Biostatistics and Bioinformatics Unit, MRC Centre for Neuropsychiatric Genetics and Genomics, Department of Psychological Medicine and Neurology, Cardiff University School of Medicine, Heath Park, Cardiff, United Kingdom.
Adv Genet. 2010;72:141-79. doi: 10.1016/B978-0-12-380862-2.00007-2.
A number of statistical methods have been developed to test for associations between pathways (collections of genes related biologically) and complex genetic traits. Pathway analysis methods were originally developed for analyzing gene expression data, but recently methods have been developed to perform pathway analysis on genome-wide association study (GWAS) data. The purpose of this review is to give an overview of these methods, enabling the reader to gain an understanding of what pathway analysis involves, and to select the method most suited to their purposes. This review describes the various types of statistical methods for pathway analysis, detailing the strengths and weaknesses of each. Factors influencing the power of pathway analyses, such as gene coverage and choice of pathways to analyze, are discussed, as well as various unresolved statistical issues. Finally, a list of computer programs for performing pathway analysis on genome-wide association data is provided.
已经开发出许多统计方法来检验通路（与生物学相关的基因集合）与复杂遗传特征之间的关联。通路分析方法最初是为分析基因表达数据而开发的，但最近已经开发出了在全基因组关联研究 (GWAS) 数据上进行通路分析的方法。本文的目的是对这些方法进行概述，使读者能够了解通路分析的内容，并选择最适合其目的的方法。本文描述了各种类型的通路分析统计方法，详细说明了每种方法的优缺点。讨论了影响通路分析效力的因素，例如基因覆盖和要分析的通路选择，以及各种未解决的统计问题。最后，提供了一份可用于在全基因组关联数据上进行通路分析的计算机程序列表。
