Current Projects

Genetic Risk Factors in Progressive Supranuclear Palsy and Alzheimer's disease

Framework to identify Rare Genetic Risk Variants in Dementia
Pictured: Framework to identify Rare Genetic Risk Variants in Alzheimer's disease and Progressive Supranuclear Palsy. We investigate gene network and prioritization of non-coding risk variants followed by functional validation.

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.

Neurodegenerative disease progression and treatment response in the Electronic Health Record

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. 

Framework to predict dementia in diverse populations e populations using genetics, electronic health records and machine learning.

Pictured: Framework to predict dementia in diverse populations using genetics, electronic health records and machine learning.

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Clinical Decision Support

Clinical decision support study design

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 

Genetics and EHR in Diverse Populations

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.

Framework to recruit and improve health outcomes for diverse populations

Dementia Screening Tool

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.