Lide Han

Project Personnel, Vanderbilt University Medical Center

5 active projects

Polygenic Risk Scores and Physical Activity CW

Our primary goal is to understand the interaction between activity levels and polygenic risk score with the development and progression of human disease. Both physical activity and polygenic risk scores have been shown to be associated with prevalence and outcomes…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and polygenic risk score with the development and progression of human disease. Both physical activity and polygenic risk scores have been shown to be associated with prevalence and outcomes in many human diseases. These analyses will generate hypotheses guiding clinical and research interventions focused on activity to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

  • Population Health
  • Social / Behavioral
  • Ancestry

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence and progression of coded human diseases, which may be modified by genetics. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, clinical outcomes (hospitalizations/mortality), and polygenic risk scores derived from the WGS dataset in AoU.

Anticipated Findings

We expect to find that lower levels of activity are associated with a higher prevalence and more rapid progression of certain diseases and that this risk may be modified by polygenic risk score. These data will provide the rationale to link wearables data with electronic health records nationwide as a window into behavioral activity choice as a modifiable risk factor for chronic diseases. We may find substantial variation in activity and disease prevalence/severity by socioeconomic status, which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Omar Costilla Reyes - Research Fellow, Massachusetts Institute of Technology

Polygenic Risk Scores and Physical Activity (Extended) CTDv7

Our primary goal is to understand the interaction between activity levels and polygenic risk score with the development and progression of human disease. Both physical activity and polygenic risk scores have been shown to be associated with prevalence and outcomes…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and polygenic risk score with the development and progression of human disease. Both physical activity and polygenic risk scores have been shown to be associated with prevalence and outcomes in many human diseases. These analyses will generate hypotheses guiding clinical and research interventions focused on activity to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

  • Population Health
  • Social / Behavioral
  • Ancestry

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence and progression of coded human diseases, which may be modified by genetics. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, clinical outcomes (hospitalizations/mortality), and polygenic risk scores derived from the WGS dataset in AoU.

Anticipated Findings

We expect to find that lower levels of activity are associated with a higher prevalence and more rapid progression of certain diseases and that this risk may be modified by polygenic risk score. These data will provide the rationale to link wearables data with electronic health records nationwide as a window into behavioral activity choice as a modifiable risk factor for chronic diseases. We may find substantial variation in activity and disease prevalence/severity by socioeconomic status, which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

  • Lide Han - Project Personnel, Vanderbilt University Medical Center
  • Jeffrey Annis - Other, Vanderbilt University Medical Center

Combined effect of rare and common variation

To understand how genetic background influence the expression of rare variants. By understanding the interplay between these factors, we can better predict who is at risk and tailor more effective treatments, improving public health outcomes

Scientific Questions Being Studied

To understand how genetic background influence the expression of rare variants. By understanding the interplay between these factors, we can better predict who is at risk and tailor more effective treatments, improving public health outcomes

Project Purpose(s)

  • Ancestry

Scientific Approaches

We will combine experimental methods with computational analyses. For example, we have generated comprehensive and prospective functional data for rare missense variants in cardiac ion channel genes. In this proposal we will leverage these data to probe how common variants interact with these functional perturbations.

Anticipated Findings

We anticipate that our study will reveal nuanced ways in which genetic background modify the expression of rare variant-associated phenotypes. These findings will improve the accuracy of existing models that predict the pathogenicity of genetic variants, making them more applicable to a broader range of populations, with the hope of better, more wholistic incorporation of both rare and common genetic variation in assessing disease risk. In doing so, this research will contribute to a more inclusive and precise form of medicine, enhancing the effectiveness of treatments and potentially other conditions influenced by genetic variability.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Douglas Ruderfer - Mid-career Tenured Researcher, Vanderbilt University Medical Center
  • Lide Han - Project Personnel, Vanderbilt University Medical Center

Combined effect of rare and common variation

To understand how genetic background influence the expression of rare variants. By understanding the interplay between these factors, we can better predict who is at risk and tailor more effective treatments, improving public health outcomes.

