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
- BST 227 Introduction to Statistical Genetics
- BIOSTAT 230 Probability Theory (for biostatistics students)
- BIOSTAT 231 Statistical Inference (or BIO 222, Basics of
Statistical Inference for epidemiology students) - BIOSTAT 232 Methods I (or BIO 210, Applied Regression
Analysis for epidemiology students) - EPI 507 Genetic Epidemiology
- BIOSTAT 234 Introduction to Data Structures and Algorithms (for
biostatistics students) - BST 270 Reproducible Data Science
- HPM 548 Responsible Conduct of Research
Recommended Elective Courses
Biostatistics
- BIOSTAT 235 Advanced Regression and Statistical Learning
- BIOSTAT 240 Probability Theory and Applications II
- BIOSTAT 241 Statistical Inference II
- BIOSTAT 244 Analysis of Failure Time Data
- BIOSTAT 245 Analysis of Multivariate and Longitudinal Data
- BIOSTAT 249 Bayesian Methods in Biostatistics or STAT 220
Bayesian Data Analysis
Computational Biology and Bioinformatics
- BST 280 Introductory Genomics & Bioinformatics for Health
Research - BIOSTAT 282 Introduction to Computational Biology and
Bioinformatics - Biophysics 170 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 244 Genetic Epidemiology of Psychiatric Disorders
- EPI 289 Models for Causal Inference
- EPI 293 Analysis of Genetic Association Studies Using
Unrelated Subjects - EPI 511 Advanced Population and Medical Genetics
- ID 542 Methods for Mediation and Interaction
- EH 298 Environmental Epigenetics
Molecular Biology, Physiology, and Genetics
- BIOSTAT 281 Genomic Data Manipulation
- BST 283 Cancer Genome Analysis
- EPI 249 Molecular Biology for Epidemiologists
- EPI 507 Genetic Epidemiology
- IID 209 Microbial Communities and the Human Microbiome
- BPH 208 / EH 205 Human Physiology
- BPH 210 /EH 208 Pathophysiology of Human Disease
- GEN 201 Principles of Genetics
- BCMP 200 Principles of Molecular Biology
The following course might be useful for students to prepare for taking the above genetic and genomic courses:
- BST 280 Introductory Genomics and Bioinformatics for Health Research