenue Louis Pasteur, Boston, MA 02115
| | - Roy H. Perlis
- Center for Experimental Drugs and Diagnostics, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 02114,Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 02114,Correspondence: Roy Perlis, MD MSc, Simches Research Building/MGH, 185 Cambridge St, 6th Floor, Boston, MA 02114, Phone: 617 726-7426, Fax: 617-726-0830,
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Yu S, Liao KP, Shaw SY, Gainer VS, Churchill SE, Szolovits P, Murphy SN, Kohane IS, Cai T. Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources. J Am Med Inform Assoc 2015; 22:993-1000. [PMID: 25929596 DOI: 10.1093/jamia/ocv034] [Citation(s) in RCA: 113] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2014] [Accepted: 03/24/2015] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVE Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy. MATERIALS AND METHODS Comprehensive medical concepts were collected from publicly available knowledge sources in an automated, unbiased fashion. Natural language processing (NLP) revealed the occurrence patterns of these concepts in EHR narrative notes, which enabled selection of informative features for phenotype classification. When combined with additional codified features, a penalized logistic regression model was trained to classify the target phenotype. RESULTS The authors applied our method to develop algorithms to identify patients with rheumatoid arthritis and coronary artery disease cases among those with rheumatoid arthritis from a large multi-institutional EHR. The area under the receiver operating characteristic curves (AUC) for classifying RA and CAD using models trained with automated features were 0.951 and 0.929, respectively, compared to the AUCs of 0.938 and 0.929 by models trained with expert-curated features. DISCUSSION Models trained with NLP text features selected through an unbiased, automated procedure achieved comparable or slightly higher accuracy than those trained with expert-curated features. The majority of the selected model features were interpretable. CONCLUSION The proposed automated feature extraction method, generating highly accurate phenotyping algorithms with improved efficiency, is a significant step toward high-throughput phenotyping.
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Affiliation(s)
- Sheng Yu
- Partners HealthCare Personalized Medicine, Boston, MA, USA Brigham and Women's Hospital, Boston, MA, USA Harvard Medical School, Boston, MA, USA
| | - Katherine P Liao
- Brigham and Women's Hospital, Boston, MA, USA Harvard Medical School, Boston, MA, USA
| | | | - Vivian S Gainer
- Research Computing, Partners HealthCare, Charlestown, MA, USA
| | | | | | - Shawn N Murphy
- Massachusetts General Hospital, Boston, MA Research Computing, Partners HealthCare, Charlestown, MA, USA
| | - Isaac S Kohane
- Harvard Medical School, Boston, MA, USA Boston Children's Hospital, Boston, MA, USA
| | - Tianxi Cai
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
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124
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McCoy TH, Castro VM, Rosenfield HR, Cagan A, Kohane IS, Perlis RH. A clinical perspective on the relevance of research domain criteria in electronic health records. Am J Psychiatry 2015; 172:316-20. [PMID: 25827030 PMCID: PMC9980718 DOI: 10.1176/appi.ajp.2014.14091177] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
OBJECTIVE The limitations of the DSM nosology for capturing dimensionality and overlap in psychiatric syndromes, and its poor correspondence to underlying neurobiology, have been well established. The Research Domain Criteria (RDoC), a proposed dimensional model of psychopathology, may offer new insights into psychiatric illness. For psychiatric clinicians, however, because tools for capturing these domains in clinical practice have not yet been established, the relevance and means of transition from the categorical system of DSM-5 to the dimensional models of RDoC remains unclear. The authors explored a method of extracting these dimensions from existing electronic health record (EHR) notes. METHOD The authors used information retrieval and natural language processing methods to extract estimates of the RDoC dimensions in the EHRs of a large health system. They parsed and scored EHR documentation for 2,484 admissions covering 2,010 patients admitted to a psychiatric inpatient unit between 2011 and 2013. These domain scores were compared with DSM-IV-based ICD-9 codes to assess face validity. As a measure of predictive validity, these scores were examined for association with two outcomes: length of hospital stay and time to all-cause hospital readmission. Together, these analyses were intended to address the extent to which RDoC symptom domains might capture clinical features already available in narrative notes but not reflected in DSM diagnoses. RESULTS In mixed-effects models, loadings for the RDoC cognitive and arousal domains were associated with length of hospital stay, while the negative valence and social domains were associated with hazard of all-cause hospital readmission. CONCLUSIONS These findings show that a computationally derived tool based on RDoC workgroup reports identifies symptom distributions in clinician notes beyond those captured by ICD-9 codes, and these domains have significant predictive validity. More generally, they point to the possibility that clinicians already document RDoC-relevant symptoms, albeit not in a quantified form.
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125
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Vassy JL, McLaughlin HM, McLaughlin HL, MacRae CA, Seidman CE, Lautenbach D, Krier JB, Lane WJ, Kohane IS, Murray MF, McGuire AL, Rehm HL, Green RC. A one-page summary report of genome sequencing for the healthy adult. Public Health Genomics 2015; 18:123-9. [PMID: 25612602 DOI: 10.1159/000370102] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2014] [Accepted: 11/25/2014] [Indexed: 11/19/2022] Open
Abstract
As genome sequencing technologies increasingly enter medical practice, genetics laboratories must communicate sequencing results effectively to nongeneticist physicians. We describe the design and delivery of a clinical genome sequencing report, including a one-page summary suitable for interpretation by primary care physicians. To illustrate our preliminary experience with this report, we summarize the genomic findings from 10 healthy participants in a study of genome sequencing in primary care.
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Affiliation(s)
- Jason L Vassy
- Division of General Internal Medicine and Primary Care, Boston, Mass., USA
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Kohane IS. An autism case history to review the systematic analysis of large-scale data to refine the diagnosis and treatment of neuropsychiatric disorders. Biol Psychiatry 2015; 77:59-65. [PMID: 25034947 PMCID: PMC4260993 DOI: 10.1016/j.biopsych.2014.05.024] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2014] [Revised: 05/05/2014] [Accepted: 05/22/2014] [Indexed: 01/18/2023]
Abstract
Analysis of large-scale systems of biomedical data provides a perspective on neuropsychiatric disease that may be otherwise elusive. Described here is an analysis of three large-scale systems of data from autism spectrum disorder (ASD) and of ASD research as an exemplar of what might be achieved from study of such data. First is the biomedical literature that highlights the fact that there are two very successful but quite separate research communities and findings pertaining to genetics and the molecular biology of ASD. There are those studies positing ASD causes that are related to immunological dysregulation and those related to disorders of synaptic function and neuronal connectivity. Second is the emerging use of electronic health record systems and other large clinical databases that allow the data acquired during the course of care to be used to identify distinct subpopulations, clinical trajectories, and pathophysiological substructures of ASD. These systems reveal subsets of patients with distinct clinical trajectories, some of which are immunologically related and others which follow pathologies conventionally thought of as neurological. The third is genome-wide genomic and transcriptomic analyses which show molecular pathways that overlap neurological and immunological mechanisms. The convergence of these three large-scale data perspectives illustrates the scientific leverage that large-scale data analyses can provide in guiding researchers in an approach to the diagnosis of neuropsychiatric disease that is inclusive and comprehensive.
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Affiliation(s)
- Isaac S Kohane
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts.
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127
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McLaughlin HM, Ceyhan-Birsoy O, Christensen KD, Kohane IS, Krier J, Lane WJ, Lautenbach D, Lebo MS, Machini K, MacRae CA, Azzariti DR, Murray MF, Seidman CE, Vassy JL, Green RC, Rehm HL. A systematic approach to the reporting of medically relevant findings from whole genome sequencing. BMC Med Genet 2014; 15:134. [PMID: 25714468 PMCID: PMC4342199 DOI: 10.1186/s12881-014-0134-1] [Citation(s) in RCA: 75] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2014] [Accepted: 12/03/2014] [Indexed: 01/22/2023]
Abstract
Background The MedSeq Project is a randomized clinical trial developing approaches to assess the impact of integrating genome sequencing into clinical medicine. To facilitate the return of results of potential medical relevance to physicians and patients participating in the MedSeq Project, we sought to develop a reporting approach for the effective communication of such findings. Methods Genome sequencing was performed on the Illumina HiSeq platform. Variants were filtered, interpreted, and validated according to methods developed by the Laboratory for Molecular Medicine and consistent with current professional guidelines. The GeneInsight software suite, which is integrated with the Partners HealthCare electronic health record, was used for variant curation, report drafting, and delivery. Results We developed a concise 5–6 page Genome Report (GR) featuring a single-page summary of results of potential medical relevance with additional pages containing structured variant, gene, and disease information along with supporting evidence for reported variants and brief descriptions of associated diseases and clinical implications. The GR is formatted to provide a succinct summary of genomic findings, enabling physicians to take appropriate steps for disease diagnosis, prevention, and management in their patients. Conclusions Our experience highlights important considerations for the reporting of results of potential medical relevance and provides a framework for interpretation and reporting practices in clinical genome sequencing. Electronic supplementary material The online version of this article (doi:10.1186/s12881-014-0134-1) contains supplementary material, which is available to authorized users.
