Our primary research focus is the detection of susceptibility genes for common diseases such as cancer, diabetes, hypertension, and cardiovascular disease, among others. The approaches will involve the development and application of new statistical methods with a focus on the detection of gene-gene interactions associated with human disease.
The study of common, complex disease can be explored through a variety of study types. One such study will involve genetic analysis of gene-gene interactions through case-control association studies. These studies will involve the development of novel statistical and computational methods, in addition to the application of existing methods. We will apply these methods to real data applications in collaboration with clinicians and epidemiologists who have collected data sets where the investigation of gene-gene interactions can be conducted. While the primary focus of the research will be gene-gene interactions, any methods developed could potentially be applied for the detection of gene-environment interactions or haplotype interactions.
Pharmacogenomics is the second type of study that we will explore. The response to treatment for many diseases is highly variable among individuals. Pharmacogenomics is based on the hypothesis that genetics will explain much of this variation. There is a growing interest in studying the association between genetic polymorphisms and drug response. Statistical methods similar to those used for detecting gene-gene interactions in case-control data may be applied to pharmacogenomics data. Our research goal is to develop a collaborative research project with pharmacologists who are collecting this exciting data. We are interested in applying currently available methods where applicable, and developing new methods as necessary for the analysis of these data.
Finally, gene expression and protein expression patterns between normal and non-normal tissues is a growing area of research that may lead to the identification of candidate genes for understanding the etiology of common, complex diseases. Microarray technologies and mass spectrometry are quickly becoming tools for many researchers in human genetics, as well as many other fields of biomedical research. While these technologies provide researchers the ability to measure expression patterns for thousands of variables, the analysis and interpretation of these data, especially in terms of complex interactions, is currently the more difficult challenge due to limited statistical methodologies. Our research goal is to apply current statistical methods to gene and protein expression data to identify candidate genes associated with common human disease. In addition, we are interested in developing new methods to analyze these data where current methods are lacking.