1
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Thomas M, Mackes N, Preuss-Dodhy A, Wieland T, Bundschus M. Assessing Privacy Vulnerabilities in Genetic Data Sets: Scoping Review. JMIR BIOINFORMATICS AND BIOTECHNOLOGY 2024; 5:e54332. [PMID: 38935957 PMCID: PMC11165293 DOI: 10.2196/54332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 03/26/2024] [Accepted: 03/29/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Genetic data are widely considered inherently identifiable. However, genetic data sets come in many shapes and sizes, and the feasibility of privacy attacks depends on their specific content. Assessing the reidentification risk of genetic data is complex, yet there is a lack of guidelines or recommendations that support data processors in performing such an evaluation. OBJECTIVE This study aims to gain a comprehensive understanding of the privacy vulnerabilities of genetic data and create a summary that can guide data processors in assessing the privacy risk of genetic data sets. METHODS We conducted a 2-step search, in which we first identified 21 reviews published between 2017 and 2023 on the topic of genomic privacy and then analyzed all references cited in the reviews (n=1645) to identify 42 unique original research studies that demonstrate a privacy attack on genetic data. We then evaluated the type and components of genetic data exploited for these attacks as well as the effort and resources needed for their implementation and their probability of success. RESULTS From our literature review, we derived 9 nonmutually exclusive features of genetic data that are both inherent to any genetic data set and informative about privacy risk: biological modality, experimental assay, data format or level of processing, germline versus somatic variation content, content of single nucleotide polymorphisms, short tandem repeats, aggregated sample measures, structural variants, and rare single nucleotide variants. CONCLUSIONS On the basis of our literature review, the evaluation of these 9 features covers the great majority of privacy-critical aspects of genetic data and thus provides a foundation and guidance for assessing genetic data risk.
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2
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Malakar Y, Lacey J, Twine NA, McCrea R, Bauer DC. Balancing the safeguarding of privacy and data sharing: perceptions of genomic professionals on patient genomic data ownership in Australia. Eur J Hum Genet 2024; 32:506-512. [PMID: 36631540 PMCID: PMC11061115 DOI: 10.1038/s41431-022-01273-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/09/2022] [Accepted: 12/15/2022] [Indexed: 01/13/2023] Open
Abstract
There are inherent complexities and tensions in achieving a responsible balance between safeguarding patients' privacy and sharing genomic data for advancing health and medical science. A growing body of literature suggests establishing patient genomic data ownership, enabled by blockchain technology, as one approach for managing these priorities. We conducted an online survey, applying a mixed methods approach to collect quantitative (using scale questions) and qualitative data (using open-ended questions). We explored the views of 117 genomic professionals (clinical geneticists, genetic counsellors, bioinformaticians, and researchers) towards patient data ownership in Australia. Data analysis revealed most professionals agreed that patients have rights to data ownership. However, there is a need for a clearer understanding of the nature and implications of data ownership in this context as genomic data often is subject to collective ownership (e.g., with family members and laboratories). This research finds that while the majority of genomic professionals acknowledge the desire for patient data ownership, bioinformaticians and researchers expressed more favourable views than clinical geneticists and genetic counsellors, suggesting that their views on this issue may be shaped by how closely they interact with patients as part of their professional duties. This research also confirms that stronger health system infrastructure is a prerequisite for enabling patient data ownership, which needs to be underpinned by appropriate digital infrastructure (e.g., central vs. decentralised data storage), patient identity ownership (e.g., limited vs. self-sovereign identity), and policy at both federal and state levels.
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Affiliation(s)
- Yuwan Malakar
- Responsible Innovation Future Science Platform, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Brisbane, Queensland, Australia.
| | - Justine Lacey
- Responsible Innovation Future Science Platform, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Brisbane, Queensland, Australia
| | - Natalie A Twine
- Transformational Bioinformatics, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney, Australia
- Applied BioSciences, Faculty of Science and Engineering, Macquarie University, Macquarie Park, Australia
| | - Rod McCrea
- Responsible Innovation Future Science Platform, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Brisbane, Queensland, Australia
| | - Denis C Bauer
- Transformational Bioinformatics, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney, Australia
- Applied BioSciences, Faculty of Science and Engineering, Macquarie University, Macquarie Park, Australia
- Department of Biomedical Sciences, Faculty of Medicine and Health Science, Macquarie University, Macquarie Park, Australia
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3
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Abstract
Following the widespread use of deep learning for genomics, deep generative modeling is also becoming a viable methodology for the broad field. Deep generative models (DGMs) can learn the complex structure of genomic data and allow researchers to generate novel genomic instances that retain the real characteristics of the original dataset. Aside from data generation, DGMs can also be used for dimensionality reduction by mapping the data space to a latent space, as well as for prediction tasks via exploitation of this learned mapping or supervised/semi-supervised DGM designs. In this review, we briefly introduce generative modeling and two currently prevailing architectures, we present conceptual applications along with notable examples in functional and evolutionary genomics, and we provide our perspective on potential challenges and future directions.
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Affiliation(s)
- Burak Yelmen
- Laboratoire Interdisciplinaire des Sciences du Numérique, CNRS UMR 9015, INRIA, Université Paris-Saclay, Orsay, France;
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Flora Jay
- Laboratoire Interdisciplinaire des Sciences du Numérique, CNRS UMR 9015, INRIA, Université Paris-Saclay, Orsay, France;
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4
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Gyngell C, Lynch F, Vears D, Bowman-Smart H, Savulescu J, Christodoulou J. Storing paediatric genomic data for sequential interrogation across the lifespan. JOURNAL OF MEDICAL ETHICS 2023:jme-2022-108471. [PMID: 37263770 DOI: 10.1136/jme-2022-108471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 03/02/2023] [Indexed: 06/03/2023]
Abstract
Genomic sequencing (GS) is increasingly used in paediatric medicine to aid in screening, research and treatment. Some health systems are trialling GS as a first-line test in newborn screening programmes. Questions about what to do with genomic data after it has been generated are becoming more pertinent. While other research has outlined the ethical reasons for storing deidentified genomic data to be used in research, the ethical case for storing data for future clinical use has not been explicated. In this paper, we examine the ethical case for storing genomic data with the intention of using it as a lifetime health resource. In this model, genomic data would be stored with the intention of reanalysis at certain points through one's life. We argue this could benefit individuals and create an important public resource. However, several ethical challenges must first be met to achieve these benefits. We explore issues related to privacy, consent, justice and equality. We conclude by arguing that health systems should be moving towards futures that allow for the sequential interrogation of genomic data throughout the lifespan.
