Susceptibility to common human diseases such as cancer and cardiovascular disease is determined by numerous genetic factors that interact in a nonlinear manner in the context of an individual's age and environmental exposure. This complex genetic architecture has important implications for the use of genome- wide association studies (GWAS) for identifying susceptibility genes. While novel methods are available for modeling gene-gene and gene-environment interactions, exhaustive testing of all combinations of SNPs is not feasible on a genome-wide scale because the number of comparisons is effectively infinite. Thus, it is critical that we develop intelligent strategies for identifying and modeling combinations of SNPs. Recent advances in grid computing systems and stochastic search algorithms able to accommodate large combinatorial problems now provide a basis for performing GWAS interaction analyses that were previously thought to be intractable. The objective of this research program is to combine the multifactor dimensionality reduction (MDR) approach to modeling nonlinear gene-gene interactions with the power of the Parabon(R) Crush" stochastic search procedure - an opportunistic evolutionary search algorithm designed for massively parallel grids (Aim 1). The combination of these two algorithms will make it feasible to identify nonlinear gene-gene interactions on a genome-wide scale. This will be made computationally feasible by harnessing the power of the Parabon Computation Grid - a brokered computation service powered by thousands of computers across the Internet. We will evaluate these new algorithms using simulated genome-wide association data (Aim 2) and then develop a web interface and corresponding service for launching Crush-MDR analyses from a browser (Aim 3). Finally, we will demonstrate the Crush-MDR analysis service using publicly available genome-wide association data (Aim 4). Accomplishment of these specific aims will position Parabon for a Phase II STTR application that will expand this service by modifying the Crush-MDR algorithm to use both statistical and biological sources of expert knowledge to enhance the search for complex patterns of gene-gene interactions. The business objectives are to bring to market three offerings: Crush-MDR atop the Parabon Computation Grid as a pay-per-use service for gene-gene interaction analysis of genome-wide association data;A knowledge base of gene-gene interactions discovered by Parabon that will be licensed to the Pharmaceutical industry;and Proprietary genotyping assays for predicting human disease endpoints that are based on knowledge discovered using the Crush-MDR algorithm.
The specific aims proposed here will transform the genetic analysis of human diseases by making available to the research community powerful analysis algorithms and high-performance computing infrastructure in a web-based "Software as a Service" (SaaS) delivery model that is accessible, affordable and easy to use. More importantly, the genetic knowledge and assays that ultimately result will help revolutionize the use of genomic information to improve the human medical condition.
Susceptibility to common human diseases such as cancer and cardiovascular disease is determined by numerous genetic factors that interact in a nonlinear manner in the context of an individual's age and environmental exposure. The goal of this research program is to develop the computer software and computing technology to enable to researchers to identify combinations of genetic and environmental factors that are associated with human health and disease.