معرفی
Marissa Ehringer is a Professor at the University of Colorado Boulder, holding dual appointments at the Institute for Behavioral Genetics and the Department of Integrative Physiology, where she serves as Chair. Her research focuses on identifying genetic mechanisms underlying substance use disorders and applying genomics and bioinformatics to behavioral genetics.
Dr. Ehringer's research interests center on the genetic basis of alcohol, tobacco, and substance use behaviors. Her work employs mouse and rat models to investigate how genetic variations influence responses to substances like alcohol, nicotine, and opioids. She has pioneered research on the High and Low Activity mouse strains, examining anxiety-related behaviors and genetic factors. Her laboratory also explores the intersection of gut microbiome and substance use disorders, particularly with opioids.
Dr. Ehringer leads multiple significant research projects including an NIDA U01 grant focused on opioid use disorder genetics, an NIH R21 examining glial expression in nicotine behaviors, and an AB Nexus grant studying the GCKR P446L variant's effect on alcohol behaviors. Her publications span top journals in genetics, neuroscience, and psychiatry, with recent work focusing on gene-microbiome interactions in addiction.
- Faculty Leadership Institute fellow (2022-2023)
- Outstanding Mentor by UROP (2020)
Dr. Ehringer maintains an active mentoring program with numerous current and former students. Her lab includes predocs, master's students, and postdocs working on various aspects of behavioral genetics. She has secured substantial grant funding supporting her research program and has been recognized for her leadership, having been appointed Chair of the Department of Integrative Physiology in 2022 after being promoted to Full Professor.
Her Genetics of Substance Abuse Laboratory employs a multidisciplinary approach combining behavioral testing, genomic analysis, and bioinformatics to unravel the complex genetic architecture of substance use disorders across different populations and model systems.
