1
|
Suver C, Harper J, Loomba J, Saltz M, Solway J, Anzalone AJ, Walters K, Pfaff E, Walden A, McMurry J, Chute CG, Haendel M. The N3C governance ecosystem: A model socio-technical partnership for the future of collaborative analytics at scale. J Clin Transl Sci 2023; 7:e252. [PMID: 38229902 PMCID: PMC10789985 DOI: 10.1017/cts.2023.681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/22/2023] [Accepted: 11/06/2023] [Indexed: 01/18/2024] Open
Abstract
The National COVID Cohort Collaborative (N3C) is a public-private-government partnership established during the Coronavirus pandemic to create a centralized data resource called the "N3C data enclave." This resource contains individual-level health data from participating healthcare sites nationwide to support rapid collaborative analytics. N3C has enabled analytics within a cloud-based enclave of data from electronic health records from over 17 million people (with and without COVID-19) in the USA. To achieve this goal of a shared data resource, N3C implemented a shared governance strategy involving stakeholders in decision-making. The approach leveraged best practices in data stewardship and team science to rapidly enable COVID-19-related research at scale while respecting the privacy of data subjects and participating institutions. N3C balanced equitable access to data, team-based scientific productivity, and individual professional recognition - a key incentive for academic researchers. This governance approach makes N3C research sustainable and effective beyond the initial days of the pandemic. N3C demonstrated that shared governance can overcome traditional barriers to data sharing without compromising data security and trust. The governance innovations described herein are a helpful framework for other privacy-preserving data infrastructure programs and provide a working model for effective team science beyond COVID-19.
Collapse
Affiliation(s)
- Christine Suver
- Research Governance & Ethics, Sage Bionetworks, Seattle, WA, USA
| | | | - Johanna Loomba
- Integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, Charlottesville, VA, USA
| | - Mary Saltz
- Department of Biomedical Informatics, Stony Brook University, New York, NY, USA
| | - Julian Solway
- Institute for Translational Medicine, University of Chicago, Chicago, IL, USA
| | - Alfred Jerrod Anzalone
- Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
| | | | - Emily Pfaff
- University of North Carolina, Chapel Hill, NC, USA
| | - Anita Walden
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Julie McMurry
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Christopher G. Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | - Melissa Haendel
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| |
Collapse
|
2
|
Sieberts SK, Marten C, Bampton E, Björling EA, Burn AM, Carey EG, Carlson S, Fernandes B, Kalha J, Lindani S, Masomera H, Neelakantan L, Pasquale L, Ranganathan S, Joy Scanlan E, Shah H, Sibisi R, Sumant S, Suver C, Thungana Y, Tummalacherla M, Velloza J, Collins PY, Fazel M, Ford T, Freeman M, Pathare S, Zingela Z, Doerr M. Young people's data governance preferences for their mental health data: MindKind Study findings from India, South Africa, and the United Kingdom. PLoS One 2023; 18:e0279857. [PMID: 37074995 PMCID: PMC10115253 DOI: 10.1371/journal.pone.0279857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 04/03/2023] [Indexed: 04/20/2023] Open
Abstract
Mobile devices offer a scalable opportunity to collect longitudinal data that facilitate advances in mental health treatment to address the burden of mental health conditions in young people. Sharing these data with the research community is critical to gaining maximal value from rich data of this nature. However, the highly personal nature of the data necessitates understanding the conditions under which young people are willing to share them. To answer this question, we developed the MindKind Study, a multinational, mixed methods study that solicits young people's preferences for how their data are governed and quantifies potential participants' willingness to join under different conditions. We employed a community-based participatory approach, involving young people as stakeholders and co-researchers. At sites in India, South Africa, and the UK, we enrolled 3575 participants ages 16-24 in the mobile app-mediated quantitative study and 143 participants in the public deliberation-based qualitative study. We found that while youth participants have strong preferences for data governance, these preferences did not translate into (un)willingness to join the smartphone-based study. Participants grappled with the risks and benefits of participation as well as their desire that the "right people" access their data. Throughout the study, we recognized young people's commitment to finding solutions and co-producing research architectures to allow for more open sharing of mental health data to accelerate and derive maximal benefit from research.
Collapse
Affiliation(s)
| | - Carly Marten
- Sage Bionetworks, Seattle, Washington, United States of America
| | - Emily Bampton
- Department of Psychiatry, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - Elin A Björling
- Human Centered Design and Engineering, University of Washington, Seattle, Washington, United States of America
| | - Anne-Marie Burn
- Department of Psychiatry, University of Cambridge, Cambridge, Cambridgeshire, United Kingdom
| | - Emma Grace Carey
- Department of Psychiatry, University of Cambridge, Cambridge, Cambridgeshire, United Kingdom
| | - Sonia Carlson
- Sage Bionetworks, Seattle, Washington, United States of America
| | - Blossom Fernandes
- Department of Psychiatry, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - Jasmine Kalha
- Centre for Mental Health Law & Policy, Indian Law Society, Pune, Maharashtra, India
| | - Simthembile Lindani
- Department of Psychiatry, Walter Sisulu University, Eastern Cape, South Africa
| | - Hedwick Masomera
- Department of Psychiatry, Walter Sisulu University, Eastern Cape, South Africa
- Nelson Mandela University, Gqeberha, Eastern Cape, South Africa
| | - Lakshmi Neelakantan
- Department of Psychiatry, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - Lisa Pasquale
- Sage Bionetworks, Seattle, Washington, United States of America
| | - Swetha Ranganathan
- Centre for Mental Health Law & Policy, Indian Law Society, Pune, Maharashtra, India
| | | | - Himani Shah
- Centre for Mental Health Law & Policy, Indian Law Society, Pune, Maharashtra, India
| | - Refiloe Sibisi
- Activate Change Drivers ZA, Johannesburg, Gauteng, South Africa
- University of Johannesburg, Johannesburg, Gauteng, South Africa
| | - Sushmita Sumant
- Centre for Mental Health Law & Policy, Indian Law Society, Pune, Maharashtra, India
| | - Christine Suver
- Sage Bionetworks, Seattle, Washington, United States of America
| | - Yanga Thungana
- Department of Psychiatry, Walter Sisulu University, Eastern Cape, South Africa
| | | | - Jennifer Velloza
- Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, California, United States of America
- Department of Global Health, University of Washington, Seattle, Washington, United States of America
| | - Pamela Y Collins
- Department of Global Health, University of Washington, Seattle, Washington, United States of America
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, United States of America
| | - Mina Fazel
- Department of Psychiatry, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - Tamsin Ford
- Department of Psychiatry, University of Cambridge, Cambridge, Cambridgeshire, United Kingdom
- Cambridgeshire and Peterborough Foundation NHS Trust, Fulbourn, Cambridgeshire, United Kingdom
| | - Melvyn Freeman
- University of Stellenbosch, Stellenbosch, Western Cape, South Africa
- Higher Health, Centurion, Gauteng, South Africa
| | - Soumitra Pathare
- Centre for Mental Health Law & Policy, Indian Law Society, Pune, Maharashtra, India
| | - Zukiswa Zingela
- Nelson Mandela University, Gqeberha, Eastern Cape, South Africa
| | - Megan Doerr
- Sage Bionetworks, Seattle, Washington, United States of America
| |
Collapse
|
3
|
Goodday S, Karlin D, Suver C, Friend S. The Post-Roe Political Landscape Demands a Morality of Caution for Women's Health. J Med Internet Res 2022; 24:e41417. [PMID: 36264611 DOI: 10.2196/41417] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 09/08/2022] [Accepted: 10/07/2022] [Indexed: 11/13/2022] Open
Abstract
The recent Supreme Court decision (ie, Dobbs v. Jackson Women's Health Organization), revoking the constitutional right to abortion in the United States, has the potential to dramatically disrupt progress in women's health research. The typical safeguards to ensure confidentiality and privacy of research participants in studies that collect certain types of personal health information may not hold against criminal investigations surrounding suspected pregnancy terminations. There are additional risks to participants in digital health research studies involving the use of wearable devices capable of tracking physiological measures, such as body temperature and heart rate, as these have shown promise for tracking conception and could be used to identify pregnancy termination signatures. There are strategies researchers can use to protect the safety of participants in health research who could get pregnant, while also maintaining integrity of research methods. The objective of this viewpoint is to discuss potential strategies to protect research participants' privacy that include the minimization of nonessential sensitive personal health information and anonymization protocols in the event of miscarriage or termination of pregnancy. We invite others to join this discussion so as to not let the current political landscape impede progress in women's health and reproductive research, while also protecting research participants.
