Comparisons of seven algorithms for pathway analysis using the WTCCC Crohn's Disease dataset.
Comparisons of seven algorithms for pathway analysis using the WTCCC Crohn's Disease dataset.
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DOI：
10.1186/1756-0500-4-386
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发表时间：
2011-10-07
影响因子：
1.8
通讯作者：
Cherny SS
中科院分区：
文献类型：
--
作者：
Gui H;Li M;Sham PC;Cherny SS
全基因组关联研究（genome-wide association studies，GWAS）的途径分析虽然起源于基因组表达研究，但由于其具有将统计学方法与生物学知识相结合来发现隐藏的疾病致病机制的潜力，因此越来越受到人们的欢迎。通常，最近提出的算法或程序可以通过不同类型的输入数据、零假设或分析阶段的计数来分类。由于SNP、基因和通路关系的复杂性，重采样策略（如排列）总是被用来推导检验统计量的经验分布，以评估候选通路的显著性。然而，这些算法的评估真实的GWAS数据集和真实的生物学途径数据库需要解决之前，我们广泛地应用它们的信心。两个算法，使用从GWAS的汇总统计作为输入，在KGG，一个新颖的和用户友好的软件工具GWAS途径分析。这两种算法以及其他五个选定的算法进行了比较，通过分析的WTCCC克罗恩病数据集利用MsigDB典型的途径。这些方法由于使用置换来获得经验p值，虽然有些方法是保守的，但大多数方法都能很好地控制I类错误率。然而，这些方法在功率和运行时间方面差异很大，其中PLINK基于截断集的测试是最强大的，KGG是最快的。只要计算能力可用，基于原始数据的算法，如PLINK中实现的算法，对于GWAS途径分析是优选的。在同一GWAS数据集上应用两种或更多种途径分析算法可能是值得的，因为这些方法的输出差异很大，并且可能为所研究的复杂疾病提供互补的发现。
Though rooted in genomic expression studies, pathway analysis for genome-wide association studies (GWAS) has gained increasing popularity, since it has the potential to discover hidden disease pathogenic mechanisms by combining statistical methods with biological knowledge. Generally, algorithms or programs proposed recently can be categorized by different types of input data, null hypothesis or counts of analysis stages. Due to complexity caused by SNP, gene and pathway relationships, re-sampling strategies like permutation are always utilized to derive an empirical distribution for test statistics for evaluating the significance of candidate pathways. However, evaluation of these algorithms on real GWAS datasets and real biological pathway databases needs to be addressed before we apply them widely with confidence. Two algorithms which use summary statistics from GWAS as input were implemented in KGG, a novel and user-friendly software tool for GWAS pathway analysis. Comparisons of these two algorithms as well as the other five selected algorithms were conducted by analyzing the WTCCC Crohn's Disease dataset utilizing the MsigDB canonical pathways. As a result of using permutation to obtain empirical p-value, most of these methods could control Type I error rate well, although some are conservative. However, the methods varied greatly in terms of power and running time, with the PLINK truncated set-based test being the most powerful and KGG being the fastest. Raw data-based algorithms, such as those implemented in PLINK, are preferable for GWAS pathway analysis as long as computational capacity is available. It may be worthwhile to apply two or more pathway analysis algorithms on the same GWAS dataset, since the methods differ greatly in their outputs and might provide complementary findings for the studied complex disease.