Research Vision

Our lab aims to improve therapeutics for underrepresented communities by investigating the influence of genetic ancestry on molecular signatures in the brain. We use computational tools and disease-relevant models such as postmortem brain tissues, brain organoids, iPSC-derived glial cells to uncover how genetic ancestry impacts complex traits in the brain. This integrative approach provides insights into the interplay between genetic and environmental factors in complex brain disorders.

We collaborate with the community to direct our efforts in the development of impactful research. Therefore, one of the main focuses of our lab is to train a diverse group of next-generation computational scientists with the ability to communicate our findings with the community.

Research Interests

Genetic ancestry in the brain

In neuroscience and genomics, individuals with recent African ancestry (AA) account for less than 5% of large-scale research cohorts for brain disorders but are 20% more likely to experience a major mental health crisis. Furthermore, divergent responses to antipsychotics between AA and European ancestry (EA) have been linked to genetic differences. Understanding these genetic and/or regulatory differences between AA and EA in the human brain, is essential to the development of effective neurotherapeutics and potentially could decrease health disparities for neurological disorders.

Schizophrenia

Caudate nucleus and schizophrenia

Most studies of gene expression in the brain of individuals with schizophrenia have focused on cortical regions. However, subcortical nuclei such as the striatum are prominently implicated in the disease, and current antipsychotic drugs target the striatum’s dense dopaminergic innervation.

Sex differences and schizophrenia

Schizophrenia is a complex neuropsychiatric disorder with sexually dimorphic features, including differential symptomatology, drug responsiveness, and male incidence rate. To date, only the prefrontal cortex has been studied in large-scale transcriptome analyses for sex differences in schizophrenia.

Software development

dRFEtools: dynamic recursive feature elimination for omics

Technology advances have generated larger ‘OMICs datasets with applications for machine learning. Even so, sample availability results in smaller sample sizes compared to features. Dynamic recursive feature elimination (RFE) provides a flexible feature elimination framework to tackle this problem. dRFEtools provides an interpretable and flexible tool to gain biological insights from ‘OMICs data using machine learning.

PyPI: dRFEtools

Collaborations

Angiotensin II receptors in the human lung

Rationale: The renin-angiotensin system is one of the most well characterized integrative pathways in humans and is known to contribute to a variety of common disorders such as hypertension, chronic renal disease, and heart failure. The wide availability of agents targeting this pathway has led to an expansion of its clinical spectrum to lung disorders. Despite this widespread interest, specific localization of this receptor family in the vertebrate lung is limited. One reason is due to the general imprecision of the available antibody reagents.
Goal: Use publicly available single-cell and bulk RNA-sequencing to identify and characterize patterns of angiotensin II receptor expression in the lung at different ages.