Collapse
Affiliation(s)
- Sarah Goodday
- 4YouandMe, Seattle, WA, United States.,Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Daniel Karlin
- 4YouandMe, Seattle, WA, United States.,Department of Psychiatry, Tufts University School of Medicine, Boston, MA, United States.,MindMed Inc, New York, NY, United States
| | | | - Stephen Friend
- 4YouandMe, Seattle, WA, United States.,Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
4
|
Pratap A, Homiar A, Waninger L, Herd C, Suver C, Volponi J, Anguera JA, Areán P. Real-world behavioral dataset from two fully remote smartphone-based randomized clinical trials for depression. Sci Data 2022; 9:522. [PMID: 36030226 PMCID: PMC9420101 DOI: 10.1038/s41597-022-01633-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [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: 06/30/2022] [Accepted: 08/15/2022] [Indexed: 11/09/2022] Open
Abstract
Most people with mental health disorders cannot receive timely and evidence-based care despite billions of dollars spent by healthcare systems. Researchers have been exploring using digital health technologies to measure behavior in real-world settings with mixed results. There is a need to create accessible and computable digital mental health datasets to advance inclusive and transparently validated research for creating robust real-world digital biomarkers of mental health. Here we share and describe one of the largest and most diverse real-world behavior datasets from over two thousand individuals across the US. The data were generated as part of the two NIMH-funded randomized clinical trials conducted to assess the effectiveness of delivering mental health care continuously remotely. The longitudinal dataset consists of self-assessment of mood, depression, anxiety, and passively gathered phone-based behavioral data streams in real-world settings. This dataset will provide a timely and long-term data resource to evaluate analytical approaches for developing digital behavioral markers and understand the effectiveness of mental health care delivered continuously and remotely.
Collapse
Affiliation(s)
- Abhishek Pratap
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada. .,Department of Psychiatry, University of Toronto, Toronto, ON, Canada. .,Vector Institute for Artificial Intelligence, Toronto, ON, Canada. .,Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK. .,Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.
| | - Ava Homiar
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.,School of Interdisciplinary Science, McMaster University, Hamilton, ON, Canada
| | - Luke Waninger
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Calvin Herd
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | | | - Joshua Volponi
- Department of Neurology, University of California San Francisco, San Francisco, WA, USA
| | - Joaquin A Anguera
- Department of Neurology, University of California San Francisco, San Francisco, WA, USA
| | - Pat Areán
- Department of Psychiatry & Behavioral Sciences, University of Washington, Seattle, WA, USA
| |
Collapse
|
5
|
Omberg L, Chaibub Neto E, Perumal TM, Pratap A, Tediarjo A, Adams J, Bloem BR, Bot BM, Elson M, Goldman SM, Kellen MR, Kieburtz K, Klein A, Little MA, Schneider R, Suver C, Tarolli C, Tanner CM, Trister AD, Wilbanks J, Dorsey ER, Mangravite LM. Remote smartphone monitoring of Parkinson's disease and individual response to therapy. Nat Biotechnol 2022; 40:480-487. [PMID: 34373643 DOI: 10.1038/s41587-021-00974-9] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 06/04/2021] [Indexed: 02/07/2023]
Abstract
Remote health assessments that gather real-world data (RWD) outside clinic settings require a clear understanding of appropriate methods for data collection, quality assessment, analysis and interpretation. Here we examine the performance and limitations of smartphones in collecting RWD in the remote mPower observational study of Parkinson's disease (PD). Within the first 6 months of study commencement, 960 participants had enrolled and performed at least five self-administered active PD symptom assessments (speeded tapping, gait/balance, phonation or memory). Task performance, especially speeded tapping, was predictive of self-reported PD status (area under the receiver operating characteristic curve (AUC) = 0.8) and correlated with in-clinic evaluation of disease severity (r = 0.71; P < 1.8 × 10-6) when compared with motor Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Although remote assessment requires careful consideration for accurate interpretation of RWD, our results support the use of smartphones and wearables in objective and personalized disease assessments.
Collapse
Affiliation(s)
| | | | | | - Abhishek Pratap
- Sage Bionetworks, Seattle, WA, USA.,Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | | | - Jamie Adams
- Center for Health and Technology, University of Rochester Medical Center, Rochester, NY, USA.,Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Bastiaan R Bloem
- Radboud University Medical Center; Donders Institute for Brain, Cognition and Behaviour; Department of Neurology, Nijmegen, the Netherlands
| | | | - Molly Elson
- Center for Health and Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Samuel M Goldman
- Department of Neurology, University of California-San Francisco and Parkinson's Disease Research, Education and Clinical Center, San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA
| | | | - Karl Kieburtz
- Center for Health and Technology, University of Rochester Medical Center, Rochester, NY, USA.,Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | | | - Max A Little
- School of Computer Science, University of Birmingham, Birmingham, UK.,Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ruth Schneider
- Center for Health and Technology, University of Rochester Medical Center, Rochester, NY, USA.,Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | | | - Christopher Tarolli
- Center for Health and Technology, University of Rochester Medical Center, Rochester, NY, USA.,Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Caroline M Tanner
- Department of Neurology, University of California-San Francisco and Parkinson's Disease Research, Education and Clinical Center, San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA
| | | | | | - E Ray Dorsey
- Center for Health and Technology, University of Rochester Medical Center, Rochester, NY, USA.,Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | | |
Collapse
|
6
|
Pfaff ER, Girvin AT, Gabriel DL, Kostka K, Morris M, Palchuk MB, Lehmann HP, Amor B, Bissell M, Bradwell KR, Gold S, Hong SS, Loomba J, Manna A, McMurry JA, Niehaus E, Qureshi N, Walden A, Zhang XT, Zhu RL, Moffitt RA, Haendel MA, Chute CG, Adams WG, Al-Shukri S, Anzalone A, Baghal A, Bennett TD, Bernstam EV, Bernstam EV, Bissell MM, Bush B, Campion TR, Castro V, Chang J, Chaudhari DD, Chen W, Chu S, Cimino JJ, Crandall KA, Crooks M, Davies SJD, DiPalazzo J, Dorr D, Eckrich D, Eltinge SE, Fort DG, Golovko G, Gupta S, Haendel MA, Hajagos JG, Hanauer DA, Harnett BM, Horswell R, Huang N, Johnson SG, Kahn M, Khanipov K, Kieler C, Luzuriaga KRD, Maidlow S, Martinez A, Mathew J, McClay JC, McMahan G, Melancon B, Meystre S, Miele L, Morizono H, Pablo R, Patel L, Phuong J, Popham DJ, Pulgarin C, Santos C, Sarkar IN, Sazo N, Setoguchi S, Soby S, Surampalli S, Suver C, Vangala UMR, Visweswaran S, von Oehsen J, Walters KM, Wiley L, Williams DA, Zai A. Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative. J Am Med Inform Assoc 2022; 29:609-618. [PMID: 34590684 PMCID: PMC8500110 DOI: 10.1093/jamia/ocab217] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [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: 07/15/2021] [Revised: 08/19/2021] [Accepted: 09/23/2021] [Indexed: 02/01/2023] Open
Abstract
OBJECTIVE In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. MATERIALS AND METHODS We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. RESULTS Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. DISCUSSION We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. CONCLUSION By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.
