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Dear all, An online course "Genetics, Epigenetics, and Mendelian Randomization: Cutting-Edge Tools for Causal Inference in Epidemiology" is being run in June by Stephen Burgess (U Cambridge), Tomi Akinyemiju (U Kentucky), and Stella Aslibekyan (U Alabama Birmingham). This course is anchored around ~20 recorded lectures (~20 hours of content) organized into three sections (Causal inference, Mendelian Randomization and Epigenetics). The recorded lectures are audio and screen recordings that will enable the instructor to teach key concepts, followed by applied sessions in the R and/or STATA software environment for applied examples of course concepts and hands on exercises. In general, each module will have a PowerPoint and video file, as well as annotated code files that can be used by participants and kept for future reference. Registration: https://reg.abcsignup.com/reg/event_page.aspx?ek=0013-0020-05e9368c2dc54da48e9058ed6951aa75 Course Description: Studies based on Mendelian randomization are increasingly being used to distinguish causal relationships from observational associations in epidemiology and to prioritize potential targets for pharmaceutical intervention. This course will provide participants with an in-depth understanding of Mendelian Randomization (MR) analysis, an approach developed to assess causal relationships in epidemiologic studies. We will teach participants the basis of instrumental variable analysis as well as the specifics on the MR approach, and provide practical skills for analyzing data and interpreting results within the MR context. A particular focus of the course will be on understanding how MR can be applied to consider the causal role of epigenetics. Genetic susceptibility and epigenetic alterations have been implicated in a wide range of chronic diseases, including cancer, diabetes, Alzheimer's and cardiovascular diseases. Epigenetic mechanisms have also been well studied as potential mediators of exposures on disease risk as well as downstream consequences of disease phenotypes. However, modifiable risk factors, including epigenetic and other biomarkers, are highly vulnerable to confounding and reverse causation. This course will provide in-depth training on the use of MR approaches to deal with confounding and reverse causation in epidemiologic studies, and outline various approaches to interrogate the causal relationships between exposures and disease outcomes. Course Objectives By the end of the course, participants will be able to understand: 1. The basic principles of causal inference in epidemiology and the role of confounding, reverse causation and measurement error 2. The basic principles of Mendelian randomization and the use of genetic variants as instrumental variables 3. How to perform Mendelian randomization analyses, understand the strength and limitations of genetic variants as instrumental variables, and interpret results 4. The basic concepts of epigenetic epidemiology, including the role of DNA methylation, histone modifications, and RNA-based mechanisms in disease processes 5. How to apply the principle of Mendelian randomization to evaluating DNA methylation as a causal risk factor 6. How to use publicly available Mendelian randomization tools and databases to conduct their own analyses Please contact Gerald Govia ([log in to unmask]<mailto:[log in to unmask]>) with any technical questions. --- Also, registration for our next residential Mendelian randomization course is now live: https://onlinesales.admin.cam.ac.uk/conferences-and-events/mrc-biostatistics-unit/mrc-biostatistics-unit-short-courses/mendelian-randomization-course-19-20-november-2018. There will also be a half-day symposium following the course: https://onlinesales.admin.cam.ac.uk/conferences-and-events/mrc-biostatistics-unit/mrc-biostatistics-unit-short-courses/research-symposium-on-mendelian-randomization-methodology-21-november-2018. For more details: http://mendelianrandomization.com/index.php/course-details. With best wishes, Stephen Burgess You may leave the list at any time by sending the command SIGNOFF allstat to [log in to unmask], leaving the subject line blank.