Genetic Programming Theory and Practice VIII

Abstract

The etiology of common human disease often involves a complex genetic architecture, where numerous points of genetic variation interact to influence disease susceptibility. Automating the detection of such epistatic genetic risk factors poses a major computational challenge, as the number of possible gene-gene interactions increases combinatorially with the number of sequence variations. Previously, we addressed this challenge with the development of a computational evolution system (CES) that incorporates greater biological realism than traditional artificial evolution methods. Our results demonstrated that CES is capable of efficiently navigating these large and rugged epistatic landscapes toward the discovery of biologically meaningful genetic models of disease predisposition. Further, we have shown that the efficacy of CES is improved dramatically when the system is provided with statistical expert knowledge. We anticipate that biological expert knowledge, such as genetic regulatory or protein-protein interaction maps, will provide complementary information, and further improve the ability of CES to model the genetic architectures of common human disease. The goal of this study is to test this hypothesis, utilizing publicly available protein-protein interaction information. We show that by incorporating this source of expert knowledge, the system is able to identify functional interactions that represent more concise models of disease susceptibility with improved accuracy. Our ability to incorporate biological knowledge into learning algorithms is an essential step toward the routine use of methods such as CES for identifying genetic risk factors for common human diseases.