Hundreds of thousands of single nucleotide polymorphisms (SNPs)are currently available for genome-wide association study (GWAS). Detecting disease-associated SNP-SNP interactions is considered an important way to capture the underlying genetic causes of complex diseases. In the combinatorially explosive search space, evolutionary algorithms are promising in solving this difficult problem because of their controllable time complexity. However, in existing evolutionary algorithms, some possible SNP-SNP interactions are evaluated multiple times by the fitness function. Such reevaluations not only waste computing resources but also make these algorithms easy to fall into local optima. To tackle this drawback, a progressive screening memetic algorithm (PSMA)is proposed in the paper. PSMA first represents all possible SNP-SNP interactions in a constructed graph. Then, the proposed algorithm uses the progressive screening strategy to guarantee that every possible SNP-SNP interaction can only be evaluated once by reducing the constructed graph. Furthermore, two types of local search algorithms are introduced to enhance the detecting power of PSMA. For detecting disease-associated SNP-SNP interactions, experimental results show that our proposed method outperforms other existing state-of-the-art methods in terms of accuracy and time.

中文翻译：

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使用渐进筛选模因算法检测疾病相关的 SNP-SNP 相互作用

目前有数十万个单核苷酸多态性 (SNP) 可用于全基因组关联研究 (GWAS)。检测疾病相关的 SNP-SNP 相互作用被认为是捕获复杂疾病潜在遗传原因的重要方法。在组合爆炸搜索空间中，进化算法因其可控的时间复杂度而有望解决这一难题。然而，在现有的进化算法中，一些可能的 SNP-SNP 相互作用是由适应度函数多次评估的。这样的重新评估不仅浪费了计算资源，而且使这些算法容易陷入局部最优。为了解决这个缺点，本文提出了一种渐进式筛选模因算法（PSMA）。PSMA 首先表示构造图中所有可能的 SNP-SNP 相互作用。然后，所提出的算法使用渐进筛选策略来保证每个可能的 SNP-SNP 交互只能通过减少构建的图来评估一次。此外，还引入了两种局部搜索算法来增强 PSMA 的检测能力。为了检测与疾病相关的 SNP-SNP 相互作用，实验结果表明，我们提出的方法在准确性和时间方面优于其他现有的最先进的方法。