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Gradient Echo Quantum Memory in Warm Atomic Vapor
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Privacy-preserving approximate GWAS computation based on homomorphic encryption.

Duhyeong Kim1, Yongha Son1, Dongwoo Kim1

  • 1Department of Mathematical Sciences, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea.

BMC Medical Genomics
|July 23, 2020
PubMed
Summary
This summary is machine-generated.

This study presents a privacy-preserving genome-wide association study (GWAS) using homomorphic encryption. The developed method ensures data security during computation, achieving high accuracy for genetic analysis.

Keywords:
Fisher scoringGWASHomomorphic encryptionPrivacy

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Last Updated: Dec 14, 2025

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cryptography

Background:

  • The iDASH 2018 competition highlighted the need for secure genome analysis.
  • Privacy-preserving Genome-Wide Association Studies (GWAS) are crucial for handling sensitive genetic data.
  • Homomorphic encryption offers a method for computation on encrypted data without decryption.

Purpose of the Study:

  • To develop a privacy-preserving GWAS computation solution using homomorphic encryption.
  • To enable secure analysis of encrypted genotype and phenotype data on untrusted servers.
  • To prevent information leakage of sensitive individual genetic data.

Main Methods:

  • Developed a privacy-preserving semi-parallel GWAS algorithm using the HEAAN homomorphic encryption scheme.
  • Modified Fisher scoring and semi-parallel GWAS algorithms for efficient computation on encrypted data.
  • Implemented optimization techniques including matrix inversion substitution and approximation for efficiency.

Main Results:

  • The homomorphic encryption-based GWAS achieved 128-bit security.
  • Computation time was 30-40 minutes for 245 samples with 10,000-15,000 SNPs.
  • The F1 score for the computed p-values exceeded 0.99 compared to unencrypted analysis.

Conclusions:

  • Privacy-preserving GWAS computation is feasible and efficient using homomorphic encryption.
  • The developed method maintains high accuracy comparable to traditional GWAS.
  • This approach enhances data security in genetic analyses without compromising results.