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128
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Namjou B, Marsolo K, Caroll RJ, Denny JC, Ritchie MD, Verma SS, Lingren T, Porollo A, Cobb BL, Perry C, Kottyan LC, Rothenberg ME, Thompson SD, Holm IA, Kohane IS, Harley JB. Phenome-wide association study (PheWAS) in EMR-linked pediatric cohorts, genetically links PLCL1 to speech language development and IL5-IL13 to Eosinophilic Esophagitis. Front Genet 2014; 5:401. [PMID: 25477900 PMCID: PMC4235428 DOI: 10.3389/fgene.2014.00401] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2014] [Accepted: 10/31/2014] [Indexed: 02/06/2023] Open
Abstract
Objective: We report the first pediatric specific Phenome-Wide Association Study (PheWAS) using electronic medical records (EMRs). Given the early success of PheWAS in adult populations, we investigated the feasibility of this approach in pediatric cohorts in which associations between a previously known genetic variant and a wide range of clinical or physiological traits were evaluated. Although computationally intensive, this approach has potential to reveal disease mechanistic relationships between a variant and a network of phenotypes. Method: Data on 5049 samples of European ancestry were obtained from the EMRs of two large academic centers in five different genotyped cohorts. Recently, these samples have undergone whole genome imputation. After standard quality controls, removing missing data and outliers based on principal components analyses (PCA), 4268 samples were used for the PheWAS study. We scanned for associations between 2476 single-nucleotide polymorphisms (SNP) with available genotyping data from previously published GWAS studies and 539 EMR-derived phenotypes. The false discovery rate was calculated and, for any new PheWAS findings, a permutation approach (with up to 1,000,000 trials) was implemented. Results: This PheWAS found a variety of common variants (MAF > 10%) with prior GWAS associations in our pediatric cohorts including Juvenile Rheumatoid Arthritis (JRA), Asthma, Autism and Pervasive Developmental Disorder (PDD) and Type 1 Diabetes with a false discovery rate < 0.05 and power of study above 80%. In addition, several new PheWAS findings were identified including a cluster of association near the NDFIP1 gene for mental retardation (best SNP rs10057309, p = 4.33 × 10−7, OR = 1.70, 95%CI = 1.38 − 2.09); association near PLCL1 gene for developmental delays and speech disorder [best SNP rs1595825, p = 1.13 × 10−8, OR = 0.65(0.57 − 0.76)]; a cluster of associations in the IL5-IL13 region with Eosinophilic Esophagitis (EoE) [best at rs12653750, p = 3.03 × 10−9, OR = 1.73 95%CI = (1.44 − 2.07)], previously implicated in asthma, allergy, and eosinophilia; and association of variants in GCKR and JAZF1 with allergic rhinitis in our pediatric cohorts [best SNP rs780093, p = 2.18 × 10−5, OR = 1.39, 95%CI = (1.19 − 1.61)], previously demonstrated in metabolic disease and diabetes in adults. Conclusion: The PheWAS approach with re-mapping ICD-9 structured codes for our European-origin pediatric cohorts, as with the previous adult studies, finds many previously reported associations as well as presents the discovery of associations with potentially important clinical implications.
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Affiliation(s)
- Bahram Namjou
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA ; College of Medicine, University of Cincinnati Cincinnati, OH, USA
| | - Keith Marsolo
- College of Medicine, University of Cincinnati Cincinnati, OH, USA ; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
| | - Robert J Caroll
- Department of Biomedical Informatics, Vanderbilt University School of Medicine Nashville, TN, USA
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University School of Medicine Nashville, TN, USA ; Department of Medicine, Vanderbilt University School of Medicine Nashville, TN, USA
| | - Marylyn D Ritchie
- Center for Systems Genomics, The Pennsylvania State University Philadelphia, PA, USA
| | - Shefali S Verma
- Center for Systems Genomics, The Pennsylvania State University Philadelphia, PA, USA
| | - Todd Lingren
- College of Medicine, University of Cincinnati Cincinnati, OH, USA ; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
| | - Aleksey Porollo
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA ; College of Medicine, University of Cincinnati Cincinnati, OH, USA ; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
| | - Beth L Cobb
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
| | - Cassandra Perry
- Division of Genetics and Genomics, Boston Children's Hospital Boston, MA, USA
| | - Leah C Kottyan
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA ; College of Medicine, University of Cincinnati Cincinnati, OH, USA ; Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
| | - Marc E Rothenberg
- Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
| | - Susan D Thompson
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA ; College of Medicine, University of Cincinnati Cincinnati, OH, USA
| | - Ingrid A Holm
- Division of Genetics and Genomics, Department of Pediatrics, The Manton Center for Orphan Disease Research, Harvard Medical School, Boston Children's Hospital Boston, MA, USA
| | - Isaac S Kohane
- Children's Hospital Informatics Program, Center for Biomedical Informatics, Harvard Medical School Boston, MA, USA
| | - John B Harley
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA ; College of Medicine, University of Cincinnati Cincinnati, OH, USA ; U.S. Department of Veterans Affairs Medical Center Cincinnati, OH, USA
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D'Amore JD, Mandel JC, Kreda DA, Swain A, Koromia GA, Sundareswaran S, Alschuler L, Dolin RH, Mandl KD, Kohane IS, Ramoni RB. Are Meaningful Use Stage 2 certified EHRs ready for interoperability? Findings from the SMART C-CDA Collaborative. J Am Med Inform Assoc 2014; 21:1060-8. [PMID: 24970839 PMCID: PMC4215060 DOI: 10.1136/amiajnl-2014-002883] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Revised: 06/03/2014] [Accepted: 06/05/2014] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND AND OBJECTIVE Upgrades to electronic health record (EHR) systems scheduled to be introduced in the USA in 2014 will advance document interoperability between care providers. Specifically, the second stage of the federal incentive program for EHR adoption, known as Meaningful Use, requires use of the Consolidated Clinical Document Architecture (C-CDA) for document exchange. In an effort to examine and improve C-CDA based exchange, the SMART (Substitutable Medical Applications and Reusable Technology) C-CDA Collaborative brought together a group of certified EHR and other health information technology vendors. MATERIALS AND METHODS We examined the machine-readable content of collected samples for semantic correctness and consistency. This included parsing with the open-source BlueButton.js tool, testing with a validator used in EHR certification, scoring with an automated open-source tool, and manual inspection. We also conducted group and individual review sessions with participating vendors to understand their interpretation of C-CDA specifications and requirements. RESULTS We contacted 107 health information technology organizations and collected 91 C-CDA sample documents from 21 distinct technologies. Manual and automated document inspection led to 615 observations of errors and data expression variation across represented technologies. Based upon our analysis and vendor discussions, we identified 11 specific areas that represent relevant barriers to the interoperability of C-CDA documents. CONCLUSIONS We identified errors and permissible heterogeneity in C-CDA documents that will limit semantic interoperability. Our findings also point to several practical opportunities to improve C-CDA document quality and exchange in the coming years.
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Affiliation(s)
- John D D'Amore
- Lantana Consulting Group, LLC,East Thetford, Vermont, USA
- Diameter Health, Inc., Newton, Massachusetts, USA
| | - Joshua C Mandel
- Children's Hospital Informatics Program at Harvard-MIT Health Sciences and Technology, Boston, Massachusetts, USA
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - David A Kreda
- SMART Platforms Project, Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Ashley Swain
- Lantana Consulting Group, LLC,East Thetford, Vermont, USA
| | | | | | | | - Robert H Dolin
- Lantana Consulting Group, LLC,East Thetford, Vermont, USA
| | - Kenneth D Mandl
- Children's Hospital Informatics Program at Harvard-MIT Health Sciences and Technology, Boston, Massachusetts, USA
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Isaac S Kohane
- Children's Hospital Informatics Program at Harvard-MIT Health Sciences and Technology, Boston, Massachusetts, USA
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Rachel B Ramoni
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Department of Oral Health Policy and Epidemiology, Harvard School of Dental Medicine, Boston, Massachusetts, USA
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Lader AS, Ramoni MF, Zetter BR, Kohane IS, Kwiatkowski DJ. Identification of a transcriptional profile associated with in vitro invasion in non-small cell lung cancer cell lines. Cancer Biol Ther 2014; 3:624-31. [PMID: 15153803 DOI: 10.4161/cbt.3.7.914] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Although much has been learned about basic mechanisms of cell invasion, the genes whose expression is required for this process by malignant cell lines have remained obscure. We assessed invasion through Matrigel using EGF as a chemoattractant and gene expression profiles using oligonucleotide microarrays for 22 non-small cell lung cancer cell lines. The expression of 22 genes were significantly correlated (p < 0.001) with the measured invasion index. Cluster analysis demonstrated that gene expression profiles classify the cell lines into low and high invasive subgroups. Considering invasiveness as a dichotomous variable, Bayesian analysis was used to identify genes that have the highest probability of being differentially expressed between the high and low invasion groups. This analysis identified 16 genes whose expression was associated with invasiveness. "Leave one out" cross validation was 91% accurate. Nine genes were identified in both correlation and Bayesian analyses. Seven of the nine genes were negatively associated with invasion and four of those genes are plasma membrane proteins. The two genes with the highest inverse association with invasion, TACSTD1 and CLDN3, are involved with cell adhesion and cell-cell interactions, respectively. Interestingly, the gene with the highest positive association with invasion, SERPINE1 (PAI-1), is a protease inhibitor. These and the other genes identified by both analyses represent targets for further study to assess their importance in non-small cell lung cancer invasion and metastasis.
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Affiliation(s)
- Alan S Lader
- Hematology Division, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA.
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Castro VM, McCoy TH, Cagan A, Rosenfield HR, Murphy SN, Churchill SE, Kohane IS, Perlis RH. Stratification of risk for hospital admissions for injury related to fall: cohort study. BMJ 2014; 349:g5863. [PMID: 25954985 PMCID: PMC4208628 DOI: 10.1136/bmj.g5863] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE To determine whether the ability to stratify an individual patient's hazard for falling could facilitate development of focused interventions aimed at reducing these adverse outcomes. DESIGN Clinical and sociodemographic data from electronic health records were utilized to derive multiple logistic regression models of hospital readmissions for injuries related to falls. Drugs used at admission were summarized based on reported adverse effect frequencies in published drug labeling. SETTING Two large academic medical centers in New England, United States. PARTICIPANTS The model was developed with 25,924 individuals age ≥ 40 with an initial hospital discharge. The resulting model was then tested in an independent set of 13,032 inpatients drawn from the same hospital and 36,588 individuals discharged from a second large hospital during the same period. MAIN OUTCOME MEASURE Hospital readmissions for injury related to falls. RESULTS Among 25,924 discharged individuals, 680 (2.6%) were evaluated in the emergency department or admitted to hospital for a fall within 30 days of discharge, 1635 (6.3%) within 180 days of discharge, 2360 (9.1%) within one year, and 3465 (13.4%) within two years. Older age, female sex, white or African-American race, public insurance, greater number of drugs taken on discharge, and score for burden of adverse effects were each independently associated with hazard for fall. For drug burden, presence of a drug with a frequency of adverse effects related to fall of 10% was associated with 3.5% increase in odds of falling over the next two years (odds ratio 1.04, 95% confidence interval 1.02 to 1.05). In an independent testing set, the area under the receiver operating characteristics curve was 0.65 for a fall within two years based on cross sectional data and 0.72 with the addition of prior utilization data including age adjusted Charlson comorbidity index. Portability was promising, with area under the curve of 0.71 for the longitudinal model in a second hospital system. CONCLUSIONS It is potentially useful to stratify risk of falls based on clinical features available as artifacts of routine clinical care. A web based tool can be used to calculate and visualize risk associated with drug treatment to facilitate further investigation and application.
