My primary research interest is how genetic variation impacts common, complex human phenotypes. The Human Genome Project, the International HapMap Project, and other public/private projects have generated an enormous amount of data for common genetic variation across human populations. Currently, more than 10 million single nucleotide polymorphisms (SNPs), the most common form of DNA variation, are available in public databases, and advances in technology now make it possible to interrogate hundreds of thousands of sites in a single assay. Despite the increasing ease of generating genetic data, we are still faced with the challenge of understanding how these genetic variants affect susceptibility to common disease in the context of environmental exposures.

To meet this challenge, my laboratory is applying genetic variation data to clinical trials and large-scale epidemiological studies linked to quantitative traits or clinical data. As an example, the Centers for Disease Control and Prevention has collected more than 7,000 DNAs with phenotypic information linked to the samples in a population-based survey known as the Third National Health and Nutrition Examination Survey (NHANES III). We are currently coordinating the study of candidate genes to determine if genetic variation within these genes is associated with various quantitative traits of coronary heart disease. As another example, we are performing a whole genome association (WGA) study in a clinical trial to identify genetic determinants of adverse reactions to the seasonal influenza vaccination in young children. These and other projects in my laboratory will involve gathering genotyping and sequencing data for various cohorts, as well as developing the necessary laboratory information systems and databases needed to store, retrieve, and ultimately analyze the combined phenotypic and genetic data. For analysis, traditional (such as regressions and haplotype inference) and experimental genetic epidemiological methods will be used to identify SNPs associated with phenotypes, and bioinformatic/genomic tools will be used to make informed decisions to target specific genes/genomic regions and to interpret these associations. Finally, many methodological issues such as controlling for population stratification and identifying interactions can be explored by mining existing data to aid in the design and analysis of future studies.


Potential Research Projects

1. WGA and candidate gene association studies and quantitative trait analysis
2. Applying methods to identify gene-environment and gene-gene interactions
3. Exploring methodological issues related to population-based association studies