Jordan W. Smoller, MD, ScD
Professor of Psychiatry, Massachusetts General Hospital and Harvard Medical School
Research Roles/Affiliations
MGH Trustees Endowed Chair in Psychiatric Neuroscience
Professor of Psychiatry, Harvard Medical School
Professor in the Department of Epidemiology, Harvard T.H. Chan School of Public Health
Director, Psychiatric and Neurodevelopmental Genetics Unit, MGH Center for Genomic Medicine
Associate Chief for Research, Department of Psychiatry, MGH
Director, Center for Precision Psychiatry, Department of Psychiatry, MGH
Director, Omics Unit, MGH Division of Clinical Research
Director, Mass General Brigham Training Program in Precision and Genomic Medicine,
Associate Member, Broad Institute
President, International Society of Psychiatric Genetics
PI, All of Us New England Consortium (All of Us Research Program)
Contact Information
Massachusetts General Hospital, Simches Research Building
185 Cambridge Street, CPZN6, Boston, MA 02114
E-mail: jsmoller@mgh.harvard.edu
Relevant Links
The Center for Precision Psychiatry
Research
Our research includes three broad domains: 1) understanding the genetic and environmental determinants of psychiatric disorders across the lifespan; 2) integrating genomics and neuroscience to unravel how genes affect brain structure and function; and 3) using “big data”, including electronic health records and genomics, to advance precision medicine.
Using genomic data, in collaboration with colleagues around the world, we have helped identify numerous genetic risk factors for psychiatric disorders and have demonstrated that these disorders have a surprising degree of shared genetics and biology.
We’ve also explored how genes effect brain structure and function and its relationship to mental illness by integrating genomics with neuroimaging and neurophysiologic phenotypes.
We have a deep interest in leveraging large scale data and computational methods to facilitate precision psychiatric approaches. This work includes using high dimensional data along with statistical and machine learning methods to identify risk and resilience factors as well as predictors of improved treatment response. Our hope is that this work will improve early detection, prevention, and patient care.
Research Interests
Precision psychiatry
Genomics
Risk prediction
Psychiatric genetics
Epidemiology