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Affiliation(s)
- Victor M Castro
- Center for Experimental Drugs and Diagnostics, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 20114, USA Partners Research Computing, Partners HealthCare System, One Constitution Center, Boston, MA 02129, USA Laboratory of Computer Science and Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Thomas H McCoy
- Center for Experimental Drugs and Diagnostics, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 20114, USA Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 02114, USA
| | - Andrew Cagan
- Center for Experimental Drugs and Diagnostics, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 20114, USA Partners Research Computing, Partners HealthCare System, One Constitution Center, Boston, MA 02129, USA Laboratory of Computer Science and Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Hannah R Rosenfield
- Center for Experimental Drugs and Diagnostics, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 20114, USA Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 02114, USA
| | - Shawn N Murphy
- Partners Research Computing, Partners HealthCare System, One Constitution Center, Boston, MA 02129, USA Laboratory of Computer Science and Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Susanne E Churchill
- Information Systems, Partners HealthCare System, New Research Building 255, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Isaac S Kohane
- Department of Medicine, Brigham and Women's Hospital, Suite 255, New Research Building, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Roy H Perlis
- Center for Experimental Drugs and Diagnostics, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 20114, USA Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 02114, USA
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Kong SW, Lee IH, Leshchiner I, Krier J, Kraft P, Rehm HL, Green RC, Kohane IS, MacRae CA. Summarizing polygenic risks for complex diseases in a clinical whole-genome report. Genet Med 2014; 17:536-44. [PMID: 25341114 DOI: 10.1038/gim.2014.143] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2014] [Accepted: 09/09/2014] [Indexed: 12/21/2022] Open
Abstract
PURPOSE Disease-causing mutations and pharmacogenomic variants are of primary interest for clinical whole-genome sequencing. However, estimating genetic liability for common complex diseases using established risk alleles might one day prove clinically useful. METHODS We compared polygenic scoring methods using a case-control data set with independently discovered risk alleles in the MedSeq Project. For eight traits of clinical relevance in both the primary-care and cardiomyopathy study cohorts, we estimated multiplicative polygenic risk scores using 161 published risk alleles and then normalized them using the population median estimated from the 1000 Genomes Project. RESULTS Our polygenic score approach identified the overrepresentation of independently discovered risk alleles in cases as compared with controls using a large-scale genome-wide association study data set. In addition to normalized multiplicative polygenic risk scores and rank in a population, the disease prevalence and proportion of heritability explained by known common risk variants provide important context in the interpretation of modern multilocus disease risk models. CONCLUSION Our approach in the MedSeq Project demonstrates how complex trait risk variants from an individual genome can be summarized and reported for the general clinician and also highlights the need for definitive clinical studies to obtain reference data for such estimates and to establish clinical utility.
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Affiliation(s)
- Sek Won Kong
- 1] Children's Hospital Informatics Program, Department of Medicine, Boston Children's Hospital, Boston, Massachusetts, USA [2] Harvard Medical School, Boston, Massachusetts, USA
| | - In-Hee Lee
- 1] Children's Hospital Informatics Program, Department of Medicine, Boston Children's Hospital, Boston, Massachusetts, USA [2] Harvard Medical School, Boston, Massachusetts, USA
| | - Ignaty Leshchiner
- 1] Harvard Medical School, Boston, Massachusetts, USA [2] Genetics Division, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Joel Krier
- 1] Harvard Medical School, Boston, Massachusetts, USA [2] Genetics Division, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Peter Kraft
- 1] Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA [2] Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Heidi L Rehm
- 1] Harvard Medical School, Boston, Massachusetts, USA [2] Laboratory for Molecular Medicine, Partners Personalized Medicine, Cambridge, Massachusetts, USA [3] Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Robert C Green
- 1] Harvard Medical School, Boston, Massachusetts, USA [2] Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Isaac S Kohane
- 1] Children's Hospital Informatics Program, Department of Medicine, Boston Children's Hospital, Boston, Massachusetts, USA [2] Harvard Medical School, Boston, Massachusetts, USA
| | - Calum A MacRae
- 1] Harvard Medical School, Boston, Massachusetts, USA [2] Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA [3] Cardiovascular Division, Brigham and Women's Hospital, Boston, Massachusetts, USA
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O’Dushlaine C, Ripke S, Ruderfer DM, Hamilton SP, Fava M, Iosifescu DV, Kohane IS, Churchill SE, Castro VM, Clements CC, Blumenthal SR, Murphy SN, Smoller JW, Perlis RH. Rare copy number variation in treatment-resistant major depressive disorder. Biol Psychiatry 2014; 76:536-41. [PMID: 24529801 PMCID: PMC4104153 DOI: 10.1016/j.biopsych.2013.10.028] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2013] [Revised: 10/03/2013] [Accepted: 10/26/2013] [Indexed: 12/28/2022]
Abstract
BACKGROUND While antidepressant treatment response appears to be partially heritable, no consistent genetic associations have been identified. Large, rare copy number variants (CNVs) play a role in other neuropsychiatric diseases, so we assessed their association with treatment-resistant depression (TRD). METHODS We analyzed data from two genome-wide association studies comprising 1263 Caucasian patients with major depressive disorder. One was drawn from a large health system by applying natural language processing to electronic health records (i2b2 cohort). The second consisted of a multicenter study of sequential antidepressant treatments, Sequenced Treatment Alternatives to Relieve Depression. The Birdsuite package was used to identify rare deletions and duplications. Individuals without symptomatic remission, despite two antidepressant treatment trials, were contrasted with those who remitted with a first treatment trial. RESULTS CNV data were derived for 778 subjects in the i2b2 cohort, including 300 subjects (37%) with TRD, and 485 subjects in Sequenced Treatment Alternatives to Relieve Depression cohort, including 152 (31%) with TRD. CNV burden analyses identified modest enrichment of duplications in cases (empirical p = .04 for duplications of 100-200 kilobase) and a particular deletion region spanning gene PABPC4L (empirical p = .02, 6 cases: 0 controls). Pathway analysis suggested enrichment of CNVs intersecting genes regulating actin cytoskeleton. However, none of these associations survived genome-wide correction. CONCLUSIONS Contribution of rare CNVs to TRD appears to be modest, individually or in aggregate. The electronic health record-based methodology demonstrated here should facilitate collection of larger TRD cohorts necessary to further characterize these effects.
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Blumenthal SR, Castro VM, Clements CC, Rosenfield HR, Murphy SN, Fava M, Weilburg JB, Erb JL, Churchill SE, Kohane IS, Smoller JW, Perlis RH. An electronic health records study of long-term weight gain following antidepressant use. JAMA Psychiatry 2014; 71:889-96. [PMID: 24898363 PMCID: PMC9980723 DOI: 10.1001/jamapsychiatry.2014.414] [Citation(s) in RCA: 89] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
IMPORTANCE Short-term studies suggest antidepressants are associated with modest weight gain but little is known about longer-term effects and differences between individual medications in general clinical populations. OBJECTIVE To estimate weight gain associated with specific antidepressants over the 12 months following initial prescription in a large and diverse clinical population. DESIGN, SETTING, AND PARTICIPANTS We identified 22,610 adult patients who began receiving a medication of interest with available weight data in a large New England health care system, including 2 academic medical centers and affiliated outpatient primary and specialty care clinics. We used electronic health records to extract prescribing data and recorded weights for any patient with an index antidepressant prescription including amitriptyline hydrochloride, bupropion hydrochloride, citalopram hydrobromide, duloxetine hydrochloride, escitalopram oxalate, fluoxetine hydrochloride, mirtazapine, nortriptyline hydrochloride, paroxetine hydrochloride, venlafaxine hydrochloride, and sertraline hydrochloride. As measures of assay sensitivity, additional index prescriptions examined included the antiasthma medication albuterol sulfate and the antiobesity medications orlistat, phentermine hydrochloride, and sibutramine hydrochloride. Mixed-effects models were used to estimate rate of weight change over 12 months in comparison with the reference antidepressant, citalopram. MAIN OUTCOME AND MEASURE Clinician-recorded weight at 3-month intervals up to 12 months. RESULTS Compared with citalopram, in models adjusted for sociodemographic and clinical features, significantly decreased rate of weight gain was observed among individuals treated with bupropion (β [SE]: -0.063 [0.027]; P = .02), amitriptyline (β [SE]: -0.081 [0.025]; P = .001), and nortriptyline (β [SE]: -0.147 [0.034]; P < .001). As anticipated, differences were less pronounced among individuals discontinuing treatment prior to 12 months. CONCLUSIONS AND RELEVANCE Antidepressants differ modestly in their propensity to contribute to weight gain. Short-term investigations may be insufficient to characterize and differentiate this risk.