Scientific Questions Being Studied

To understand how genetic background influence the expression of rare variants. By understanding the interplay between these factors, we can better predict who is at risk and tailor more effective treatments, improving public health outcomes.

Project Purpose(s)

  • Ancestry

Scientific Approaches

We will combine experimental methods with computational analyses. For example, we have generated comprehensive and prospective functional data for rare missense variants in cardiac ion channel genes. In this proposal we will leverage these data to probe how common variants interact with these functional perturbations.

Anticipated Findings

We anticipate that our study will reveal nuanced ways in which genetic background modify the expression of rare variant-associated phenotypes. These findings will improve the accuracy of existing models that predict the pathogenicity of genetic variants, making them more applicable to a broader range of populations, with the hope of better, more wholistic incorporation of both rare and common genetic variation in assessing disease risk. In doing so, this research will contribute to a more inclusive and precise form of medicine, enhancing the effectiveness of treatments and potentially other conditions influenced by genetic variability.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Douglas Ruderfer - Mid-career Tenured Researcher, Vanderbilt University Medical Center
  • Lide Han - Project Personnel, Vanderbilt University Medical Center

Ruderfer - Brittain Collaboration

The demo project aims to explore the strategies to leverage Fitbit along with genomics, survey and EHR data on the cloud-based platform in a cost-efficient fashion. These strategies can lay the foundation to multiple research studies which can drive evidence-based…

Scientific Questions Being Studied

The demo project aims to explore the strategies to leverage Fitbit along with genomics, survey and EHR data on the cloud-based platform in a cost-efficient fashion. These strategies can lay the foundation to multiple research studies which can drive evidence-based care for all. Specifically, the project aims to develop the workspace on Researcher Workbench to develop use cases for digital biomarker development for the Fitbit data and its integration with other AoU data types. One existing challenge is how to ensure that information about multiple streams of health data can be conveyed appropriately to enable fit-for-purpose analyses. Therefore, the intent of the demonstration projects is to understand the challenges that users might face who would like to leverage Fitbit data in tandem with surveys, measurements, genomics and EHR data.

Project Purpose(s)

  • Population Health
  • Social / Behavioral
  • Educational
  • Ancestry

Scientific Approaches

Data wrangling strategies to meaningfully combine Fitbit data with EHR and genomics data on the Researcher Workbench. Develop strategy in R and Python (RMarkdown and Jupyter Notebooks), including calculation of summary statistics and data visualizations, for users of varying levels of digital health literacy.

Develop educational materials to acquaint researchers with the benefits and limitations of combining Fitbit, EHR and genomics data. Materials to be developed include peer-reviewed manuscript, articles/blogs, videos, and user guides.

Anticipated Findings

All of Us Research program (AoURP) currently provides multiple streams of health data (i.e., genomics, surveys, electronic healthcare records (EHR) and Fitbit) to registered users on Researcher Workbench - cloud based platform. This in turn provides a unique opportunity to answer clinically relevant questions. Wearable devices enable continuous monitoring of physiological signals, which may be used for discovery, diagnostic, and prognostic purposes. The Fitbit study as a part of the AoURP includes Fitbit data from approximately 12,000 patients. The information available from the Fitbits (such as activity, heart rate, sleep patterns and device metadata) can be used to develop new digital biomarkers by exploring their correlations with clinical measurements, genetic risk scores and information from surveys such as social determinants of health.

Demographic Categories of Interest

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

  • Douglas Ruderfer - Mid-career Tenured Researcher, Vanderbilt University Medical Center
  • Hiral Master - Project Personnel, All of Us Program Operational Use
  • Lide Han - Project Personnel, Vanderbilt University Medical Center
  • Jeffrey Annis - Other, Vanderbilt University Medical Center

Collaborators:

  • Brandon Lowery - Other, Vanderbilt University Medical Center
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