GIS mapping and gene-environment interaction
Gene-environment interaction and GIS mapping were two major methods for spatial data analysis in public health sector. In the past, researchers often used the gene-environment interaction method to study the relationship between the environmental exposures and genetics factors, and how they affect each other. However, gene-environment interaction method only focused on the environmental factors at personal level. Along with the rapid development of geographic information systems (GIS), spatial data analysis has gained considerable attention, and has played a major role in public health.  The Geographic information system (GIS) is widely used in the public health sector, because it can combine the factors such as incidence of the disease, health services, geographic characteristics and environmental factors together when analyzing. The overall objective of this thesis was to present a comprehensive analysis for the spatial distribution of disease rate data and their linkage with location information and environmental risk factors through the application of GIS and spatial statistics. The research aims for this study included the investigation of: (1) whether there is gene-environment interaction (2) whether the data points are equally distributed across the area, and (3) whether there is spatial autocorrelation analysis. The simulation datasets I built included (4) study groups: Environmental exposures case group (the group who have disease gene), Environmental exposures control group (the group who do not have disease gene), People who have the gene for specific disease case group, People who do not have the gene for specific disease control group. Using 60 disease rate data in Boston area with location information, I employed gene-environment interaction, Quadrate methods, K function estimation, L function, Kriging Density, Spatial Autocorrelation Analysis (Moran's I) to analyze disease data. Throughout this article, I demonstrated that the disease risk near Boston area tended to cluster by both gene-environment interaction and GIS analysis. And both environment and gene risk will affect the disease risk. The similar environment and life style may be the reason that caused spatial autocorrelation.
Biostatistics|Geographic information science
Zhu, Jialing, "GIS mapping and gene-environment interaction" (2014). Texas Medical Center Dissertations (via ProQuest). AAI1569959.