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Affiliation(s)
- Sarah R. Blumenthal
- Center for Experimental Drugs and Diagnostics, Department of Psychiatry, Massachusetts General Hospital, Boston; Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston
| | - Victor M. Castro
- Partners Research Computing, Partners HealthCare System, Boston, Massachusetts, Laboratory of Computer Science, Department of Neurology, Massachusetts General Hospital, Boston
| | - Caitlin C. Clements
- Center for Experimental Drugs and Diagnostics, Department of Psychiatry, Massachusetts General Hospital, Boston, Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston
| | - Hannah R. Rosenfield
- Center for Experimental Drugs and Diagnostics, Department of Psychiatry, Massachusetts General Hospital, Boston, Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston
| | - Shawn N. Murphy
- Partners Research Computing, Partners HealthCare System, Boston, Massachusetts, Laboratory of Computer Science, Department of Neurology, Massachusetts General Hospital, Boston
| | - Maurizio Fava
- Depression Clinic and Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston
| | - Jeffrey B. Weilburg
- Depression Clinic and Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston
| | - Jane L. Erb
- Department of Psychiatry, Brigham and Women’s Hospital, Boston, Massachusetts
| | | | - Isaac S. Kohane
- Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Jordan W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston
| | - Roy H. Perlis
- Center for Experimental Drugs and Diagnostics, Department of Psychiatry, Massachusetts General Hospital, Boston, Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston
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Abstract
Newly released definitions of autism spectrum disorder demonstrate the need for precise diagnoses informed by the integration of clinical, molecular, and biochemical characteristics in a patient-information commons.
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Affiliation(s)
- Isaac S Kohane
- Isaac S. Kohane is Co-Director of the Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA, and the Lawrence J. Henderson Professor of Pediatrics, Children's Hospital, Boston, MA 02115, USA.
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Mandl KD, Kohane IS, McFadden D, Weber GM, Natter M, Mandel J, Schneeweiss S, Weiler S, Klann JG, Bickel J, Adams WG, Ge Y, Zhou X, Perkins J, Marsolo K, Bernstam E, Showalter J, Quarshie A, Ofili E, Hripcsak G, Murphy SN. Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS): architecture. J Am Med Inform Assoc 2014; 21:615-20. [PMID: 24821734 PMCID: PMC4078286 DOI: 10.1136/amiajnl-2014-002727] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2014] [Accepted: 03/08/2014] [Indexed: 11/06/2022] Open
Abstract
We describe the architecture of the Patient Centered Outcomes Research Institute (PCORI) funded Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS, http://www.SCILHS.org) clinical data research network, which leverages the $48 billion dollar federal investment in health information technology (IT) to enable a queryable semantic data model across 10 health systems covering more than 8 million patients, plugging universally into the point of care, generating evidence and discovery, and thereby enabling clinician and patient participation in research during the patient encounter. Central to the success of SCILHS is development of innovative 'apps' to improve PCOR research methods and capacitate point of care functions such as consent, enrollment, randomization, and outreach for patient-reported outcomes. SCILHS adapts and extends an existing national research network formed on an advanced IT infrastructure built with open source, free, modular components.
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Affiliation(s)
- Kenneth D Mandl
- Children's Hospital Informatics Program at Harvard–MIT Health Sciences and Technology, Boston, Massachusetts, USA
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Harvard Catalyst, Harvard Medical School, Boston, Massachusetts, USA
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Isaac S Kohane
- Children's Hospital Informatics Program at Harvard–MIT Health Sciences and Technology, Boston, Massachusetts, USA
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Harvard Catalyst, Harvard Medical School, Boston, Massachusetts, USA
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Douglas McFadden
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Harvard Catalyst, Harvard Medical School, Boston, Massachusetts, USA
| | - Griffin M Weber
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Biomedical Research Informatics Core, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Marc Natter
- Children's Hospital Informatics Program at Harvard–MIT Health Sciences and Technology, Boston, Massachusetts, USA
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Joshua Mandel
- Children's Hospital Informatics Program at Harvard–MIT Health Sciences and Technology, Boston, Massachusetts, USA
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Sebastian Schneeweiss
- Division of Pharmacoepidemiology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Sarah Weiler
- Harvard Catalyst, Harvard Medical School, Boston, Massachusetts, USA
| | - Jeffrey G Klann
- Laboratory of Computer Science, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jonathan Bickel
- Children's Hospital Informatics Program at Harvard–MIT Health Sciences and Technology, Boston, Massachusetts, USA
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
- Information Services Department, Boston Children's Hospital, Boston, Massachusetts,USA
| | - William G Adams
- Boston University School of Medicine/Boston Medical Center, Boston, Massachusetts, USA
- Boston University Clinical and Translational Sciences Institute, Boston, Massachusetts, USA
| | - Yaorong Ge
- College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, North Carolina, USA
| | - Xiaobo Zhou
- Department of Radiology, Center for Bioinformatics & Systems Biology, Wake Forest University Health Science, Winston-Salem, North Carolina, USA
| | - James Perkins
- Clark Atlanta University, Atlanta, Georgia, USA
- Research Centers in Minority Institutions Translational Research Network, Data Coordinating Center, Jackson State University, Jackson, Mississippi, USA
| | - Keith Marsolo
- Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Elmer Bernstam
- Division of Biomedical Informatics, Biomedical Informatics and Department of Internal Medicine, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - John Showalter
- University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Alexander Quarshie
- Department of Internal Medicine, Community Health and Preventive Medicine and Clinical Research Center, Morehouse School of Medicine, Atlanta, Georgia, USA
| | - Elizabeth Ofili
- Department of Internal Medicine, Clinical Research Center, Morehouse School of Medicine, Atlanta, Georgia, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Shawn N Murphy
- Laboratory of Computer Science, Massachusetts General Hospital, Boston, Massachusetts, USA
- Partners HealthCare Systems, Information Systems, Charlestown, Massachusetts, USA
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Hwang KB, Lee IH, Park JH, Hambuch T, Choe Y, Kim M, Lee K, Song T, Neu MB, Gupta N, Kohane IS, Green RC, Kong SW. Reducing false-positive incidental findings with ensemble genotyping and logistic regression based variant filtering methods. Hum Mutat 2014; 35:936-44. [PMID: 24829188 DOI: 10.1002/humu.22587] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2013] [Accepted: 04/29/2014] [Indexed: 12/29/2022]
Abstract
As whole genome sequencing (WGS) uncovers variants associated with rare and common diseases, an immediate challenge is to minimize false-positive findings due to sequencing and variant calling errors. False positives can be reduced by combining results from orthogonal sequencing methods, but costly. Here, we present variant filtering approaches using logistic regression (LR) and ensemble genotyping to minimize false positives without sacrificing sensitivity. We evaluated the methods using paired WGS datasets of an extended family prepared using two sequencing platforms and a validated set of variants in NA12878. Using LR or ensemble genotyping based filtering, false-negative rates were significantly reduced by 1.1- to 17.8-fold at the same levels of false discovery rates (5.4% for heterozygous and 4.5% for homozygous single nucleotide variants (SNVs); 30.0% for heterozygous and 18.7% for homozygous insertions; 25.2% for heterozygous and 16.6% for homozygous deletions) compared to the filtering based on genotype quality scores. Moreover, ensemble genotyping excluded > 98% (105,080 of 107,167) of false positives while retaining > 95% (897 of 937) of true positives in de novo mutation (DNM) discovery in NA12878, and performed better than a consensus method using two sequencing platforms. Our proposed methods were effective in prioritizing phenotype-associated variants, and an ensemble genotyping would be essential to minimize false-positive DNM candidates.
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Affiliation(s)
- Kyu-Baek Hwang
- Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Boston Children's Hospital, Boston, Massachusetts; School of Computer Science and Engineering, Soongsil University, Seoul, 156-743, South Korea
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Affiliation(s)
- Arjun K Manrai
- Harvard-MIT Health Sciences and Technology, Cambridge, Massachusetts2Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Gaurav Bhatia
- Harvard-MIT Health Sciences and Technology, Cambridge, Massachusetts3The Eli and Edythe L Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Judith Strymish
- Department of Veterans Affairs (VA) Boston Healthcare System, Harvard Medical School, Boston, Massachusetts
| | - Isaac S Kohane
- Harvard-MIT Health Sciences and Technology, Cambridge, Massachusetts2Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Sachin H Jain
- Department of Veterans Affairs (VA) Boston Healthcare System, Harvard Medical School, Boston, Massachusetts5Merck Medical Information and Innovation, Merck and Company, Boston, Massachusetts6Department of Health Care Policy, Harvard Medical School, Boston
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Abstract
IMPORTANCE Epilepsy is a debilitating condition, often with neither a known etiology nor an effective treatment. Autoimmune mechanisms have been increasingly identified. OBJECTIVE To conduct a population-level study investigating the relationship between epilepsy and several common autoimmune diseases. DESIGN, SETTING, AND PARTICIPANTS A retrospective population-based study using claims from a nationwide employer-provided health insurance plan in the United States. Participants were beneficiaries enrolled between 1999 and 2006 (N = 2 518 034). MAIN OUTCOMES AND MEASURES We examined the relationship between epilepsy and 12 autoimmune diseases: type 1 diabetes mellitus, psoriasis, rheumatoid arthritis, Graves disease, Hashimoto thyroiditis, Crohn disease, ulcerative colitis, systemic lupus erythematosus, antiphospholipid syndrome, Sjögren syndrome, myasthenia gravis, and celiac disease. RESULTS The risk of epilepsy was significantly heightened among patients with autoimmune diseases (odds ratio, 3.8; 95% CI, 3.6-4.0; P < .001) and was especially pronounced in children (5.2; 4.1-6.5; P < .001). Elevated risk was consistently observed across all 12 autoimmune diseases. CONCLUSIONS AND RELEVANCE Epilepsy and autoimmune disease frequently co-occur; patients with either condition should undergo surveillance for the other. The potential role of autoimmunity must be given due consideration in epilepsy so that we are not overlooking a treatable cause.