Collapse
Affiliation(s)
- Emily R Pfaff
- Department of Medicine, UNC Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA
| | | | - Davera L Gabriel
- Section of Biomedical Informatics and Data Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kristin Kostka
- The OHDSI Center at the Roux Institute, Northeastern University, Portland, Maine, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | - Harold P Lehmann
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | | | | | | | - Sigfried Gold
- Section of Biomedical Informatics and Data Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Stephanie S Hong
- Section of Biomedical Informatics and Data Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - Amin Manna
- Palantir Technologies, Denver, Colorado, USA
| | - Julie A McMurry
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | | | | | - Anita Walden
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | | | - Richard L Zhu
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Richard A Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Melissa A Haendel
- University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
7
|
Chin E, Suver C, Lah JJ, Goldstein FC, Manzanares C, Cobb NL, Blazel H, Edwards DF. An analysis of the study participant and study coordinator experience utilizing an electronic consent (eConsent) in REDCap. Alzheimers Dement 2021. [DOI: 10.1002/alz.056079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Erin Chin
- University of Wisconsin School of Medicine and Public Health Madison WI USA
- Alzheimer's Disease Research Center Madison WI USA
| | | | - James J Lah
- Emory University School of Medicine Atlanta GA USA
- Emory Goizueta Alzheimer's Disease Research Center Atlanta GA USA
| | | | - Cecelia Manzanares
- Emory University School of Medicine Atlanta GA USA
- Emory University Goizueta Alzheimer’s Disease Research Center Atlanta GA USA
| | - Nichelle L Cobb
- University of Wisconsin School of Medicine and Public Health Madison WI USA
| | - Hanna Blazel
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health Madison WI USA
| | - Dorothy Farrar Edwards
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health Madison WI USA
- University of Wisconsin Madison School of Medicine and Public Health Madison WI USA
| |
Collapse
|
8
|
Haendel MA, Chute CG, Bennett TD, Eichmann DA, Guinney J, Kibbe WA, Payne PRO, Pfaff ER, Robinson PN, Saltz JH, Spratt H, Suver C, Wilbanks J, Wilcox AB, Williams AE, Wu C, Blacketer C, Bradford RL, Cimino JJ, Clark M, Colmenares EW, Francis PA, Gabriel D, Graves A, Hemadri R, Hong SS, Hripscak G, Jiao D, Klann JG, Kostka K, Lee AM, Lehmann HP, Lingrey L, Miller RT, Morris M, Murphy SN, Natarajan K, Palchuk MB, Sheikh U, Solbrig H, Visweswaran S, Walden A, Walters KM, Weber GM, Zhang XT, Zhu RL, Amor B, Girvin AT, Manna A, Qureshi N, Kurilla MG, Michael SG, Portilla LM, Rutter JL, Austin CP, Gersing KR. The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment. J Am Med Inform Assoc 2021; 28:427-443. [PMID: 32805036 PMCID: PMC7454687 DOI: 10.1093/jamia/ocaa196] [Citation(s) in RCA: 285] [Impact Index Per Article: 95.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 08/14/2020] [Indexed: 01/12/2023] Open
Abstract
Objective Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. Materials and Methods The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. Results Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. Conclusions The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.
Collapse
Affiliation(s)
- Melissa A Haendel
- Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, Oregon, USA.,Translational and Integrative Sciences Center, Department of Molecular Toxicology, Oregon State University, Corvallis, Oregon, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland, USA
| | - Tellen D Bennett
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora, Colorado, USA
| | - David A Eichmann
- School of Library and Information Science, The University of Iowa, Iowa City, Iowa, USA
| | | | | | - Philip R O Payne
- Institute for Informatics, Washington University in St. Louis, Saint Louis,Missouri, USA
| | - Emily R Pfaff
- North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill,North Carolina, USA
| | | | - Joel H Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Heidi Spratt
- University of Texas Medical Branch, Galveston, Texas, USA
| | | | | | | | - Andrew E Williams
- Tufts Medical Center Clinical and Translational Science Institute, Tufts Medical Center, Boston,Massachusetts, USA
| | - Chunlei Wu
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA
| | - Clair Blacketer
- Janssen Research and Development, LLC, Raritan, New Jersey, USA
| | - Robert L Bradford
- North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill,North Carolina, USA
| | - James J Cimino
- University of Alabama-Birmingham, Birmingham, Alabama, USA
| | - Marshall Clark
- North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill,North Carolina, USA
| | - Evan W Colmenares
- Department of Pharmaceutical Outcomes and Policy, University of North Carolina at Chapel Hill, Chapel Hill,North Carolina, USA
| | | | - Davera Gabriel
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Alexis Graves
- University of Iowa Institute for Clinical and Translational Science, The University of Iowa, Iowa City, Iowa, USA
| | - Raju Hemadri
- National Center for Advancing Translational Science, Bethesda, Maryland, USA
| | - Stephanie S Hong
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - George Hripscak
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Dazhi Jiao
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | | | - Adam M Lee
- University of North Carolina at Chapel Hill, Chapel Hill,North Carolina, USA
| | - Harold P Lehmann
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - Robert T Miller
- Tufts Clinical and Translational Science Institute, Tufts University, Boston,Massachusetts, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh,Pennsylvania, USA
| | | | | | | | - Usman Sheikh
- National Center for Advancing Translational Science, Bethesda, Maryland, USA
| | - Harold Solbrig
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh,Pennsylvania, USA
| | - Anita Walden
- Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, Oregon, USA.,Sage Bionetworks, Seattle, Washington, USA
| | - Kellie M Walters
- North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill,North Carolina, USA
| | - Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston,Massachusetts, USA
| | | | - Richard L Zhu
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | | | - Amin Manna
- Palantir Technologies, Palo Alto, California, USA
| | | | - Michael G Kurilla
- Division of Clinical Innovation, National Center for Advancing Translational Science, Bethesda, Maryland, USA
| | - Sam G Michael
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, USA
| | - Lili M Portilla
- Office of Strategic Alliances, National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, USA
| | - Joni L Rutter
- Office of the Director, National Center for Advancing Translational Science, Bethesda, Maryland, USA
| | - Christopher P Austin
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, USA
| | - Ken R Gersing
- National Center for Advancing Translational Science, Bethesda, Maryland, USA
| | | |
Collapse
|
9
|
Abstract
Informed consent is the gateway to research participation. We report on the results of the formative evaluation that follows the electronic informed consent process for the All of Us Research Program. Of the nearly 250,000 participants included in this analysis, more than 95% could correctly answer questions distinguishing the program from medical care, the voluntary nature of participation, and the right to withdraw; comparatively, participants were less sure of privacy risk of the program. We also report on a small mixed-methods study of the experience of persons of very low health literacy with All of Us informed consent materials. Of note, many of the words commonly employed in the consent process were unfamiliar to or differently defined by informants. In combination, these analyses may inform participant-centered development and highlight areas for refinement of informed consent materials for the All of Us Research Program and similar studies.
Collapse
Affiliation(s)
| | | | | | - Scott Sutherland
- Broad Institute, Cambridge, Massachusetts.,Vibrent Health, Fairfax, Virginia
| | | | | | | |
Collapse
|
10
|
Chin E, Croes K, Dykema J, Suver C, Hamann J, Truong A, Doerr M, Lah JJ, Goldstein FC, Blazel H, Manzanares C, Edwards DF. A qualitative analysis of study participant and study partner experiences with the consent process: Assessments guiding the development of an electronic consent (ECONSENT). Alzheimers Dement 2020. [DOI: 10.1002/alz.043917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Erin Chin
- University of Wisconsin School of Medicine and Public Health Madison WI USA
- Alzheimer's Disease Research Center Madison WI USA
| | - Kenneth Croes
- University of Wisconsin Survey Center Madison WI USA
| | | | | | | | | | | | - James J Lah
- Emory University School of Medicine Atlanta GA USA
| | | | - Hanna Blazel
- University of Wisconsin School of Medicine and Public Health Madison WI USA
| | | | - Dorothy Farrar Edwards
- University of Wisconsin School of Medicine and Public Health Department of Medicine Division of Geriatrics Madison WI USA
| |
Collapse
|
11
|
Deering S, Pratap A, Suver C, Borelli AJ, Amdur A, Headapohl W, Stepnowsky CJ. Real-world longitudinal data collected from the SleepHealth mobile app study. Sci Data 2020; 7:418. [PMID: 33247114 PMCID: PMC7695828 DOI: 10.1038/s41597-020-00753-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 09/04/2020] [Indexed: 11/09/2022] Open
Abstract
Conducting biomedical research using smartphones is a novel approach to studying health and disease that is only beginning to be meaningfully explored. Gathering large-scale, real-world data to track disease manifestation and long-term trajectory in this manner is quite practical and largely untapped. Researchers can assess large study cohorts using surveys and sensor-based activities that can be interspersed with participants' daily routines. In addition, this approach offers a medium for researchers to collect contextual and environmental data via device-based sensors, data aggregator frameworks, and connected wearable devices. The main aim of the SleepHealth Mobile App Study (SHMAS) was to gain a better understanding of the relationship between sleep habits and daytime functioning utilizing a novel digital health approach. Secondary goals included assessing the feasibility of a fully-remote approach to obtaining clinical characteristics of participants, evaluating data validity, and examining user retention patterns and data-sharing preferences. Here, we provide a description of data collected from 7,250 participants living in the United States who chose to share their data broadly with the study team and qualified researchers worldwide.