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Affiliation(s)
- Christopher Gyngell
- Biomedical Ethics Research Group, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Fiona Lynch
- Biomedical Ethics Research Group, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Melbourne Law School, The University of Melbourne, Parkville, VIC, Australia
| | - Danya Vears
- Biomedical Ethics Research Group, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Hilary Bowman-Smart
- Biomedical Ethics Research Group, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- University of South Australia, Adeliade, South Australia, Australia
| | - Julian Savulescu
- Biomedical Ethics Research Group, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Faculty of Philosophy, University of Oxford, Oxford, UK
- Centre for Biomedical Ethics - Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - John Christodoulou
- Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
- Brain and Mitochondrial Research Group, Murdoch Children's Research Institute, Parkville, VIC, Australia
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5
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Wan Z, Hazel JW, Clayton EW, Vorobeychik Y, Kantarcioglu M, Malin BA. Sociotechnical safeguards for genomic data privacy. Nat Rev Genet 2022; 23:429-445. [PMID: 35246669 PMCID: PMC8896074 DOI: 10.1038/s41576-022-00455-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/24/2022] [Indexed: 12/21/2022]
Abstract
Recent developments in a variety of sectors, including health care, research and the direct-to-consumer industry, have led to a dramatic increase in the amount of genomic data that are collected, used and shared. This state of affairs raises new and challenging concerns for personal privacy, both legally and technically. This Review appraises existing and emerging threats to genomic data privacy and discusses how well current legal frameworks and technical safeguards mitigate these concerns. It concludes with a discussion of remaining and emerging challenges and illustrates possible solutions that can balance protecting privacy and realizing the benefits that result from the sharing of genetic information.
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Affiliation(s)
- Zhiyu Wan
- Center for Genetic Privacy and Identity in Community Settings, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - James W Hazel
- Center for Genetic Privacy and Identity in Community Settings, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Biomedical Ethics and Society, Vanderbilt University, Nashville, TN, USA
| | - Ellen Wright Clayton
- Center for Genetic Privacy and Identity in Community Settings, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Biomedical Ethics and Society, Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Law School, Nashville, TN, USA
| | - Yevgeniy Vorobeychik
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Murat Kantarcioglu
- Department of Computer Science, University of Texas at Dallas, Richardson, TX, USA
| | - Bradley A Malin
- Center for Genetic Privacy and Identity in Community Settings, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
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6
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Krishnamoorthy M, Ranjan P, Erb-Downward JR, Dickson RP, Wiens J. AMAISE: a machine learning approach to index-free sequence enrichment. Commun Biol 2022; 5:568. [PMID: 35681015 PMCID: PMC9184628 DOI: 10.1038/s42003-022-03498-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 05/18/2022] [Indexed: 11/21/2022] Open
Abstract
Metagenomics holds potential to improve clinical diagnostics of infectious diseases, but DNA from clinical specimens is often dominated by host-derived sequences. To address this, researchers employ host-depletion methods. Laboratory-based host-depletion methods, however, are costly in terms of time and effort, while computational host-depletion methods rely on memory-intensive reference index databases and struggle to accurately classify noisy sequence data. To solve these challenges, we propose an index-free tool, AMAISE (A Machine Learning Approach to Index-Free Sequence Enrichment). Applied to the task of separating host from microbial reads, AMAISE achieves over 98% accuracy. Applied prior to metagenomic classification, AMAISE results in a 14-18% decrease in memory usage compared to using metagenomic classification alone. Our results show that a reference-independent machine learning approach to host depletion allows for accurate and efficient sequence detection.
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Affiliation(s)
- Meera Krishnamoorthy
- Division of Computer Science and Engineering, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Piyush Ranjan
- Division of Pulmonary & Critical Care Medicine, Department of Medicine, University of Michigan, Ann Arbor, MI, USA
| | - John R Erb-Downward
- Division of Pulmonary & Critical Care Medicine, Department of Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
| | - Robert P Dickson
- Division of Pulmonary & Critical Care Medicine, Department of Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
- Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, MI, USA
| | - Jenna Wiens
- Division of Computer Science and Engineering, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.
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7
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Cohen S, Zultan R. Genomic privacy, identity and dignity. JOURNAL OF MEDICAL ETHICS 2022; 48:317-322. [PMID: 33910975 DOI: 10.1136/medethics-2020-106979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 01/10/2021] [Accepted: 02/17/2021] [Indexed: 06/12/2023]
Abstract
Significant advancements towards a future of big data genomic medicine, associated with large-scale public dataset repositories, intensify dilemmas of genomic privacy. To resolve dilemmas adequately, we need to understand the relative force of the competing considerations that make them up. Attitudes towards genomic privacy are complex and not well understood; understanding is further complicated by the vague claim of 'genetic exceptionalism'. In this paper, we distinguish between consequentialist and non-consequentialist privacy interests: while the former are concerned with harms secondary to exposure, the latter represent the interest in a private sphere for its own sake, as an essential component of human dignity. Empirical studies of attitudes towards genomic privacy have almost never targeted specifically this important dignitary component of the privacy interest. In this paper we first articulate the question of a non-consequentialist genomic privacy interest, and then present results of an empirical study that probed people's attitudes towards that interest. This was done via comparison to other non-consequentialist privacy interests, which are more tangible and can be more easily assessed. Our results indicate that the non-consequentialist genomic privacy interest is rather weak. This insight can assist in adjudicating dilemmas involving genomic privacy.
