Introduction to Epigenomics - Online Course
A 3-Day Livestream Seminar Taught by
Jennifer SmithThursday, January 23 –
Saturday, January 25, 2025
10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
Epigenetic mechanisms are modifications to the DNA or its surrounding proteins that regulate the expression of genes. A deep understanding of the epigenome lends insight into the biological mechanisms underlying health and aging, and epigenomic datasets offer the opportunity to capture the biological signatures of social, behavioral, and environmental exposures. The availability of multi-omic data (e.g., genomics, epigenomics, transcriptomics) in large social, behavioral, and health research datasets provides exciting opportunities to integrate exposures with biological outcomes across the life course. However, the start-up costs of learning the specialized tools and techniques of epigenomic analysis can be a deterrent.
The goal of this seminar is to impart a thorough conceptual understanding of epigenomics, particularly methylomics (i.e., the study of DNA methylation), and to equip you with the technical skills to conduct a range of popular statistical epigenomic analyses. Topics covered include: managing high dimensional epigenomic data, linear and logistic epigenome-wide association studies (EWAS) and differentially methylated region analyses (DMR), multiple testing, data visualization, epigenetic aging clocks, polyepigenetic scores, machine learning for DNAm surrogate biomarker creation, the interface of genomics and epigenomics, and an overview of key software tools and epigenomic data sources.
Starting January 23, we are offering this seminar as a 3-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
More details about the course content
It can be difficult to start analyzing epigenetic data due to its large size, correlation structure, and relationship with genetic data. To address these challenges, we teach a full suite of methods for managing, cleaning, and modeling epigenomic data. After completing the course, you will be able to manage and analyze your own epigenetic data, including running epigenome-wide association studies (EWAS) and differentially methylated region analysis (DMR).
You will learn best practices for EWAS, including modeling strategies and diagnostics to minimize bias and confounding in the results. You will also be able to use and create epigenetic surrogate biomarkers such as epigenetic aging clocks and polyepigenetic scores. Finally, you will gain a theoretical understanding of social epigenomics, anchoring epigenomics as a mechanism for biological embedding of socioenvironmental exposures that influence downstream health and disease.
This seminar will first introduce epigenomic mechanisms and propose epigenetics as a biomarker for health status and environmental exposures. Next, it will introduce the computing environment, R and RStudio, and discuss the management of epigenomic data matrices. Measurement and quality control of epigenetic data will be presented before a comprehensive description of methods for EWAS and DMR analysis including basic linear and logistic regression, common covariate specifications, and accounting for ancestry. We then discuss post-EWAS processing, including addressing multiple testing and plotting results for diagnostic and visualization purposes.
Subsequently, we will discuss post-EWAS analyses, such as gene enrichment testing, that emphasize biological relevance of EWAS results. Next, we explore the use of data reduction methods in epigenetics to construct summary measures like epigenetic aging clocks and polyepigenetic scores, and we discuss the use of machine learning approaches to create and evaluate epigenetic surrogate biomarkers of environmental exposures or health outcomes. We then discuss genetic and environmental influences on the epigenome, and we present the concepts behind integration of epigenomic data with genomic and transcriptomic data. Finally, we provide resources for locating and analyzing epigenomic datasets.
Throughout the course, you will gain experience with these methods through hands-on exercises.
It can be difficult to start analyzing epigenetic data due to its large size, correlation structure, and relationship with genetic data. To address these challenges, we teach a full suite of methods for managing, cleaning, and modeling epigenomic data. After completing the course, you will be able to manage and analyze your own epigenetic data, including running epigenome-wide association studies (EWAS) and differentially methylated region analysis (DMR).
You will learn best practices for EWAS, including modeling strategies and diagnostics to minimize bias and confounding in the results. You will also be able to use and create epigenetic surrogate biomarkers such as epigenetic aging clocks and polyepigenetic scores. Finally, you will gain a theoretical understanding of social epigenomics, anchoring epigenomics as a mechanism for biological embedding of socioenvironmental exposures that influence downstream health and disease.
This seminar will first introduce epigenomic mechanisms and propose epigenetics as a biomarker for health status and environmental exposures. Next, it will introduce the computing environment, R and RStudio, and discuss the management of epigenomic data matrices. Measurement and quality control of epigenetic data will be presented before a comprehensive description of methods for EWAS and DMR analysis including basic linear and logistic regression, common covariate specifications, and accounting for ancestry. We then discuss post-EWAS processing, including addressing multiple testing and plotting results for diagnostic and visualization purposes.