Collapse
Affiliation(s)
- Sean Deering
- Health Services Research & Development, VA San Diego Healthcare System, San Diego, CA, 92161, USA
| | | | | | | | - Adam Amdur
- American Sleep Apnea Association, Washington, DC, 20001, USA
| | - Will Headapohl
- American Sleep Apnea Association, Washington, DC, 20001, USA
| | - Carl J Stepnowsky
- Health Services Research & Development, VA San Diego Healthcare System, San Diego, CA, 92161, USA. .,Department of Medicine, University of California at San Diego, La Jolla, CA, 92037, USA.
| |
Collapse
|
12
|
Suver C, Thorogood A, Doerr M, Wilbanks J, Knoppers B. Bringing Code to Data: Do Not Forget Governance. J Med Internet Res 2020; 22:e18087. [PMID: 32540846 PMCID: PMC7420687 DOI: 10.2196/18087] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Revised: 05/21/2020] [Accepted: 06/11/2020] [Indexed: 11/13/2022] Open
Abstract
Developing or independently evaluating algorithms in biomedical research is difficult because of restrictions on access to clinical data. Access is restricted because of privacy concerns, the proprietary treatment of data by institutions (fueled in part by the cost of data hosting, curation, and distribution), concerns over misuse, and the complexities of applicable regulatory frameworks. The use of cloud technology and services can address many of the barriers to data sharing. For example, researchers can access data in high performance, secure, and auditable cloud computing environments without the need for copying or downloading. An alternative path to accessing data sets requiring additional protection is the model-to-data approach. In model-to-data, researchers submit algorithms to run on secure data sets that remain hidden. Model-to-data is designed to enhance security and local control while enabling communities of researchers to generate new knowledge from sequestered data. Model-to-data has not yet been widely implemented, but pilots have demonstrated its utility when technical or legal constraints preclude other methods of sharing. We argue that model-to-data can make a valuable addition to our data sharing arsenal, with 2 caveats. First, model-to-data should only be adopted where necessary to supplement rather than replace existing data-sharing approaches given that it requires significant resource commitments from data stewards and limits scientific freedom, reproducibility, and scalability. Second, although model-to-data reduces concerns over data privacy and loss of local control when sharing clinical data, it is not an ethical panacea. Data stewards will remain hesitant to adopt model-to-data approaches without guidance on how to do so responsibly. To address this gap, we explored how commitments to open science, reproducibility, security, respect for data subjects, and research ethics oversight must be re-evaluated in a model-to-data context.
Collapse
Affiliation(s)
| | - Adrian Thorogood
- Centre of Genomics and Policy, McGill University, Montreal, QC, Canada
| | - Megan Doerr
- Sage Bionetworks, Seattle, WA, United States
| | | | - Bartha Knoppers
- Centre of Genomics and Policy, McGill University, Montreal, QC, Canada
| |
Collapse
|
13
|
Pratap A, Neto EC, Snyder P, Stepnowsky C, Elhadad N, Grant D, Mohebbi MH, Mooney S, Suver C, Wilbanks J, Mangravite L, Heagerty PJ, Areán P, Omberg L. Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants. NPJ Digit Med 2020; 3:21. [PMID: 32128451 PMCID: PMC7026051 DOI: 10.1038/s41746-020-0224-8] [Citation(s) in RCA: 171] [Impact Index Per Article: 42.8] [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: 10/01/2019] [Accepted: 01/17/2020] [Indexed: 12/13/2022] Open
Abstract
Digital technologies such as smartphones are transforming the way scientists conduct biomedical research. Several remotely conducted studies have recruited thousands of participants over a span of a few months allowing researchers to collect real-world data at scale and at a fraction of the cost of traditional research. Unfortunately, remote studies have been hampered by substantial participant attrition, calling into question the representativeness of the collected data including generalizability of outcomes. We report the findings regarding recruitment and retention from eight remote digital health studies conducted between 2014-2019 that provided individual-level study-app usage data from more than 100,000 participants completing nearly 3.5 million remote health evaluations over cumulative participation of 850,000 days. Median participant retention across eight studies varied widely from 2-26 days (median across all studies = 5.5 days). Survival analysis revealed several factors significantly associated with increase in participant retention time, including (i) referral by a clinician to the study (increase of 40 days in median retention time); (ii) compensation for participation (increase of 22 days, 1 study); (iii) having the clinical condition of interest in the study (increase of 7 days compared with controls); and (iv) older age (increase of 4 days). Additionally, four distinct patterns of daily app usage behavior were identified by unsupervised clustering, which were also associated with participant demographics. Most studies were not able to recruit a sample that was representative of the race/ethnicity or geographical diversity of the US. Together these findings can help inform recruitment and retention strategies to enable equitable participation of populations in future digital health research.
Collapse
Affiliation(s)
- Abhishek Pratap
- Sage Bionetworks, Seattle, WA USA
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA USA
| | | | | | - Carl Stepnowsky
- University of California, San Diego, CA USA
- American Sleep Apnea Association, Washington, DC USA
| | | | - Daniel Grant
- Novartis Pharmaceutical Corporation, East Hanover, NJ USA
| | | | - Sean Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA USA
| | | | | | | | | | - Pat Areán
- Department of Psychiatry & Behavioral Sciences, University of Washington, Seattle, WA USA
| | | |
Collapse
|
14
|
Chin E, Suver C, Hamann J, Truong A, Doerr M, Legreid D, Lah JJ, Goldstein FC, Manzanares C, Blazel H, Edwards DF. P3-504: A QUALITATIVE ANALYSIS OF COORDINATOR PERCEPTIONS OF THE CONSENT PROCESS: LESSONS GUIDING THE DEVELOPMENT OF AN ELECTRONIC CONSENT PLATFORM. Alzheimers Dement 2019. [DOI: 10.1016/j.jalz.2019.06.3540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Erin Chin
- University of Wisconsin School of Medicine and Public Health; Madison WI USA
- Alzheimer's Disease Research Center; Madison WI USA
| | | | | | | | | | - Dayne Legreid
- University of Wisconsin School of Medicine and Public Health; Madison WI USA
- Alzheimer's Disease Research Center; Madison WI USA
| | - James J. Lah
- Emory University School of Medicine; Atlanta GA USA
| | | | | | - Hanna Blazel
- University of Wisconsin School of Medicine and Public Health; Madison WI USA
- Wisconsin Alzheimer's Disease Research Center; Madison WI USA
| | - Dorothy Farrar Edwards
- University of Wisconsin-Madison; Madison WI USA
- Wisconsin Alzheimer's Disease Research Center; University of Wisconsin School of Medicine and Public Health; Madison WI USA
| |
Collapse
|
15
|
Logsdon BA, Greenwood AK, Daily K, Leanza Z, Woo KH, Suver C, Montgomery K, Mukherjee S, Sieberts SK, Peters MA, Omberg L, Mangravite LM. P4-120: COMMUNITY-POWERED, RADICALLY OPEN TECHNOLOGIES TO ACCELERATE ALZHEIMER'S DISEASE THERAPEUTIC DISCOVERY. Alzheimers Dement 2019. [DOI: 10.1016/j.jalz.2019.06.3781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
16
|
Doerr M, Grayson S, Moore S, Suver C, Wilbanks J, Wagner J. Implementing a universal informed consent process for the All of Us Research Program. Pac Symp Biocomput 2019; 24:427-438. [PMID: 30963079 PMCID: PMC6417826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The United States' All of Us Research Program is a longitudinal research initiative with ambitious national recruitment goals, including of populations traditionally underrepresented in biomedical research, many of whom have high geographic mobility. The program has a distributed infrastructure, with key programmatic resources spread across the US. Given its planned duration and geographic reach both in terms of recruitment and programmatic resources, a diversity of state and territory laws might apply to the program over time as well as to the determination of participants' rights. Here we present a listing and discussion of state and territory guidance and regulation of specific relevance to the program, and our approach to their incorporation within the program's informed consent processes.
