基于通路的全基因组关联研究分析方法。
Pathway-based approaches for analysis of genomewide association studies.
机构信息
Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA.
出版信息
Am J Hum Genet. 2007 Dec;81(6):1278-83. doi: 10.1086/522374.
Abstract
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.
摘要
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性 (SNP) 及其邻近基因（“最显著 SNPs/基因”方法），而很少关注其余的。我们借鉴微阵列数据分析的思路，证明基于途径的方法可以共同考虑同一途径中的多个贡献因素，可能补充最显著 SNPs/基因方法，并为复杂疾病的 GWA 数据分析的解释提供更多的见解。
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