Please click here for information on coursework for the Program in Quantitative Genomics training grant.  

Students primarily interested in Genetic Epidemiology and Statistical Genetics should consider courses from our PGSG sequence.

Genetic Epidemiology and Statistical Genetics Suggested Schedule
Courses Titles Credits
Faculty Term
EPI 249 Molecular Biology for Epidemiologists 2.5 / 2.0 Immaculata De Vivo Fall 1
EPI 507 Genetic Epidemiology 2.5 / 2.0 Peter Kraft Fall 2
EPI 293 Analysis of Genetic of Association Studies 2.5 / 2.0 Liming Liang Spring 1
EPI 511 Advanced Population & Med Genetics 5.0 / 4.0 Alkes Price Full Spring (even years)
EPI 535 Epidemiologic challenges to the interpretation of genetic analyses 2.5 / 2.0 Elise Robinson Spring 2
BST 227 Introduction to Statistical Genetics 2.5 / 2.0 Martin Aryee Fall 2
BST 247 Advanced Statistical Genetics 2.5 / 2.0 Liming Liang Spring 2

EPI 249 (Molecular Biology for Epidemiologists), taught by offers an overview of fundamental molecular biology concepts and techniques commonly used in the laboratory and in epidemiological research. During the term, we will cover a broad range of topics including — but not limited to — the mechanisms and regulatory processes involved in different steps of the central dogma of molecular biology, how cellular mechanisms go awry and how these cells can be repaired, Mendelian and non-Mendelian genetics, meiosis, mitosis, and both novel and classical molecular techniques. This course is geared towards individuals with diverse backgrounds, and prior molecular biology experience is not required. In fact, this course will be of most interest to those who have not taken a recent college-level course in molecular biology, or equivalent.

EPI 507 (Genetic Epidemiology), taught by Drs. Peter Kraft and Simin Liu, introduces the basic principles and methods of genetic epidemiology. After a brief review of the history of genetic epidemiology, methods for the study of both high penetrance and low penetrance alleles will be described and discussed. Methods of analysis of genome-wide association studies are a particular focus. Examples of the contribution of genetic analysis to major diseases will be reviewed.

EPI 293 (Analysis of Genetic Association Studies), taught by Dr. Liming Liang.  At the end of this course students will grasp Concept and Theory, Methods and Software Tools needed to critically evaluate and conduct genetic association studies in unrelated individuals and family samples, including: basic molecular and population genetics, marker selection algorithms, haplotyping, multiple comparisons issues, population stratification, genome-wide association studies, genotype imputation, gene-gene and gene-environment interaction, analysis of microarray data (including gene expression, methylation data analysis, eQTL mapping), next-generation sequencing data analysis and genetics simulation studies.  Useful software tools will be introduced and practiced in lab and project.
Course note: Familiarity with SAS or S-PLUS/R and UNIX computing environment also highly recommended.

EPI 511 (Advanced Population and Medical Genetics), taught by Dr. Alkes Price, covers quantitative topics in human population genetics and applications to medical genetics, including the HapMap project, linkage disequilibrium, population structure and stratification, population admixture, admixture mapping, and natural selection. The course is aimed at Epidemiology and Biostatistics students with a strong interest in statistical genetics, and will be accessible to students with a sufficient statistical background. The course will emphasize hands-on analysis of large empirical data sets, thus requiring prior experience with a general-purpose high-level programming language such as Python or PERL. After taking this course, each student will have the experience and skills to develop and apply statistical methods to population genetic data.

EPI 535 (Epidemiologic challenges to the interpretation of genetic analyses), taught by Elise Robinson, is designed for students in Epidemiology and Biostatistics concentrating on genetic analysis, particularly those who will use quantitative genetic approaches in their theses and future careers. We will examine challenges to interpreting genetic association data: phenotypic heterogeneity, sample selection, heritable covariates, gene-environment correlation, survival bias, and more. It will offer students opportunity to think critically about their own interests and develop testable research questions.

BST 227 (Introduction to Statistical Genetics), taught by Dr. Martin Aryee, teaches students the diverse statistical methods used in genetic epidemiology, from familial aggregation and segregation studies to linkage scans candidate-gene association studies. While some familiarity with molecular biology and statistical hypothesis testing (e.g. material covered in EPI249 and BST201) is helpful, it is not required since relevant concepts will be reviewed in lectures and labs. Students should leave with a basic understanding of how to read and evaluate statistical studies of genetics epidemiology.

BST 247 (Advanced Statistical Genetics), taught by Dr. Liming Liang and guest faculty speakers, is a seminar style course with readings selected from the literature in the areas of expertise of the participating faculty.  Content may vary from year to year. At the end of the course the student will be able to critically read foundational papers and current journal articles in statistical genetics, present sophisticated ideas to an audience of peers and engage in doctoral level research in the area. Students are expected to have an in-depth and broad understanding on important topics of statistical genetics research after this course.