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Affiliation(s)
- Mei-Sing Ong
- Australian Institute of Health Innovation, University of New South Wales, Sydney, Australia2Children's Hospital Informatics Program at Harvard-Massachusetts Institute of Technology Health Sciences and Technology, Boston Children's Hospital, Boston, Massac
| | - Isaac S Kohane
- Children's Hospital Informatics Program at Harvard-Massachusetts Institute of Technology Health Sciences and Technology, Boston Children's Hospital, Boston, Massachusetts3Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Tianxi Cai
- Department of Biostatics, Harvard School of Public Health, Boston, Massachusetts
| | - Mark P Gorman
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts5Department of Neurology, Boston Children's Hospital, Boston, Massachusetts
| | - Kenneth D Mandl
- Children's Hospital Informatics Program at Harvard-Massachusetts Institute of Technology Health Sciences and Technology, Boston Children's Hospital, Boston, Massachusetts3Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
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Galkina EI, Shin A, Coser KR, Shioda T, Kohane IS, Seong IS, Wheeler VC, Gusella JF, MacDonald ME, Lee JM. HD CAGnome: a search tool for huntingtin CAG repeat length-correlated genes. PLoS One 2014; 9:e95556. [PMID: 24751919 PMCID: PMC3994101 DOI: 10.1371/journal.pone.0095556] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2013] [Accepted: 03/28/2014] [Indexed: 11/19/2022] Open
Abstract
Background The length of the huntingtin (HTT) CAG repeat is strongly correlated with both age at onset of Huntington’s disease (HD) symptoms and age at death of HD patients. Dichotomous analysis comparing HD to controls is widely used to study the effects of HTT CAG repeat expansion. However, a potentially more powerful approach is a continuous analysis strategy that takes advantage of all of the different CAG lengths, to capture effects that are expected to be critical to HD pathogenesis. Methodology/Principal Findings We used continuous and dichotomous approaches to analyze microarray gene expression data from 107 human control and HD lymphoblastoid cell lines. Of all probes found to be significant in a continuous analysis by CAG length, only 21.4% were so identified by a dichotomous comparison of HD versus controls. Moreover, of probes significant by dichotomous analysis, only 33.2% were also significant in the continuous analysis. Simulations revealed that the dichotomous approach would require substantially more than 107 samples to either detect 80% of the CAG-length correlated changes revealed by continuous analysis or to reduce the rate of significant differences that are not CAG length-correlated to 20% (n = 133 or n = 206, respectively). Given the superior power of the continuous approach, we calculated the correlation structure between HTT CAG repeat lengths and gene expression levels and created a freely available searchable website, “HD CAGnome,” that allows users to examine continuous relationships between HTT CAG and expression levels of ∼20,000 human genes. Conclusions/Significance Our results reveal limitations of dichotomous approaches compared to the power of continuous analysis to study a disease where human genotype-phenotype relationships strongly support a role for a continuum of CAG length-dependent changes. The compendium of HTT CAG length-gene expression level relationships found at the HD CAGnome now provides convenient routes for discovery of candidates influenced by the HD mutation.
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Affiliation(s)
- Ekaterina I. Galkina
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Aram Shin
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Kathryn R. Coser
- Massachusetts General Hospital Cancer Center, Charlestown, Massachusetts, United States of America
| | - Toshi Shioda
- Massachusetts General Hospital Cancer Center, Charlestown, Massachusetts, United States of America
| | - Isaac S. Kohane
- Children’s Hospital Informatics program, Children’s Hospital, Boston, Massachusetts, United States of America
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
- i2b2 National center for Biomedical Computing, Boston, Massachusetts, United States of America
| | - Ihn Sik Seong
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Vanessa C. Wheeler
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - James F. Gusella
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Marcy E. MacDonald
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Jong-Min Lee
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- * E-mail:
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Brownstein CA, Beggs AH, Homer N, Merriman B, Yu TW, Flannery KC, DeChene ET, Towne MC, Savage SK, Price EN, Holm IA, Luquette LJ, Lyon E, Majzoub J, Neupert P, McCallie D, Szolovits P, Willard HF, Mendelsohn NJ, Temme R, Finkel RS, Yum SW, Medne L, Sunyaev SR, Adzhubey I, Cassa CA, de Bakker PIW, Duzkale H, Dworzyński P, Fairbrother W, Francioli L, Funke BH, Giovanni MA, Handsaker RE, Lage K, Lebo MS, Lek M, Leshchiner I, MacArthur DG, McLaughlin HM, Murray MF, Pers TH, Polak PP, Raychaudhuri S, Rehm HL, Soemedi R, Stitziel NO, Vestecka S, Supper J, Gugenmus C, Klocke B, Hahn A, Schubach M, Menzel M, Biskup S, Freisinger P, Deng M, Braun M, Perner S, Smith RJH, Andorf JL, Huang J, Ryckman K, Sheffield VC, Stone EM, Bair T, Black-Ziegelbein EA, Braun TA, Darbro B, DeLuca AP, Kolbe DL, Scheetz TE, Shearer AE, Sompallae R, Wang K, Bassuk AG, Edens E, Mathews K, Moore SA, Shchelochkov OA, Trapane P, Bossler A, Campbell CA, Heusel JW, Kwitek A, Maga T, Panzer K, Wassink T, Van Daele D, Azaiez H, Booth K, Meyer N, Segal MM, Williams MS, Tromp G, White P, Corsmeier D, Fitzgerald-Butt S, Herman G, Lamb-Thrush D, McBride KL, Newsom D, Pierson CR, Rakowsky AT, Maver A, Lovrečić L, Palandačić A, Peterlin B, Torkamani A, Wedell A, Huss M, Alexeyenko A, Lindvall JM, Magnusson M, Nilsson D, Stranneheim H, Taylan F, Gilissen C, Hoischen A, van Bon B, Yntema H, Nelen M, Zhang W, Sager J, Zhang L, Blair K, Kural D, Cariaso M, Lennon GG, Javed A, Agrawal S, Ng PC, Sandhu KS, Krishna S, Veeramachaneni V, Isakov O, Halperin E, Friedman E, Shomron N, Glusman G, Roach JC, Caballero J, Cox HC, Mauldin D, Ament SA, Rowen L, Richards DR, San Lucas FA, Gonzalez-Garay ML, Caskey CT, Bai Y, Huang Y, Fang F, Zhang Y, Wang Z, Barrera J, Garcia-Lobo JM, González-Lamuño D, Llorca J, Rodriguez MC, Varela I, Reese MG, De La Vega FM, Kiruluta E, Cargill M, Hart RK, Sorenson JM, Lyon GJ, Stevenson DA, Bray BE, Moore BM, Eilbeck K, Yandell M, Zhao H, Hou L, Chen X, Yan X, Chen M, Li C, Yang C, Gunel M, Li P, Kong Y, Alexander AC, Albertyn ZI, Boycott KM, Bulman DE, Gordon PMK, Innes AM, Knoppers BM, Majewski J, Marshall CR, Parboosingh JS, Sawyer SL, Samuels ME, Schwartzentruber J, Kohane IS, Margulies DM. An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge. Genome Biol 2014; 15:R53. [PMID: 24667040 PMCID: PMC4073084 DOI: 10.1186/gb-2014-15-3-r53] [Citation(s) in RCA: 90] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2013] [Accepted: 03/25/2014] [Indexed: 12/30/2022] Open
Abstract
Background There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. Results A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. Conclusions The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.
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Vassy JL, Lautenbach DM, McLaughlin HM, Kong SW, Christensen KD, Krier J, Kohane IS, Feuerman LZ, Blumenthal-Barby J, Roberts JS, Lehmann LS, Ho CY, Ubel PA, MacRae CA, Seidman CE, Murray MF, McGuire AL, Rehm HL, Green RC. The MedSeq Project: a randomized trial of integrating whole genome sequencing into clinical medicine. Trials 2014; 15:85. [PMID: 24645908 PMCID: PMC4113228 DOI: 10.1186/1745-6215-15-85] [Citation(s) in RCA: 112] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2013] [Accepted: 02/28/2014] [Indexed: 11/28/2022] Open
Abstract
Background Whole genome sequencing (WGS) is already being used in certain clinical and research settings, but its impact on patient well-being, health-care utilization, and clinical decision-making remains largely unstudied. It is also unknown how best to communicate sequencing results to physicians and patients to improve health. We describe the design of the MedSeq Project: the first randomized trials of WGS in clinical care. Methods/Design This pair of randomized controlled trials compares WGS to standard of care in two clinical contexts: (a) disease-specific genomic medicine in a cardiomyopathy clinic and (b) general genomic medicine in primary care. We are recruiting 8 to 12 cardiologists, 8 to 12 primary care physicians, and approximately 200 of their patients. Patient participants in both the cardiology and primary care trials are randomly assigned to receive a family history assessment with or without WGS. Our laboratory delivers a genome report to physician participants that balances the needs to enhance understandability of genomic information and to convey its complexity. We provide an educational curriculum for physician participants and offer them a hotline to genetics professionals for guidance in interpreting and managing their patients’ genome reports. Using varied data sources, including surveys, semi-structured interviews, and review of clinical data, we measure the attitudes, behaviors and outcomes of physician and patient participants at multiple time points before and after the disclosure of these results. Discussion The impact of emerging sequencing technologies on patient care is unclear. We have designed a process of interpreting WGS results and delivering them to physicians in a way that anticipates how we envision genomic medicine will evolve in the near future. That is, our WGS report provides clinically relevant information while communicating the complexity and uncertainty of WGS results to physicians and, through physicians, to their patients. This project will not only illuminate the impact of integrating genomic medicine into the clinical care of patients but also inform the design of future studies. Trial registration ClinicalTrials.gov identifier
NCT01736566
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Robert C Green
- Genomes2People and Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Broad Institute and Harvard Medical School, 41 Avenue Louis Pasteur, Suite 301, 02115 Boston, MA, USA.
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Lee IH, Lee K, Hsing M, Choe Y, Park JH, Kim SH, Bohn JM, Neu MB, Hwang KB, Green RC, Kohane IS, Kong SW. Prioritizing disease-linked variants, genes, and pathways with an interactive whole-genome analysis pipeline. Hum Mutat 2014; 35:537-47. [PMID: 24478219 DOI: 10.1002/humu.22520] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2013] [Accepted: 01/23/2014] [Indexed: 01/02/2023]
Abstract
Whole-genome sequencing (WGS) studies are uncovering disease-associated variants in both rare and nonrare diseases. Utilizing the next-generation sequencing for WGS requires a series of computational methods for alignment, variant detection, and annotation, and the accuracy and reproducibility of annotation results are essential for clinical implementation. However, annotating WGS with up to date genomic information is still challenging for biomedical researchers. Here, we present one of the fastest and highly scalable annotation, filtering, and analysis pipeline-gNOME-to prioritize phenotype-associated variants while minimizing false-positive findings. Intuitive graphical user interface of gNOME facilitates the selection of phenotype-associated variants, and the result summaries are provided at variant, gene, and genome levels. Moreover, the enrichment results of specific variants, genes, and gene sets between two groups or compared with population scale WGS datasets that is already integrated in the pipeline can help the interpretation. We found a small number of discordant results between annotation software tools in part due to different reporting strategies for the variants with complex impacts. Using two published whole-exome datasets of uveal melanoma and bladder cancer, we demonstrated gNOME's accuracy of variant annotation and the enrichment of loss-of-function variants in known cancer pathways. gNOME Web server and source codes are freely available to the academic community (http://gnome.tchlab.org).