Subsequently, we will discuss post-EWAS analyses, such as gene enrichment testing, that emphasize biological relevance of EWAS results. Next, we explore the use of data reduction methods in epigenetics to construct summary measures like epigenetic aging clocks and polyepigenetic scores, and we discuss the use of machine learning approaches to create and evaluate epigenetic surrogate biomarkers of environmental exposures or health outcomes. We then discuss genetic and environmental influences on the epigenome, and we present the concepts behind integration of epigenomic data with genomic and transcriptomic data. Finally, we provide resources for locating and analyzing epigenomic datasets.
Throughout the course, you will gain experience with these methods through hands-on exercises.
Computing
This seminar will use R. All software and the datasets used for computing exercises will be made available via an easy-to-access cloud-based virtual machine. This will spare you the effort of manually installing the various software used in the course.
Basic familiarity with R is highly desirable, but even novice R coders should be able to follow the presentation and do the exercises.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
This seminar will use R. All software and the datasets used for computing exercises will be made available via an easy-to-access cloud-based virtual machine. This will spare you the effort of manually installing the various software used in the course.
Basic familiarity with R is highly desirable, but even novice R coders should be able to follow the presentation and do the exercises.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
Who should register?
If you want to learn the fundamental principles of epigenomics and apply them to enrich your biomedical, behavioral, or social research, this course is for you. It will impart the skills to clean and analyze DNA methylation data, perform epigenome-wide association studies (EWAS), and use or create epigenomic biomarkers including epigenetic aging clocks and polyepigenomic scores. You will also gain a deeper understanding of genomic and environmental influences on the epigenome. You should have basic knowledge of linear and logistic regression.
If you want to learn the fundamental principles of epigenomics and apply them to enrich your biomedical, behavioral, or social research, this course is for you. It will impart the skills to clean and analyze DNA methylation data, perform epigenome-wide association studies (EWAS), and use or create epigenomic biomarkers including epigenetic aging clocks and polyepigenomic scores. You will also gain a deeper understanding of genomic and environmental influences on the epigenome. You should have basic knowledge of linear and logistic regression.
Seminar outline
Day 1: Overview of epigenomics, quality control, and fundamentals of epigenome-wide association studies
-
- Epigenomics primer (e.g., epigenetic mechanisms, epigenetics as a biomarker of health and the environment)
- Intro to computing environment, R and RStudio, and working with high dimensional data
- Measuring epigenetics
- Quality control and data cleaning
- Introduction to epigenome-wide association studies (EWAS)
Day 2: Epigenome-wide association studies, differentially methylated regions, and data reduction
-
- EWAS considerations (covariates, ancestry)
- Multiple testing
- Plotting (e.g., QQ-plots, Manhattan plots, regional plots)
- Post-EWAS analysis (e.g., genomic feature enrichment, gene-set analysis, EWAS Catalog)
- Differentially methylated region (DMR) analysis
- Introduction to data reduction in epigenetics
Day 3: Applications of epigenetic biomarkers, genetic and environmental influences on epigenetics, and resources for epigenomic analysis
-
- Epigenetic aging clocks
- Polyepigenetic scores (PEGS)
- Machine learning approaches to DNAm surrogate biomarker creation
- Genetic and environmental influences on methylation (e.g., heritability, mQTLs, eQTMs, social epigenomics)
- Sources of epigenomic data and resources for analysis
Day 1: Overview of epigenomics, quality control, and fundamentals of epigenome-wide association studies
-
- Epigenomics primer (e.g., epigenetic mechanisms, epigenetics as a biomarker of health and the environment)
- Intro to computing environment, R and RStudio, and working with high dimensional data
- Measuring epigenetics
- Quality control and data cleaning
- Introduction to epigenome-wide association studies (EWAS)
Day 2: Epigenome-wide association studies, differentially methylated regions, and data reduction
-
- EWAS considerations (covariates, ancestry)
- Multiple testing
- Plotting (e.g., QQ-plots, Manhattan plots, regional plots)
- Post-EWAS analysis (e.g., genomic feature enrichment, gene-set analysis, EWAS Catalog)
- Differentially methylated region (DMR) analysis
- Introduction to data reduction in epigenetics
Day 3: Applications of epigenetic biomarkers, genetic and environmental influences on epigenetics, and resources for epigenomic analysis
-
- Epigenetic aging clocks
- Polyepigenetic scores (PEGS)
- Machine learning approaches to DNAm surrogate biomarker creation
- Genetic and environmental influences on methylation (e.g., heritability, mQTLs, eQTMs, social epigenomics)
- Sources of epigenomic data and resources for analysis
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.