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Affiliation(s)
- Shlomo Cohen
- Department of Philosophy, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Ro'i Zultan
- Department of Economics, Ben-Gurion University of the Negev, Be'er Sheva, Israel
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8
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Alsaffar MM, Hasan M, McStay GP, Sedky M. Digital DNA lifecycle security and privacy: an overview. Brief Bioinform 2022; 23:6518049. [PMID: 35106557 DOI: 10.1093/bib/bbab607] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 12/29/2021] [Accepted: 12/30/2021] [Indexed: 11/14/2022] Open
Abstract
DNA sequencing technologies have advanced significantly in the last few years leading to advancements in biomedical research which has improved personalised medicine and the discovery of new treatments for diseases. Sequencing technology advancement has also reduced the cost of DNA sequencing, which has led to the rise of direct-to-consumer (DTC) sequencing, e.g. 23andme.com, ancestry.co.uk, etc. In the meantime, concerns have emerged over privacy and security in collecting, handling, analysing and sharing DNA and genomic data. DNA data are unique and can be used to identify individuals. Moreover, those data provide information on people's current disease status and disposition, e.g. mental health or susceptibility for developing cancer. DNA privacy violation does not only affect the owner but also affects their close consanguinity due to its hereditary nature. This article introduces and defines the term 'digital DNA life cycle' and presents an overview of privacy and security threats and their mitigation techniques for predigital DNA and throughout the digital DNA life cycle. It covers DNA sequencing hardware, software and DNA sequence pipeline in addition to common privacy attacks and their countermeasures when DNA digital data are stored, queried or shared. Likewise, the article examines DTC genomic sequencing privacy and security.
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Affiliation(s)
- Muhalb M Alsaffar
- Department of Computing, AI and Robotics, School of Digital, Technologies and Arts, Staffordshire University, College Road, ST4 2DE, Staffordshire, United Kingdom
| | | | - Gavin P McStay
- Department of Biological Sciences, School of Health, Science and Wellbeing, Staffordshire University, College Road, Stoke-on-Trent, Staffordshire, ST4 2DE, United Kingdom
| | - Mohamed Sedky
- Department of Computing, AI and Robotics, School of Digital, Technologies and Arts, Staffordshire University, College Road, ST4 2DE, Staffordshire, United Kingdom
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9
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Alrefaei AF, Hawsawi YM, Almaleki D, Alafif T, Alzahrani FA, Bakhrebah MA. Genetic data sharing and artificial intelligence in the era of personalized medicine based on a cross-sectional analysis of the Saudi human genome program. Sci Rep 2022; 12:1405. [PMID: 35082362 PMCID: PMC8791994 DOI: 10.1038/s41598-022-05296-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 01/07/2022] [Indexed: 12/21/2022] Open
Abstract
The success of the Saudi Human Genome Program (SHGP), one of the top ten genomic programs worldwide, is highly dependent on the Saudi population embracing the concept of participating in genetic testing. However, genetic data sharing and artificial intelligence (AI) in genomics are critical public issues in medical care and scientific research. The present study was aimed to examine the awareness, knowledge, and attitude of the Saudi society towards the SHGP, the sharing and privacy of genetic data resulting from the SHGP, and the role of AI in genetic data analysis and regulations. Results of a questionnaire survey with 804 respondents revealed moderate awareness and attitude towards the SHGP and minimal knowledge regarding its benefits and applications. Respondents demonstrated a low level of knowledge regarding the privacy of genetic data. A generally positive attitude was found towards the outcomes of the SHGP and genetic data sharing for medical and scientific research. The highest level of knowledge was detected regarding AI use in genetic data analysis and privacy regulation. We recommend that the SHGP’s regulators launch awareness campaigns and educational programs to increase and improve public awareness and knowledge regarding the SHGP’s benefits and applications. Furthermore, we propose a strategy for genetic data sharing which will facilitate genetic data sharing between institutions and advance Personalized Medicine in genetic diseases’ diagnosis and treatment.
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Affiliation(s)
- Abdulmajeed F Alrefaei
- Department of Biology, Genetic and Molecular Biology Central Lab, Jamoum University College, Umm Al-Qura University, Makkah, 21955, Saudi Arabia.
| | - Yousef M Hawsawi
- Research Centre, King Faisal Specialist Hospital and Research Centre, P.O. Box 40047, Jeddah, 21499, Saudi Arabia.,MBC: J04/ College of Medicine, Al-Faisal University, P.O. Box 50927, Riyadh, 11533, Kingdom of Saudi Arabia
| | - Deyab Almaleki
- Department of Evaluation, Measurement, and Research, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
| | - Tarik Alafif
- Computer Science Department, Jamoum University College, Umm Al-Qura University, Jamoum, 25375, Saudi Arabia
| | - Faisal A Alzahrani
- Department of Biochemistry, Faculty of Science, Embryonic Stem Cells Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
| | - Muhammed A Bakhrebah
- King Abdulaziz City for Science and Technology (KACST), Life Science and Environment Research Institute, P.O. Box 6086, Riyadh, 11442, Saudi Arabia
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10
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Zolotareva O, Nasirigerdeh R, Matschinske J, Torkzadehmahani R, Bakhtiari M, Frisch T, Späth J, Blumenthal DB, Abbasinejad A, Tieri P, Kaissis G, Rückert D, Wenke NK, List M, Baumbach J. Flimma: a federated and privacy-aware tool for differential gene expression analysis. Genome Biol 2021; 22:338. [PMID: 34906207 PMCID: PMC8670124 DOI: 10.1186/s13059-021-02553-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 11/22/2021] [Indexed: 12/13/2022] Open
Abstract
Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma ( https://exbio.wzw.tum.de/flimma/ ) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.
