BACKGROUND In genome-wide association study (GWAS), conventional interaction detection methods such as BOOST are mostly based on SNP-SNP interactions. Although single nucleotides are the building blocks of human genome, single nucleotide polymorphisms (SNPs) are not necessarily the smallest functional unit for complex phenotypes. Region-based strategies have been proved to be successful in studies aiming at marginal effects. METHODS We propose a novel region-region interaction detection method named RRIntCC (region-region interaction detection for case-control studies). RRIntCC uses the correlations between individual SNP-SNP interactions based on linkage disequilibrium (LD) contrast test. RESULTS Simulation experiments showed that our method can achieve a higher power than conventional SNP-based methods with similar type-I-error rates. When applied to two real datasets, RRIntCC was able to find several significant regions, while BOOST failed to identify any significant results. The source code and the sample data of RRIntCC are available at http://bioinformatics.ust.hk/RRIntCC.html. CONCLUSION In this paper, a new region-based interaction detection method with better performance than SNP-based interaction detection methods has been proposed.

中文翻译：

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全基因组病例对照研究中基于区域的相互作用检测。

背景技术在全基因组关联研究（GWAS）中，诸如BOOST之类的常规相互作用检测方法主要基于SNP-SNP相互作用。尽管单核苷酸是人类基因组的组成部分，但单核苷酸多态性（SNP）不一定是复杂表型的最小功能单元。基于区域的策略已被证明在针对边际效应的研究中是成功的。方法我们提出了一种新的区域-区域交互检测方法，称为RRIntCC（用于案例对照研究的区域-区域交互检测）。RRIntCC使用基于连锁不平衡（LD）对比测试的各个SNP-SNP相互作用之间的相关性。结果仿真实验表明，与具有类似I型错误率的传统基于SNP的方法相比，我们的方法可以实现更高的功率。当将RRIntCC应用于两个真实数据集时，它能够找到几个重要区域，而BOOST未能识别出任何重要结果。RRIntCC的源代码和示例数据可从http://bioinformatics.ust.hk/RRIntCC.html获得。结论本文提出了一种新的基于区域的交互检测方法，该方法具有比基于SNP的交互检测方法更好的性能。