SecureMA
Computational Tool
FAIR Metrics
0 evaluations
General Information
DescriptionProtecting participant privacy in genetic association meta-analysis
Homepagehttp://github.com/XieConnect/CircuitService
Publications
SecureMA: protecting participant privacy in genetic association meta-analysis
Wei Xie; Murat Kantarcioglu; William S. Bush; Dana Crawford; Joshua C. Denny; Raymond Heatherly; Bradley A. Malin
Motivation: Sharing genomic data is crucial to support scientific investigation such as genome-wide association studies. However, recent investigations suggest the privacy of the individual participants in these studies can be compromised, leading to serious concerns and consequences, such as overly restricted access to data.
Results: We introduce a novel cryptographic strategy to securely perform meta-analysis for genetic association studies in large consortia. Our methodology is useful for supporting joint studies among disparate data sites, where privacy or confidentiality is of concern. We validate our method using three multisite association studies. Our research shows that genetic associations can be analyzed efficiently and accurately across substudy sites, without leaking information on individual participants and site-level association summaries.
Availability and implementation: Our software for secure meta-analysis of genetic association studies, SecureMA, is publicly available at http://github.com/XieConnect/SecureMA. Our customized secure computation framework is also publicly available at http://github.com/XieConnect/CircuitService
Contact: b.malin@vanderbilt.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
DOI: https://doi.org/10.1093/bioinformatics/btu561
Metrics:
7
Canned Analyses generated by the tool
There are no indexed analyses by this tool at the moment.
Datasets analyzed by the tool
There are no indexed analyses by this tool at the moment.
FAIR Evaluation
Sorry, you must be signed in to evaluate this object.