Integrating domain knowledge with statistical and data mining methods for high-density genomic SNP disease association analysis
Integrating domain knowledge with statistical and data mining methods for high-density genomic SNP disease association analysis
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DOI：
10.1016/j.jbi.2007.06.002
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发表时间：
2007-12-01
影响因子：
4.5
通讯作者：
Miller, Perry L.
中科院分区：
文献类型：
--
作者：
Dinu, Valentin;Zhao, Hongyu;Miller, Perry L.
全基因组关联研究可以帮助确定多基因对疾病的贡献。然而，随着检测的高密度基因组标记数量的增加，与疾病偶然相关的基因座数量也在增加。鉴于目前的计算限制，对四个或更多个高密度基因组位点的相互作用进行蛮力测试是不可行的。启发式必须采用限制的统计tests.In本文中，我们探讨了使用生物领域的知识，以补充统计分析和数据挖掘方法，以确定与疾病相关的基因和途径的数量。我们描述了Pathway/SNP，一个旨在帮助评估通路与疾病之间关联的软件应用程序。Pathway/SNP将领域知识-SNP，来自多个来源的基因和途径注释-与统计和数据挖掘算法集成为可用于探索复杂疾病病因的工具。(C)2007年爱思唯尔公司All rights reserved.
Genome-wide association studies can help identify multi-gene contributions to disease. As the number of high-density genomic markers tested increases, however, so does the number of loci associated with disease by chance. Performing a brute-force test for the interaction of four or more high-density genomic loci is unfeasible given the current computational limitations. Heuristics must be employed to limit the number of statistical tests performed.In this paper we explore the use of biological domain knowledge to supplement statistical analysis and data mining methods to identify genes and pathways associated with disease. We describe Pathway/SNP, a software application designed to help evaluate the association between pathways and disease. Pathway/SNP integrates domain knowledge-SNP, gene and pathway annotation from multiple sources-with statistical and data mining algorithms into a tool that can be used to explore the etiology of complex diseases. (C) 2007 Elsevier Inc. All rights reserved.