Biofilter: a knowledge-integration system for the multi-locus analysis of genome-wide association studies

Abstract

Genome-wide association studies provide an unprecedented opportunity to identify combinations of genetic variants that contribute to disease susceptibility. The combinatorial problem of jointly analyzing the millions of genetic variations accessible by high-throughput genotyping technologies is a difficult challenge. One approach to reducing the search space of this variable selection problem is to assess specific combinations of genetic variations based on prior statistical and biological knowledge. In this work, we provide a systematic approach to integrate multiple public databases of gene groupings and sets of disease-related genes to produce multi-SNP models that have an established biological foundation. This approach yields a collection of models which can be tested statistically in genome-wide data, along with an ordinal quantity describing the number of data sources that support any given model. Using this knowledge-driven approach reduces the computational and statistical burden of large-scale interaction analysis while simultaneously providing a biological foundation for the relevance of any significant statistical result that is found.

Figure 1

Overview of the Biofilter process. GWAS platform SNPs are mapped to Ensembl gene IDs and related to disease-independent sources (left) and to disease-dependent sources (right). Multi-marker models are generated from SNPs within knowledge-related genes. Derived models are overlaid to assess overall model implication.

Figure 2

Two-gene model types. Each box represents a gene, and each line a connection between genes. Boxes that are dashed have been previously linked to disease by at least one data source.