Collapse
Affiliation(s)
- Megan Doerr
- Sage Bionetworks, 2901 Third Avenue Seattle, WA 98121, USA,
| | - Shira Grayson
- Sage Bionetworks, 2901 Third Avenue Seattle, WA 98121, USA,
| | - Sarah Moore
- Sage Bionetworks, 2901 Third Avenue Seattle, WA 98121, USA,
| | | | - John Wilbanks
- Sage Bionetworks, 2901 Third Avenue Seattle, WA 98121, USA,
| | - Jennifer Wagner
- Center for Translational Bioethics & Health Care Policy, Geisinger, 100 N. Academy Ave., MC 30-42, Danville, PA 17822, USA
| |
Collapse
|
17
|
Chan YFY, Bot BM, Zweig M, Tignor N, Ma W, Suver C, Cedeno R, Scott ER, Gregory Hershman S, Schadt EE, Wang P. The asthma mobile health study, smartphone data collected using ResearchKit. Sci Data 2018; 5:180096. [PMID: 29786695 PMCID: PMC5963336 DOI: 10.1038/sdata.2018.96] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Accepted: 03/26/2018] [Indexed: 02/06/2023] Open
Abstract
Widespread adoption of smart mobile platforms coupled with a growing ecosystem of sensors including passive location tracking and the ability to leverage external data sources create an opportunity to generate an unprecedented depth of data on individuals. Mobile health technologies could be utilized for chronic disease management as well as research to advance our understanding of common diseases, such as asthma. We conducted a prospective observational asthma study to assess the feasibility of this type of approach, clinical characteristics of cohorts recruited via a mobile platform, the validity of data collected, user retention patterns, and user data sharing preferences. We describe data and descriptive statistics from the Asthma Mobile Health Study, whereby participants engaged with an iPhone application built using Apple's ResearchKit framework. Data from 6346 U.S. participants, who agreed to share their data broadly, have been made available for further research. These resources have the potential to enable the research community to work collaboratively towards improving our understanding of asthma as well as mobile health research best practices.
Collapse
Affiliation(s)
- Yu-Feng Yvonne Chan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Center for Digital Health, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Micol Zweig
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Center for Digital Health, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nicole Tignor
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Center for Digital Health, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Weiping Ma
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Center for Digital Health, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Rafhael Cedeno
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Center for Digital Health, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Erick R Scott
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Sema4, a Mount Sinai venture, Stamford, Connecticut, USA
| | - Steven Gregory Hershman
- Department of Medicine, Stanford University, Stanford, California, USA.,Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Center for Digital Health, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Sema4, a Mount Sinai venture, Stamford, Connecticut, USA
| | - Pei Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Center for Digital Health, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| |
Collapse
|
18
|
Doerr M, Maguire Truong A, Bot BM, Wilbanks J, Suver C, Mangravite LM. Formative Evaluation of Participant Experience With Mobile eConsent in the App-Mediated Parkinson mPower Study: A Mixed Methods Study. JMIR Mhealth Uhealth 2017; 5:e14. [PMID: 28209557 PMCID: PMC5334514 DOI: 10.2196/mhealth.6521] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Revised: 12/14/2016] [Accepted: 12/18/2016] [Indexed: 11/13/2022] Open
Abstract
Background To fully capitalize on the promise of mobile technology to enable scalable, participant-centered research, we must develop companion self-administered electronic informed consent (eConsent) processes. As we do so, we have an ethical obligation to ensure that core tenants of informed consent—informedness, comprehension, and voluntariness—are upheld. Furthermore, we should be wary of recapitulating the pitfalls of “traditional” informed consent processes. Objective Our objective was to describe the essential qualities of participant experience, including delineation of common and novel themes relating to informed consent, with a self-administered, smartphone-based eConsent process. We sought to identify participant responses related to informedness, comprehension, and voluntariness as well as to capture any emergent themes relating to the informed consent process in an app-mediated research study. Methods We performed qualitative thematic analysis of participant responses to a daily general prompt collected over a 6-month period within the Parkinson mPower app. We employed a combination of a priori and emergent codes for our analysis. A priori codes focused on the core concepts of informed consent; emergent codes were derived to capture additional themes relating to self-administered consent processes. We used self-reported demographic information from the study’s baseline survey to characterize study participants and respondents. Results During the study period, 9846 people completed the eConsent process and enrolled in the Parkinson mPower study. In total, 2758 participants submitted 7483 comments; initial categorization identified a subset of 3875 germane responses submitted by 1678 distinct participants. Respondents were more likely to self-report a Parkinson disease diagnosis (30.21% vs 11.10%), be female (28.26% vs 20.18%), be older (42.89 years vs 34.47 years), and have completed more formal education (66.23% with a 4-year college degree or more education vs 55.77%) than all the mPower participants (P<.001 for all values). Within our qualitative analysis, 3 conceptual domains emerged. First, consistent with fully facilitated in-person informed consent settings, we observed a broad spectrum of comprehension of core research concepts following eConsent. Second, we identified new consent themes born out of the remote mobile research setting, for example the impact of the study design on the engagement of controls and the misconstruction of the open response field as a method for responsive communication with researchers, that bear consideration for inclusion within self-administered eConsent. Finally, our findings highlighted participants’ desire to be empowered as partners. Conclusions Our study serves as a formative evaluation of participant experience with a self-administered informed consent process via a mobile app. Areas for future investigation include direct comparison of the efficacy of self-administered eConsent with facilitated informed consent processes, exploring the potential benefits and pitfalls of smartphone user behavioral habits on participant engagement in research, and developing best practices to increase informedness, comprehension, and voluntariness via participant coengagement in the research endeavor.
Collapse
Affiliation(s)
- Megan Doerr
- Sage Bionetworks, Seattle, WA, United States
| | | | - Brian M Bot
- Sage Bionetworks, Seattle, WA, United States
| | | | | | | |
Collapse
|
19
|
Bot BM, Suver C, Neto EC, Kellen M, Klein A, Bare C, Doerr M, Pratap A, Wilbanks J, Dorsey ER, Friend SH, Trister AD. The mPower study, Parkinson disease mobile data collected using ResearchKit. Sci Data 2016; 3:160011. [PMID: 26938265 PMCID: PMC4776701 DOI: 10.1038/sdata.2016.11] [Citation(s) in RCA: 276] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2015] [Accepted: 02/02/2016] [Indexed: 01/12/2023] Open
Abstract
Current measures of health and disease are often insensitive, episodic, and subjective. Further, these measures generally are not designed to provide meaningful feedback to individuals. The impact of high-resolution activity data collected from mobile phones is only beginning to be explored. Here we present data from mPower, a clinical observational study about Parkinson disease conducted purely through an iPhone app interface. The study interrogated aspects of this movement disorder through surveys and frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from large enrollment and repeated measurements on many individuals, these data may help establish baseline variability of real-world activity measurement collected via mobile phones, and ultimately may lead to quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection modules are available through an open source license for use in studies of other conditions. We hope that releasing data contributed by engaged research participants will seed a new community of analysts working collaboratively on understanding mobile health data to advance human health.
Collapse
Affiliation(s)
- Brian M Bot
- Sage Bionetworks, Seattle, Washington 98109, USA
| | | | | | | | - Arno Klein
- Sage Bionetworks, Seattle, Washington 98109, USA
| | | | - Megan Doerr
- Sage Bionetworks, Seattle, Washington 98109, USA
| | | | | | - E Ray Dorsey
- Center for Human Experimental Therapeutics, University of Rochester Medical Center, Rochester, New York 14642, USA
| | | | | |
Collapse
|
20
|
Narayanan M, Huynh JL, Wang K, Yang X, Yoo S, McElwee J, Zhang B, Zhang C, Lamb JR, Xie T, Suver C, Molony C, Melquist S, Johnson AD, Fan G, Stone DJ, Schadt EE, Casaccia P, Emilsson V, Zhu J. Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases. Mol Syst Biol 2014; 10:743. [PMID: 25080494 PMCID: PMC4299500 DOI: 10.15252/msb.20145304] [Citation(s) in RCA: 123] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Using expression profiles from postmortem prefrontal cortex samples of 624 dementia patients and non-demented controls, we investigated global disruptions in the co-regulation of genes in two neurodegenerative diseases, late-onset Alzheimer's disease (AD) and Huntington's disease (HD). We identified networks of differentially co-expressed (DC) gene pairs that either gained or lost correlation in disease cases relative to the control group, with the former dominant for both AD and HD and both patterns replicating in independent human cohorts of AD and aging. When aligning networks of DC patterns and physical interactions, we identified a 242-gene subnetwork enriched for independent AD/HD signatures. This subnetwork revealed a surprising dichotomy of gained/lost correlations among two inter-connected processes, chromatin organization and neural differentiation, and included DNA methyltransferases, DNMT1 and DNMT3A, of which we predicted the former but not latter as a key regulator. To validate the inter-connection of these two processes and our key regulator prediction, we generated two brain-specific knockout (KO) mice and show that Dnmt1 KO signature significantly overlaps with the subnetwork (P = 3.1 × 10−12), while Dnmt3a KO signature does not (P = 0.017).
