GWGGI: software for genome-wide gene-gene interaction analysis.
GWGGI: software for genome-wide gene-gene interaction analysis.
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GWGGI：全基因组基因 - 基因相互作用分析的软件。
DOI：
10.1186/s12863-014-0101-z
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发表时间：
2014-10-16
期刊：
影响因子：
2.9
通讯作者：
Lu Q
中科院分区：
文献类型：
--
作者：
Wei C;Lu Q
虽然基因-基因相互作用在人类疾病中的重要性已经得到很好的认识，但识别它们一直是一个巨大的挑战，特别是通过对数百万个遗传标记和数千个个体进行关联研究。在高维关联研究中，迫切需要高效和强大的计算工具来识别新的基因-基因相互作用。我们开发了用于全基因组基因-基因互作分析的C++软件(GWGGI)。GWGGI利用基于树的算法在大量遗传标记中搜索与疾病相关的联合关联，并考虑高阶交互作用，然后使用非参数统计量对联合关联进行检验。该软件包包括两个函数，似然比Mann-Whitney(LRMW)和树装配Mann-Whitney(TAMW)。我们优化了软件的数据存储和计算效率，使其能够在个人计算机上运行全基因组分析。GWGGI的使用通过使用两个具有近500个遗传标记的真实数据集进行了演示。通过实证研究，我们证明使用GWGGI可以在个人计算机上在合理的时间内(即LRMW约3.5小时，TAMW约10小时)完成全基因组基因-基因互作分析。我们还发现，LRMW适合于检测少数具有中到强边际效应的遗传变异之间的交互作用，而TAMW适用于检测大量低边际效应遗传变异之间的交互作用。本文的在线版本(doi：10.1186/s12863-0140101-z)包含补充材料，授权用户可以使用。
While the importance of gene-gene interactions in human diseases has been well recognized, identifying them has been a great challenge, especially through association studies with millions of genetic markers and thousands of individuals. Computationally efficient and powerful tools are in great need for the identification of new gene-gene interactions in high-dimensional association studies. We develop C++ software for genome-wide gene-gene interaction analyses (GWGGI). GWGGI utilizes tree-based algorithms to search a large number of genetic markers for a disease-associated joint association with the consideration of high-order interactions, and then uses non-parametric statistics to test the joint association. The package includes two functions, likelihood ratio Mann–Whitney (LRMW) and Tree Assembling Mann–Whitney (TAMW). We optimize the data storage and computational efficiency of the software, making it feasible to run the genome-wide analysis on a personal computer. The use of GWGGI was demonstrated by using two real data-sets with nearly 500 k genetic markers. Through the empirical study, we demonstrated that the genome-wide gene-gene interaction analysis using GWGGI could be accomplished within a reasonable time on a personal computer (i.e., ~3.5 hours for LRMW and ~10 hours for TAMW). We also showed that LRMW was suitable to detect interaction among a small number of genetic variants with moderate-to-strong marginal effect, while TAMW was useful to detect interaction among a larger number of low-marginal-effect genetic variants. The online version of this article (doi:10.1186/s12863-014-0101-z) contains supplementary material, which is available to authorized users.
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通讯作者：
Worthington, Jane