Jordan W. Smoller - Center for Genomic Medicine

Dr. Smoller is Professor of Psychiatry at HMS, and Professor in Epidemiology at the Harvard T.H. Chan School of Public Health. At MGH, he is the Trustees Endowed Chair in Psychiatric Neuroscience, a Tepper Family MGH Research Scholar, Associate Chief for Research for the Department of Psychiatry, Director of the Psychiatric and Neurodevelopmental Genetics Unit, Director of the Omics Unit of the Division of Clinical Research and co-Director of the Partners HealthCare Biobank. He is also an Associate Member of the Broad Institute, a Senior Scientist at the Broad’s Stanley Center for Psychiatric Research, and Vice President of the International Society of Psychiatric Genetics.

Questions being addressed in the lab

What are the genetic and environmental determinants of psychiatric disorders across the lifespan?
Can we unravel the genetic and neural basis of psychiatric disorders by integrating genomics and neuroscience?
Can we leverage electronic health records and genomic data to advance precision medicine?

Projects underway to answer these questions

Genomewide analyses and cross-disorder analyses of autism, ADHD, bipolar disorder, depression, PTSD, schizophrenia, and others
Brain genomics: the neural and genetic architecture of mental illness
The New England Precision Medicine Consortium

Answers Found

Identification of shared genetic loci and genetic correlations among multiple psychiatric disorders. As part of the Cross-Disorder Group of the Psychiatric Genomics Consortium, we identified genomic loci with pleiotropic effects across childhood- and adult-onset psychiatric disorders and demonstrated significant co-heritability among these disorders.
Novel methods reveal the heritability of brain phenotypes relevant to neuropsychiatric disorders. We developed novel computational approaches to enabling, for the first time, heritability estimation for high-dimensional phenotypes including vertex-wise cortical thickness, surface area and neuroanatomical shape. We have also developed and used statistical methods to conduct “phenomewide” heritability analyses in large-scale biobanks.
Leveraging longitudinal electronic health records (EHRs) for phenotyping, genomic analysis and risk prediction. We have developed algorithms enabling high-throughput phenotyping for large-scale genomic studies using natural language processing of EHRs. We also developed and validated EHR-based models that were able to identify nearly half of all suicides and suicidal behaviors an average of 3 – 4 years in advance.