DCEG investigates the biological basis of inherited and acquired genetic variants associated with cancer susceptibility, utilizing genome-wide association studies, candidate gene studies, exome sequencing, and genetic mosaicism studies. DCEG scientists and their collaborators employ and array of advanced statistical methods to support these studies.
Candidate Gene Studies
A candidate gene is a gene suspected to be involved with a particular disease, condition, or abnormality, based on previous findings. Candidate gene studies test an identified gene and mutation in a group of subjects both with the disease (cases) and without the disease (controls).
Whole exome sequencing enables researchers to sequence all of the exons (protein coding parts of a gene) in the genome, with the goal of identifying the genetic cause of a specific disease.
Genetic Mosaicism Studies
Genetic mosaicism results from an acquired DNA mutation that is present in only some of the body's cells. DCEG investigators are exploring the mechanisms that initiate and select for mosaic alterations and seek to elucidate how genetic mosaicism may serve as an intermediate between normal and disease states. Read more about genetic mosaicism studies.
Genome-wide Association Studies
A genome-wide association study (GWAS) is an approach that involves scanning the genomes from many different people and looking for genetic markers that can be used to predict the presence of a disease. The goal is to understand how genes contribute to the disease and to use that understanding to help develop better prevention and treatment strategies. DCEG has developed a robust research program with GWAS for a number of cancers, and more recently, exposures and survival. Read more about genome-wide association studies.
DCEG investigators collaborate with scientists at the Cancer Genomics Research (CGR) Laboratory (formerly the Core Genotyping Facility) to formulate design and analysis strategies in support of genetic association. Such strategies include determining the number of SNPs to be followed in various stages of multistage GWAS; choosing association test statistics; analyzing and adjusting for population stratification using principal component methods; conducting haplotype-based association scans; and exploring genetic pathways and interactions.
In addition, investigators have developed a number of tools and resources that are made available to the general scientific public for download and use. For example, Biostatistics Branch investigators have developed a number of genetic analysis software tools.