Pathway-based approaches for analysis of genomewide association studies
Pathway-based approaches for analysis of genomewide association studies
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DOI：
10.1086/522374
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发表时间：
2007-12-01
影响因子：
9.8
通讯作者：
Bucan, Maja
中科院分区：
文献类型：
--
作者：
Wang, Kai;Li, Mingyao;Bucan, Maja
已发表的全基因组关联(GWA)研究通常分析和报告具有最强关联证据的单核苷酸多态(SNPs)及其邻近基因(“最显著的SNPs/基因”方法)，而对其余的关注较少。借用微阵列数据分析的思想，我们证明了基于途径的方法，即联合考虑同一途径中的多个致病因素，可能是对最重要的SNPs/基因方法的补充，并为解释GWA关于复杂疾病的数据提供了额外的见解。
Published genomewide association (GWA) studies typically analyze and report single-nucleotide polymorphisms (SNPs) and their neighboring genes with the strongest evidence of association (the "most-significant SNPs/genes" approach), while paying little attention to the rest. Borrowing ideas from microarray data analysis, we demonstrate that pathway-based approaches, which jointly consider multiple contributing factors in the same pathway, might complement the most-significant SNPs/genes approach and provide additional insights into interpretation of GWA data on complex diseases.