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Affiliation(s)
- Olga Zolotareva
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany. .,Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany.
| | - Reza Nasirigerdeh
- AI in Medicine and Healthcare, Technical University of Munich, Munich, Germany.,Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Julian Matschinske
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | | | - Mohammad Bakhtiari
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Tobias Frisch
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Julian Späth
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - David B Blumenthal
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Amir Abbasinejad
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.,Sapienza University of Rome, Rome, Italy
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy.,Sapienza University of Rome, Rome, Italy
| | - Georgios Kaissis
- AI in Medicine and Healthcare, Technical University of Munich, Munich, Germany.,Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Biomedical Image Analysis Group, Imperial College London, London, UK.,OpenMined, Oxford, UK
| | - Daniel Rückert
- AI in Medicine and Healthcare, Technical University of Munich, Munich, Germany.,Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Biomedical Image Analysis Group, Imperial College London, London, UK
| | - Nina K Wenke
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany.,Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
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11
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Vesteghem C, Brøndum RF, Sønderkær M, Sommer M, Schmitz A, Bødker JS, Dybkær K, El-Galaly TC, Bøgsted M. Implementing the FAIR Data Principles in precision oncology: review of supporting initiatives. Brief Bioinform 2021; 21:936-945. [PMID: 31263868 PMCID: PMC7299292 DOI: 10.1093/bib/bbz044] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 03/13/2019] [Accepted: 03/21/2019] [Indexed: 12/26/2022] Open
Abstract
Compelling research has recently shown that cancer is so heterogeneous that single research centres cannot produce enough data to fit prognostic and predictive models of sufficient accuracy. Data sharing in precision oncology is therefore of utmost importance. The Findable, Accessible, Interoperable and Reusable (FAIR) Data Principles have been developed to define good practices in data sharing. Motivated by the ambition of applying the FAIR Data Principles to our own clinical precision oncology implementations and research, we have performed a systematic literature review of potentially relevant initiatives. For clinical data, we suggest using the Genomic Data Commons model as a reference as it provides a field-tested and well-documented solution. Regarding classification of diagnosis, morphology and topography and drugs, we chose to follow the World Health Organization standards, i.e. ICD10, ICD-O-3 and Anatomical Therapeutic Chemical classifications, respectively. For the bioinformatics pipeline, the Genome Analysis ToolKit Best Practices using Docker containers offer a coherent solution and have therefore been selected. Regarding the naming of variants, we follow the Human Genome Variation Society's standard. For the IT infrastructure, we have built a centralized solution to participate in data sharing through federated solutions such as the Beacon Networks.
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Affiliation(s)
- Charles Vesteghem
- Department of Clinical Medicine, Aalborg University, Denmark.,Department of Haematology, Aalborg University Hospital, Denmark
| | | | - Mads Sønderkær
- Department of Haematology, Aalborg University Hospital, Denmark
| | - Mia Sommer
- Department of Clinical Medicine, Aalborg University, Denmark.,Department of Haematology, Aalborg University Hospital, Denmark
| | | | | | - Karen Dybkær
- Department of Clinical Medicine, Aalborg University, Denmark.,Department of Haematology, Aalborg University Hospital, Denmark.,Clinical Cancer Research Center, Aalborg University Hospital, Denmark
| | - Tarec Christoffer El-Galaly
- Department of Clinical Medicine, Aalborg University, Denmark.,Department of Haematology, Aalborg University Hospital, Denmark.,Clinical Cancer Research Center, Aalborg University Hospital, Denmark
| | - Martin Bøgsted
- Department of Clinical Medicine, Aalborg University, Denmark.,Department of Haematology, Aalborg University Hospital, Denmark.,Clinical Cancer Research Center, Aalborg University Hospital, Denmark
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12
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Ficek J, Wang W, Chen H, Dagne G, Daley E. Differential privacy in health research: A scoping review. J Am Med Inform Assoc 2021; 28:2269-2276. [PMID: 34333623 DOI: 10.1093/jamia/ocab135] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 06/11/2021] [Accepted: 06/16/2021] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE Differential privacy is a relatively new method for data privacy that has seen growing use due its strong protections that rely on added noise. This study assesses the extent of its awareness, development, and usage in health research. MATERIALS AND METHODS A scoping review was conducted by searching for ["differential privacy" AND "health"] in major health science databases, with additional articles obtained via expert consultation. Relevant articles were classified according to subject area and focus. RESULTS A total of 54 articles met the inclusion criteria. Nine articles provided descriptive overviews, 31 focused on algorithm development, 9 presented novel data sharing systems, and 8 discussed appraisals of the privacy-utility tradeoff. The most common areas of health research where differential privacy has been discussed are genomics, neuroimaging studies, and health surveillance with personal devices. Algorithms were most commonly developed for the purposes of data release and predictive modeling. Studies on privacy-utility appraisals have considered economic cost-benefit analysis, low-utility situations, personal attitudes toward sharing health data, and mathematical interpretations of privacy risk. DISCUSSION Differential privacy remains at an early stage of development for applications in health research, and accounts of real-world implementations are scant. There are few algorithms for explanatory modeling and statistical inference, particularly with correlated data. Furthermore, diminished accuracy in small datasets is problematic. Some encouraging work has been done on decision making with regard to epsilon. The dissemination of future case studies can inform successful appraisals of privacy and utility. CONCLUSIONS More development, case studies, and evaluations are needed before differential privacy can see widespread use in health research.
