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

RFMix-reader: Accelerated reading and processing for local ancestry studies

Local ancestry inference is crucial for understanding population history and disease genetics, especially for eQTL studies in admixed populations. While RFMix is widely used, handling its output for large datasets is challenging due to high memory and processing demands. To address this, RFMix-reader efficiently processes large local ancestry datasets, leveraging GPUs for speed and minimizing memory usage, enabling deeper insights into human health and health disparities.

PyPI: https://pypi.org/project/rfmix-reader/

Documentation: http://rfmix-reader.readthedocs.io/

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: https://pypi.org/project/drfetools/

Documentation: http://drfetools.readthedocs.io/

Collaborations

Angiotensin II receptors in the human lung

Understanding the precise distribution and function of angiotensin receptors within the lung is crucial for developing effective treatments for lung diseases like COPD and IPF. Here, our goal is to provide a foundational framework by mapping the expression patterns of AGTR1 and AGTR2 across different lung cell types and identifying their involvement in specific disease states. Our findings will offer new insights into the complex role of the renin-angiotensin system in lung health and disease, paving the way for targeted therapeutic interventions.