Collapse
Affiliation(s)
| | - Jimmy L Huynh
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kai Wang
- Merck Research Laboratories Merck & Co., Inc., Whitehouse Station, NJ, USA
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
| | - Seungyeul Yoo
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joshua McElwee
- Merck Research Laboratories Merck & Co., Inc., Whitehouse Station, NJ, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Chunsheng Zhang
- Merck Research Laboratories Merck & Co., Inc., Whitehouse Station, NJ, USA
| | - John R Lamb
- Merck Research Laboratories Merck & Co., Inc., Whitehouse Station, NJ, USA
| | - Tao Xie
- Merck Research Laboratories Merck & Co., Inc., Whitehouse Station, NJ, USA
| | | | - Cliona Molony
- Merck Research Laboratories Merck & Co., Inc., Whitehouse Station, NJ, USA
| | - Stacey Melquist
- Merck Research Laboratories Merck & Co., Inc., Whitehouse Station, NJ, USA
| | | | - Guoping Fan
- Department of Human Genetics, University of California, Los Angeles, CA, USA
| | - David J Stone
- Merck Research Laboratories Merck & Co., Inc., Whitehouse Station, NJ, USA
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Patrizia Casaccia
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Valur Emilsson
- Icelandic Heart Association, Kopavogur, Iceland Faculty of Pharmaceutical Sciences, University of Iceland, Reykjavik, Iceland
| | - Jun Zhu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| |
Collapse
|
21
|
Boutros PC, Ewing AD, Ellrott K, Norman TC, Dang KK, Hu Y, Kellen MR, Suver C, Bare JC, Stein LD, Spellman PT, Stolovitzky G, Friend SH, Margolin AA, Stuart JM. Global optimization of somatic variant identification in cancer genomes with a global community challenge. Nat Genet 2014; 46:318-319. [PMID: 24675517 DOI: 10.1038/ng.2932] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Paul C Boutros
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada
| | - Adam D Ewing
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, California, USA
| | - Kyle Ellrott
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, California, USA
| | | | | | - Yin Hu
- Sage Bionetworks, Seattle, Washington, USA
| | | | | | | | - Lincoln D Stein
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Paul T Spellman
- Department of Molecular and Medical Genetics, Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA
| | - Gustavo Stolovitzky
- IBM Computational Biology Center, T.J. Watson Research Center, Yorktown Heights, New York, USA
| | | | | | - Joshua M Stuart
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, California, USA
| |
Collapse
|
22
|
Zhang B, Gaiteri C, Bodea LG, Wang Z, McElwee J, Podtelezhnikov AA, Zhang C, Xie T, Tran L, Dobrin R, Fluder E, Clurman B, Melquist S, Narayanan M, Suver C, Shah H, Mahajan M, Gillis T, Mysore J, MacDonald ME, Lamb JR, Bennett DA, Molony C, Stone DJ, Gudnason V, Myers AJ, Schadt EE, Neumann H, Zhu J, Emilsson V. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 2013; 153:707-20. [PMID: 23622250 DOI: 10.1016/j.cell.2013.03.030] [Citation(s) in RCA: 1161] [Impact Index Per Article: 105.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2012] [Revised: 10/17/2012] [Accepted: 03/22/2013] [Indexed: 12/11/2022]
Abstract
The genetics of complex disease produce alterations in the molecular interactions of cellular pathways whose collective effect may become clear through the organized structure of molecular networks. To characterize molecular systems associated with late-onset Alzheimer's disease (LOAD), we constructed gene-regulatory networks in 1,647 postmortem brain tissues from LOAD patients and nondemented subjects, and we demonstrate that LOAD reconfigures specific portions of the molecular interaction structure. Through an integrative network-based approach, we rank-ordered these network structures for relevance to LOAD pathology, highlighting an immune- and microglia-specific module that is dominated by genes involved in pathogen phagocytosis, contains TYROBP as a key regulator, and is upregulated in LOAD. Mouse microglia cells overexpressing intact or truncated TYROBP revealed expression changes that significantly overlapped the human brain TYROBP network. Thus the causal network structure is a useful predictor of response to gene perturbations and presents a framework to test models of disease mechanisms underlying LOAD.
Collapse
Affiliation(s)
- Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
23
|
Huan T, Zhang B, Wang Z, Joehanes R, Zhu J, Johnson AD, Ying S, Munson PJ, Raghavachari N, Wang R, Liu P, Courchesne P, Hwang SJ, Assimes TL, McPherson R, Samani NJ, Schunkert H, Meng Q, Suver C, O'Donnell CJ, Derry J, Yang X, Levy D. A systems biology framework identifies molecular underpinnings of coronary heart disease. Arterioscler Thromb Vasc Biol 2013; 33:1427-34. [PMID: 23539213 DOI: 10.1161/atvbaha.112.300112] [Citation(s) in RCA: 128] [Impact Index Per Article: 11.6] [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: 12/29/2022]
Abstract
OBJECTIVE Genetic approaches have identified numerous loci associated with coronary heart disease (CHD). The molecular mechanisms underlying CHD gene-disease associations, however, remain unclear. We hypothesized that genetic variants with both strong and subtle effects drive gene subnetworks that in turn affect CHD. APPROACH AND RESULTS We surveyed CHD-associated molecular interactions by constructing coexpression networks using whole blood gene expression profiles from 188 CHD cases and 188 age- and sex-matched controls. Twenty-four coexpression modules were identified, including 1 case-specific and 1 control-specific differential module (DM). The DMs were enriched for genes involved in B-cell activation, immune response, and ion transport. By integrating the DMs with gene expression-associated single-nucleotide polymorphisms and with results of genome-wide association studies of CHD and its risk factors, the control-specific DM was implicated as CHD causal based on its significant enrichment for both CHD and lipid expression-associated single-nucleotide polymorphisms. This causal DM was further integrated with tissue-specific Bayesian networks and protein-protein interaction networks to identify regulatory key driver genes. Multitissue key drivers (SPIB and TNFRSF13C) and tissue-specific key drivers (eg, EBF1) were identified. CONCLUSIONS Our network-driven integrative analysis not only identified CHD-related genes, but also defined network structure that sheds light on the molecular interactions of genes associated with CHD risk.
Collapse
Affiliation(s)
- Tianxiao Huan
- From the National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA 01702, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
24
|
Hao K, Bossé Y, Nickle DC, Paré PD, Postma DS, Laviolette M, Sandford A, Hackett TL, Daley D, Hogg JC, Elliott WM, Couture C, Lamontagne M, Brandsma CA, van den Berge M, Koppelman G, Reicin AS, Nicholson DW, Malkov V, Derry JM, Suver C, Tsou JA, Kulkarni A, Zhang C, Vessey R, Opiteck GJ, Curtis SP, Timens W, Sin DD. Lung eQTLs to help reveal the molecular underpinnings of asthma. PLoS Genet 2012; 8:e1003029. [PMID: 23209423 PMCID: PMC3510026 DOI: 10.1371/journal.pgen.1003029] [Citation(s) in RCA: 222] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2012] [Accepted: 08/14/2012] [Indexed: 12/26/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified loci reproducibly associated with pulmonary diseases; however, the molecular mechanism underlying these associations are largely unknown. The objectives of this study were to discover genetic variants affecting gene expression in human lung tissue, to refine susceptibility loci for asthma identified in GWAS studies, and to use the genetics of gene expression and network analyses to find key molecular drivers of asthma. We performed a genome-wide search for expression quantitative trait loci (eQTL) in 1,111 human lung samples. The lung eQTL dataset was then used to inform asthma genetic studies reported in the literature. The top ranked lung eQTLs were integrated with the GWAS on asthma reported by the GABRIEL consortium to generate a Bayesian gene expression network for discovery of novel molecular pathways underpinning asthma. We detected 17,178 cis- and 593 trans- lung eQTLs, which can be used to explore the functional consequences of loci associated with lung diseases and traits. Some strong eQTLs are also asthma susceptibility loci. For example, rs3859192 on chr17q21 is robustly associated with the mRNA levels of GSDMA (P = 3.55×10−151). The genetic-gene expression network identified the SOCS3 pathway as one of the key drivers of asthma. The eQTLs and gene networks identified in this study are powerful tools for elucidating the causal mechanisms underlying pulmonary disease. This data resource offers much-needed support to pinpoint the causal genes and characterize the molecular function of gene variants associated with lung diseases. Recent genome-wide association studies (GWAS) have identified genetic variants associated with lung diseases. The challenge now is to find the causal genes in GWAS–nominated chromosomal regions and to characterize the molecular function of disease-associated genetic variants. In this paper, we describe an international effort to systematically capture the genetic architecture of gene expression regulation in human lung. By studying lung specimens from 1,111 individuals of European ancestry, we found a large number of genetic variants affecting gene expression in the lung, or lung expression quantitative trait loci (eQTL). These lung eQTLs will serve as an important resource to aid in the understanding of the molecular underpinnings of lung biology and its disruption in disease. To demonstrate the utility of this lung eQTL dataset, we integrated our data with previous genetic studies on asthma. Through integrative techniques, we identified causal variants and genes in GWAS–nominated loci and found key molecular drivers for asthma. We feel that sharing our lung eQTLs dataset with the scientific community will leverage the impact of previous large-scale GWAS on lung diseases and function by providing much needed functional information to understand the molecular changes introduced by the susceptibility genetic variants.
