Dataset Information

Privacy-preserving genomic testing in the clinic: a model using HIV treatment.

ABSTRACT: The implementation of genomic-based medicine is hindered by unresolved questions regarding data privacy and delivery of interpreted results to health-care practitioners. We used DNA-based prediction of HIV-related outcomes as a model to explore critical issues in clinical genomics.We genotyped 4,149 markers in HIV-positive individuals. Variants allowed for prediction of 17 traits relevant to HIV medical care, inference of patient ancestry, and imputation of human leukocyte antigen (HLA) types. Genetic data were processed under a privacy-preserving framework using homomorphic encryption, and clinical reports describing potentially actionable results were delivered to health-care providers.A total of 230 patients were included in the study. We demonstrated the feasibility of encrypting a large number of genetic markers, inferring patient ancestry, computing monogenic and polygenic trait risks, and reporting results under privacy-preserving conditions. The average execution time of a multimarker test on encrypted data was 865?ms on a standard computer. The proportion of tests returning potentially actionable genetic results ranged from 0 to 54%.The model of implementation presented herein informs on strategies to deliver genomic test results for clinical care. Data encryption to ensure privacy helps to build patient trust, a key requirement on the road to genomic-based medicine.Genet Med 18 8, 814-822.

SUBMITTER: McLaren PJ

PROVIDER: S-EPMC4985613 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Privacy-preserving genomic testing in the clinic: a model using HIV treatment.

McLaren Paul J PJ Raisaro Jean Louis JL Aouri Manel M Rotger Margalida M Ayday Erman E Bartha István I Delgado Maria B MB Vallet Yannick Y Günthard Huldrych F HF Cavassini Matthias M Furrer Hansjakob H Doco-Lecompte Thanh T Marzolini Catia C Schmid Patrick P Di Benedetto Caroline C Decosterd Laurent A LA Fellay Jacques J Hubaux Jean-Pierre JP Telenti Amalio A

Genetics in medicine : official journal of the American College of Medical Genetics 20160114 8

<h4>Purpose</h4>The implementation of genomic-based medicine is hindered by unresolved questions regarding data privacy and delivery of interpreted results to health-care practitioners. We used DNA-based prediction of HIV-related outcomes as a model to explore critical issues in clinical genomics.<h4>Methods</h4>We genotyped 4,149 markers in HIV-positive individuals. Variants allowed for prediction of 17 traits relevant to HIV medical care, inference of patient ancestry, and imputation of human ...[more]

PMID: 26765343

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Project description:OBJECTIVE:To facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a "flattened" topology, while real-world research networks may consist of "network-of-networks" which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks. MATERIALS AND METHODS:We propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology. RESULTS:HierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level. DISCUSSION:HierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns. CONCLUSION:We demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction.

Dataset Information

Privacy-preserving genomic testing in the clinic: a model using HIV treatment.

Publications

Privacy-preserving genomic testing in the clinic: a model using HIV treatment.

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