Sensible Initialization of a Computational Evolution System Using Expert Knowledge for Epistasis Analysis in Human Genetics

Abstract

High throughput sequencing technologies now routinely measure over one million DNA sequence variations on the human genome. Analyses of these data have demonstrated that single sequence variants predictive of common human disease are rare. Instead, disease risk is thought to be the result of a confluence of many genes acting in concert, often with no statistically significant individual effects. The detection and characterization of such gene-gene interactions that predispose for human disease is a computationally daunting task, since the search space grows exponentially with the number of measured genetic variations. Traditional artificial evolution methods have offered some promise in this problem domain, but they are plagued by the lack of marginal effects of individual sequence variants. To address this problem, we have developed a computational evolution system that allows for the evolution of solutions and solution operators of arbitrary complexity. In this study, we incorporate a linkage learning technique into the population initialization method of the computational evolution system and investigate its influence on the ability to detect and characterize gene-gene interactions in synthetic data sets. These data sets are generated to exhibit characteristics of real genomewide association studies for purely epistatic diseases with various heritabilities. Our results demonstrate that incorporating linkage learning in population initialization via expert knowledge sources improves classification accuracy, enhancing our ability to automate the discovery and characterization of the genetic causes of common human diseases.