Privacy-preserving decision tree for epistasis detection
journal contributionposted on 05.02.2021, 05:49 by Qingfeng ChenQingfeng Chen, X Zhang, R Zhang
© 2019, The Author(s). The interaction between gene loci, namely epistasis, is a widespread biological genetic phenomenon. In genome-wide association studies(GWAS), epistasis detection of complex diseases is a major challenge. Although many approaches using statistics, machine learning, and information entropy were proposed for epistasis detection, the privacy preserving for single nucleotide polymorphism(SNP) data has been largely ignored. Thus, this paper proposes a novel two-stage approach. A fusion strategy assists in combining and sorting the SNPs importance scores obtained by the relief and mutual information, thereby obtaining a candidate set of SNPs. This avoids missing some SNPs with strong interaction. Furthermore, differentially private decision tree is applied to search for SNPs. This achieves the efficient epistasis detection of complex diseases on the basis of privacy preserving compared with heuristic methods. The recognition rate on simulation data set is more than 90%. Also, several susceptible loci including rs380390 and rs1329428 are found in the real data set for Age-related Macular Degeneration (AMD). This demonstrates that our method is promising in epistasis detection.