Please click here for a Word document containing all suggested Genetics and Genomics courses for the 2013-2014 academic year.
Students primarily interested in Genetic Epidemiology and Statistical Genetics should consider courses from our PGSG sequence.
EPI249 (Molecular Biology for Epidemiologists), taught by Dr. Immaculata De Vivo, 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.
EPI507 (Genetic Epidemiology), taught by Drs. Peter Kraft and Liming Liang, 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 222 (Genetic Epidemiology of Diabetes), taught by Drs. Lu Qi, Frank Hu and Alessandro Doria. The genetics of diabetes and its complications, together with the descriptive epidemiology of these conditions, will be used to illustrate the process of generating etiologic hypotheses that can be studied by the methods of genetic epidemiology. Techniques of molecular genetics relevant to epidemiologic studies will be reviewed and demonstrated. Data sets that include genotype information will be analyzed with an emphasis placed on the examination of various gene/environment interaction.
BIO227 (Fundamental Concepts in Gene Mapping), taught by Dr. Nan Laird, 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 BIO201) 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.