Integrating domain knowledge with statistical and data mining methods for high-density genomic SNP disease association analysis
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 knowledgeSNP, gene and pathway annotation from multiple sourceswith statistical and data mining algorithms into a tool that can be used to explore the etiology of complex diseases.
data mining false discovery rate (fdr) data integration genome-wide association (gwa) pathway-based disease association single nucleotide polymorphisms (snp)