A distance-based cluster algorithm for genomic analysis in genetic disease

Both environmental and genetic factors play roles in the development of some diseases. Complex diseases, such as Crohn's disease or Type II diabetes, are caused by a combination of environmental factors and mutations in multiple genes. Patients who have been diagnosed with such diseases cannot easily be treated. However, many diseases can be avoided if people at high risk change their living style, one example being their diet. But how can we tell their susceptibility to diseases before symptoms are found and help them make informed decisions about their health? The susceptibility to complex diseases can be predicted through the analysis of the genetic data. With the development of DNA microarray technique, it is possible to access the human genetic information related to specific diseases. This paper used a combinatorial method to analyze the genetic casecontrol data for Crohn's disease. A distance based cluster method has been applied to publicly available genotype data on Crohn's disease for epidemiological study and achieved a highly accurate result.