Gene set analysis of genome-wide association studies: methodological issues and perspectives.

Gene set analysis of genome-wide association studies: methodological issues and perspectives.
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DOI:
10.1016/j.ygeno.2011.04.006
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发表时间:
2011-07
期刊:
影响因子:
4.4
通讯作者:
Zhao, Zhongming
Zhao, Zhongming
中科院分区:
生物学3区
文献类型:
--
作者:
Wang, Lily;Jia, Peilin;Wolfinger, Russell D.;Chen, Xi;Zhao, Zhongming

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最近的研究表明,基因集分析,测试疾病与一组功能相关基因的遗传变异的关联,是一种有前途的方法,用于分析和解释全基因组关联研究(GWAS)数据。这些方法旨在通过组合来自同一基因集中的多个基因的关联信号来增加功率。此外,基因集分析还可以更多地揭示复杂疾病背后的生物学过程。然而,目前的基因集分析方法仍处于早期发展阶段,因为分析结果往往容易受到偏倚的影响,包括基因集大小和基因长度、连锁不平衡模式和重叠基因的存在。在本文中,我们提供了一个深入的审查的基因集分析程序,沿着参数的选择和特定的方法在每个阶段的挑战。除了提供最近开发的工具的调查,我们还将分析方法分为更大的类别,并讨论其优势和局限性。在最后一节中,我们概述了几个重要领域,以改善基因集分析的分析策略。
Recent studies have demonstrated that gene set analysis, which tests disease association with genetic variants in a group of functionally related genes, is a promising approach for analyzing and interpreting genome-wide association studies (GWAS) data. These approaches aim to increase power by combining association signals from multiple genes in the same gene set. In addition, gene set analysis can also shed more light on the biological processes underlying complex diseases. However, current approaches for gene set analysis are still in an early stage of development in that analysis results are often prone to sources of bias, including gene set size and gene length, linkage disequilibrium patterns and the presence of overlapping genes. In this paper, we provide an in-depth review of the gene set analysis procedures, along with parameter choices and the particular methodology challenges at each stage. In addition to providing a survey of recently developed tools, we also classify the analysis methods into larger categories and discuss their strengths and limitations. In the last section, we outline several important areas for improving the analytical strategies in gene set analysis.
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