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HiSSI: high-order SNP-SNP interactions detection based on efficient significant pattern and differential evolution

机译：Hissi：高阶SNP-SNP交互式检测，基于有效的显着图案和差分演变

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Detecting single nucleotide polymorphism (SNP) interactions is an important and challenging task in genome-wide association studies (GWAS). Various efforts have been devoted to detect SNP interactions. However, the large volume of SNP datasets results in such a big number of high-order SNP combinations that restrict the power of detecting interactions. In this paper, to combat with this challenge, we propose a two-stage approach (called HiSSI) to detect high-order SNP-SNP interactions. In the screening stage, HiSSI employs a statistically significant pattern that takes into account family wise error rate, to control false positives and to effectively screen two-locus combinations candidate set. In the searching stage, HiSSI applies two different search strategies (exhaustive search and heuristic search based on differential evolution along with χ2-test) on candidate pairwise SNP combinations to detect high-order SNP interactions. Extensive experiments on simulated datasets are conducted to evaluate HiSSI and recently proposed and related approaches on both two-locus and three-locus disease models. A real genome-wide dataset: breast cancer dataset collected from the Wellcome Trust Case Control Consortium (WTCCC) is also used to test HiSSI. Simulated experiments on both two-locus and three-locus disease models show that HiSSI is more powerful than other related approaches. Real experiment on breast cancer dataset, in which HiSSI detects some significantly two-locus and three-locus interactions associated with breast cancer, again corroborate the effectiveness of HiSSI in high-order SNP-SNP interaction identification.
机译：检测单一核苷酸多态性（SNP）相互作用是基因组 - 宽协会研究（GWAS）中的重要且挑战性的任务。已经致力于检测SNP相互作用的各种努力。然而，大量的SNP数据集导致如此大量的高阶SNP组合，其限制了检测交互的力量。在本文中，为了解决这一挑战，我们提出了一种两级方法（称为Hissi）来检测高阶SNP-SNP相互作用。在筛选阶段，Hissi采用统计上重要的模式，考虑了家庭明智的错误率，以控制误报并有效地筛选两个轨迹组合候选集。在搜索阶段，Hissi在候选成对SNP组合上应用两种不同的搜索策略（基于差分演进以及χ2-test），以检测高阶SNP交互。对模拟数据集进行了广泛的实验，以评估Hissi和最近提出的两位轨迹和三位遗尿病模型的相关方法。真正的基因组数据集：从惠康信托案例控制联盟（WTCCC）收集的乳腺癌数据集也用于测试Hissi。两种轨迹和三位源病疾病模型的模拟实验表明，Hissi比其他相关方法更强大。乳腺癌数据集的真实实验，其中Hissi检测与乳腺癌相关的一些显着的两基因座和三位基因座相互作用，再次证实了Hissi在高阶SNP-SNP交互识别中的有效性。

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