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Affiliation(s)
- In-Hee Lee
- Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Department of Medicine, Boston Children's Hospital, Boston, Massachusetts, 02115
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145
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Hoogenboom WS, Perlis RH, Smoller JW, Zeng-Treitler Q, Gainer VS, Murphy SN, Churchill SE, Kohane IS, Shenton ME, Iosifescu DV. Limbic system white matter microstructure and long-term treatment outcome in major depressive disorder: a diffusion tensor imaging study using legacy data. World J Biol Psychiatry 2014; 15:122-34. [PMID: 22540406 PMCID: PMC6450652 DOI: 10.3109/15622975.2012.669499] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
OBJECTIVES Treatment-resistant depression is a common clinical occurrence among patients with major depressive disorder (MDD), but its neurobiology is poorly understood. We used data collected as part of routine clinical care to study white matter integrity of the brain's limbic system and its association to treatment response. METHODS Electronic medical records of multiple large New England hospitals were screened for patients with an MDD billing diagnosis, and natural language processing was subsequently applied to find those with concurrent diffusion-weighted images, but without any diagnosed brain pathology. Treatment outcome was determined by review of clinical charts. MDD patients (n = 29 non-remitters, n = 26 partial-remitters, and n = 37 full-remitters), and healthy control subjects (n = 58) were analyzed for fractional anisotropy (FA) of the fornix and cingulum bundle. RESULTS Failure to achieve remission was associated with lower FA among MDD patients, statistically significant for the medial body of the fornix. Moreover, global and regional-selective age-related FA decline was most pronounced in patients with treatment-refractory, non-remitted depression. CONCLUSIONS These findings suggest that specific brain microstructural white matter abnormalities underlie persistent, treatment-resistant depression. They also demonstrate the feasibility of investigating white matter integrity in psychiatric populations using legacy data.
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Affiliation(s)
- Wouter S. Hoogenboom
- Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, United States,Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02215, United States,Corresponding author: Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, 1249 Boylston Street, Boston, MA 02215, United States, Tel: +1 617 455 8929, Fax: +1 617 525 6150,
| | - Roy H. Perlis
- Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, United States,Center for Human Genetic Research, Laboratory of Psychiatric Pharmacogenomics, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Jordan W. Smoller
- Psychiatric Genetics Program in Mood and Anxiety Disorders, Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Qing Zeng-Treitler
- University of Utah, Department of Biomedical Informatics, Salt Lake City, UT 84112, United States,VA Salt Lake City Health Care System, Salt Lake City, UT, 84148, United States
| | - Vivian S. Gainer
- Information Systems, Partners HealthCare System, Inc., Charlestown, MA 02129, United States
| | - Shawn N. Murphy
- Information Systems, Partners HealthCare System, Inc., Charlestown, MA 02129, United States,Laboratory of Computer Science, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Susanne E. Churchill
- i2b2 National Center for Biomedical Computing, Brigham and Women’s Hospital, Boston, MA 02115, United States
| | - Isaac S. Kohane
- i2b2 National Center for Biomedical Computing, Brigham and Women’s Hospital, Boston, MA 02115, United States
| | - Martha E. Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02215, United States,Clinical Neuroscience Division, Laboratory of Neuroscience, Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA 02301 and Harvard Medical School, Boston, MA 02115, United States
| | - Dan V. Iosifescu
- Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, United States,Mood and Anxiety Disorders Program, Mount Sinai School of Medicine, New York, NY 10029, United States
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146
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Kolker E, Özdemir V, Martens L, Hancock W, Anderson G, Anderson N, Aynacioglu S, Baranova A, Campagna SR, Chen R, Choiniere J, Dearth SP, Feng WC, Ferguson L, Fox G, Frishman D, Grossman R, Heath A, Higdon R, Hutz MH, Janko I, Jiang L, Joshi S, Kel A, Kemnitz JW, Kohane IS, Kolker N, Lancet D, Lee E, Li W, Lisitsa A, Llerena A, MacNealy-Koch C, Marshall JC, Masuzzo P, May A, Mias G, Monroe M, Montague E, Mooney S, Nesvizhskii A, Noronha S, Omenn G, Rajasimha H, Ramamoorthy P, Sheehan J, Smarr L, Smith CV, Smith T, Snyder M, Rapole S, Srivastava S, Stanberry L, Stewart E, Toppo S, Uetz P, Verheggen K, Voy BH, Warnich L, Wilhelm SW, Yandl G. Toward more transparent and reproducible omics studies through a common metadata checklist and data publications. OMICS 2014; 18:10-4. [PMID: 24456465 PMCID: PMC3903324 DOI: 10.1089/omi.2013.0149] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.
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Affiliation(s)
- Eugene Kolker
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Vural Özdemir
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Office of the President, Gaziantep University, International Affairs and Global Development Strategy
- Faculty of Communications, Universite Bulvarı, Kilis Yolu, Turkey
| | - Lennart Martens
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Medical Protein Research, Vlaams Instituut voor Biotechnologie, Ghent, Belgium
- Department of Biochemistry, Ghent University; Ghent, Belgium
| | - William Hancock
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Chemistry, Barnett Institute, Northeastern University, Boston, Massachusetts
| | - Gordon Anderson
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington
| | - Nathaniel Anderson
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Sukru Aynacioglu
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Pharmacology, Gaziantep University, Gaziantep, Turkey
| | - Ancha Baranova
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- School of Systems Biology, George Mason University, Manassas, Virginia
| | - Shawn R. Campagna
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Chemistry, University of Tennessee Knoxville, Knoxville, Tennessee
| | - Rui Chen
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Genetics, Stanford University, Stanford, California
| | - John Choiniere
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Stephen P. Dearth
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Chemistry, University of Tennessee Knoxville, Knoxville, Tennessee
| | - Wu-Chun Feng
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia
- Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia
- Department of SyNeRGy Laboratory, Virginia Tech, Blacksburg, Virginia
| | - Lynnette Ferguson
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Nutrition, Auckland Cancer Society Research Centre, University of Auckland, Auckland, New Zealand
| | - Geoffrey Fox
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- School of Informatics and Computing, Indiana University, Bloomington, Indiana
| | - Dmitrij Frishman
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Technische Universitat Munchen, Wissenshaftzentrum Weihenstephan, Freising, Germany
| | - Robert Grossman
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois
- Department of Medicine, University of Chicago, Chicago, Illinois
| | - Allison Heath
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois
- Knapp Center for Biomedical Discovery, University of Chicago, Chicago, Illinois
| | - Roger Higdon
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Mara H. Hutz
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Departamento de Genetica, Instituto de Biociencias, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Imre Janko
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- High-Throughput Analysis Core, Seattle Children's Research Institute, Seattle, Washington
| | - Lihua Jiang
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Genetics, Stanford University, Stanford, California
| | - Sanjay Joshi
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Life Sciences, EMC, Hopkinton, Massachusetts
| | - Alexander Kel
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- GeneXplain GmbH, Wolfenbüttel, Germany
| | - Joseph W. Kemnitz
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, Wisconsin
- Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, Wisconsin
| | - Isaac S. Kohane
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Pediatrics and Health Sciences Technology, Children's Hospital and Harvard Medical School, Boston, Massachusetts
- HMS Center for Biomedical Informatics, Countway Library of Medicine, Boston, Massachusetts
| | - Natali Kolker
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- High-Throughput Analysis Core, Seattle Children's Research Institute, Seattle, Washington
| | - Doron Lancet
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Molecular Genetics, Crown Human Genome Center, Weizmann Institute of Science, Rehovot, Israel
| | - Elaine Lee
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- High-Throughput Analysis Core, Seattle Children's Research Institute, Seattle, Washington
| | - Weizhong Li
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Center for Research in Biological Systems, University of California, San Diego, La Jolla, California
| | - Andrey Lisitsa
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Russian Human Proteome Organization (RHUPO), Moscow, Russia
- Institute of Biomedical Chemistry, Moscow, Russia
| | - Adrian Llerena
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Clinical Research Center, Extremadura University Hospital and Medical School, Badajoz, Spain
| | - Courtney MacNealy-Koch
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Jean-Claude Marshall
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Center for Translational Research, Catholic Health Initiatives, Towson, Maryland
| | - Paola Masuzzo
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Medical Protein Research, Vlaams Instituut voor Biotechnologie, Ghent, Belgium
- Department of Biochemistry, Ghent University; Ghent, Belgium
| | - Amanda May
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Chemistry, University of Tennessee Knoxville, Knoxville, Tennessee
| | - George Mias
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Genetics, Stanford University, Stanford, California
| | - Matthew Monroe
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington
| | - Elizabeth Montague
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Sean Mooney
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- The Buck Institute for Research on Aging, Novato, California
| | - Alexey Nesvizhskii
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Pathology, University of Michigan, Ann Arbor, Michigan
- Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
| | - Santosh Noronha
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, India
| | - Gilbert Omenn
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor Michigan
- Department of Molecular Medicine & Genetics and Human Genetics, University of Michigan, Ann Arbor Michigan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor Michigan
- School of Public Health, University of Michigan, Ann Arbor Michigan
| | - Harsha Rajasimha
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Jeeva Informatics Solutions LLC, Derwood, Maryland
| | - Preveen Ramamoorthy
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Molecular Diagnostics Department, National Jewish Health, Denver, Colorado
| | - Jerry Sheehan
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- California Institute for Telecommunications and Information Technology, University of California-San Diego, La Jolla, California
| | - Larry Smarr
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- California Institute for Telecommunications and Information Technology, University of California-San Diego, La Jolla, California
| | - Charles V. Smith
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Center for Developmental Therapeutics, Seattle Children's Research Institute, Seattle, Washington
| | - Todd Smith
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Digital World Biology, Seattle, Washington
| | - Michael Snyder
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Genetics, Stanford University, Stanford, California
- Stanford Center for Genomics and Personalized Medicine, Stanford University, Stanford, California
| | - Srikanth Rapole
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Proteomics Laboratory, National Centre for Cell Science, University of Pune, Pune, India
| | - Sanjeeva Srivastava
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Proteomics Laboratory, Indian Institute of Technology Bombay, Mumbai, India
| | - Larissa Stanberry
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Elizabeth Stewart
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Stefano Toppo
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | - Peter Uetz
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Center for the Study of Biological Complexity (CSBC), Virginia Commonwealth University, Richmond, Virginia
| | - Kenneth Verheggen
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Medical Protein Research, Vlaams Instituut voor Biotechnologie, Ghent, Belgium
- Department of Biochemistry, Ghent University; Ghent, Belgium
| | - Brynn H. Voy
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Animal Science, University of Tennessee Institute of Agriculture, Knoxville, Tennessee
| | - Louise Warnich
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Genetics, Faculty of AgriSciences, University of Stellenbosch, Stellenbosch, South Africa
| | - Steven W. Wilhelm
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Microbiology, University of Tennessee-Knoxville, Knoxville, Tennessee
| | - Gregory Yandl
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
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147
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Prilutsky D, Palmer NP, Smedemark-Margulies N, Schlaeger TM, Margulies DM, Kohane IS. iPSC-derived neurons as a higher-throughput readout for autism: promises and pitfalls. Trends Mol Med 2013; 20:91-104. [PMID: 24374161 DOI: 10.1016/j.molmed.2013.11.004] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2013] [Revised: 11/20/2013] [Accepted: 11/21/2013] [Indexed: 12/13/2022]
Abstract
The elucidation of disease etiologies and establishment of robust, scalable, high-throughput screening assays for autism spectrum disorders (ASDs) have been impeded by both inaccessibility of disease-relevant neuronal tissue and the genetic heterogeneity of the disorder. Neuronal cells derived from induced pluripotent stem cells (iPSCs) from autism patients may circumvent these obstacles and serve as relevant cell models. To date, derived cells are characterized and screened by assessing their neuronal phenotypes. These characterizations are often etiology-specific or lack reproducibility and stability. In this review, we present an overview of efforts to study iPSC-derived neurons as a model for autism, and we explore the plausibility of gene expression profiling as a reproducible and stable disease marker.
