Graphical algorithm for integration of genetic and biological data: proof of principle using psoriasis as a model

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Motivation: Pathway analysis to reveal biological mechanisms for results from genetic association studies have great potential to better understand complex traits with major human disease impact. However, current approaches have not been optimized to maximize statistical power to identify enriched functions/pathways, especially when the genetic data derives from studies using platforms (e.g. Immunochip and Metabochip) customized to have pre-selected markers from previously identified top-rank loci. We present here a novel approach, called Minimum distance-based Enrichment Analysis for Genetic Association (MEAGA), with the potential to address both of these important concerns.

Results: MEAGA performs enrichment analysis using graphical algorithms to identify sub-graphs among genes and measure their closeness in interaction database. It also incorporates a statistic summarizing the numbers and total distances of the sub-graphs, depicting the overlap between observed genetic signals and defined function/pathway gene-sets. MEAGA uses sampling technique to approximate empirical and multiple testing-corrected P-values. We show in simulation studies that MEAGA is more powerful compared to count-based strategies in identifying disease-associated functions/pathways, and the increase in power is influenced by the shortest distances among associated genes in the interactome. We applied MEAGA to the results of a meta-analysis of psoriasis using Immunochip datasets, and showed that associated genes are significantly enriched in immune-related functions and closer with each other in the protein–protein interaction network.

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Supplementary information: Supplementary data are available at Bioinformatics online.

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