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iLOCi: a SNP interaction prioritization technique for detecting epistasis in genome-wide association studies

机译：iLOCi：一种用于在全基因组关联研究中检测上位性的SNP相互作用优先技术

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Background Genome-wide association studies (GWAS) do not provide a full account of the heritability of genetic diseases since gene-gene interactions, also known as epistasis are not considered in single locus GWAS. To address this problem, a considerable number of methods have been developed for identifying disease-associated gene-gene interactions. However, these methods typically fail to identify interacting markers explaining more of the disease heritability over single locus GWAS, since many of the interactions significant for disease are obscured by uninformative marker interactions e.g., linkage disequilibrium (LD). Results In this study, we present a novel SNP interaction prioritization algorithm, named iLOCi (Interacting Loci). This algorithm accounts for marker dependencies separately in case and control groups. Disease-associated interactions are then prioritized according to a novel ranking score calculated from the difference in marker dependencies for every possible pair between case and control groups. The analysis of a typical GWAS dataset can be completed in less than a day on a standard workstation with parallel processing capability. The proposed framework was validated using simulated data and applied to real GWAS datasets using the Wellcome Trust Case Control Consortium (WTCCC) data. The results from simulated data showed the ability of iLOCi to identify various types of gene-gene interactions, especially for high-order interaction. From the WTCCC data, we found that among the top ranked interacting SNP pairs, several mapped to genes previously known to be associated with disease, and interestingly, other previously unreported genes with biologically related roles. Conclusion iLOCi is a powerful tool for uncovering true disease interacting markers and thus can provide a more complete understanding of the genetic basis underlying complex disease. The program is available for download at http://www4a.biotec.or.th/GI/tools/iloci.
机译：背景技术全基因组关联研究（GWAS）并未全面说明遗传疾病的遗传力，因为在单基因座GWAS中未考虑基因与基因的相互作用，也称为上位性。为了解决这个问题，已经开发出许多方法来鉴定与疾病相关的基因-基因相互作用。但是，这些方法通常无法识别出相互作用的标记物，从而解释了单基因座GWAS上更多的疾病遗传力，因为许多对疾病重要的相互作用被无信息的标记物相互作用（例如连锁不平衡（LD））所遮盖。结果在这项研究中，我们提出了一种新的SNP交互优先算法，名为iLOCi（交互基因座）。该算法分别区分案例和对照组中的标记依赖性。然后，根据从病例组和对照组之间每个可能对的标记依赖关系中的差异计算出的新的排名分数，对与疾病相关的相互作用进行优先排序。在具有并行处理能力的标准工作站上，可以在不到一天的时间内完成对典型GWAS数据集的分析。所提出的框架已使用模拟数据进行了验证，并使用惠康信任案例控制协会（WTCCC）数据应用于了实际的GWAS数据集。模拟数据的结果表明，iLOCi能够识别各种类型的基因-基因相互作用，特别是对于高阶相互作用。从WTCCC数据中，我们发现在相互作用最强的SNP对中，有几个映射到先前已知与疾病相关的基因，而有趣的是，其他先前未报道的具有生物学相关作用的基因。结论iLOCi是揭示真实疾病相互作用标记的强大工具，因此可以提供对复杂疾病潜在遗传基础的更完整理解。该程序可从http://www4a.biotec.or.th/GI/tools/iloci下载。

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