At the Health Equity for Advancing Research and Technology using Genomic Neuroscience (HEART-GeN) Lab, our goal is to improve treatments for brain disorders with health disparities.
While 99.99% of our genome is shared across global ancestral populations, the very small fraction unique to a specific ancestral population can affect how genes turn on or off. We use this human variation to find these genes that may play a role in explaining health disparities for brain disorders.
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.
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.
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.
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.
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/
py-qvalue is a Python package that provides a robust solution for controlling
the False Discovery Rate (FDR) in multiple hypothesis testing. This package is a
direct port of the well-regarded qvalue R package and is designed for
researchers who need to estimate the proportion of true null hypotheses
(qvalue, pi0est, and lfdr, it enables
straightforward FDR control within the Python ecosystem, making it an essential
tool for statistical analysis and multiple hypothesis testing workflows.
PyPI: https://pypi.org/project/py-qvalue/
Documentation: https://github.com/heart-gen/py-qvalue
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/
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.
Collaborator(s):
- Enid R. Neptune, M.D., at Johns Hopkins University School of Medicine
While significant advances have been made in bulk postmortem functional genomics for schizophrenia, understanding cell-specific molecular alterations is still challenging due to the cellular heterogeneity of brain tissue. Single-cell and single-nucleus RNA-sequencing has helped by revealing disease-associated changes and cell type-specific enrichment of schizophrenia risk genes. Additionally, cerebral organoids, which model early human brain development and are amenable to genetic manipulation, provide a powerful platform for studying cell type-specific disease mechanisms in a controlled environment. By combining these approaches, we can examine the specific cellular differences in schizophrenia within cerebral organoids.
Collaborator(s):
- Richard E. Straub, Ph.D., at Lieber Institute for Brain Development
- Brady J. Maher, Ph.D., at Lieber Institute for Brain Development