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Affiliation(s)
- Joseph Ficek
- College of Public Health, University of South Florida, Tampa, Florida, USA
| | - Wei Wang
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Henian Chen
- College of Public Health, University of South Florida, Tampa, Florida, USA
| | - Getachew Dagne
- College of Public Health, University of South Florida, Tampa, Florida, USA
| | - Ellen Daley
- College of Public Health, University of South Florida, Tampa, Florida, USA
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13
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Rosenbaum JN, Berry AB, Church AJ, Crooks K, Gagan JR, López-Terrada D, Pfeifer JD, Rennert H, Schrijver I, Snow AN, Wu D, Ewalt MD. A Curriculum for Genomic Education of Molecular Genetic Pathology Fellows: A Report of the Association for Molecular Pathology Training and Education Committee. J Mol Diagn 2021; 23:1218-1240. [PMID: 34245921 DOI: 10.1016/j.jmoldx.2021.07.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 06/16/2021] [Accepted: 07/01/2021] [Indexed: 12/19/2022] Open
Abstract
Molecular genetic pathology (MGP) is a subspecialty of pathology and medical genetics and genomics. Genomic testing, which we define as that which generates large data sets and interrogates large segments of the genome in a single assay, is increasingly recognized as essential for optimal patient care through precision medicine. The most common genomic testing technologies in clinical laboratories are next-generation sequencing and microarray. It is essential to train in these methods and to consider the data generated in the context of the diagnosis, medical history, and other clinical findings of individual patients. Accordingly, updating the MGP fellowship curriculum to include genomics is timely, important, and challenging. At the completion of training, an MGP fellow should be capable of independently interpreting and signing out results of a wide range of genomic assays and, given the appropriate context and institutional support, of developing and validating new assays in compliance with applicable regulations. The Genomics Task Force of the MGP Program Directors, a working group of the Association for Molecular Pathology Training and Education Committee, has developed a genomics curriculum framework and recommendations specific to the MGP fellowship. These recommendations are presented for consideration and implementation by MGP fellowship programs with the understanding that MGP programs exist in a diversity of clinical practice environments with a spectrum of available resources.
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Affiliation(s)
- Jason N Rosenbaum
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Anna B Berry
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Swedish Cancer Institute and Institute of Systems Biology, Seattle, Washington
| | - Alanna J Church
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Boston Children's Hospital, Boston, Massachusetts
| | - Kristy Crooks
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Jeffrey R Gagan
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Dolores López-Terrada
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Baylor College of Medicine, Houston, Texas
| | - John D Pfeifer
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Washington University School of Medicine, St. Louis, Missouri
| | - Hanna Rennert
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York
| | - Iris Schrijver
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Stanford University School of Medicine, Stanford, California
| | - Anthony N Snow
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, University of Iowa Hospitals and Clinics, Iowa City, Iowa
| | - David Wu
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington
| | - Mark D Ewalt
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.
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Lemieux VL, Hofman D, Hamouda H, Batista D, Kaur R, Pan W, Costanzo I, Regier D, Pollard S, Weymann D, Fraser R. Having Our “Omic” Cake and Eating It Too?: Evaluating User Response to Using Blockchain Technology for Private and Secure Health Data Management and Sharing. FRONTIERS IN BLOCKCHAIN 2021. [DOI: 10.3389/fbloc.2020.558705] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
This paper reports on end users' perspectives on the use of a blockchain solution for private and secure individual “omics” health data management and sharing. This solution is one output of a multidisciplinary project investigating the social, data, and technical issues surrounding application of blockchain technology in the context of personalized healthcare research. The project studies potential ethical, legal, social, and cognitive constraints of self-sovereign healthcare data management and sharing, and whether such constraints can be addressed through careful design of a blockchain solution.
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Schumacher GJ, Sawaya S, Nelson D, Hansen AJ. Genetic Information Insecurity as State of the Art. Front Bioeng Biotechnol 2020; 8:591980. [PMID: 33381496 PMCID: PMC7768984 DOI: 10.3389/fbioe.2020.591980] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 11/16/2020] [Indexed: 11/16/2022] Open
Abstract
Genetic information is being generated at an increasingly rapid pace, offering advances in science and medicine that are paralleled only by the threats and risk present within the responsible systems. Human genetic information is identifiable and contains sensitive information, but genetic information security is only recently gaining attention. Genetic data is generated in an evolving and distributed cyber-physical system, with multiple subsystems that handle information and multiple partners that rely and influence the whole ecosystem. This paper characterizes a general genetic information system from the point of biological material collection through long-term data sharing, storage and application in the security context. While all biotechnology stakeholders and ecosystems are valuable assets to the bioeconomy, genetic information systems are particularly vulnerable with great potential for harm and misuse. The security of post-analysis phases of data dissemination and storage have been focused on by others, but the security of wet and dry laboratories is also challenging due to distributed devices and systems that are not designed nor implemented with security in mind. Consequently, industry standards and best operational practices threaten the security of genetic information systems. Extensive development of laboratory security will be required to realize the potential of this emerging field while protecting the bioeconomy and all of its stakeholders.
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Affiliation(s)
- Garrett J. Schumacher
- GeneInfoSec Inc., Boulder, CO, United States
- Technology, Cybersecurity and Policy Program, College of Engineering and Applied Science, University of Colorado Boulder, Boulder, CO, United States
- Department of Computer Science, College of Engineering and Applied Science, University of Colorado Boulder, Boulder, CO, United States
| | | | | | - Aaron J. Hansen
- Technology, Cybersecurity and Policy Program, College of Engineering and Applied Science, University of Colorado Boulder, Boulder, CO, United States
- Department of Computer Science, College of Engineering and Applied Science, University of Colorado Boulder, Boulder, CO, United States
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16
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Botkin JR. Informed Consent for Genetic and Genomic Research. ACTA ACUST UNITED AC 2020; 108:e104. [PMID: 33202103 DOI: 10.1002/cphg.104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Genetic research often utilizes or generates information that is potentially sensitive to individuals, families, or communities. For these reasons, genetic research may warrant additional scrutiny from investigators and governmental regulators, compared to other types of biomedical research. The informed consent process should address the range of social and psychological issues that may arise in genetic research. This article addresses a number of these issues, including recruitment of participants, disclosure of results, psychological impact of results, insurance and employment discrimination, community engagement, consent for tissue banking, and intellectual property issues. Points of consideration are offered to assist in the development of protocols and consent processes in light of contemporary debates on a number of these issues. © 2020 Wiley Periodicals LLC.
