Gloria M. Petersen, Ph.D., holds appointments in the departments of Health Sciences Research, Gastroenterology and Medical Genetics. She is a professor of epidemiology in the College of Medicine, Mayo Clinic, and holds the Purvis and Roberta Tabor Professorship.
Dr. Petersen is certified as a Ph.D. Medical Geneticist by the American Board of Medical Genetics and is a founding member of the American College of Medical Genetics.
As a result of her research reputation, she is on the leadership teams of the National Cancer Institute-based PanScan studies and the Pancreatic Cancer Case-Control Consortium (PANC4).
For more information about Dr. Petersen's research, visit the "Gastrointestinal genetic epidemiology" link on the right.
Dr. Petersen's research interests and expertise are in the application of genetic epidemiology to cancer etiology, including genetic linkage analysis of cancer families for gene discovery and genetic association studies for characterizing gene-environment interaction. Her disease research focus is pancreatic and other gastrointestinal cancers.
Dr. Petersen's funded research programs include two R01 grants. One project is the Pancreatic Cancer Genetic Epidemiology (PACGENE) Consortium, a seven-center consortium that is prospectively recruiting high-risk familial pancreatic cancer kindreds and genotyping them to localize the chromosomal regions that harbor susceptibility loci and identify the gene(s) themselves. She and her colleagues have developed a resource of more than 2,700 families for study.
The second project explores the bioethical issues involved in informing members of pancreatic cancer families of incidental genetic research findings.
Dr. Petersen also directs the Mayo Clinic Specialized Program of Research Excellence (SPORE) in Pancreatic Cancer, as well as one of the largest and most comprehensive patient registry and biobank resources for the study of pancreatic cancer.
Significance to patient care
Dr. Petersen's goal is to translate gene discoveries into clinical application, with respect to improving risk assessment through modeling and studying the impact of genetic testing.