Genome-wide association studies have succeeded in identifying genetic variants associated with complex diseases, but the findings have not been well interpreted biologically. Although it is widely accepted that epistatic interactions of high-order single nucleotide polymorphisms (SNPs) [(1) Single nucleotide polymorphisms (SNP) are mainly deoxyribonucleic acid (DNA) sequence polymorphisms caused by variants at a single nucleotide at the genome level. They are the most common type of heritable variation in humans.] are important causes of complex diseases, the combinatorial explosion of millions of SNPs and multiple tests impose a large computational burden. Moreover, it is extremely challenging to correctly distinguish high-order SNP epistatic interactions from other high-order SNP combinations due to small sample sizes. In this study, a multitasking harmony search algorithm (MTHSA-DHEI) is proposed for detecting high-order epistatic interactions [(2) In classical genetics, if genes X1 and X2 are mutated and each mutation by itself produces a unique disease status (phenotype) but the mutations together cause the same disease status as the gene X1 mutation, gene X1 is epistatic and gene X2 is hypostatic, and gene X1 has an epistatic effect (main effect) on disease status. In this work, a high-order epistatic interaction occurs when two or more SNP loci have a joint influence on disease status.], with the goal of simultaneously detecting multiple types of high-order (k₁-order, k₂-order, …, k_n-order) SNP epistatic interactions. Unified coding is adopted for multiple tasks, and four complementary association evaluation functions are employed to improve the capability of discriminating the high-order SNP epistatic interactions. We compare the proposed MTHSA-DHEI method with four excellent methods for detecting high-order SNP interactions for 8 high-order epistatic interaction models with no marginal effect (EINMEs) and 12 epistatic interaction models with marginal effects (EIMEs) ^(*) and implement the MTHSA-DHEI algorithm with a real dataset: age-related macular degeneration (AMD). The experimental results indicate that MTHSA-DHEI has power and an F1-score exceeding 90% for all EIMEs and five EINMEs and reduces the computational time by more than 90%. It can efficiently perform multiple high-order detection tasks for high-order epistatic interactions and improve the discrimination ability for diverse epistasis models.

全基因组关联研究已成功识别与复杂疾病相关的遗传变异，但研究结果尚未得到很好的生物学解释。尽管人们普遍认为高阶单核苷酸多态性（SNP）的上位相互作用[（1）单核苷酸多态性（SNP）主要是由基因组水平的单核苷酸变异引起的脱氧核糖核酸（DNA）序列多态性。它们是人类最常见的遗传变异类型。] 是复杂疾病的重要原因，数百万个 SNP 的组合爆炸和多重测试带来了巨大的计算负担。此外，正确区分高阶是极具挑战性的。由于样本量小，来自其他高阶SNP 组合的 SNP 上位相互作用。在这项研究中，提出了一种多任务和谐搜索算法 (MTHSA-DHEI) 来检测高阶上位相互作用 [(2) 在经典遗传学中，如果基因 X1 和 X2 发生突变并且每个突变本身都会产生独特的疾病状态（表型) 但突变共同导致与基因 X1 突变相同的疾病状态，基因 X1 是上位性的，基因 X2 是原位性的，基因 X1 对疾病状态具有上位性效应（主效应）。在这项工作中，高阶上位相互作用当两个或多个 SNP 位点对疾病状态有共同影响时发生。]，目的是同时检测多种类型的高阶（k ₁阶，k ₂阶，...，k n_阶） SNP上位相互作用. 多任务采用统一编码，并采用四个互补的关联评价函数来提高区分高阶SNP上位相互作用的能力。我们将提出的 MTHSA-DHEI 方法与用于检测8 个高阶e的高阶SNP 相互作用的四种优秀方法进行比较 具有无边际效应( EINME ) 的pistatic交互模型和具有边际效应 (EIME) (*) 的 12 e pistatic交互模型，^并使用真实数据集实现MTHSA - DHEI算法：年龄相关性黄斑变性 ( AMD）。实验结果表明，MTHSA-DHEI 对于所有 EIME 和 5 个 EINME 都具有超过 90% 的功率和 F1 分数，并且计算时间减少了 90% 以上。它可以高效地执行多个高阶检测任务，用于高阶上位相互作用并提高对不同上位模型的区分能力。