Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets
Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets
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DOI：
10.1101/gr.124370.111
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发表时间：
2012-02-01
期刊：
影响因子：
7
通讯作者：
Furey, Terrence S.
中科院分区：
文献类型：
--
作者：
Xiong, Qing;Ancona, Nicola;Furey, Terrence S.
单变异或单基因分析通常只占复杂性状表型变异的一小部分。另外，基因组或途径关联分析在揭示复杂性状的遗传结构中发挥着越来越重要的作用，通过识别系统的遗传相互作用。基因集分析的两个主要范式是基于SNP基因型的关联分析和基于基因表达谱的关联分析。然而，基因疾病关联可以以多种方式表现，例如基因表达、基因型和拷贝数的改变;因此，结合多种形式证据的综合方法可以更准确和全面地捕获途径关联。我们已经开发了一个单一的统计框架，基因集关联分析（GSAA），同时测量基因组范围内的遗传变异和基因表达变异的模式，以确定富集差异表达和/或性状相关的遗传标记的基因集。模拟研究表明，基因组数据的联合分析增加了检测真实的关联的能力，与只使用一种基因组数据类型的基因集方法相比。对两种人类疾病成胶质细胞瘤和克罗恩病的分析检测到了先前鉴定的疾病相关通路的异常，例如与PI 3 K信号传导、DNA损伤反应和NF κ B活化相关的通路。此外，GSAA预测了新的途径协会，例如，从ABC转运蛋白家族的胶质母细胞瘤和克罗恩病的HLA系统的基因的差异遗传和表达特征。这表明GSAA可以帮助揭示人类疾病和复杂特征的生物学途径。
Single variant or single gene analyses generally account for only a small proportion of the phenotypic variation in complex traits. Alternatively, gene set or pathway association analyses are playing an increasingly important role in uncovering genetic architectures of complex traits through the identification of systematic genetic interactions. Two dominant paradigms for gene set analyses are association analyses based on SNP genotypes and those based on gene expression profiles. However, gene disease association can manifest in many ways, such as alterations of gene expression, genotype, and copy number; thus, an integrative approach combining multiple forms of evidence can more accurately and comprehensively capture pathway associations. We have developed a single statistical framework, Gene Set Association Analysis (GSAA), that simultaneously measures genome-wide patterns of genetic variation and gene expression variation to identify sets of genes enriched for differential expression and/or trait-associated genetic markers. Simulation studies illustrate that joint analyses of genomic data increase the power to detect real associations when compared with gene set methods that use only one genomic data type. The analysis of two human diseases, glioblastoina and Crohn's disease, detected abnormalities in previously identified disease-associated pathways, such as pathways related to PI3K signaling, DNA damage response, and the activation of NFKB. In addition, GSAA predicted novel pathway associations, for example, differential genetic and expression characteristics in genes from the ABC transporter family in glioblastoma and from the HLA system in Crohn's disease. These demonstrate that GSAA can help uncover biological pathways underlying human diseases and complex traits.