Implementation and Analysis of Conditional Statement Algorithms in Fully Homomorphic Encryption for Genome-Wide Association Studies

Abstract

This study addressed the privacy challenges in genome-wide association studies (GWAS) by developing a secure system that performs computations directly on encrypted genotype data using Fully Homomorphic Encryption (FHE). The system was implemented with the CKKS scheme via the TenSEAL library and structured using a Django client and Flask server. Five conditional logic algorithms were evaluated—Polynomial Approximation, Multiplexer, Blind Evaluation, Conditional Branching, and Minimax Approximation—to compute GWAS statistics such as allelic odds ratio, chi-square, minor allele frequency, and Hardy-Weinberg equilibrium. Results showed that Multiplexer and Conditional Branching achieved the highest accuracy, while Polynomial and Minimax approaches offered trade-offs in speed and flexibility. The system demonstrated that secure GWAS analysis is feasible without compromising data privacy.