School of Information and Control Engineering, China University of Mining and Technology, Xuzhou221116, China.
School of Medical Information & Engineering, Xuzhou Medical University, Xuzhou 221000, China.
Comput Math Methods Med. 2022 Apr 21;2022:4471736. doi: 10.1155/2022/4471736. eCollection 2022.
Infectious diseases pose a serious threat to human life, the Genome Wide Association Studies (GWAS) can analyze susceptibility genes of infectious diseases from the genetic level and carry out targeted prevention and treatment. The susceptibility genes for infectious diseases often act in combination with multiple susceptibility sites; therefore, high-order epistasis detection has become an important means. However, due to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power, high computation cost, and preference for some types of disease models. Furthermore, these methods are exposed to repeated query and model inversion attacks in the process of iterative optimization, which may disclose Single Nucleotide Polymorphism (SNP) information associated with individual privacy. Therefore, in order to solve these problems, this paper proposed a safe harmony search algorithm for high-order gene interaction detection, termed as HS-DP. Firstly, the linear weighting method was used to integrate 5 objective functions to screen out high-order SNP sets with high correlation, including K2-Score, JS divergence, logistic regression, mutual information, and Gini. Then, based on the Differential Privacy (DP) theory, the function disturbance mechanism was introduced to protect the security of individual privacy information associated with the objective function, and we proved the rationality of the disturbance mechanism theoretically. Finally, the practicability and superiority of the algorithm were verified by experiments. Experimental results showed that the algorithm proposed in this paper could improve the detection accuracy to the greatest extent while guaranteeing privacy.
传染病对人类生命构成严重威胁，全基因组关联研究（GWAS）可以从遗传水平分析传染病的易感基因，并进行针对性的预防和治疗。传染病的易感基因通常与多个易感位点共同作用；因此，高阶上位性检测已成为一种重要手段。然而，由于计算负担大且疾病模型多样化，现有方法在检测能力低、计算成本高以及对某些类型疾病模型的偏好方面存在缺陷。此外，这些方法在迭代优化过程中容易受到重复查询和模型反转攻击，这可能会泄露与个人隐私相关的单核苷酸多态性（SNP）信息。因此，为了解决这些问题，本文提出了一种用于高阶基因互作检测的安全和声搜索算法，称为 HS-DP。首先，采用线性加权法整合 5 个目标函数，筛选出相关性较高的高阶 SNP 集，包括 K2-Score、JS 散度、逻辑回归、互信息和 Gini。然后，基于差分隐私（DP）理论，引入函数干扰机制来保护与目标函数相关的个体隐私信息的安全性，并从理论上证明了干扰机制的合理性。最后，通过实验验证了算法的实用性和优越性。实验结果表明，本文提出的算法在保证隐私的同时，能够最大限度地提高检测精度。
