The Division of Biostatistics offers a master’s degree in genetic epidemiology as an option for those who have completed a doctoral degree (PhD, MD, or equivalent). The Master of Science in Genetic Epidemiology (GEMS) provides a multidisciplinary educational opportunity for people who want to work at the dynamic nexus of genetics and medicine. There are growing needs for scientists with this training both in academia and industry. With the wealth of data from the Human Genome Project and the availability of powerful new computational approaches, abundant opportunities are now available to explore and characterize the interplay between genes and the environment that affect the biological processes that underlie disease.
Genetic Epidemiology is the scientific discipline that deals with the analysis of the familial distribution of traits, with a view to understanding any possible genetic basis. However, one cannot study genes except as they are expressed in people living in certain environments, and one cannot study environmental factors except as they affect people who have certain genotypes. Genetic Epidemiology is a uniquely interdisciplinary field that seeks to understand both the genetic and environmental factors and how they interact to produce various diseases and traits in humans. These studies are carried out in relatively large samples of subjects from relevant populations, thus, the population history and dynamics often come into play. Population dynamics alter the frequency and distribution of both genetic and environmental factors, and thus, their net effect on the phenotype of interest. Some population characteristics also can be exploited for the purposes of gene discovery and mapping because the history has affected the genomic structure in a way that specific genotypes associated with disease can be identified.
Human diseases have been the focal point of genetic epidemiologic studies and recent efforts are directed toward complex disorders such as coronary heart disease, hypertension, diabetes, obesity, cancer, atopy and allergies, and neurological and psychiatric disorders, to name a few. It is commonly thought that an understanding of the genetic underpinnings of such diseases will revolutionize medicine in the 21st century enabling better preventive measures, diagnosis, prognosis, and novel treatments. Given progress in the Human Genome Project, in computing power, and in the creation of powerful statistical methods of analysis, we are poised to shepherd this revolution. It is an exciting time in science, and opportunities for careers in genetic epidemiology abound.
Since genetic epidemiology is a multidisciplinary field, we expect applicants to come from a variety of backgrounds, but primarily those who have earned a terminal degree such as physician scientists and other clinical investigators, particularly postdoctoral fellows and people with terminal degrees in other (related) disciplines who seek to gain expertise in genetic epidemiology. All prospective students must provide evidence of basic skills in genetics, mathematics, and computer programming through course work, documented experience or by passing a proficiency exam. We expect all applicants to have taken the following courses or their equivalents. If you have any questions about whether a course will meet our prerequisites, please email OHIDS-Education@wustl.edu.
Introduction to Biology
Introduction to Genetics
Elementary Probability & Statistics
The GEMS degree consists of a total of 30 credits and can be pursued either full-time or part-time but must be completed within three years. There are 8 core courses, listed below, as well as two electives. The GEMS degree begins in the summer semester, last week of June or first week of July, depending on the academic calendar.
Core courses (course descriptions»)
M21-503 Statistical Computing with SAS
M21-506 R for Data Science
M21-515 Fundamentals of Genetic Epidemiology
M21-550 Introduction to Bioinformatics
M21-5483 Human Genetic Analysis
M21-560 Biostatistics I
M21-512 Ethics in Biostatistics and Data Science
M21-610 Mentored Research