The program has three tracks for students to pick the one that best suits their needs. For all three tracks:
- Required courses are in bold
- Total credits are 30
- Letter graded credits are at least 21
- GENE 505, IBMS 500 and GENE 601 are Pass (P)/Fail (F)
Standard Track
Year 1
Fall |
Spring |
| GENE 520 (4) | GENE 500 (6) |
| GENE 524 (2) | GENE 505 (1) |
| Elective (3) | GENE 503 (1) |
| IBMS 500 (1) |
Year 2
Fall |
Spring |
| GENE 526 (2) | GENE 601 (3) |
| GENE 601 (4) | |
| Elective (3) |
Accelerated Track
Year 1
Fall |
Spring |
Summer |
| GENE 520 (4) | GENE 500 (6) | GENE 601 (3) |
| GENE 524 (2) | GENE 505 (1) | |
| Elective (3) | GENE 503 (1) | |
| IBMS 500 (1) |
Year 2
| Fall |
| GENE 526 (2) |
| GENE 601 (4) |
| Elective (3) |
Part Time Track
Year 1
| Fall | Spring |
| GENE 520 (4) | GENE 500 (6) |
| GENE 524 (2) | GENE 503 (1) |
Year 2
| Fall | Spring |
| GENE 526 (2) | IBMS 500 (1) |
| Elective (3) | GENE 505 (1) |
| Elective (3) |
Year 3
| Fall | Spring |
| GENE 601 (4) | GENE 601 (3) |
Required Courses
GENE 520 Computational Human Genomics and Epigenomics, 4 credits
This course will teach students the cutting-edge computational technologies of data management and analytics for genomics studies. The most important feature of this course is to provide the students hands-on learning experience, so they learn how to use the computational pipelines to conduct genomics and epigenomic research in the GGS labs.
Current topics: Array- and sequencing-based technologies; standard next-generation sequencing processing pipelines; single-cell and spatial transcriptomic technologies and pipelines; epigenetics and transcriptional regulation; ATAC-seq; chromatin architecture, pipelines for HiChIP, Hi-C, 4C as well as sample bias and down-sampling; data structures and file formats; single cell- and ChIPseq-related programming; cancer genomics analyses: mutation, copy number, purity, ploidy; data mining, statistical analysis; machine learning; analysis of CRISPR knockout screens; DepMap; debugging and testing code; Docker; ChatGPT in programming; generative language learning; Amazon Web Services (AWS); coding collaboratively as a team, paired programming; GitHub and cloud computing.
GENE 524 Advanced Medical Genetics: Molecular & Cytogenetics, 2 credits
An in-depth forum for discussion of fundamental principles regarding clinical cytogenetics and molecular genetics and their relevance to medical genetics, genomics and genetic counseling. Following a historical overview, topics include a discussion of numerical and structural aberrations, sex chromosome abnormalities, issues regarding population cytogenetics, clinical relevance of such findings as marker chromosomes, mosaicism, contiguous gene deletions and uniparental disomy. The course will cover principles of molecular genetics including structure, function and regulations of genes (DNA, RNA, proteins), genetic variation, inheritance patterns and both cytogenetic and molecular laboratory techniques (fluorescence in situ hybridization, micro-array, SNP analyses, sequencing) in the clinical laboratory.
GENE 500 Fundamentals and current topics in genetics and genomics research, 6 credits
This course is aimed towards first year Ph.D. and MS students in the Department of GGS. At the end of this course students should be able to (1) read and critically evaluate studies in the primary literature and communicate the significance and impact of that study and (2) identify the important open questions and be able to design a research strategy to begin to address those questions.
GENE 503 Readings and Discussions in Genetics, 1 credit
In-depth consideration of special selected topics through critical evaluation of classic and current literature.
GENE 505 Genetics Journal Club, 1 credit
Students are required to present at Genetics Journal Club while enrolled in this course. Attending presentations is important to expose students to recent research advances and promotes the development of critical thinking skills. Preparing and delivering talks on important findings from the literature is also important for learning how to organize and present data in a format that is both engaging and informative.
IBMS 500 Being a Professional Scientist, 1 credit
The course is organized by faculty in the Department of Bioethics and provides information on each of the NIH nine-points, (research misconduct, animal research, authorship, mentoring, data management, human subjects, conflict of interest, peer review, collaborative science).