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Beskow LM, Hammack-Aviran CM, Brelsford KM. Developing model biobanking consent language: what matters to prospective participants? BMC Med Res Methodol 2020; 20:119. [PMID: 32414333 PMCID: PMC7227271 DOI: 10.1186/s12874-020-01001-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 04/30/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Efforts to improve informed consent have led to calls for providing information a reasonable person would want to have, in a way that facilitates understanding of the reasons why one might or might not want to participate. At the same time, advances in large-scale genomic research have expanded both the opportunities and the risks for participants, families, and communities. To advance the use of effective consent materials that reflect this landscape, we used empirical data to develop model consent language, as well as brief questions to assist people in thinking about their own values relative to participation. METHODS We conducted in-person interviews to gather preliminary input on these materials from a diverse sample (n = 32) of the general population in Nashville, Tennessee. We asked them to highlight information they found especially reassuring or concerning, their hypothetical willingness to participate, and their opinions about the values questions. RESULTS Consent information most often highlighted as reassuring included the purpose of the biobank, the existence and composition of a multidisciplinary oversight committee, the importance of participants' privacy and efforts to protect it, and controlled access to a scientific database. Information most often highlighted as concerning included the deposition of data in a publicly accessible database, the risk of unintended access to data, the potential for non-research use of data, and use of medical record information in general. Seventy-five percent of participants indicated initial willingness to participate in the hypothetical biobank; this decreased to 66% as participants more closely considered the information over the course of the interview. A large majority rated the values questions as helpful. CONCLUSIONS These results are consistent with other research on public perspectives on biobanking and genomic cohort studies, suggesting that our model language effectively captures commonly expressed reasons for and against participation. Our study enriches this literature by connecting specific consent form disclosures with qualitative data regarding what participants found especially reassuring or concerning and why. Interventions that facilitate individuals' closer engagement with consent information may result in participation decisions more closely aligned with their values.
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Affiliation(s)
- Laura M Beskow
- Center for Biomedical Ethics and Society, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 400, Nashville, TN, 37203, USA.
| | - Catherine M Hammack-Aviran
- Center for Biomedical Ethics and Society, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 400, Nashville, TN, 37203, USA
| | - Kathleen M Brelsford
- Center for Biomedical Ethics and Society, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 400, Nashville, TN, 37203, USA
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Alkaraki AK, Khabour OF, Alzoubi KH, Al-Ebbini LMK, Altaany Z. Informed Consent Form Challenges for Genetic Research in Jordan. J Multidiscip Healthc 2020; 13:235-239. [PMID: 32184613 PMCID: PMC7062388 DOI: 10.2147/jmdh.s243669] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2019] [Accepted: 02/13/2020] [Indexed: 01/27/2023] Open
Abstract
Background Informed consent is an obligatory requirement for research engaging human subjects. Informed consent form (ICF) should be provided for human subjects to confirm their willingness for voluntary participation in a study. Ethical and legal obligations necessitate the presence of informed consent essential items to be built into the ICF. Objective To evaluate the content of ICFs obtained from different genetic studies accomplished in Jordan and their adherence to ethical guidelines proposed by the International Conference on Harmonization—Good Clinical Practice (ICHGCP). Methods and Measures A total of 44 ICFs obtained from master theses and grant proposals at two major universities in Jordan were analyzed according to the good clinical practice criteria proposed by ICHGCP. ICFs were scored for the presence or absence of ICF main items/categories. Results Results show inadequate information present in the examined ICFs. The highest information score was 17 out of 20, while the lowest score was one out of 20. The average score for all studied ICFs was 6.18±3.65. Among essential items/categories that were absent from the majority of studied ICFs were a statement about voluntary participation, confidentiality of data, compensation to study participants, risk/benefits of the study, and researchers’ contact information. Conclusion The ICFs were missing a number of required items. This could reflect inadequate knowledge about minimal informed consent requirements among Jordanian investigators highlighting the need for research ethical training in the country.
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Affiliation(s)
- Almuthanna K Alkaraki
- Department of Biological Sciences, Faculty of Science, Yarmouk University, Irbid 21163, Jordan
| | - Omar F Khabour
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Karem H Alzoubi
- Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Lina M K Al-Ebbini
- Department of Biomedical Systems and Informatics Engineering, Hijjawi for Engineering Technology, Yarmouk University, Irbid 21163, Jordan
| | - Zaid Altaany
- Department of Basic Medical Sciences, Faculty of Medicine, Yarmouk University, Irbid 21163, Jordan
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20
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Inference attacks on genomic privacy with an improved HMM and an RCNN model for unrelated individuals. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2019.09.036] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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21
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Duggal P, Ladd-Acosta C, Ray D, Beaty TH. The Evolving Field of Genetic Epidemiology: From Familial Aggregation to Genomic Sequencing. Am J Epidemiol 2019; 188:2069-2077. [PMID: 31509181 PMCID: PMC7036654 DOI: 10.1093/aje/kwz193] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 08/15/2019] [Accepted: 08/19/2019] [Indexed: 12/21/2022] Open
Abstract
The field of genetic epidemiology is relatively young and brings together genetics, epidemiology, and biostatistics to identify and implement the best study designs and statistical analyses for identifying genes controlling risk for complex and heterogeneous diseases (i.e., those where genes and environmental risk factors both contribute to etiology). The field has moved quickly over the past 40 years partly because the technology of genotyping and sequencing has forced it to adapt while adhering to the fundamental principles of genetics. In the last two decades, the available tools for genetic epidemiology have expanded from a genetic focus (considering 1 gene at a time) to a genomic focus (considering the entire genome), and now they must further expand to integrate information from other “-omics” (e.g., epigenomics, transcriptomics as measured by RNA expression) at both the individual and the population levels. Additionally, we can now also evaluate gene and environment interactions across populations to better understand exposure and the heterogeneity in disease risk. The future challenges facing genetic epidemiology are considerable both in scale and techniques, but the importance of the field will not diminish because by design it ties scientific goals with public health applications.
