Format

Send to

Choose Destination
See comment in PubMed Commons below
Bioinformatics. 2014 Dec 1;30(23):3334-41. doi: 10.1093/bioinformatics/btu561. Epub 2014 Aug 21.

SecureMA: protecting participant privacy in genetic association meta-analysis.

Author information

  • 1Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA, Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA, Department of Biomedical Informatics, Center for Human Genetics Research, Department of Molecular Physiology and Biophysics and Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA.
  • 2Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA, Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA, Department of Biomedical Informatics, Center for Human Genetics Research, Department of Molecular Physiology and Biophysics and Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA, Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA, Department of Biomedical Informatics, Center for Human Genetics Research, Department of Molecular Physiology and Biophysics and Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA.

Abstract

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.

PMID:
25147357
PMCID:
PMC4296153
DOI:
10.1093/bioinformatics/btu561
[PubMed - indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Silverchair Information Systems Icon for PubMed Central
    Loading ...
    Support Center