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Affiliation(s)
- Daria Prilutsky
- Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Nathan P Palmer
- Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | | | | | - David M Margulies
- Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Divisions of Genetics and Developmental Medicine, Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Isaac S Kohane
- Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.
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148
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Namjou B, Keddache M, Marsolo K, Wagner M, Lingren T, Cobb B, Perry C, Kennebeck S, Holm IA, Li R, Crimmins NA, Martin L, Solti I, Kohane IS, Harley JB. EMR-linked GWAS study: investigation of variation landscape of loci for body mass index in children. Front Genet 2013; 4:268. [PMID: 24348519 PMCID: PMC3847941 DOI: 10.3389/fgene.2013.00268] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2013] [Accepted: 11/16/2013] [Indexed: 12/21/2022] Open
Abstract
UNLABELLED Common variations at the loci harboring the fat mass and obesity gene (FTO), MC4R, and TMEM18 are consistently reported as being associated with obesity and body mass index (BMI) especially in adult population. In order to confirm this effect in pediatric population five European ancestry cohorts from pediatric eMERGE-II network (CCHMC-BCH) were evaluated. METHOD Data on 5049 samples of European ancestry were obtained from the Electronic Medical Records (EMRs) of two large academic centers in five different genotyped cohorts. For all available samples, gender, age, height, and weight were collected and BMI was calculated. To account for age and sex differences in BMI, BMI z-scores were generated using 2000 Centers of Disease Control and Prevention (CDC) growth charts. A Genome-wide association study (GWAS) was performed with BMI z-score. After removing missing data and outliers based on principal components (PC) analyses, 2860 samples were used for the GWAS study. The association between each single nucleotide polymorphism (SNP) and BMI was tested using linear regression adjusting for age, gender, and PC by cohort. The effects of SNPs were modeled assuming additive, recessive, and dominant effects of the minor allele. Meta-analysis was conducted using a weighted z-score approach. RESULTS The mean age of subjects was 9.8 years (range 2-19). The proportion of male subjects was 56%. In these cohorts, 14% of samples had a BMI ≥95 and 28 ≥ 85%. Meta analyses produced a signal at 16q12 genomic region with the best result of p = 1.43 × 10(-) (7) [p (rec) = 7.34 × 10(-) (8)) for the SNP rs8050136 at the first intron of FTO gene (z = 5.26) and with no heterogeneity between cohorts (p = 0.77). Under a recessive model, another published SNP at this locus, rs1421085, generates the best result [z = 5.782, p (rec) = 8.21 × 10(-) (9)]. Imputation in this region using dense 1000-Genome and Hapmap CEU samples revealed 71 SNPs with p < 10(-) (6), all at the first intron of FTO locus. When hetero-geneity was permitted between cohorts, signals were also obtained in other previously identified loci, including MC4R (rs12964056, p = 6.87 × 10(-) (7), z = -4.98), cholecystokinin CCK (rs8192472, p = 1.33 × 10(-) (6), z = -4.85), Interleukin 15 (rs2099884, p = 1.27 × 10(-) (5), z = 4.34), low density lipoprotein receptor-related protein 1B [LRP1B (rs7583748, p = 0.00013, z = -3.81)] and near transmembrane protein 18 (TMEM18) (rs7561317, p = 0.001, z = -3.17). We also detected a novel locus at chromosome 3 at COL6A5 [best SNP = rs1542829, minor allele frequency (MAF) of 5% p = 4.35 × 10(-) (9), z = 5.89]. CONCLUSION An EMR linked cohort study demonstrates that the BMI-Z measurements can be successfully extracted and linked to genomic data with meaningful confirmatory results. We verified the high prevalence of childhood rate of overweight and obesity in our cohort (28%). In addition, our data indicate that genetic variants in the first intron of FTO, a known adult genetic risk factor for BMI, are also robustly associated with BMI in pediatric population.
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Affiliation(s)
- Bahram Namjou
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
| | - Mehdi Keddache
- Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA ; School of Medicine, University of Cincinnati Cincinnati, OH, USA
| | - Keith Marsolo
- School of Medicine, University of Cincinnati Cincinnati, OH, USA ; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
| | - Michael Wagner
- School of Medicine, University of Cincinnati Cincinnati, OH, USA ; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
| | - Todd Lingren
- School of Medicine, University of Cincinnati Cincinnati, OH, USA ; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
| | - Beth Cobb
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
| | - Cassandra Perry
- Division of Genetics and Genomics, Boston Children's Hospital Boston, MA, USA
| | - Stephanie Kennebeck
- Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA ; School of Medicine, University of Cincinnati Cincinnati, OH, USA
| | - Ingrid A Holm
- Division of Genetics and Genomics, Department of Pediatrics, The Manton Center for Orphan Disease Research, Boston Children's Hospital, Harvard Medical School Boston, MA, USA
| | - Rongling Li
- National Human Genome Research Institute, National Institutes of Health Bethesda, MD, USA
| | - Nancy A Crimmins
- Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA ; School of Medicine, University of Cincinnati Cincinnati, OH, USA
| | - Lisa Martin
- Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA ; School of Medicine, University of Cincinnati Cincinnati, OH, USA
| | - Imre Solti
- School of Medicine, University of Cincinnati Cincinnati, OH, USA ; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
| | - Isaac S Kohane
- Center for Biomedical Informatics, Harvard Medical School and Children's Hospital Informatics Program Boston, MA, USA
| | - John B Harley
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA ; Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA ; School of Medicine, University of Cincinnati Cincinnati, OH, USA ; Department of Veteran Affairs Medical Center Cincinnati, OH, USA
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149
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Kolker E, Özdemir V, Martens L, Hancock W, Anderson G, Anderson N, Aynacioglu S, Baranova A, Campagna SR, Chen R, Choiniere J, Dearth SP, Feng WC, Ferguson L, Fox G, Frishman D, Grossman R, Heath A, Higdon R, Hutz MH, Janko I, Jiang L, Joshi S, Kel A, Kemnitz JW, Kohane IS, Kolker N, Lancet D, Lee E, Li W, Lisitsa A, Llerena A, MacNealy-Koch C, Marshall JC, Masuzzo P, May A, Mias G, Monroe M, Montague E, Mooney S, Nesvizhskii A, Noronha S, Omenn G, Rajasimha H, Ramamoorthy P, Sheehan J, Smarr L, Smith CV, Smith T, Snyder M, Rapole S, Srivastava S, Stanberry L, Stewart E, Toppo S, Uetz P, Verheggen K, Voy BH, Warnich L, Wilhelm SW, Yandl G. Toward More Transparent and Reproducible Omics Studies Through a Common Metadata Checklist and Data Publications. Big Data 2013; 1:196-201. [PMID: 27447251 DOI: 10.1089/big.2013.0039] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.