Collapse
Affiliation(s)
- Ke Hao
- Merck Research Laboratories, Boston, Massachusetts, United States of America
- Merck, Rahway, New Jersey, United States of America
- Genetics, Rosetta Inpharmatics, Merck, Seattle, Washington, United States of America
| | - Yohan Bossé
- Department of Molecular Medicine, Laval University, Québec City, Canada
- Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University, Québec City, Canada
| | - David C. Nickle
- Merck Research Laboratories, Boston, Massachusetts, United States of America
- Merck, Rahway, New Jersey, United States of America
- Genetics, Rosetta Inpharmatics, Merck, Seattle, Washington, United States of America
- * E-mail:
| | - Peter D. Paré
- The University of British Columbia James Hogg Research Laboratory, St Paul's Hospital, Vancouver, Canada
- Respiratory Division, Department of Medicine, University of British Columbia, Vancouver, Canada
| | - Dirkje S. Postma
- Department of Pulmonology, University Medical Center Groningen, GRIAC Research Institute, University of Groningen, Groningen, The Netherlands
| | - Michel Laviolette
- Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University, Québec City, Canada
| | - Andrew Sandford
- The University of British Columbia James Hogg Research Laboratory, St Paul's Hospital, Vancouver, Canada
- Respiratory Division, Department of Medicine, University of British Columbia, Vancouver, Canada
| | - Tillie L. Hackett
- The University of British Columbia James Hogg Research Laboratory, St Paul's Hospital, Vancouver, Canada
| | - Denise Daley
- The University of British Columbia James Hogg Research Laboratory, St Paul's Hospital, Vancouver, Canada
- Respiratory Division, Department of Medicine, University of British Columbia, Vancouver, Canada
| | - James C. Hogg
- The University of British Columbia James Hogg Research Laboratory, St Paul's Hospital, Vancouver, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
| | - W. Mark Elliott
- The University of British Columbia James Hogg Research Laboratory, St Paul's Hospital, Vancouver, Canada
| | - Christian Couture
- Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University, Québec City, Canada
| | - Maxime Lamontagne
- Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University, Québec City, Canada
| | - Corry-Anke Brandsma
- Department of Pathology and Medical Biology, University Medical Center Groningen, GRIAC Research Institute, University of Groningen, Groningen, The Netherlands
| | - Maarten van den Berge
- Department of Pulmonology, University Medical Center Groningen, GRIAC Research Institute, University of Groningen, Groningen, The Netherlands
| | - Gerard Koppelman
- Department of Pediatric Pulmonology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | | | | | | | | | - Christine Suver
- Sage Bionetworks, Seattle, Washington, United States of America
| | | | | | | | | | | | | | - Wim Timens
- Department of Pathology and Medical Biology, University Medical Center Groningen, GRIAC Research Institute, University of Groningen, Groningen, The Netherlands
| | - Don D. Sin
- The University of British Columbia James Hogg Research Laboratory, St Paul's Hospital, Vancouver, Canada
- Respiratory Division, Department of Medicine, University of British Columbia, Vancouver, Canada
| |
Collapse
|
25
|
Lanktree M, Guo Y, Murtaza M, Glessner J, Bailey S, Onland-Moret N, Lettre G, Ongen H, Rajagopalan R, Johnson T, Shen H, Nelson C, Klopp N, Baumert J, Padmanabhan S, Pankratz N, Pankow J, Shah S, Taylor K, Barnard J, Peters B, Maloney C, Lobmeyer M, Stanton A, Zafarmand M, Romaine S, Mehta A, van Iperen E, Gong Y, Price T, Smith E, Kim C, Li Y, Asselbergs F, Atwood L, Bailey K, Bhatt D, Bauer F, Behr E, Bhangale T, Boer J, Boehm B, Bradfield J, Brown M, Braund P, Burton P, Carty C, Chandrupatla H, Chen W, Connell J, Dalgeorgou C, de Boer A, Drenos F, Elbers C, Fang J, Fox C, Frackelton E, Fuchs B, Furlong C, Gibson Q, Gieger C, Goel A, Grobbee D, Hastie C, Howard P, Huang GH, Johnson W, Li Q, Kleber M, Klein B, Klein R, Kooperberg C, Ky B, LaCroix A, Lanken P, Lathrop M, Li M, Marshall V, Melander O, Mentch F, Meyer N, Monda K, Montpetit A, Murugesan G, Nakayama K, Nondahl D, Onipinla A, Rafelt S, Newhouse S, Otieno F, Patel S, Putt M, Rodriguez S, Safa R, Sawyer D, Schreiner P, Simpson C, Sivapalaratnam S, Srinivasan S, Suver C, Swergold G, Sweitzer N, Thomas K, Thorand B, Timpson N, Tischfield S, Tobin M, Tomaszewski M, Verschuren W, Wallace C, Winkelmann B, Zhang H, Zheng D, Zhang L, Zmuda J, Clarke R, Balmforth A, Danesh J, Day I, Schork N, de Bakker P, Delles C, Duggan D, Hingorani A, Hirschhorn J, Hofker M, Humphries S, Kivimaki M, Lawlor D, Kottke-Marchant K, Mega J, Mitchell B, Morrow D, Palmen J, Redline S, Shields D, Shuldiner A, Sleiman P, Smith G, Farrall M, Jamshidi Y, Christiani D, Casas J, Hall A, Doevendans P, Christie J, Berenson G, Murray S, Illig T, Dorn G, Cappola T, Boerwinkle E, Sever P, Rader D, Reilly M, Caulfield M, Talmud P, Topol E, Engert J, Wang K, Dominiczak A, Hamsten A, Curtis S, Silverstein R, Lange L, Sabatine M, Trip M, Saleheen D, Peden J, Cruickshanks K, März W, O'Connell J, Klungel O, Wijmenga C, Maitland-van der Zee A, Schadt E, Johnson J, Jarvik G, Papanicolaou G, Grant S, Munroe P, North K, Samani N, Koenig W, Gaunt T, Anand S, van der Schouw Y, Soranzo N, FitzGerald G, Reiner A, Hegele R, Hakonarson H, Keating B. Meta-analysis of Dense Genecentric Association Studies Reveals Common and Uncommon Variants Associated with Height. Am J Hum Genet 2012. [DOI: 10.1016/j.ajhg.2012.05.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
|
26
|
Derry JMJ, Mangravite LM, Suver C, Furia MD, Henderson D, Schildwachter X, Bot B, Izant J, Sieberts SK, Kellen MR, Friend SH. Developing predictive molecular maps of human disease through community-based modeling. Nat Genet 2012; 44:127-30. [PMID: 22281773 DOI: 10.1038/ng.1089] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
|
27
|
Greenawalt DM, Dobrin R, Chudin E, Hatoum IJ, Suver C, Beaulaurier J, Zhang B, Castro V, Zhu J, Sieberts SK, Wang S, Molony C, Heymsfield SB, Kemp DM, Reitman ML, Lum PY, Schadt EE, Kaplan LM. A survey of the genetics of stomach, liver, and adipose gene expression from a morbidly obese cohort. Genome Res 2011; 21:1008-16. [PMID: 21602305 DOI: 10.1101/gr.112821.110] [Citation(s) in RCA: 148] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
To map the genetics of gene expression in metabolically relevant tissues and investigate the diversity of expression SNPs (eSNPs) in multiple tissues from the same individual, we collected four tissues from approximately 1000 patients undergoing Roux-en-Y gastric bypass (RYGB) and clinical traits associated with their weight loss and co-morbidities. We then performed high-throughput genotyping and gene expression profiling and carried out a genome-wide association analyses for more than 100,000 gene expression traits representing four metabolically relevant tissues: liver, omental adipose, subcutaneous adipose, and stomach. We successfully identified 24,531 eSNPs corresponding to about 10,000 distinct genes. This represents the greatest number of eSNPs identified to our knowledge by any study to date and the first study to identify eSNPs from stomach tissue. We then demonstrate how these eSNPs provide a high-quality disease map for each tissue in morbidly obese patients to not only inform genetic associations identified in this cohort, but in previously published genome-wide association studies as well. These data can aid in elucidating the key networks associated with morbid obesity, response to RYGB, and disease as a whole.
