Department of Computer Science, School of Mathematics and Informatics, School of Software Engineering, South China Agricultural University, Guangzhou, 510642, PR China.
School of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, PR China.
BMC Genomics. 2024 May 13;25(1):462. doi: 10.1186/s12864-024-10373-4.
Detecting epistatic interactions (EIs) involves the exploration of associations among single nucleotide polymorphisms (SNPs) and complex diseases, which is an important task in genome-wide association studies. The EI detection problem is dependent on epistasis models and corresponding optimization methods. Although various models and methods have been proposed to detect EIs, identifying EIs efficiently and accurately is still a challenge.
Here, we propose a linear mixed statistical epistasis model (LMSE) and a spherical evolution approach with a feedback mechanism (named SEEI). The LMSE model expands the existing single epistasis models such as LR-Score, K2-Score, Mutual information, and Gini index. The SEEI includes an adaptive spherical search strategy and population updating strategy, which ensures that the algorithm is not easily trapped in local optima. We analyzed the performances of 8 random disease models, 12 disease models with marginal effects, 30 disease models without marginal effects, and 10 high-order disease models. The 60 simulated disease models and a real breast cancer dataset were used to evaluate eight algorithms (SEEI, EACO, EpiACO, FDHEIW, MP-HS-DHSI, NHSA-DHSC, SNPHarvester, CSE). Three evaluation criteria (pow1, pow2, pow3), a T-test, and a Friedman test were used to compare the performances of these algorithms. The results show that the SEEI algorithm (order 1, averages ranks = 13.125) outperformed the other algorithms in detecting EIs.
Here, we propose an LMSE model and an evolutionary computing method (SEEI) to solve the optimization problem of the LMSE model. The proposed method performed better than the other seven algorithms tested in its ability to identify EIs in genome-wide association datasets. We identified new SNP-SNP combinations in the real breast cancer dataset and verified the results. Our findings provide new insights for the diagnosis and treatment of breast cancer.
检测上位性相互作用（EIs）涉及到单核苷酸多态性（SNPs）和复杂疾病之间的关联的探索，这是全基因组关联研究中的一个重要任务。EIs 检测问题依赖于上位性模型和相应的优化方法。虽然已经提出了各种模型和方法来检测 EIs，但高效准确地识别 EIs 仍然是一个挑战。
在这里，我们提出了一种线性混合统计上位性模型（LMSE）和一种具有反馈机制的球形进化方法（称为 SEEI）。LMSE 模型扩展了现有的单一位点模型，如 LR-Score、K2-Score、互信息和基尼指数。SEEI 包括自适应球形搜索策略和群体更新策略，以确保算法不易陷入局部最优。我们分析了 8 个随机疾病模型、12 个具有边缘效应的疾病模型、30 个没有边缘效应的疾病模型和 10 个高阶疾病模型的性能。使用 60 个模拟疾病模型和一个真实的乳腺癌数据集来评估 8 种算法（SEEI、EACO、EpiACO、FDHEIW、MP-HS-DHSI、NHSA-DHSC、SNPHarvester、CSE）。使用三个评估标准（pow1、pow2、pow3）、T 检验和 Friedman 检验来比较这些算法的性能。结果表明，SEEI 算法（阶数 1，平均等级=13.125）在检测 EIs 方面优于其他算法。
在这里，我们提出了一种 LMSE 模型和一种进化计算方法（SEEI）来解决 LMSE 模型的优化问题。所提出的方法在识别全基因组关联数据集中的 EIs 方面的能力优于测试的其他七种算法。我们在真实的乳腺癌数据集上识别了新的 SNP-SNP 组合，并验证了结果。我们的研究结果为乳腺癌的诊断和治疗提供了新的见解。
