Genomics Training Grant
This grants seeks to train the next generation of quantitative genomic scientists to have a strong understanding of cutting edge methodological and collaborative research in statistical genetics/genomics and bioinformatics/computational biology with applications in genetic epidemiology, molecular biology and genomic medicine.
Biostatistics Courses
For Ph.D. Students with an Emphasis in Quantitative Genomics in Biostatistics
A Biostatistics Ph.D. student is required to satisfy all degree requirements as specified in the Department’s Graduate Student Handbook. As part of their program, students with an emphasis in quantitative genomics are required to follow the program described below and satisfy the 8 credits (GSAS credit hours) for a cognate field in an area related to quantitative genomics. Students are expected to take at least one course in the category of Data Structures and Programming and one course in the category of Molecular Biology, Physiology, and Genetics. Through a careful selection of courses, most of the courses below will be useful towards satisfying Ph.D. degree requirements.
Required Core Courses
- BIOSTAT 230 Probability I (for biostatistics students)
- BIOSTAT 231 Statistical Inference I (or BIO 222, Basics of
Statistical Inference for epidemiology students) - BIOSTAT 232 Methods I (or BIO 210, Applied Regression
Analysis for epidemiology students) - BIOSTAT 233 Methods II
- BIOSTAT 236 Computing I
- HPM 548 Responsible Conduct of Research
Recommended Elective Courses
Biostatistics
- BIOSTAT 234 Introduction to Data Structures and Algorithms
- BIOSTAT 235 Advanced Regression & Statistical Learning
- BIOSTAT 240 Probability II
- BIOSTAT 241 Statistical Inference II
- BIOSTAT 244 Analysis of Failure Time Data
- BIOSTAT 245 Analysis of Multivariate & Longitudinal Data
- BIOSTAT 249 Bayesian Methods in Biostatistics
Computational Biology and Bioinformatics
- BST 227 Introduction to Statistical Genetics
- BST 280 Introductory Genomics & Bioinformatics for Health
Research - BST 282 Introduction to Computational Biology and
Bioinformatics - Biophysics 170 Evolutionary and Quantitative Genomics
- Biophysics 205 Computational and Functional Genomics
Data Science and Big Data Computing
- BST 260 Introduction to Data Science
- BST 261 Data Science II
- BST 262 Computing for Big Data
- BST 267 Intro to Social and Biological Networks
Epidemiology and Genetic Epidemiology
- EPI 207 Advanced Epidemiologic Methods
- EPI 289 Models for Causal Inference
- EPI 293 Analysis of Genetic Association Studies
- EPI 511 Advanced Population and Medical Genetics
- ID 542 Methods for Mediation and Interaction
Molecular Biology, Physiology, and Genetics
- BIOSTAT 281 Genomic Data Manipulation
- EPI 249 Molecular Biology for Epidemiologists
- EPI 507 Principles of Genetic Epidemiology
- IID 209 Microbial Communities and the Human Microbiome
- BPH 208 / EH 205 Human Physiology
- BPH 210 /EH 208 Pathophysiology of Human Disease
- GENETIC 201 Principles of Genetics
- BCMP 200 Principles of Molecular Biology