Our ultimate goal is to the contribute to research that will improve patient lives. While research can be challenging and stressful at times, remembering our motivation can keep us grounded. Toward these goals, we work with integrity, with curiosity, and collaboratively, supporting the principles of inclusivity, equity, and diversity. As an academic laboratory, we are committed to mentorship, training, and professional growth. We expect a strong work ethic and enjoy life outside of research.
Genetic Risk Factors in Progressive Supranuclear Palsy and Alzheimer's disease
Neurodegenerative diseases are clinically and pathologically defined syndromes with heterogeneous genetic endophenotypes, some of which are unique to one disease while others are shared across diseases. Despite the economic and healthcare burden, treatments targeted to endophenotypes are not available for neurodegenerative tauopathies such as Alzheimer’s disease (AD) and Progressive Supranuclear Palsy (PSP). AD and PSP are pathologically related by abnormal tau protein inclusions and the tau gene is a shared genetic risk factor. Unique genetic risk in common variants have been identified. However, a significant part of genetic heritability remains unexplained; a proportion of which is likely due to rare variants, which can be determined from whole genome sequencing. Identification of rare genetic risk factors may delineate the endophenotypes of AD and PSP, unlocking future therapeutic targets.
Genes do not act in isolation, but rather interact with one another in networks. Grouping genes in a network can improve can improve of understanding of disease processes. We will develop and apply rare variant statistical tests that incorporate network connectivity to identify dementia risk genes using whole genome sequencing data. We will identify noncoding rare genetic risk variants by developing a noncoding prioritization and gene assignment strategy. Lastly, we will validate rare variants that are unique to or shared by AD and PSP with replication datasets and functional valiation. We plan to integrate genetic analyses with multi-omic post-mortem brain tissue to robustly identify gene networks dysregulated in disease
The discovery of disease insights using Electronic Health Records (EHRs) is in its infancy. Advantages of EHR cohorts over traditional cohorts include the longitudinal large sample size, wide range of phenotypes, and inclusion of underrepresented populations (minorities, elderly). There are a limited number of EHRs linked with genetic data, which could facilitate novel disease endophenotyping. Using EHRs with a diverse patient population linked with genetic data, this project will use genetic risk stratification to characterize neurodegenerative disease progression and treatment response. We will perform phenome wide association studies to identify gene networks associated with neurodegenerative diseases. Because genetic risk factors are ripe to become therapeutic targets, we will use gene-drug interaction databases to hypothesize potential treatments.
Biomedical informatic models have predicted future health events and treatment response. However, few models have been translated into clinical practice. One hurtle is integrating predictive models into clinical workflow. Clinical decision support (CDS) tools within the EHR helped ameliorate this issue to some exist. While CDS may be based on models with high accuracy, it is not a foregone conclusion the CDS will improve health outcomes. In this project, will plan to evaluate CDS impact on health outcome, identifying high value CDS tools which improve patient care, and develop novel CDS tools that incorporate genetics
A major objective of the laboratory is to increase the representation of under-represented groups in dementia research. Along these lines, we plan to leverage electronic health records linked with genetics to study disease etiology, progression, treatment response in diverse populations. An advantage of identifying these patients within the EHR is that longitudinal data will be captured through the patient’s continued interaction with the health system. Our objective is to bolster research recruitment of diverse populations at health systems such as Harbor-UCLA and Olive View Medical Center.
Growing evidence suggest dementias, like Alzheimer's disease, are under-diagnosed in the community. This may be particularly evident in under-represented groups like Hispanic/Latinx and Black Americans. The goal of this project is to develop and implement an early detection dementia screening toolkit that can be administered efficiently and effectively at primary care settings in diverse populations.
With the continuing coronavirus disease 2019 (COVID-19) pandemic, it is critical to identify risk factors associated with susceptibility and severity of disease in a diverse population to help shape government policies, guide clinical decision making, and prioritize future COVID-19 research. Our objective are to identify pre-existing risk factors unique to COVID-19 diagnosis susceptibility, inpatient admission, and severe outcome in minority groups through automated Electronic Health Record extraction. We will also collect and analyze genetic risk factors for COVID-19.