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Affiliation(s)
- Priya Duggal
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Christine Ladd-Acosta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Debashree Ray
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Terri H Beaty
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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Raghuram Pillai P, Prows CA, Martin LJ, Myers MF. Decisional conflict among adolescents and parents making decisions about genomic sequencing results. Clin Genet 2019; 97:312-320. [PMID: 31654527 DOI: 10.1111/cge.13658] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 09/23/2019] [Accepted: 10/18/2019] [Indexed: 01/08/2023]
Abstract
Genomic testing of adolescents is increasing yet engaging them in decision-making is not routine. We assessed decisional conflict in adolescents and a parent making independent decisions about actual genomic testing results and factors that influenced their choices. We enrolled 163 dyads consisting of an adolescent (13-17 years) not selected based on a specific clinical indication and one parent. After independently choosing categories of conditions to learn for the adolescent, participants completed the validated Decisional Conflict Scale and a survey assessing factors influencing their respective choices. Adolescents had higher decisional conflict scores than parents (15.6 [IQR:4.7-25.6] vs 9.4 [IQR:1.6-21.9]; P = .0007). Adolescents with clinically significant decisional conflict were less likely to choose to learn all results than adolescents with lower decisional conflict (19.6% vs 80.4%; P < .0001) and less likely to report their choices were influenced by actionability of results (33.3% vs 18.9%; P = .044) and feeling confident they can deal with the results (71.2% vs 91.9%; P = .0005). Our findings suggest higher decisional conflict in adolescents may influence the type and amount of genomic results they wish to learn. Additional research assessing decisional conflict and factors influencing testing choices among adolescents in clinical settings are required.
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Affiliation(s)
- Preethi Raghuram Pillai
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center and University of Cincinnati, Cincinnati, Ohio
| | - Cynthia A Prows
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center and University of Cincinnati, Cincinnati, Ohio.,Division of Patient Services, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Lisa J Martin
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center and University of Cincinnati, Cincinnati, Ohio
| | - Melanie F Myers
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center and University of Cincinnati, Cincinnati, Ohio
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Savatt J, Pisieczko CJ, Zhang Y, Lee MTM, Faucett WA, Williams JL. Biobanks in the Era of Genomic Data. CURRENT GENETIC MEDICINE REPORTS 2019. [DOI: 10.1007/s40142-019-00171-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Carter AB. Considerations for Genomic Data Privacy and Security when Working in the Cloud. J Mol Diagn 2019; 21:542-552. [DOI: 10.1016/j.jmoldx.2018.07.009] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Revised: 05/16/2018] [Accepted: 07/02/2018] [Indexed: 01/21/2023] Open
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Zhang L, Pan Q, Wang Y, Wu X, Shi X. Bayesian Network Construction and Genotype-Phenotype Inference Using GWAS Statistics. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:475-489. [PMID: 29990020 PMCID: PMC6499495 DOI: 10.1109/tcbb.2017.2779498] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Genome-wide association studies (GWASs) have received increasing attention to understand how genetic variation affects different human traits. In this paper, we study whether and to what extent exploiting the GWAS statistics can be used for inferring private information about a human individual. We first provide a method to construct a three-layered Bayesian network explicitly revealing the conditional dependency between single-nucleotide polymorphisms (SNPs) and traits from public GWAS catalog. The key challenge in building a Bayesian network from GWAS statistics is the specification of the conditional probability table of a variable with multiple parent variables. We employ the models of independence of causal influences which assume that the causal mechanism of each parent variable is mutually independent. We then formulate three inference problems based on the dependency relationship captured in the Bayesian network, namely trait inference given SNP genotype, genotype inference given trait, and trait inference given known traits, and develop efficient formulas and algorithms. Different from previous work, the possible target of these inference problems we study may be any individual, not limited to GWAS participants. Empirical evaluations show the effectiveness of our proposed methods. In summary, our work implies that meaningful information can be inferred from modeling GWAS statistics, and appropriate privacy protection mechanisms need to be developed to protect genetic privacy not only of GWAS participants but also regular individuals.
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26
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Manzoni C, Kia DA, Vandrovcova J, Hardy J, Wood NW, Lewis PA, Ferrari R. Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences. Brief Bioinform 2019; 19:286-302. [PMID: 27881428 PMCID: PMC6018996 DOI: 10.1093/bib/bbw114] [Citation(s) in RCA: 376] [Impact Index Per Article: 75.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Indexed: 02/07/2023] Open
Abstract
Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.
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Affiliation(s)
- Claudia Manzoni
- School of Pharmacy, University of Reading, Whiteknights, Reading, United Kingdom.,Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
| | - Demis A Kia
- Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
| | - Jana Vandrovcova
- Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
| | - John Hardy
- Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
| | - Nicholas W Wood
- Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
| | - Patrick A Lewis
- School of Pharmacy, University of Reading, Whiteknights, Reading, United Kingdom.,Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
| | - Raffaele Ferrari
- Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
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Systematizing Genome Privacy Research: A Privacy-Enhancing Technologies Perspective. PROCEEDINGS ON PRIVACY ENHANCING TECHNOLOGIES 2018. [DOI: 10.2478/popets-2019-0006] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Abstract
Rapid advances in human genomics are enabling researchers to gain a better understanding of the role of the genome in our health and well-being, stimulating hope for more effective and cost efficient healthcare. However, this also prompts a number of security and privacy concerns stemming from the distinctive characteristics of genomic data. To address them, a new research community has emerged and produced a large number of publications and initiatives. In this paper, we rely on a structured methodology to contextualize and provide a critical analysis of the current knowledge on privacy-enhancing technologies used for testing, storing, and sharing genomic data, using a representative sample of the work published in the past decade. We identify and discuss limitations, technical challenges, and issues faced by the community, focusing in particular on those that are inherently tied to the nature of the problem and are harder for the community alone to address. Finally, we report on the importance and difficulty of the identified challenges based on an online survey of genome data privacy experts.
