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In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 3-Day Livestream Seminar Taught by Jennifer Smith, Ph.D., M.P.H.

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 users 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.

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.