GENE 526 Quantitative genetics and genomics, 2 credits
This course provides a foundation in quantitative genetics as well as genomic approaches and technologies which have greatly expanded our understanding of not only rare genetic disorders but common ones as well. Concepts related to risk assessment and calculation and its application to medical genetics including principles and application of Hardy Weinberg equilibrium and applying Bayes' Theorem as a mechanism to refine risk assessment based on patient specific data are covered. The clinical implications of interpreting next generation sequencing results, identifying limitations of genomic technologies, and practicing annotation and interpretation of genomic testing results are also covered. In addition, resources and bioinformatics tools including national databases and clinical labs to aid in the interpretation of genomic test results including variants of uncertain significance are discussed.
GENE 601 Research in Genetics and Genome Sciences, 3 credits
Students are required to conduct research in GGS labs for a total of 7 credits. They register under the GGS PI mentor whose lab they will carry out research in.
Elective Coursework
PQHS431 Statistical Methods I, 3 credits
This course is the first half of a two-semester sequence focused on modern data analysis, advanced statistical modeling, and programming in R and R Markdown. The course emphasizes placing biological, medical and health research questions into a statistical context, and thinking effectively about practical questions of design and analysis, while minimizing theory. In the first semester, we use tools from the tidyverse and literate programming to produce replicable research on public data. Course projects focus on using modern tools to ingest, tidy, manage, explore (transform, visualize and model) and communicate about data. Foundations of the first semester include exploratory data analysis, estimation strategies for means and proportions, and linear models for prediction and exploration of quantitative outcomes. The course attracts people with varied backgrounds in statistics/data science or coding/programming or biomedical science, and a common interest in using data effectively in scientific research. Instructor permission is required for enrollment. Offered as CRSP 431, MPHP 431, and PQHS 431.
PQHS413 Introduction to Data Structures and Algorithms in Python, 3 credits
This course is an introduction to data types and algorithm design in computational analysis, specifically using Python. It has two main parts: The first part focuses on data structures and includes topics such as files, expressions, strings, lists, arrays, control flow, functions, object-oriented programming, and computation complexity and efficiency. This part aims to provide students with a solid understanding of general data structures in computer science and introduce key concepts for computational purposes. The second part covers algorithm design in Python and includes topics like searching trees, sorting, graph algorithms, random walks, Monte Carlo simulation, sampling, confidence intervals, and machine learning. This part emphasizes algorithm design, particularly in statistical programming. While the class prioritizes computation implementation over statistical theories and research projects, students will gain computational skills and practical experience in simulations and statistical modeling using Python programming.
PQHS432 Statistical Methods II, 3 credits
Methods of analysis of variance, regression and analysis of quantitative data. Emphasis on computer solution of problems drawn from the biomedical sciences. Design of experiments, power of tests, and adequacy of models. Offered as BIOL 432, PQHS 432, CRSP 432 and MPHP 432.
GENE 451, PQHS451, MPHP 451 Principles of Genetic Epidemiology, 3 credits
This course introduces the foundational concepts of genomics and genetic epidemiology through four key principles: 1) Teaching students how to query relational databases using Structure Query Language (SQL); 2) Exposing students to the most current data used in genomics and bioinformatics research, providing a quantitative understanding of biological concepts; 3) Integrating newly learned concepts with prior ones to discover new relationships among biological concepts; and 4) providing historical context to how and why data were generated and stored in the way they were, and how this gave rise to modern concepts in genomics.
PQHS452 Statistical Methods in Genetic Epidemiology, 3 credits
Analytic methods for evaluating the role of genetic factors in human disease, and their interactions with environmental factors. Statistical methods for the estimation of genetic parameters and testing of genetic hypotheses, emphasizing maximum likelihood methods. Models to be considered will include such components as genetic loci of major effect, polygenic inheritance, and environmental, cultural and developmental effects. Topics will include familial aggregation, segregation and linkage analysis, ascertainment, linkage disequilibrium, and disease marker association studies. Recommended preparation: PQHS431 and PQHS451.
GENE 525 Advanced Medical Genetics: Clinical Genetics, 2 credits
Fundamental principles regarding congenital malformations, dysmorphology and syndromes. Discussion of a number of genetic disorders from a systems approach: CNS malformations, neurodegenerative disorders, craniofacial disorders, skeletal dysplasias, connective tissue disorders, hereditary cancer syndromes, etc. Discussions also include diagnosis, etiology, genetics, prognosis and management.
GENE 531 Clinical Cancer Genetics, 2 credits
This required seminar during spring semester discusses basic concepts in cancer epidemiology, principles of cancer genetics, inherited cancer syndromes, cytogenetics of cancers, pedigree analysis for familial cancer risk, approaches to differential diagnosis, risk assessment, genetic testing, screening and management of patients with familial or inherited cancer disorders and psychosocial issues.