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Middleton A. Society and personal genome data. Hum Mol Genet 2018; 27:R8-R13. [PMID: 29522190 PMCID: PMC5946868 DOI: 10.1093/hmg/ddy084] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Revised: 03/05/2018] [Accepted: 03/06/2018] [Indexed: 12/18/2022] Open
Abstract
Genomic data offer a goldmine of information for understanding the contribution of genetic variation makes to health and disease. The potential of genomic medicine, to predict, diagnose, manage and treat genetic disease, is underpinned by accurate variant interpretation. This in itself hinges on the ability to access large and varied genomic databases. There is now recognition that international collaboration between research and healthcare systems are paramount to delivering the scale of genomic data required. No single research group, institute or country will liberate our understanding, it is only through global cooperation, together with super computing power, will we truly make sense of how genotype and phenotype correlate. Whilst it is logistically possible to create computing systems that talk to each other and aggregate datasets ready to reveal novel correlations, the bottom line is that this will only happen if people (whether they be scientists, clinicians, patients, research participants, policy makers, politicians, law makers) support the principle that we should be donating, accessing and sharing our DNA data in this way. And in order to make the most sense of genomics, given the geographical and ancestral variation between us, such people are likely to be the majority of society. Within this review, a perspective is proffered on the human story that underpins genomic 'big data' access and how we are at a tipping point as a society-we need to decide collectively, are we in? and if so, what needs to be in place to protect us? or are we out?
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Affiliation(s)
- Anna Middleton
- Society and Ethics Research Group, Connecting Science, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
- Faculty of Education, University of Cambridge, UK
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29
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Haga SB, Friedman B, Richard G. Considering the Benefits and Risks of Research Participants' Access to Sequence Data. Genet Test Mol Biomarkers 2017; 21:717-721. [PMID: 29045186 DOI: 10.1089/gtmb.2017.0143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The use of sequencing technologies has greatly expanded in both research and clinical settings. The generation of voluminous datasets has raised several issues regarding data sharing and access. Current regulations require clinical laboratories and some research laboratories to provide access to test data, including sequencing data, directly to patients upon request. There is some controversy over whether this access right may be somewhat broader, encompassing research data as well-a question beyond the scope of this article. It is clear that in the research setting, deposition of sequencing data into public or private databases often occurs, although little information exists about the return of data files to research participants (in contrast to the extensive deliberations regarding return of results). Thus, further consideration of the issue of access to data files is warranted as well as more effort to understand both patients' and research participants' use of the data.
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Affiliation(s)
- Susanne B Haga
- 1 Center for Applied Genomics and Precision Medicine, Duke University School of Medicine , Durham, North Carolina
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30
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Abstract
BACKGROUND With the enormous need for federated eco-system for holding global genomic and clinical data, Global Alliance for Genomic and Health (GA4GH) has created an international website called beacon service which allows a researcher to find out whether a specific dataset can be utilized to his or her research beforehand. This simple webservice is quite useful as it allows queries like whether a certain position of a target chromosome has a specific nucleotide. However, the increased integration of individuals genomic data into clinical practice and research raised serious privacy concern. Though the answer of such queries are yes or no in Bacon network, it results in serious privacy implication as demonstrated in a recent work from Shringarpure and Bustamante. In their attack model, the authors demonstrated that with a limited number of queries, presence of an individual in any dataset can be determined. METHODS We propose two lightweight algorithms (based on randomized response) which captures the efficacy while preserving the privacy of the participants in a genomic beacon service. We also elaborate the strength and weakness of the attack by explaining some of their statistical and mathematical models using real world genomic database. We extend their experimental simulations for different adversarial assumptions and parameters. RESULTS We experimentally evaluated the solutions on the original attack model with different parameters for better understanding of the privacy and utility tradeoffs provided by these two methods. Also, the statistical analysis further elaborates the different aspects of the prior attack which leads to a better risk management for the participants in a beacon service. CONCLUSIONS The differentially private and lightweight solutions discussed here will make the attack much difficult to succeed while maintaining the fundamental motivation of beacon database network.
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Affiliation(s)
- Md Momin Al Aziz
- Department of Computer Science, University of Manitoba, Winnipeg, Canada
| | - Reza Ghasemi
- Department of Mathematics, Faculty of Sciences, Bu-Ali Sina University, Hamedan, Iran
| | - Md Waliullah
- Department of Computer Science, University of Manitoba, Winnipeg, Canada
| | - Noman Mohammed
- Department of Computer Science, University of Manitoba, Winnipeg, Canada
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Adam NR, Wieder R, Ghosh D. Data science, learning, and applications to biomedical and health sciences. Ann N Y Acad Sci 2017; 1387:5-11. [DOI: 10.1111/nyas.13309] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Accepted: 12/13/2016] [Indexed: 12/25/2022]
Affiliation(s)
- Nabil R. Adam
- Rutgers University Institute for Data Science; Learning, and Applications; Newark New Jersey
| | - Robert Wieder
- Rutgers University Biomedical and Health Sciences; Newark New Jersey
| | - Debopriya Ghosh
- Rutgers University Institute for Data Science; Learning, and Applications; Newark New Jersey
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