Paper 2019/145

Achieving GWAS with Homomorphic Encryption

Jun Jie Sim, Fook Mun Chan, Shibin Chen, Benjamin Hong Meng Tan, and Khin Mi Mi Aung


One way of investigating how genes affect human traits would be with a genome-wide association study (GWAS). Genetic markers, known as single-nucleotide polymorphism (SNP), are used in GWAS. This raises privacy and security concerns as these genetic markers can be used to identify individuals uniquely. This problem is further exacerbated by a large number of SNPs needed, which produce reliable results at a higher risk of compromising the privacy of participants. We describe a method using homomorphic encryption (HE) to perform GWAS in a secure and private setting. This work is based on a semi-parallel logistic regression algorithm proposed to accelerate GWAS computations. Our solution involves homomorphically encrypted matrices and suitable approximations that adapts the original algorithm to be HE-friendly. Our best implementation took $24.70$ minutes for a dataset with $245$ samples, $4$ covariates and $10643$ SNPs. We demonstrate that it is possible to achieve GWAS with homomorphic encryption with suitable approximations.

Note: Added figures for Replicate and Duplicate.

Available format(s)
Publication info
Preprint. MINOR revision.
GWASHomomorphic EncryptionLogistic Regression
Contact author(s)
simjj @ i2r a-star edu sg
2019-08-01: last of 2 revisions
2019-02-14: received
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Creative Commons Attribution


      author = {Jun Jie Sim and Fook Mun Chan and Shibin Chen and Benjamin Hong Meng Tan and Khin Mi Mi Aung},
      title = {Achieving GWAS with Homomorphic Encryption},
      howpublished = {Cryptology ePrint Archive, Paper 2019/145},
      year = {2019},
      note = {\url{}},
      url = {}
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