Integrating domain knowledge with statistical and data mining methods for high-density genomic SNP disease...

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Genome-wide association studies (GWAS) identify multi-gene disease contributions.
• Increasing genomic markers raise the number of spurious locus associations.
• Computational limits hinder testing interactions of four or more genomic loci.

Purpose of the Study:

• To explore using biological domain knowledge to enhance statistical analysis for disease gene discovery.
• To introduce Pathway/SNP, a software tool for evaluating pathway-disease associations.
• To aid in exploring the etiology of complex diseases.

Main Methods:

• Supplementing statistical analysis and data mining with biological domain knowledge.
• Developing the Pathway/SNP software application.
• Integrating SNP, gene, and pathway annotations from multiple sources.

Main Results:

• Pathway/SNP integrates diverse data with statistical and data mining algorithms.
• The tool facilitates the exploration of gene and pathway associations with disease.
• Biological domain knowledge is shown to be a valuable supplement for complex disease research.

Conclusions:

• Pathway/SNP offers a novel approach to identify disease-associated genes and pathways.
• Integrating domain knowledge overcomes computational limitations in multi-locus interaction analysis.
• This approach aids in understanding the complex etiology of diseases.