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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Towards practical privacy-preserving genome-wide association study.

Charlotte Bonte1, Eleftheria Makri2,3, Amin Ardeshirdavani4

  • 1imec-COSIC, Department of Electrical Engineering, KU Leuven, Leuven, Belgium. cbonte@esat.kuleuven.be.

BMC Bioinformatics
|December 22, 2018
PubMed
Summary

We developed secure methods for Genome-wide association studies (GWAS) to protect patient privacy. Our techniques enable privacy-preserving GWAS by securely calculating statistics, ensuring data confidentiality and enabling large-scale genetic research.

Keywords:
Genome-wide association study (GWAS)Homomorphic encryption (HE)Secure multiparty computation (MPC)

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) require large population genomic data.
  • Privacy concerns hinder participation in genetic studies.
  • Existing methods may compromise patient data confidentiality.

Purpose of the Study:

  • To develop privacy-preserving computational methods for GWAS.
  • To address data security challenges in large-scale genetic research.
  • To enable reliable GWAS without compromising individual genetic information.

Main Methods:

  • Proposed two provably secure solutions: somewhat homomorphic encryption (HE) and secure multiparty computation (MPC).
  • Developed protocols to compute the chi-squared (χ²) statistic privately.
  • Introduced a novel masking technique to enhance the efficiency of secure comparisons in HE protocols.

Main Results:

  • Achieved privacy-preserving calculation of the χ² statistic, returning only significance (yes/no).
  • Protocols prevent data breaches by not revealing the statistic's value.
  • Demonstrated efficiency, with MPC completing in ~2 ms for one million subjects.

Conclusions:

  • Full-scale privacy-preserving GWAS is practical and efficient.
  • The proposed HE and MPC methods ensure data confidentiality.
  • Secure computation techniques facilitate large-scale genomic data analysis.