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Affiliation(s)
- Eugene Kolker
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 2 Predictive Analytics , Seattle Children's, Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Vural Özdemir
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 4 Office of the President, Gaziantep University , International Affairs and Global Development Strategy
- 5 Faculty of Communications, Universite Bulvarı , Kilis Yolu, Turkey
| | - Lennart Martens
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 6 Department of Medical Protein Research, Vlaams Instituut voor Biotechnologie , Ghent, Belgium
- 7 Department of Biochemistry, Ghent University, Ghent , Belgium
| | - William Hancock
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 8 Department of Chemistry, Barnett Institute, Northeastern University , Boston, Massachusetts
| | - Gordon Anderson
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 9 Fundamental & Computational Sciences Directorate, Pacific Northwest National Laboratory , Richland, Washington
| | - Nathaniel Anderson
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Sukru Aynacioglu
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 10 Department of Pharmacology, Gaziantep University , Gaziantep, Turkey
| | - Ancha Baranova
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 11 School of Systems Biology, George Mason University , Manassas, Virginia
| | - Shawn R Campagna
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 12 Department of Chemistry, University of Tennessee Knoxville , Knoxville, Tennessee
| | - Rui Chen
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 13 Department of Genetics, Stanford University , Stanford, California
| | - John Choiniere
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Stephen P Dearth
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 12 Department of Chemistry, University of Tennessee Knoxville , Knoxville, Tennessee
| | - Wu-Chun Feng
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 14 Department of Computer Science, Virginia Tech, Blacksburg Virginia
- 15 Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg Virginia
- 16 SyNeRGy Laboratory, Virginia Tech, Blacksburg, Virginia
| | - Lynnette Ferguson
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 17 Department of Nutrition, Auckland Cancer Society Research Centre, University of Auckland , Auckland, New Zealand
| | - Geoffrey Fox
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 18 School of Informatics and Computing, Indiana University , Bloomington, Indiana
| | - Dmitrij Frishman
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 19 Technische Universitat Munchen , Wissenshaftzentrum Weihenstephan, Freising, Germany
| | - Robert Grossman
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 20 Institute for Genomics and Systems Biology, University of Chicago , Chicago Illinois
- 21 Department of Medicine, University of Chicago , Chicago, Illinois
| | - Allison Heath
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 20 Institute for Genomics and Systems Biology, University of Chicago , Chicago Illinois
- 22 Knapp Center for Biomedical Discovery, University of Chicago , Chicago, Illinois
| | - Roger Higdon
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 2 Predictive Analytics , Seattle Children's, Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Mara H Hutz
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 23 Departamento de Genetica, Instituto de Biociencias, Federal University of Rio Grande do Sul , Porto Alegre, Brazil
| | - Imre Janko
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 24 High-Throughput Analysis Core, Seattle Children's Research Institute , Seattle, Washington
| | - Lihua Jiang
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 13 Department of Genetics, Stanford University , Stanford, California
| | - Sanjay Joshi
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 25 Life Sciences , EMC, Hopkinton, Massachusetts
| | - Alexander Kel
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 26 GeneXplain GmbH , Wolfenbüttel, Germany
| | - Joseph W Kemnitz
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 27 Department of Cell and Regenerative Biology, University of Wisconsin-Madison , Madison, Wisconsin
- 28 Wisconsin National Primate Research Center, University of Wisconsin-Madison , Madison, Wisconsin
| | - Isaac S Kohane
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 29 Pediatrics and Health Sciences Technology, Children's Hospital and Harvard Medical School , Boston, Massachusetts
- 30 HMS Center for Biomedical Informatics, Countway Library of Medicine , Boston, Massachusetts
| | - Natali Kolker
- 2 Predictive Analytics , Seattle Children's, Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 24 High-Throughput Analysis Core, Seattle Children's Research Institute , Seattle, Washington
| | - Doron Lancet
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 31 Department of Molecular Genetics, Crown Human Genome Center , Weizmann Institute of Science, Rehovot, Israel
| | - Elaine Lee
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 24 High-Throughput Analysis Core, Seattle Children's Research Institute , Seattle, Washington
| | - Weizhong Li
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 32 Center for Research in Biological Systems, University of California , San Diego, La Jolla, California
| | - Andrey Lisitsa
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 33 Russian Human Proteome Organization (RHUPO) , Moscow, Russia
- 34 Institute of Biomedical Chemistry , Moscow, Russia
| | - Adrian Llerena
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 35 Clinical Research Center, Extremadura University Hospital and Medical School , Badajoz, Spain
| | - Courtney MacNealy-Koch
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Jean-Claude Marshall
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 36 Center for Translational Research, Catholic Health Initiatives , Towson, Maryland
| | - Paola Masuzzo
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 6 Department of Medical Protein Research, Vlaams Instituut voor Biotechnologie , Ghent, Belgium
- 7 Department of Biochemistry, Ghent University, Ghent , Belgium
| | - Amanda May
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 12 Department of Chemistry, University of Tennessee Knoxville , Knoxville, Tennessee
| | - George Mias
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 13 Department of Genetics, Stanford University , Stanford, California
| | - Matthew Monroe
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 37 Biological Sciences Division, Pacific Northwest National Laboratory , Richland, Washington
| | - Elizabeth Montague
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 2 Predictive Analytics , Seattle Children's, Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Sean Mooney
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 38 The Buck Institute for Research on Aging , Novato, California
| | - Alexey Nesvizhskii
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 39 Department of Pathology, University of Michigan , Ann Arbor, Michigan
- 40 Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, Michigan
| | - Santosh Noronha
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 41 Department of Chemical Engineering, Indian Institute of Technology Bombay , Powai, Mumbai, India
| | - Gilbert Omenn
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 42 Center for Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, Michigan
- 43 Departments of Molecular Medicine & Genetics and Human Genetics, University of Michigan , Ann Arbor Michigan
- 44 Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, Michigan
- 45 School of Public Health, University of Michigan , Ann Arbor, Michigan
| | - Harsha Rajasimha
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 46 J eeva Informatics Solutions LLC , Derwood, Maryland
| | - Preveen Ramamoorthy
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 47 Molecular Diagnostics Department, National Jewish Health , Denver Colorado
| | - Jerry Sheehan
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 48 California Institute for Telecommunications and Information Technology, University of California-San Diego , La Jolla, California
| | - Larry Smarr
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 48 California Institute for Telecommunications and Information Technology, University of California-San Diego , La Jolla, California
| | - Charles V Smith
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 49 Center for Developmental Therapeutics, Seattle Children's Research Institute , Seattle, Washington
| | - Todd Smith
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 50 Digital World Biology , Seattle, Washington
| | - Michael Snyder
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 13 Department of Genetics, Stanford University , Stanford, California
- 51 Stanford Center for Genomics and Personalized Medicine, Stanford University , Stanford, California
| | - Srikanth Rapole
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 52 Proteomics Laboratory, National Centre for Cell Science, University of Pune , Pune, India
| | - Sanjeeva Srivastava
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 53 Proteomics Laboratory, Indian Institute of Technology Bombay , Mumbai, India
| | - Larissa Stanberry
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 2 Predictive Analytics , Seattle Children's, Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Elizabeth Stewart
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Stefano Toppo
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 54 Department of Molecular Medicine, University of Padova , Padova, Italy
| | - Peter Uetz
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 55 Center for the Study of Biological Complexity (CSBC), Virginia Commonwealth University , Richmond, Virginia
| | - Kenneth Verheggen
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 6 Department of Medical Protein Research, Vlaams Instituut voor Biotechnologie , Ghent, Belgium
- 7 Department of Biochemistry, Ghent University, Ghent , Belgium
| | - Brynn H Voy
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 56 Department of Animal Science, University of Tennessee Institute of Agriculture , Knoxville, Tennessee
| | - Louise Warnich
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 57 Department of Genetics, Faculty of AgriSciences, University of Stellenbosch , Stellenbosch, South Africa
| | - Steven W Wilhelm
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 58 Department of Microbiology, University of Tennessee-Knoxville , Knoxville, Tennessee
| | - Gregory Yandl
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
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150
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Xia Z, Secor E, Chibnik LB, Bove RM, Cheng S, Chitnis T, Cagan A, Gainer VS, Chen PJ, Liao KP, Shaw SY, Ananthakrishnan AN, Szolovits P, Weiner HL, Karlson EW, Murphy SN, Savova GK, Cai T, Churchill SE, Plenge RM, Kohane IS, De Jager PL. Modeling disease severity in multiple sclerosis using electronic health records. PLoS One 2013; 8:e78927. [PMID: 24244385 PMCID: PMC3823928 DOI: 10.1371/journal.pone.0078927] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2013] [Accepted: 09/17/2013] [Indexed: 12/28/2022] Open
Abstract
Objective To optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings. Methods In a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume). Results The EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R2 = 0.38±0.05, and that between EHR-derived and true BPF has a mean R2 = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10−12). Conclusion Incorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.
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Affiliation(s)
- Zongqi Xia
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
| | - Elizabeth Secor
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Lori B. Chibnik
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
| | - Riley M. Bove
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
| | - Suchun Cheng
- Department of Biostatistics, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
| | - Tanuja Chitnis
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Andrew Cagan
- Research Computing and Informatics Service, Partners HealthCare, Charlestown, Massachusetts, United States of America
| | - Vivian S. Gainer
- Research Computing and Informatics Service, Partners HealthCare, Charlestown, Massachusetts, United States of America
| | - Pei J. Chen
- Department of Pediatrics, Boston Children’s Hospital, Boston, Massachusetts, United States of America
| | - Katherine P. Liao
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Stanley Y. Shaw
- Harvard Medical School, Boston, Massachusetts, United States of America
- Center for System Biology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Ashwin N. Ananthakrishnan
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Peter Szolovits
- Laboratory for Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Howard L. Weiner
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Elizabeth W. Karlson
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Shawn N. Murphy
- Harvard Medical School, Boston, Massachusetts, United States of America
- Research Computing and Informatics Service, Partners HealthCare, Charlestown, Massachusetts, United States of America
- Laboratory of Computer Science, Massachusetts General Hospital, Charlestown, Massachusetts, United States of America
| | - Guergana K. Savova
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Pediatrics, Boston Children’s Hospital, Boston, Massachusetts, United States of America
| | - Tianxi Cai
- Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Susanne E. Churchill
- i2b2/National Center for Biomedical Computing, Partners HealthCare, Boston, Massachusetts, United States of America
| | - Robert M. Plenge
- Harvard Medical School, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
- Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Isaac S. Kohane
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Pediatrics, Boston Children’s Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Philip L. De Jager
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
- * E-mail:
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