[Pipelining for sequence variant analysis]

             [Disease risk prediction service]

This study found the relationship of disease, genetic factors and environment as etiological factors using literature data and make interaction network up. The network gives information of etiology and it can help to interpret personal genome data.

• The development of priority weighting approach for disease-environment interaction based on personal genome

Both genetic and environmental factors contributed to chronic diseases. Individuals who are genetically susceptible to the environmental exposure should increase the magnitude of relative risk in chronic diseases. So, this research develops priority weighting approach for disease-environment interaction based on personal genome. Taking account of diseases-environment interactions considered personal genome can have important implications for public health and personalized medicine and provide behavior modification for high-priority environment exposure to prevent relevant to diseases.

Many human diseases and traits are influenced by genetic factors, but the known genetic variations explain only a small proportion of trait heritability. Here we demonstrate that genomic data can be interpreted through knowledge data. First we construct a Phenotypic Semantic map using Phenopedia and Unified Medical Language System(UMLS). Then, we find signature differences in the phenotypes in certain group of cases by using knowledge of their genetic variation on semantic map. Lastly, we confirm that similar traits are clustered in phenotypes which have shorter distances among controlled vocabularies.