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Genome-wide association studies have become a standard way of discovering novel causative alleles by looking for statisticaly significant associations in patient genotyping data. The present challenge for these methods is to discover associations involving multiple interacting loci, a common phenomenon in diseases often related to epistasis. The main problem is the exponential increase in necessary computational power for every additional interacting locus considered in association tests. Several approaches have been proposed to manage this problem, including limiting analysis to interacting pairs and filtering SNPs according to external biological knowledge. Here we explore the possibilities of using public protein interaction data and pathway maps to filter out only pairs of SNPs that are likely to interact, perhaps because of epistatic mechanisms working at the protein level. After filtering all possible pairs of SNPs by their presence in common protein-protein interactions or proteins sharing a metabolic or signalling pathway, we calculate the possible reduction in computational requirements under different scenarios. We discuss these exploratory results in the context of the so-called "lost heredity" and the usefulness of this approach for similar scenarios.

The Possibilities of Filtering Pairs of SNPs in GWAS Studies - Exploratory Study on Public Protein-interaction and Pathway Data