Collapse
|
28
|
Cotsapas C, Speliotes EK, Hatoum IJ, Greenawalt DM, Dobrin R, Lum PY, Suver C, Chudin E, Kemp D, Reitman M, Voight BF, Neale BM, Schadt EE, Hirschhorn JN, Kaplan LM, Daly MJ. Common body mass index-associated variants confer risk of extreme obesity. Hum Mol Genet 2010. [DOI: 10.1093/hmg/ddq287] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
|
29
|
Yang X, Zhang B, Molony C, Chudin E, Hao K, Zhu J, Gaedigk A, Suver C, Zhong H, Leeder JS, Guengerich FP, Strom SC, Schuetz E, Rushmore TH, Ulrich RG, Slatter JG, Schadt EE, Kasarskis A, Lum PY. Systematic genetic and genomic analysis of cytochrome P450 enzyme activities in human liver. Genome Res 2010; 20:1020-36. [PMID: 20538623 DOI: 10.1101/gr.103341.109] [Citation(s) in RCA: 198] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Liver cytochrome P450s (P450s) play critical roles in drug metabolism, toxicology, and metabolic processes. Despite rapid progress in the understanding of these enzymes, a systematic investigation of the full spectrum of functionality of individual P450s, the interrelationship or networks connecting them, and the genetic control of each gene/enzyme is lacking. To this end, we genotyped, expression-profiled, and measured P450 activities of 466 human liver samples and applied a systems biology approach via the integration of genetics, gene expression, and enzyme activity measurements. We found that most P450s were positively correlated among themselves and were highly correlated with known regulators as well as thousands of other genes enriched for pathways relevant to the metabolism of drugs, fatty acids, amino acids, and steroids. Genome-wide association analyses between genetic polymorphisms and P450 expression or enzyme activities revealed sets of SNPs associated with P450 traits, and suggested the existence of both cis-regulation of P450 expression (especially for CYP2D6) and more complex trans-regulation of P450 activity. Several novel SNPs associated with CYP2D6 expression and enzyme activity were validated in an independent human cohort. By constructing a weighted coexpression network and a Bayesian regulatory network, we defined the human liver transcriptional network structure, uncovered subnetworks representative of the P450 regulatory system, and identified novel candidate regulatory genes, namely, EHHADH, SLC10A1, and AKR1D1. The P450 subnetworks were then validated using gene signatures responsive to ligands of known P450 regulators in mouse and rat. This systematic survey provides a comprehensive view of the functionality, genetic control, and interactions of P450s.
Collapse
Affiliation(s)
- Xia Yang
- Rosetta Inpharmatics, LLC, Merck & Co., Inc., Seattle, Washington 98109, USA.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
30
|
Cotsapas C, Speliotes EK, Hatoum IJ, Greenawalt DM, Dobrin R, Lum PY, Suver C, Chudin E, Kemp D, Reitman M, Voight BF, Neale BM, Schadt EE, Hirschhorn JN, Kaplan LM, Daly MJ. Common body mass index-associated variants confer risk of extreme obesity. Hum Mol Genet 2009; 18:3502-7. [PMID: 19553259 DOI: 10.1093/hmg/ddp292] [Citation(s) in RCA: 83] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
To investigate the genetic architecture of severe obesity, we performed a genome-wide association study of 775 cases and 3197 unascertained controls at approximately 550,000 markers across the autosomal genome. We found convincing association to the previously described locus including the FTO gene. We also found evidence of association at a further six of 12 other loci previously reported to influence body mass index (BMI) in the general population and one of three associations to severe childhood and adult obesity and that cases have a higher proportion of risk-conferring alleles than controls. We found no evidence of homozygosity at any locus due to identity-by-descent associating with phenotype which would be indicative of rare, penetrant alleles, nor was there excess genome-wide homozygosity in cases relative to controls. Our results suggest that variants influencing BMI also contribute to severe obesity, a condition at the extreme of the phenotypic spectrum rather than a distinct condition.
Collapse
Affiliation(s)
- Chris Cotsapas
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
31
|
Schadt EE, Molony C, Chudin E, Hao K, Yang X, Lum PY, Kasarskis A, Zhang B, Wang S, Suver C, Zhu J, Millstein J, Sieberts S, Lamb J, GuhaThakurta D, Derry J, Storey JD, Avila-Campillo I, Kruger MJ, Johnson JM, Rohl CA, van Nas A, Mehrabian M, Drake TA, Lusis AJ, Smith RC, Guengerich FP, Strom SC, Schuetz E, Rushmore TH, Ulrich R. Mapping the genetic architecture of gene expression in human liver. PLoS Biol 2008; 6:e107. [PMID: 18462017 PMCID: PMC2365981 DOI: 10.1371/journal.pbio.0060107] [Citation(s) in RCA: 760] [Impact Index Per Article: 47.5] [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: 12/03/2007] [Accepted: 03/18/2008] [Indexed: 01/28/2023] Open
Abstract
Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process. Genome-wide association studies seek to identify regions of the genome in which changes in DNA in a given population are correlated with disease, drug response, or other phenotypes of interest. However, changes in DNA that associate with traits like common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in the higher-order disease traits. Therefore, identifying molecular phenotypes that vary in response to changes in DNA that also associate with changes in disease traits can provide the functional information necessary to not only identify and validate the susceptibility genes directly affected by changes in DNA, but to understand as well the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. To enable this type of approach we profiled the expression levels of 39,280 transcripts and genotyped 782,476 SNPs in 427 human liver samples, identifying thousands of DNA variants that strongly associated with liver gene expression. These relationships were then leveraged by integrating them with genotypic and expression data from other human and mouse populations, leading to the direct identification of candidate susceptibility genes corresponding to genetic loci identified as key drivers of disease. Our analysis is able to provide much needed functional support for these candidate susceptibility genes. Identifying changes in DNA that associate with changes in gene expression in human tissues elucidates the genetic architecture of gene expression in human populations and enables the direct identification of functionally supported candidate susceptibility genes in genomic regions associated with disease.
Collapse
MESH Headings
- Adolescent
- Adult
- Aged
- Aged, 80 and over
- Animals
- Child
- Child, Preschool
- Cholesterol, LDL/blood
- Cholesterol, LDL/genetics
- Coronary Artery Disease/genetics
- Diabetes Mellitus, Type 1/genetics
- Female
- Gene Expression Profiling
- Genes, MHC Class II/genetics
- Genetic Predisposition to Disease/genetics
- Genome, Human
- Genotype
- Humans
- Infant
- Liver/metabolism
- Male
- Mice
- Middle Aged
- Oligonucleotide Array Sequence Analysis
- Polymorphism, Single Nucleotide/genetics
- Quantitative Trait Loci/genetics
- RNA, Messenger/analysis
- RNA, Messenger/genetics
- Transcription, Genetic/genetics
Collapse
Affiliation(s)
- Eric E Schadt
- Rosetta Inpharmatics, Seattle, Washington, United States of America.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|