Genetics of Addiction is geared toward students of all experience levels, from undergraduate and graduate students who seek an introduction to the field, to experienced addiction researchers who wish to hone their genetic skills and knowledge. Course attendees are invited to bring their own data for analysis during the hands-on laboratory sessions.
Registration is Open
Sep 16 - 22
2018
Millions of people worldwide struggle with addiction, a chronic, complex condition involving genes, environment, and behavior. To provide a path toward deeper understanding and improved public health measures, scientists must first unlock the fundamental biology of the disorder. This requires advanced genetic tools and methods to reveal the genes and biological networks that contribute to addiction.
This JAX short course brings together world-renowned experts in addiction, human genetics, and mouse genetics. Through a combination of lectures and hands-on computational modules, the course will feature:
The course also provides key opportunities to network with students, researchers, and other professionals; and explore potential scientific collaborations.
Genetics of Addiction is geared toward students of all experience levels, from undergraduate and graduate students who seek an introduction to the field, to experienced addiction researchers who wish to hone their genetic skills and knowledge. Course attendees are invited to bring their own data for analysis during the hands-on laboratory sessions.
Don’t miss this unique opportunity to learn the latest tools and approaches in addiction and genetic research, allowing students and researchers alike to return to their own communities and make meaningful contributions to science and society.
Research reported in this publication was supported by the National Institute On Drug Abuse of the National Institutes of Health under Award Number R13DA032192. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
The 2018 schedule is currently being developed. Please refer to the 2017 schedule for an idea of content and flow.
Registration Fee: The $1300 fee includes all course materials, meals and shared lodging. Payment is not due at the time of application.
Registration Fee: The $350 fee includes live webcast of the majority of lecture sessions, and online access to recorded lectures for six months.
Lodging accommodations will be at Highseas Conference Center, a Georgian style mansion built in 1912. Highseas is perched on the cliffs overlooking the entrance to Frenchman Bay and is surrounded by beautiful Acadia National Park. This is casual, dormitory-style housing consisting of shared bedrooms and bathrooms (minimum double occupancy, some triple and quad rooms). Wireless internet access is provided throughout the building. Laundry facilities are available on site. Transportation will be provided from the conference center to the course venue for the duration of the workshop. Highseas cannot accommodate families, please contact the event planner for information about off-campus lodging alternatives. Participants are responsible for all travel booking, including airport transportation.
Bar Harbor is served by two airports:
Alumni Endowed Professor of Psychiatry, Vice Chair for Faculty Development
Professor of Pharmacology
Professor of Medicine
Empire Innovation Professor of Behavioral Neuroscience
Associate Professor
Bioinformatics Analyst III
Professor and Head, Division of Bioinformatics & Computational Biology
Associate Research Scientist
Director, Computational Sciences
Professor of Neuroscience in Psychiatry
Chief, Genetics, Epigenetics, and Developmental Neuroscience Branch
Associate Professor, University of Pittsburgh School of Medicine; Director PITT Preclinical Phenotyping Core; Co-Head MODEL-AD Preclinical Testing Core
Assistant Professor, Department of Psychiatry
Senior Director, Tech Evaluation & Development
Professor, The Ann Watson Symington Chair in Addiction Research and Senior Director, Integrative Data Science
Karl E. Rickles Professor of Psychiatry