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Identifying disease-causing mutations with privacy protection

MOTIVATION: The use of genome data for diagnosis and treatment is becoming increasingly common. Researchers need access to as many genomes as possible to interpret the patient genome, to obtain some statistical patterns and to reveal disease–gene relationships. The sensitive information contained in...

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Autores principales: Akgün, Mete, Ünal, Ali Burak, Ergüner, Bekir, Pfeifer, Nico, Kohlbacher, Oliver
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7850099/
https://www.ncbi.nlm.nih.gov/pubmed/32683440
http://dx.doi.org/10.1093/bioinformatics/btaa641
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author Akgün, Mete
Ünal, Ali Burak
Ergüner, Bekir
Pfeifer, Nico
Kohlbacher, Oliver
author_facet Akgün, Mete
Ünal, Ali Burak
Ergüner, Bekir
Pfeifer, Nico
Kohlbacher, Oliver
author_sort Akgün, Mete
collection PubMed
description MOTIVATION: The use of genome data for diagnosis and treatment is becoming increasingly common. Researchers need access to as many genomes as possible to interpret the patient genome, to obtain some statistical patterns and to reveal disease–gene relationships. The sensitive information contained in the genome data and the high risk of re-identification increase the privacy and security concerns associated with sharing such data. In this article, we present an approach to identify disease-associated variants and genes while ensuring patient privacy. The proposed method uses secure multi-party computation to find disease-causing mutations under specific inheritance models without sacrificing the privacy of individuals. It discloses only variants or genes obtained as a result of the analysis. Thus, the vast majority of patient data can be kept private. RESULTS: Our prototype implementation performs analyses on thousands of genomic data in milliseconds, and the runtime scales logarithmically with the number of patients. We present the first inheritance model (recessive, dominant and compound heterozygous) based privacy-preserving analyses of genomic data to find disease-causing mutations. Furthermore, we re-implement the privacy-preserving methods (MAX, SETDIFF and INTERSECTION) proposed in a previous study. Our MAX, SETDIFF and INTERSECTION implementations are 2.5, 1122 and 341 times faster than the corresponding operations of the state-of-the-art protocol, respectively. AVAILABILITY AND IMPLEMENTATION: https://gitlab.com/DIFUTURE/privacy-preserving-genomic-diagnosis. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-78500992021-02-03 Identifying disease-causing mutations with privacy protection Akgün, Mete Ünal, Ali Burak Ergüner, Bekir Pfeifer, Nico Kohlbacher, Oliver Bioinformatics Original Papers MOTIVATION: The use of genome data for diagnosis and treatment is becoming increasingly common. Researchers need access to as many genomes as possible to interpret the patient genome, to obtain some statistical patterns and to reveal disease–gene relationships. The sensitive information contained in the genome data and the high risk of re-identification increase the privacy and security concerns associated with sharing such data. In this article, we present an approach to identify disease-associated variants and genes while ensuring patient privacy. The proposed method uses secure multi-party computation to find disease-causing mutations under specific inheritance models without sacrificing the privacy of individuals. It discloses only variants or genes obtained as a result of the analysis. Thus, the vast majority of patient data can be kept private. RESULTS: Our prototype implementation performs analyses on thousands of genomic data in milliseconds, and the runtime scales logarithmically with the number of patients. We present the first inheritance model (recessive, dominant and compound heterozygous) based privacy-preserving analyses of genomic data to find disease-causing mutations. Furthermore, we re-implement the privacy-preserving methods (MAX, SETDIFF and INTERSECTION) proposed in a previous study. Our MAX, SETDIFF and INTERSECTION implementations are 2.5, 1122 and 341 times faster than the corresponding operations of the state-of-the-art protocol, respectively. AVAILABILITY AND IMPLEMENTATION: https://gitlab.com/DIFUTURE/privacy-preserving-genomic-diagnosis. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-07-19 /pmc/articles/PMC7850099/ /pubmed/32683440 http://dx.doi.org/10.1093/bioinformatics/btaa641 Text en © The Author(s) 2020. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
Akgün, Mete
Ünal, Ali Burak
Ergüner, Bekir
Pfeifer, Nico
Kohlbacher, Oliver
Identifying disease-causing mutations with privacy protection
title Identifying disease-causing mutations with privacy protection
title_full Identifying disease-causing mutations with privacy protection
title_fullStr Identifying disease-causing mutations with privacy protection
title_full_unstemmed Identifying disease-causing mutations with privacy protection
title_short Identifying disease-causing mutations with privacy protection
title_sort identifying disease-causing mutations with privacy protection
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7850099/
https://www.ncbi.nlm.nih.gov/pubmed/32683440
http://dx.doi.org/10.1093/bioinformatics/btaa641
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