Yang Can, He Zengyou, Wan Xiang, Yang Qiang, Xue Hong, Yu Weichuan
Laboratory for Bioinformatics and Computational Biology, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.
Bioinformatics. 2009 Feb 15;25(4):504-11. doi: 10.1093/bioinformatics/btn652. Epub 2008 Dec 19.
Hundreds of thousands of single nucleotide polymorphisms (SNPs) are available for genome-wide association (GWA) studies nowadays. The epistatic interactions of SNPs are believed to be very important in determining individual susceptibility to complex diseases. However, existing methods for SNP interaction discovery either suffer from high computation complexity or perform poorly when marginal effects of disease loci are weak or absent. Hence, it is desirable to develop an effective method to search epistatic interactions in genome-wide scale.
We propose a new method SNPHarvester to detect SNP-SNP interactions in GWA studies. SNPHarvester creates multiple paths in which the visited SNP groups tend to be statistically associated with diseases, and then harvests those significant SNP groups which pass the statistical tests. It greatly reduces the number of SNPs. Consequently, existing tools can be directly used to detect epistatic interactions. By using a wide range of simulated data and a real genome-wide data, we demonstrate that SNPHarvester outperforms its recent competitor significantly and is promising for practical disease prognosis.
如今，全基因组关联（GWA）研究有数十万种单核苷酸多态性（SNP）可供使用。SNP的上位性相互作用在决定个体对复杂疾病的易感性方面被认为非常重要。然而，现有的SNP相互作用发现方法要么存在高计算复杂度，要么在疾病位点的边际效应较弱或不存在时表现不佳。因此，需要开发一种在全基因组范围内搜索上位性相互作用的有效方法。
我们提出了一种新方法SNPHarvester来检测GWA研究中的SNP - SNP相互作用。SNPHarvester创建多条路径，其中被访问的SNP组往往与疾病存在统计学关联，并收获那些通过统计检验的显著SNP组。它大大减少了SNP的数量。因此，现有工具可直接用于检测上位性相互作用。通过使用广泛的模拟数据和真实的全基因组数据，我们证明SNPHarvester明显优于其最近的竞争对手，并且在实际疾病预后方面很有前景。
