BOOST: A Fast Approach to Detecting Gene-Gene Interactions in Genome-wide Case-Control Studies
BOOST: A Fast Approach to Detecting Gene-Gene Interactions in Genome-wide Case-Control Studies
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DOI：
10.1016/j.ajhg.2010.07.021
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发表时间：
2010-09-10
影响因子：
9.8
通讯作者：
Yu, Weichuan
中科院分区：
文献类型：
--
作者：
Wan, Xiang;Yang, Can;Yu, Weichuan
基因-基因相互作用长期以来被认为是理解复杂疾病性状的遗传原因的重要基础。目前，从全基因组病例对照研究中识别基因-基因相互作用在计算和方法上都具有挑战性。在本文中，我们介绍了一个简单而强大的方法，命名为“BOOST操作为基础的筛选和测试”（BOOST）。为了发现复杂疾病背后的未知基因-基因相互作用，BOOST允许以非常快的方式检查全基因组病例对照研究中的所有成对相互作用。我们已经进行了互动分析的七个数据集从威康信托病例控制联盟（WTCCC）。每次分析花费不到60小时，在运行Windows XP系统的具有4G内存的标准3.0 GHz台式机上完全评估所有约360，000个SNP对。从1型糖尿病数据集识别的相互作用模式与从类风湿性关节炎数据集识别的相互作用模式显示出显著差异，尽管两个数据集在WTCCC报告中共享非常相似的命中区域。BOOST还在1型糖尿病数据集中确定了主要组织相容性复合体区域基因之间的一些疾病相关相互作用。我们相信，我们的方法可以作为一个计算和统计学上有用的工具，在未来的大规模相互作用映射在全基因组病例对照研究的时代。
Gene-gene interactions have long been recognized to be fundamentally important for understanding genetic causes of complex disease traits. At present, identifying gene-gene interactions from genome-wide case-control studies is computationally and methodologically challenging. In this paper, we introduce a simple but powerful method, named "BOolean Operation-based Screening and Testing" (BOOST). For the discovery of unknown gene-gene interactions that underlie complex diseases, BOOST allows examination of all pairwise interactions in genome-wide case-control studies in a remarkably fast manner. We have carried out interaction analyses on seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). Each analysis took less than 60 hr to completely evaluate all pairs of roughly 360,000 SNPs on a standard 3.0 GHz desktop with 4G memory running the Windows XP system. The interaction patterns identified from the type 1 diabetes data set display significant difference from those identified from the rheumatoid arthritis data set, although both data sets share a very similar hit region in the WTCCC report. BOOST has also identified some disease-associated interactions between genes in the major histocompatibility complex region in the type 1 diabetes data set. We believe that our method can serve as a computationally and statistically useful tool in the coming era of large-scale interaction mapping in genome-wide case-control studies.