• Our research interests
• Genetic and Phenotypic Architecture of Neuropsychiatric Disorders
• Global collaborations in gene discovery for severe neuropsychiatric disorders
• Cellular Genomics to Study Mental Illnesses
• Functional Genomics of the Brain

Our research interests

Mental, behavioral, and emotional disorders, which include schizophrenia, anxiety, depression, and bipolar disorders, are highly debilitating and diminish a person’s ability to function and quality of life. Unfortunately, few novel and effective therapies have developed in recent decades, hampered by our limited understanding of disease mechanisms.

We are interested in using genomics to answer specific questions related to the causes of mental illnesses. For instance, which genes are associated with an increased risk of schizophrenia or major depression? What biological pathways and processes are these genes involved in? Which cell types and neuronal circuits are perturbed by the risk variants and genes linked to each psychiatric disorder?

In the last decade, large global efforts have generated genetic data for hundreds of thousands of individuals. These large data sets were initially designed to study specific traits and conditions,  As part of these collaborations, we have identified specific genes associated with severe psychiatric disorders. More recently, genetic data of national biobanks allow us to explore thousands of phenotypes in hundreds of thousands of individuals. We will use the depth and diversity of these datasets to study and characterize the effects of genetic risk factors for psychiatric and neurodevelopmental disorders.

To this end, our team generates, analyzes, and integrates genetic and functional data to understand the causes of mental illnesses. We will use these findings to build specific biological hypotheses on disease pathogenesis that lead to new therapies. Broadly, we: (i) develop and apply methods to identify robust genetic associations and pinpoint disease-specific genes through the meta-analyses of sequence data of psychiatric disorders, (ii) characterize the range of clinical outcomes of common and rare genetic risk by studying clinical collections and population biobanks, and (iii) develop methods and approaches to integrate genetic and functional data to prioritize biological tissues, cell types, and processes relevant to disease.

Genetic and Phenotypic Architecture of Neuropsychiatric Disorders

We are exploring the genetic and phenotypic architecture of severe psychiatric illnesses, focusing on sequence data of severe cases from the Genome Psychiatry Cohort (GPC) in collaboration with Drs. Carlos and Michelle Pato at Rutgers University. The GPC is a large, diverse, and deeply phenotyped patient cohort with clinician-verified psychiatric diagnoses and screened controls. We will leverage the deeper phenotyping in this collection to study the genetic underpinnings of psychiatric symptomology, including age of onset, treatment resistance, and long-term hospitalization. We will perform these analyses as complementary approaches to identify novel variants and realize clinical utility.

Global collaborations in gene discovery for severe neuropsychiatric disorders

Our team is interested in exploring, developing, and applying methods to study the risk factors, socioeconomic factors, medication, treatment response, and disease progression for severe human brain disorders, focusing on neuropsychiatric and neurodevelopmental disorders. We leverage data sets that include thousands of features and parameters derived from electronic health record (EHR) data: detailed descriptions of risk factors, socioeconomic challenges, medication, treatment response, and disease progression. We are interested in rich multimodal data that augment standard EHR data, including brain neuroimaging and DEXA imaging. We are interested in combining genetic methods with machine learning approaches on the phenotypes for prediction and gene discovery.

We participate in the recently launched Breakthrough Discoveries for Thriving with Bipolar Disorder (BD2) Genetics Platform. Led by collaborators at the Broad Institute and UCLA, we aim to sequence one of the largest and most diverse cohorts of people with Bipolar Disorders (BD).

Cellular Genomics to Study Mental Illnesses

We want to delineate causal mechanisms of BD and other severe psychiatric disorders using scalable functional models. As part of the BD2 Discovery Platform, we use patient-derived cellular models to characterize the etiology of BD. In collaboration with Rutgers University (Drs. Carlos Pato, Michelle Pato, Ron Hart) and the New York Genome Center (Drs. Thomas Lehner, Neville Sanjana, Tuuli Lappalainen), we have ascertained a large cohort of individuals of African ancestry living with psychotic disorders with matched controls. We are generating stem cell-derived neurons from 70 bipolar patients with very high genetic risk and 70 control individuals with low genetic risk. We will use cutting-edge cellular genomics and genome engineering technologies to identify molecular signatures in single neurons at high resolution and link them directly to genetic variation computationally. We hypothesize that different BD-associated genetic risk factors converge onto shared genes, biological processes, and pathways. Characterizing these processes will clarify causal mechanisms underlying BD etiology, opening new avenues for translational and therapeutic target discovery.

Functional Genomics of the Brain

Complementing the study of patient-derived cells is the study of brain tissue from deceased patients, which captures the full complexity of cell and tissue types in the human brain. We are leveraging bulk- and single-cell multi-omics approaches to characterize molecular signatures of the postmortem brain. We collaborate with different brain banks in the United States toward this goal, including the New York State Psychiatric Institute/Columbia University Brain Collection, which is dedicated to the study of suicide and where all suicide decedents and comparator brain samples are deeply phenotyped clinically. We want to integrate the deeper phenotyping from these brain banks and whole-genome sequence data to generate disease-specific QTL maps and identify genes more specific to each psychiatric disorder.