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Title page for ETD etd-11052014-180906

Type of Document Master's Thesis
Author Xie, Wei
Author's Email Address wei.xie@vanderbilt.edu
URN etd-11052014-180906
Title Protecting Participant Privacy in Genotype-Phenotype Association Meta-analysis
Degree Master of Science
Department Computer Science
Advisory Committee
Advisor Name Title
Bradley A. Malin Committee Chair
Yuan Xue Committee Member
  • genome privacy
  • meta-analysis
  • privacy-preserving association studies
  • secure multi-party computation
Date of Defense 2014-08-08
Availability unrestricted
Sharing genomic data is crucial to support scientific investigation such as genome-wide association studies (GWAS). However, various 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.

This thesis introduces a novel cryptographic strategy to securely perform meta-analysis for genotype-phenotype association studies in large consortia. Our methodology is useful for supporting joint studies among disparate data sites, where data privacy or confidentiality is of concern. We validate our method using three multi-site meta-analyses from two large consortia. This work shows that genetic associations can be analyzed efficiently and accurately across study sites, without leaking information on individual participants and site-level study summaries.

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