ABSTRACT
Human genetics has a rich history of using biochemistry, biostatistics, epidemiology, molecular biology, physiology and other disciplines to determine the mapping relationship between DNA sequence information and measures of human health. This is an exciting time in human genetics because we now have access to technology that allows us to efficiently measure most of the relevant DNA sequence variations from across the human genome. We will within the next 10 years likely have access to cutting-edge technology that will deliver the entire genomic sequence for all subjects in our genetic and epidemiologic studies. Now that we have access to the basic hereditary information it is time to shift our focus toward the analysis of this data. The focus of this chapter is on the important role of computer science, and, more specifically, machine learning for mining patterns of genetic variations that are associated with susceptibility to common human diseases. Specifically, we will focus on computational methods for identifying gene-gene interactions or epistasis.
