Dana C. Crawford, PhD

Associate Professor, Molecular Physiology and Biophysics

515 B Light Hall
(615) 343-7852

My primary research interest is how genetic variation impacts common, complex human phenotypes.

Research Description

My primary research interest is how genetic variation impacts common, complex human phenotypes. The Human Genome Project, the International HapMap Project, the 1000 Genomes Project, and other public/private projects have generated an enormous amount of data for common and rare genetic variation across human populations. Currently, greater 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 a million 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, large-scale epidemiological studies, and biobanks linked to quantitative traits and extensive clinical data. As an example, the Centers for Disease Control and Prevention has collected >15,000 DNAs with phenotypic information linked to the samples in a population-based survey known as the National Health and Nutrition Examination Surveys (NHANES). We are using NHANES for candidate gene studies as well as for a recently funded U01 to characterize the genetic architecture of genetics variants identified in genome-wide association studies (GWAS) with an emphasis on African Americans and Mexican Americans. In addition to NHANES, my laboratory also accesses BioVU, the Vanderbilt DNA biobank containing >125,000 DNA samples linked to de-identified electronic medical records. These and other projects in my laboratory cut across various phenotypes and traits and will involve gathering genotyping and sequencing data for various cohorts, as well as developing tools to store, retrieve, and ultimately analyze the combined phenotypic and genetic data. For analysis, traditional (e.g., regressions; 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 adjusting for population stratification and identifying interactions can be explored by mining existing data to aid in the design and analysis of future studies.