Abstract
AbstractGenome-wide association studies (GWAS) have become a standard method for finding genetic variations that contribute to common, complex diseases. Recently, it is suggested that these diseases may be caused by epistatic interactions of multiple genetic variations. Although tens of software tools have been developed for epistasis detection, few are able to infer pathway importance from the identified epistatic interactions. AntEpiSeeker is originally an algorithm for detecting epistatic interactions in case-control studies, using a two-stage ant colony optimization (ACO) algorithm. We have developed AntEpiSeeker2.0, which extends the AntEpiSeeker algorithm to inference of epistasis-associated pathways, based on a natural use of the ACO pheromones. By looking at pheromone distribution across pathways, epistasis-associated pathways can be easily identified. The effectiveness of AntEpiSeeker2.0 in inferring epistasis-associated pathways is demonstrated through a simulation study and a real data application. AntEpiSeeker 2.0 was designed to provide efficient inference of epistasis-associated pathways based on ant colony optimization and is freely available at http://lambchop.ads.uga.edu/antepiseeker2/.
Highlights
Genome-wide association studies (GWAS) have become a standard method for finding genetic variations that contribute to common, complex diseases [1]
AntEpiSeeker1.0, a software tool for epistasis detection in GWAS based on a two-stage Ant colony optimization (ACO) algorithm, was shown to outperform its recent competitors based on a series of simulation studies and a real GWAS example [6]
Simulation study To evaluate the performance of AntEpiSeeker2.0 on detecting epistasis-associated pathways, the real data based simulation presented by [6] was adopted and extended
Summary
Genome-wide association studies (GWAS) have become a standard method for finding genetic variations that contribute to common, complex diseases [1]. To interpret the result of epistatic interactions, inference of epistasis-associated pathways could be a promising approach. The pheromone of a pathway can be approximated by the average pheromone of its associated SNPs, which provides a method for unbiasedly ranking the contribution of each investigated pathway to epistatic interactions. Based on this idea, we have developed AntEpiSeeker2.0, which provides efficient inference of epistasis-associated pathways. "AntEpiSeeker.log" and “results_maximized.txt” record intermediate results and all detected epistatic interactions respectively, and two user-specified output files show the epistatic interactions with minimized false positives and sorted pathways with pheromones respectively.
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