期刊
JOURNAL OF BIOMEDICAL INFORMATICS
卷 40, 期 6, 页码 750-760出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2007.06.002
关键词
false discovery rate (FDR); data integration; data mining; genome-wide association (GWA); pathway-based disease association; single nucleotide polymorphisms (SNP)
资金
- NCRR NIH HHS [UL1 RR024139] Funding Source: Medline
- NIGMS NIH HHS [GM59507] Funding Source: Medline
- NINDS NIH HHS [U24 NS051869] Funding Source: Medline
- NLM NIH HHS [P20 LM07253, T15 LM07056] Funding Source: Medline
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.
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